MONITORING AND MAPPING OF THE EXTENT OF INDUSTRIAL FORESTS IN MALAYSIA  By UY DUC PHAM        A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements  for the degree of  Forestry  Doctor of Philosophy   2016 ABSTRACT MONITORING AND MAPPING OF THE EXTENT OF  INDUSTRIAL FORESTS IN MALAYSIA  By UY DUC PHAM There are scattered studies in the international forestry sector that Industrial Forests (IFs) have been expanding as a newly-emerging Land Use and Land Cover Change (LULCC) in the tropics, especially in the Asia-Pacific region. However, these new tree plantations have not yet been well-documented; the area, along with its geography and land use dynamics, are not well known. Additionally, the drivers are not well understood, but it is widely believed that changes in tropical silviculture and increased international demand for wood and fiber are shifting to new demand centers in Asia. These trends have the potential to create global shifts in source producing areas, from long-standing IFs in North America and Europe to newer areas to the tropics. Considerable remote sensing research and product development have been focusing on monitoring closed canopy natural forests, but less work has been done on intensively managed IFs, which involve techniques for remote characterization of the establishment, management, and rotation. Moreover, the studies to date have been geographically limited to some key areas, such as the Amazon and Indonesia, and more work needs to be done outside of these closed natural forest regions. This research is conducted in the tropical Asia-Pacific region with a focus on the new IFs in the Sabah and Sarawak states of Malaysia. This study aims to improve the knowledge base and understanding of the extent and characteristics of new IFs as a new agent of LULCC, and to develop the methods for Landsat data, in particular by using forest fractional cover (fC) and vegetation indices (VIs) analyses in time series integrated with textural, spectral, visual, and other analyses to detect and quantify IF LULCC patterns and dynamics in the country.  Results showed that the selected IFs-including acacia, rubber, and other IFs-have expanded quickly from 2000 to 2014 with a net increase of 288,547 ha at the annual mean rate of 20.1% in Sabah, and 459,898 ha at the annual mean rate of 59.9% in Sarawak. The annual mean expansion rate of faster-growing, shorter-rotation acacia IFs at 28.4% in Sabah and 376.5% in Sarawak was much faster than that of slower-growing, longer-rotation rubber IFs at 13.7% in Sabah and 5.8% in Sarawak, as well as other IFs at 10.9 % in Sabah and 78.2% in Sarawak. The development of IFs in both states was primarily dominated by the larger scale holdings; however, the role of the small-scale IFs in developing new IFs in the region grew through an increase of its total area and rate of change in area. The expansion of IFs in Sabah and Sarawak significantly contributed to a LULCC in the regions. Most of these new IFs replaced disturbed natural forests (81-95%), followed by agricultural land (4-18%), and waste land (< 1%). These have caused a significant decline for the aboveground C stock in Sabah (11.5 Tg C) and Sarawak (24.7 Tg C), and resulted in an emission of 42.1 Tg CO2 in Sabah and 90.5 Tg CO2 in Sarawak over the period. The expansion of these new IFs had also led to a reduction in biodiversity in Sabah at 2.79-4.98% and in Sarawak at 2.77-4.96%. The results also showed a possibility of developing the fC and VIs-based methods in a time series for Landsat datasets that could detect and monitor the extent, pattern, and scale of IFs in the tropics. The accuracy for detecting the IF land using the fC-based method (with its accuracy at 83% and Kappa coefficient at 0.46) was higher than that of the VIs-based method. Among VIs, ARVI worked the best with its produc at 64% and Kappa coefficient at 0.4, followed by SAVI, SARVI, EVI, NDVIaf, and MSAVIaf. For both the fC-based method and the VIs-based method, the accuracy of detecting acacia and rubber IFs was better than that of other IFs in the region. In brief, this study successfully developed the fC- and VIs-based methods in multi-dated Landsat data to detect and quantify IF LULCC.                      Copyright by UY DUC PHAM 2016 v           This dissertation is gratefully dedicated to my family, especially to my beloved wife, Lien Hoang Thi Pham, and to my adored daughter, Elise Pham or Linh Khanh Pham, who have encouraged, inspired me, and sacrificed a lot throughout my Ph.D. life. I would also like to dedicate it to my whole family, my parents, and my parents-in-law, who tirelessly support me during my entire doctorate program. Without their supports and love, I could not complete this dissertation study and my doctorate program. vi  ACKNOWLEDGEMENTS  First and foremost, I would like to deeply express my sincere gratitude to my main advisor Professor David L. Skole for the continuous, generous, and tireless support of my Ph.D. study, for his patience, motivation, kindness, and immense knowledge. His priceless guidance and comments helped me in all the time of research and writing this dissertation.  Besides, I would like to sincerely thank all my guidance committee members: Prof. Stuart Gage, Prof. Larry A. Leefers, and Prof. Pascal Nzokou, for their insightful comments and encouragements. Without their guidance and supports, I could not complete this study. On this occasion, I would like to give special thanks to Professor Richard Kobe, who has offered me an opportunity to study here, at the Department of Forestry, Michigan State University, USA. My thanks also go to my lab-mates and all staff at the Center of Global Observatory for Ecosystem Services (GOES), Michigan State University, especially to Daniel Zelenak and Hartanto Sanjaya for remote sensing and GIS technical discussions and supports, to Jay Samek and James Gray for many supports and insightful recommendations for my study, and also to Mr. James Gray and Ms. Cat Olenick for their English proofreading assistance for this dissertation. I would also like to say thank you to all of Forestry Department Faculties, particularly to Dr. Phu Nguyen, Dr.  Runsheng Yin, and Dr. David Rothstein for their early discussions, supports, and encouragement for my study; and to all my family members and friends, who have supported spiritually throughout my doctorate program.  Lastly, I would like to say that my words are never enough to say thank you to all of you for all what you have done and given to me. I really appreciate that and thank you very much again.  vii  TABLE OF CONTENT  LIST OF TABLE ix LIST OF FIGURE xii KEY TO ABBREVIATIONS xx CHAPTER 1:   OVERVIEW 1 1.1 Introduction 1 1.2 Industrial Forest: Concepts and Definitions 5 1.3 The Development of Industrial Forests (IFs) in the Asia-Pacific Region 7 1.4 The Development of Industrial Forests in Malaysia 13 1.5 Literature Review of the Studies on Industrial Forests 16 1.5.1 In the Asia-Pacific Region 16 1.5.2 In Malaysia 19 1.6 Literature Review of the Studies on the Methods Development for Detecting and Mapping Industrial Forests  21 1.7 The Significance of this Study: Problems and Rationale 23 1.8 Selection of the Study Area and Industrial Forest Systems 26 Industrial Forest Systems Studied 27 1.9 Research Questions and Objectives  29 Research Questions 29 Objectives 30 1.10 Research Methods 30 Validation 32 1.11 The Flowchart of the Study 37 CHAPTER 2:   DEVELOPING THE VEGETATION INDICES-BASED  INDUSTRIAL FOREST DETECTION METHOD FOR LANDSAT DATASETS  38 2.1    Introduction 38 2.2 Acquiring and Preprocessing Images 40 2.3  Developing the Method 44 2.3.1    General Principles 44 2.3.2    Results 45 Textural Analysis 62 Spectral Analysis 70 Visual Interpretation and Using Other Data 79 Making IF Maps 83 2.4 Validation 91 2.5  Discussions and Conclusions  97     viii  CHAPTER 3:   DEVELOPING THE VEGETATION/FOREST FRACTIONAL COVER-BASED INDUSTRAL FOREST DETECTION METHOD FOR LANDSAT DATASETS  101 3.1    Introduction 101 3.2 Acquiring and Preprocessing Images 102 3.3  Developing the Method 103 3.3.1    General Principles 103 3.3.2    Results 105 Spectral Analysis 120 Textural Analysis 120 Visual Interpretation and Using Other Data 122 Making IF Maps 123 3.4  Validation 128 3.5  Discussions and Conclusions 130 CHAPTER 4: ASSESSING THE INDUSTRIAL FOREST LAND USE AND  LAND   COVER CHANGES, AND THEIR CONSEQUENCES  133 4.1    Industrial Forest Land Use and Land Cover Changes 133 4.1.1.   The fC-based LULCC The Pattern Indices for IF LULC Changes 4.1.2.   The Vegetation Indices-based LULCC 133 140 145 4.2    Assessments of the IF LULC Changes and their Consequences 154 The Consequences of the IF LULC Changes 161 4.3    Discussions and Conclusions 171 CHAPTER 5:   SYNTHESIS 180 5.1    Introduction 180 5.2    Shortcoming 180 5.3    Applicability 184 APPENDIX 185 REFERENCES 236  ix  LIST OF TABLES  Table 1.1.   A summary of plantation areas and the rate of their change in Malaysia .   28 Table 1.2.   The principal requirements for a sampling scheme to validate the developed methods  35  Table 1.3.   The ways for assessing accuracy of IF maps derived from Landsat datasets   36 Table 2.1. Sequences of the vegetation cover changes based on the changes of VI values (MSAVIaf) in 30 key areas chosen to observe in Sabah and Sarawak, 2000-2014    54 Table 2.2. The changes of MSAVIaf values in some key areas in Sabah, 2000-2014..........................................................................................................   56 Table 2.3. The accuracy assessment results for ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI-based IF land detection methods for Landsat data   93 Table 2.4. The accuracy assessment results specific for acacia, rubber, and other IFs for ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI-based IF     95 Table 3.1. The fC value changes in 30 monitored key locations in Sabah, 2000-2014..........................................................................................................   112 Table 3.2. The accuracy assessment results for the fC-based IF land detection method ...   128 Table 3.3. The accuracy assessment results specific for acacia, rubber, and other IFs for the fC-based IF detect   129 Table 4.1. The IF area expansion in Sabah, 2000-2014.....  135 Table 4.2. The IF area expansion in Sarawak, 2000-2014.....  135 Table 4.3a. The area (in ha) of large-scale and small-scale IFs in Sabah, 2000-   138 Table 4.3b. The percentage of large-scale and small-scale IFs in Sabah, 2000-   138 x  Table 4.4a. The area (in ha) of large-scale and small-scale IFs in Sarawak, 2000-..   138 Table 4.4b. The percentage of large-scale and small-scale IFs in Sarawak, 2000-   138 Table 4.5. The new IF areas and their LULC replacements in Sabah, 2000-  156 Table 4.6. The new IF areas and their LULC replacements in Sarawak, 2000-2014  159 Table 4.7. The above ground carbon stock values (tC ha-1/MgC ha-1) for the classified LULC types in Sabah and Sarawak (adapted from Agus et al., 2013a; Agus et al., 2013b; RSPO, 2014)    163 Table 4.8. Comparisons of ABG stocks (Mg) of new IFs and their LULC replacements in Sabah   163 Table 4.9. Comparisons of C stocks (Mg) of new IFs and their LULC ..   164 Table 4.10. The number of species in the different   167 Table 4.11. The percentage of declining or increasing number of species if UF, DF    168 Table 4.12. Estimating the biodiversity loss caused by the expansion of the new IFs in the study area from 2000 to 2014 (adapted from Brook et al., 2003)..   170 Table A.1. The full list of Landsat scenes used for the study in Sarawak, Malaysia, 2000-2014   186 Table A.2. The date, type, and cloud coverage of Landsat scenes used for the study in Sarawak, Malaysia, 2000-2014.   187 Table A.3. The full list of Landsat scenes used for the study in Sabah, Malaysia, 2000-2014   188 Table A.4. The date, type, and cloud coverage of Landsat scenes used for the study in Sabah, Malaysia, 2000-2014.   189 Table A.5. The changes of ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in 30 key areas in Sabah, 2000-2014   201 Table A.6. The result for calculating the growth rate of VIs in Sabah, 2000-2014  207    xi  Table A.7. The GLCM_MEA, DIS, and HOM values for different LULC types in VIs, band 4 and 5 images in Sarawak, 2000-2014   213 Table A.8. The GLCM_MEA, DIS, and HOM values for different LULC types in VIs, band 4 and 5 images in Sabah, 2000-2014   215 Table A.9. The PCA, ICA, TCA, band 4 and band 5 values for different Land Use Land Cover (LULC) types in Sabah, 2000-2014   219 Table A.10. The PCA, ICA, TCA, band 4 and band 5 values for different Land Use Land Cover (LULC) types in Sarawak, 2000-2014   220 Table A.11. The fC value changes and its change sequence in 30 monitored key locations in Sarawak, 2000-2014   225 Table A.12. Details of the high resolution imagery data used for the validation in S   228 Table A.13. Details of the high resolution imagery data used for the validation in   228        xii  LIST OF FIGURES  Figure 1.1.        The commercial plantation by species in Malaysia in 2005 (ITTO, 2009)... 13 Figure 1.2.      The distribution of plantations (rubber in 2005 & other IFs in 2009) in Malaysia (adapted from (1) Malik et al., 2013); (2) Malaysia Timber     13 Figure 1.3.        Industrial plantation development in Sarawak, 1997-2012 (adapted from    15 Figure 1.4.     Map of Malaysia showing the selected study sites (Sarawak & Sabah States)   27 Figure 1.5.  The general flowchart for the development of forest fractional cover (fC)- and vegetation indices (VIs)-based industrial forest detection methods for Landsat datasets    33 Figure 1.6. The general flowchart of the study 37 Figure 1.7.  The system diagram of the study 37 Figure 2.1.  The general procedures for preprocessing images 43 Figure 2.2.  The flowchart of development of the VIs-based IF detection method 46 Figure 2.3.  The stacked MSAVIaf for Sabah and Sarawak, 2000-2014 49 Figure 2.4.  The change detection graph (adapted from Cakir et al.,  51 Figure 2.5.  The changes of MSAVIaf value from 2012 to 2014 in Sabah and Sarawak, Malaysia..................   51 Figure 2.6. The sequence of the VI (MSAVIaf) value changes, 2000-2014, in the study area   52 Figure 2.7. The key locations for monitoring the VI value changes in Sabah and Sarawak   53 Figure 2.8. The cycle of rotation (clearing and regrowth) of vegetation cover based on the changes of MSAVIaf values in Sarawak, 2000-2014   55 Figure 2.9. The changes of The MSAVIaf values at 6 locations (No. 3, 7, 17, 23, 25, & 30) selected as an example in Sabah  57 xiii  Figure 2.10. Possibly shorter- and longer-rotation plantations based on MSAVIaf, 2000-2014 in Sabah   58 Figure 2.11. The growth rates of the MSAVIaf values in some locations (location numbers 3, 7, 17, 23, 25, & 30) chosen to monitor their value changes in Sabah, 2000-2014    59 Figure 2.12. The possibly faster-growing and slower-growing plantations based on MSAVIaf  values  in Sabah, 2000-2014   60 Figure 2.13. Possibly faster-growing, shorter-rotation and slower-growing, longer-rotation plantations based on MSAVIaf, 2000-2014 in Sabah   61 Figure 2.14. The difference between natural forest and plantation 63 Figure 2.15. The Mean (MEA) index in the GLCM is calculated for an NDVIaf image in 2014 in Sabah and Sarawak   65 Figure 2.16. The identification of different land uses/land covers used to acquire the textural values in the study sites   67 Figure 2.17. The values of GLCM_MEA, HOM, and DIS for different Land Uses/Land Covers in the NDVIaf product, band 4, & band 5 grey level images in Sabah, 2000-2014    68 Figure 2.18. The texture-based models for the VI datasets to detect the focused IF systems   69 Figure 2.19. Detecting the targeted IF systems based on textural analysis in Sabah, 2012   69 Figure 2.20. Spectral profiles for bare lands, natural forests, and plantations in the study area   71 Figure 2.21. The Principal Components Analysis for Sabah and Sarawak in 2000..  72 Figure 2.22. The mean values of acacias, natural forests, oil palms, rubbers, and other industrial forests of layer 1, 2, and 3 in the PCA product in Sabah, 2000- 2014    73 Figure 2.23. The Independent Components Analysis for Landsat data in the study area in 2000   74 Figure 2.24. The Independent Components mean values (acacias, natural forests, oil palms, rubbers, and other industrial forests) of layer 1, 2, and 3 in Sabah, 2000-2014   75 xiv  Figure 2.25. The Tasseled Cap Analysis for Landsat data in the study area in 2000 76 Figure 2.26. The Tasseled Cap values (acacias, natural forests, oil palms, rubbers, and other industrial forests) of layer 1, 2, 3, 4, 5, and 6 in Sabah, 2000-2014   78 Figure 2.27. The mean values of band 4 and band 5 for the different land use/land cover areas of interest in Sabah, 2000-2014   79 Figure 2.28. The spectra-based models for the VI datasets to detect the focused IF systems   80 Figure 2.29. The spectral analysis-based industrial forest detection in Sabah, 2012 80 Figure 2.30. An example of how to interpret the different land uses/land covers based on their interpretation keys in Sabah in 2009   82 Figure 2.31. The visual interpretation-based industrial forest map in Sabah, 2000 83 Figure 2.32. The final algorithm to identify industrial forest areas and species based on textural analysis, spectral analysis, visual interpretation, and faster-growing shorter-rotation (FGSR) and slower-growing longer-rotation (SGLR) IF      84 Figure 2.33. The ARVI-based industrial forest maps in Sabah and Sarawak, 2000-2014...............................................................................................................   85 Figure 2.34. The EVI-based industrial forest maps in Sabah and Sarawak, 2000-2014... 86 Figure 2.35. The MSAVIaf-based industrial forest maps in Sabah and Sarawak, 2000-2014   87 Figure 2.36. The NDVIaf-based industrial forest maps in Sabah and Sarawak, 2000-2014   88 Figure 2.37. The SARVI-based industrial forest maps in Sabah and Sarawak, 2000-2014   89 Figure 2.38. The SAVI-based industrial forest maps in Sabah and Sarawak, 2000-2014. 90 Figure 2.39. The locations, areas, years, and sensors of the high resolution imagery scenes used to validate the Landsat-derived IF maps in Sarawak and     91 Figure 3.1. The flowchart of development for the forest factional cover (fC)-based IF detection method   104 xv  Figure 3.2. A test for different VIs to choose the best index applied to the fC method...  106 Figure 3.3. An example of choosing the areas for closed forest and bare land endmembers...   107 Figure 3.4. The endmember values of closed forest and bare soil/land in Sarawak and Sabah, 2000-2014   107 Figure 3.5. The forest/vegetation fractional cover (fC) map produced from the MSAVIaf products in 2014 for Sarawak and Sabah   109 Figure 3.6. The fC changes detection for 2012-2014 in Sarawak and Sabah  110 Figure 3.7. The key locations for monitoring the fC changes in Sabah and Sarawak, 2000-2014   111 Figure 3.8. The possibly shorter- and longer-rotation industrial forests in Sabah and Sarawak, 2000-2014   113 Figure 3.9. The possibly faster-growing and slower-growing industrial forests in Sabah and Sarawak, 2000-2014   115 Figure 3.10. The possibly faster-growing, shorter-rotation and slower-growing, longer-rotation industrial forests in Sabah and Sarawak   116 Figure 3.11. The band 4 values in the same vegetation cover in Sa  117 Figure 3.12. The band 4 values for different vegetation cover types in Sabah, 2000-2014   117 Figure 3.13. The MSAVIaf-based LAI for different vegetation cover types in Sabah and Sarawak, 2000-2014   119 Figure 3.14. The spectral analysis-based land use/land cover map in Sabah and Sarawak, 2003   121 Figure 3.15. The textural analysis-based land use/land cover map in Sabah and Sarawak, 2000   122 Figure 3.16. The simple diagram for developing the final algorithm to detect and map industrial forest areas and species based on the fC dataset analysis   124 Figure 3.17. The fC-based IF map for Sabah and Sarawak in 2000  125 Figure 3.18. The fC-based IF map for Sabah and Sarawak in 2003  125 xvi  Figure 3.19. The fC-based IF map for Sabah and Sarawak in 2006  126 Figure 3.20. The fC-based IF map for Sabah and Sarawak in 2009  126 Figure 3.21. The fC-based IF map for Sabah and Sarawak in 2012  127 Figure 3.22. The fC-based IF map for Sabah and Sarawak in 2014  127 Figure 4.1. The IF areas in 2000, 2003, 2006, 2009, 2012 and 2014 in Sabah and Sarawak   133 Figure 4.2. The annual rate of change in area in Sabah (a) and Sarawak (b), 2000-2014   135 Figure 4.3. The total large-scale and small-scale IF area in Sabah and Sarawak, 2000-   137 Figure 4.4. The expansion of the large- and-small-scale IFs in Sabah and Sarawak, 2000-   139 Figure 4.5. The annual rate of change in large- and small-scale IF area by type in Sabah and Sarawak, 2000-   140 Figure 4.6. The total number of patches and the largest IF patch area (in ha) in Sabah and Sarawak, 2000-   141 Figure 4.7. The largest patch size of acacia, rubber and other IFs in Sabah and Sarawak, 2000-   141 Figure 4.8. The mean patch size index of acacia, rubber and other IFs in Sabah and Sarawak, 2000-   142 Figure 4.9. IF areas by type and by patch size class in Sabah, 2000-  143 Figure 4.10. IF areas by type and by patch size class in Sarawak, 2000-  143 Figure 4.11. The total large-scale patch number and the mean patch size of IFs in Sabah, 2000-   145 Figure 4.12. The total large-scale patch number and the mean patch size of IFs in Sarawak, 2000-   145 Figure 4.13. The VIs-based IF areas in Sabah and Sarawak, 2000-  149 Figure 4.14. The VIs-based rates of change in IF areas in Sabah and Sarawak, 2000-  150 xvii  Figure 4.15. The VIs-based large-scale and small-scale IF areas in Sabah and Sarawak, 2000-   152 Figure 4.16. The VIs-based rate of changes in large- and small-scale IF areas in Sabah and Sarawak, 2000-   153 Figure 4.17. The procedure to assess the IF LULC changes in Sabah and Sarawak,    154 Figure 4.18. The new IF areas and their other-LULC-types-replacements percentage and area in Sabah, 2000-   157 Figure 4.19. The percentage of the different LULC types converted to new acacia, rubber, and other IFs in Sabah, 2000-   157 Figure 4.20. The different LULC types area and their percentage converted to the new acacia, rubber, and other IFs in Sabah, 2000-   158 Figure 4.21. The new IF areas and their other-LULC-types-replacements percentage and area in Sarawak, 2000-   159 Figure 4.22. The percentage of the different LULC types converted to new acacia, rubber, and other IFs in Sarawak, 2000-   160 Figure 4.23. The different LULC area and percentage converted to the new acacia, rubber and other IFs in Sarawak, 2000-   161 Figure 4.24. The ABG stock changes as a consequence of the IF LULCC in Sabah, 2000-..   164 Figure 4.25. The C stock changes as a consequence of the IF LULCC in Sarawak, 2000-   165 Figure 4.26. CO2 emissions caused by the IF LULCC in Sabah and Sarawak, 2000-   166 Figure 4.27. A number of species for different ecosystems (UF, DF, IF, and OP) in the    168 Figure 4.28. The percentage of change in number of species in IFs compared with other    169 Figure A.1. The stacked VI images by type for Sabah, 2000-2014  192 Figure A.2. The stacked VI images by type for Sarawak, 2000-2014  193 xviii  Figure A.3. The changes of EVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia   194 Figure A.4. The changes of MSAVIaf values from 2000 to 2014 in Sabah and Sarawak, Malaysia   195 Figure A.5. The changes of NDVIaf values from 2000 to 2014 in Sabah and Sarawak, Malaysia   196 Figure A.6. The changes of SARVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia   197 Figure A.7. The changes of SAVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia   198 Figure A.8. The changes of ARVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia   199 Figure A.9. The clearing and regrowth cycle (rotation) of vegetation cover based on the changes of ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in Sabah and Sarawak, 2000-2014    200 Figure A.10. Possibly shorter- and longer-rotation plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI in Sabah, 2000-2014   205 Figure A.11. Possibly shorter- and longer-rotation plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI in Sarawak, 2000-2014   206 Figure A.12. The possibly faster-growing and slower-growing plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in Sabah, 2000- 2014    209 Figure A.13. The possibly faster-growing and slower-growing plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in Sarawak, 2000- 2014    210 Figure A.14. Possibly faster-growing, shorter-rotation and slower-growing, longer-rotation plantations based on VIs values in Sabah, 2000-2014   211 Figure A.15. Possibly faster-growing, shorter-rotation and slower-growing, longer-rotation plantations based on VIs values in Sarawak, 2000-2014   212 Figure A.16. Vegetation/forest fractional cover maps of 2000, 2003, 2006, 2009, 2012, and 2014 in Sarawak and Sabah   223    xix  Figure A.17. Vegetation/forest cover change detection for 2000-2014 in Sarawak and Sabah   224 Figure A.18. The spectral analysis-based LULC maps in Sabah and Sarawak, 2000-2014   226 Figure A.19. The textural analysis-based LULC maps in Sabah and Sarawak, 2000-2014   227 Figure A.20. The location and distribution of the samples in the ARVI-based IF maps in   229  Figure A.21. The location and distribution of the samples in the EVI-based IF maps in Sarawak and Sabah   230 Figure A.22. The location and distribution of the samples in the MSAVIaf-based IF    231 Figure A.23. The location and distribution of the samples in the NDVIaf-based IF maps    232 Figure A.24. The location and distribution of the samples in the SARVI-based IF maps    233 Figure A.25. The location and distribution of the samples in the SAVI-based IF maps in Sarawak and    234 Figure A.26. The location and distribution of the samples in the fC-based IF maps in    235  xx  KEY TO ABBREVIATIONS  LULCC:  Land Use and Land Cover Change LULC:  Land Use and Land Cover FAO:  Food and Agriculture Organization of the United Nations ITTO:  International Tropical Timber Organization NASA: National Aeronautics and Space Administration Mha:  Millions of hectares IF/IFs:  Industrial Forest/Industrial Forests IF LULCC: Industrial Forest Land Use and Land Cover Change fC:  Forest/Vegetation Fractional Cover VI/VIs: Vegetation Index/Vegetation Indices ICFRE: India Council of Forestry Research and Education MARD: Ministry of Agriculture and Rural Development of Viet Nam RS:  Remote Sensing NDVI:  Normalized Difference Vegetation Index SAVI:  Soil-Adjusted Vegetation Index ARVI:  Atmospherically Resistant Vegetation Index  SARVI: Soil-Adjusted Atmospherically Resistant Vegetation Index  MSAVI2: Modified Soil Adjusted Vegetation Index 2  EVI:  Enhanced Vegetation Index  AFRI:  Aerosol Free Vegetation Index  MSAVIaf: Modified Soil Adjusted Vegetation Index Aerosol Resistant xxi  NDVIaf: Normalized Difference Vegetation Index Aerosol Resistant LAI:  Leaf Area Index SWIR:  Short-Wave Infrared EROS:  Earth Resources Observation and Science Center DNs:  Digital Numbers NV/NF: Non Vegetation/Non Forest V/F:  Vegetation/Forest SR:  Short Rotation LR:  Long Rotation FG:  Fast-Growing SG:  Slow-Growing FGSR:  Fast-Growing, Short-Rotation SGLR:  Slow-Growing, Long-Rotation GLCM: Grey Level Co-occurrence Matrix  MEA:  Mean  HOM:  Homogeneity  DIS:  Dissimilarity AOI:  Area of Interest PCA:  Principal Component Analysis  ICA:  Independent Component Analysis TCA:  Tasseled Cap Analysis  UNFCCC: The United Nations Framework Convention on Climate Change DF:  Disturbed Forest xxii  UF:  Undisturbed Forest AL:  Agricultural Land OP:  Oil Palm Land WL:  Waste/Degraded Land RL:  Residential Land Mg C ha-1: Million grams of Carbon per hectare Tg C or CO2: Trillion grams of Carbon or Carbon dioxide  tC ha-1:  Tonne of Carbon per hectare MtC or CO2: Millions of tonnes of Carbon or Carbon dioxide  IPCC:  Intergovernmental Panel on Climate Change of the United Nations  1  CHAPTER 1 OVERVIEW  1.1 Introduction Forests play an extremely important role in maintaining life on Earth. They contain most terrestrial species on this planet and provide livelihood support to millions of people. Forests provide many precious and important ecological services for billions of people, even those living outside their immediate vicinity.  We also know that today forests, a specific Land Cover (LC) type, have been reduced in area through the activities of humans at the global scale. Forest decline, particularly tropical forests, can affect the global climate system, global carbon cycle, water resource systems, global energy balance, and biodiversity. Tropical forests contain very high carbon stocks and energy, sustain very high biodiversity, and are especially susceptible to significant Land Use and Land Cover Change (LULCC). Currently, the rate of human disturbance of the forests is high compared to other forest biomes. The United Nations Food and Agriculture Organization (FAO, 2000, 2005, & 2010) and the International Tropical Timber Organization (ITTO, 2009) estimated that 16 million hectares (Mha) and 13 Mha of tropical forests have been cleared and degraded annually for the 1990s and 2000s, respectively. As a result, the United Nations Intergovernmental Panel on Climate Change (IPCC, 2007) estimated that tropical forest conversion accounted for nearly 20% of the total anthropogenic global emissions of carbon dioxide to the atmosphere, and was a major driver of climate change.   Current research is now focused on understanding tropical LULCC dynamics, identifying the drivers of tropical deforestation and forest degradation, as well as quantifying their rates, extent, 2  and patterns. Researchers have found that one of the main drivers of deforestation over the last four decades has been the conversion of closed canopy tropical forests to agriculture (Skole & Tucker, 1993; Gibbs et al., 2010; Tollefson, 2015); and selective logging has been a main factor for degrading the forests (Matricardi et al., 2005; Matricardi et al., 2007; Matricardi et al., 2010; & Matricardi et al., 2013). In response to these challenges, and in recognition of the timber supply shortage from forests, and other benefits of multiple forest uses (ITTO, 2009), government policies in most tropical countries have attempted to address the drivers of deforestation and forest degradation. They also seek to constrain the responsible agents by encouraging and developing solutions such as afforestation, reforestation, and the expansion of plantations.   As a result, FAO (2000, 2006, & 2010) reported that approximately 3.0-4.5 Mha of new tree plantations (equal to the annual average planting rate of 8.6%) have been established worldwide between 1995 and 2005, with the most significant increase occurring in tropical climate zones. ITTO (2009) also indicated that, among the three primary tropical regions over the world - including Asia-Pacific, Latin America and the Caribbean, and Africa - the Asia-Pacific region showed the highest rate of annual growth in land cover devoted to tree plantation area at 9.4%. This is compared to 8.8% in Africa and 4.3% in Latin America and Caribbean during the period. The Asia-Pacific region was also the location of approximatplantation area and 80% of its increase in area. Of this quantity, about 90% of the total plantation area in the region was established in a few key countries, including India, Indonesia, Thailand, Malaysia, and Viet Nam. However, unlike deforestation and forest degradation, these tree plantations have not yet been widely studied with respect to the new widespread LULCC. We 3  know little about them in terms of their specific processes, drivers, locations, rates, extent, and patterns (FAO, 2006; ITTO, 2009; Skole et al., 2013).   There are a few reports from the international forestry sector suggesting that tree plantations have been expanding in recent years. This portends to be an important emerging Land Use and Land Cover Change (LULCC) in the tropics, especially in the Asia-Pacific region.  However, these new tree plantations have not been well documented - the area, geography and land use dynamics are not well known. The drivers are not well understood either, but it is widely believed that advances in tropical silviculture technology and methods, and increased international demand for wood and fiber are shifting industrial wood source areas from North American and European areas closer to new demand centers in Asia (ITTO, 2009; Skole & Simpson, 2010; Skole et al., 2013). These trends have the potential to create global shifts in the location of source-producing areas, where long-standing industrial timber plantations in North America and Europe are now moving to the tropics. In spite of this understanding, questions remain: What is the magnitude? What size class are the new forest plantations and their rotations? What are the uses and drivers of these plantations? Are the new plantations replacing natural forests? Furthermore, robust tools to detect, map, and monitor them are also lacking (Skole et al., 2013).   Considerable remote-sensing research and product development have been focused on monitoring closed canopy tropical forests, while less work has been done on intensively managed industrial timber plantations. To do so would involve techniques for remote-sensing characterization of the establishment, management, and rotation of even-aged stands of industrial plantations. Moreover, studies done to-date have been geographically limited to some key areas of closed canopy tropical forest, such as the Amazon and Indonesia, and more work needs to be 4  done outside of these closed natural forest regions. Moreover, many institutions (e.g., NASA) and researchers (e.g., Skole et al., 2013) have emphasized the plantation phenomenon-along with open forests, woodlands, savanna, and trees outside of forests-as a high-priority topic for the next stage of research on drivers and dynamics of LULCC.  An investigation of the expansion of new tree plantations, including their underlying and proximate LULCC processes and drivers, requires a new and innovative approach that includes development of new remote sensing methods and an analysis of spatial patterns. This approach helps us better understand the extent and dynamics of IFs from perspectives of both driver analysis and monitoring (Skole et al., 2013).   Therefore, the research that supports this dissertation has been aimed at recently-established tree plantations in the tropics, with a focus on the Asia-Pacific region and selection of Malaysia as a case study.  Representative plantation species or systems studied consist of Acacia spp., Eucalyptus spp., Pinus spp., Hevea spp., and Tectona species in terms of both methods development and quantification of their rates, extent, and patterns of establishments. The selection of the above plantation species results from the fact that nearly 90% of tree plantations in the Asia-Pacific region utilize these species (FAO, 2000, 2006; ITTO, 2009). Meanwhile, Malaysia provides a compelling case for the examination of new tree plantations because the annual expansion rate of faster-growing, shorter-rotation industrial timber plantations (such as acacia) is surprisingly high, while the slower-growing, longer-rotation plantation areas (such as rubber) are decreasing remarkably. Additionally, developing and testing new methods for detecting and mapping these new tree plantations are especially challenging in Malaysia due to heavy cloud contamination and haze.  5  1.2 Industrial Forest: Concepts and Definitions  In this section, in this study will be developed. The concept and definition of industrial forests are both derived from the concepts and definitions of tree plantations. This study will utilize a widely-accepted and widely-used concept and definition from FAO (2000)1: Plantation forests are forest stands established by planting or/and seeding in the process of afforestation or reforestation. They are either of introduced species (all planted stands), or intensively managed stands of indigenous species, which meet all the following criteria: one or two species at planting, even age class,  A plantation could be established on lands which previously did not carry any type of plantations (a new tree plantation) or re-established on already-existing plantation lands.  Plantations are generally divided into two sub-groups: productive plantations and protective plantations (Kanninen, 2010). The productive plantation is a forest plantation mainly established for the provision of wood, fiber (e.g., roundwood, sawnwood, and pulpwood), and non-wood products, while the protective plantation is a forest plantation established chiefly for the provision of such forest ecosystem services as water and soil resource protection. Kanninen (2010) found that was productive plantation. Specifically, the general ratio of productive and protective plantation forests was 3.6 (equal to the ratio of natural forests allocated for production and protection purposes), but distributed unevenly in different countries, regions, and continents. A plantation could also be classified as hardwoods/broad-leaved (e.g., Eucalyptus and Acacia spp.) and softwoods/conifers (also known as needle-leaved; e.g., Pinus spp.), or for industrial use or non-industrial use (FAO, 2000)1. For industrial and non-industrial use, an industrial forest (IF) could be a productive plantation, which is extremely diverse - ranging from horticultural types such as orchards, to fuel oils, to saw logs -                                                       1 http://www.fao.org/docrep/007/ae347e/ae347e02.htm  6  and covering many varying worldwide land cover characteristics. It can also be established in a new area or already-existing IF lands. Therefore, this study only focuses on the most important form of new tree plantation LULCC in the tropics, i.e., new tree plantations for timber and biomass feedstock, including the following types of tree systems: timber, saw log, veneer, and pulp in addition to other biomass feedstock plantation systems with the focused species of Acacia spp., Eucalyptus spp., Pinus spp., Hevea spp., and Tectona spp. These new tree plantation systems involve the replacement of natural forests and other land uses with plantations of commercial trees using forest management and silvicultural rotations. This is a new phenomenon and has not yet been widely studied. This focus also fits with the definition from FAO for the industrial plantations, as those for the production of wood for industry (saw-logs, veneer-log, pulpwood, and mining pillars/pit pros) (FAO, 2003).   In brief, in this study, an industrial forest (IF) is a productive plantation established for the industrial use, as defined here, which involves the planting and harvesting of trees for timber, saw log, veneer, pulp, and other biomaterial feedstock. A new IF could be understood as a new productive plantation created from other land uses and land covers, which do not previously include any types of tree plantations. An industrial forest could come to many different names in different countries in the national forestry statistics. For instance, in Indonesia, industrial forests are called Hutan Tanaman Industri (HTI), meaning industrial plantation forest (Indonesia Forestry Statistics, 2012); in Malaysia they are called plantation forest (Malaysia Timber Council, 2009); and in Vietnam they are called , or productive plantation forest (Vietnamese Ministry of Agriculture & Rural Development [MARD], 2012).They are even simply called a plantation in many cases.  7  1.3 The Development of Industrial Forests (IFs) in the Asia-Pacific Region heterogeneous in their spatial distribution and cover many different biophysical characteristics. These plantations consist of diverse types, including rubber plantations, saw-log plantations, pulpwood plantations, and more.  In spite of their total area being small compared to natural forests, they provide one for industrial wood (ABARE-Jaakko Pöyry Consulting, 1999). The importance and impact of IFs on humans and LULCC will continue to increase as a result of rapidly increasing their area, especially in the tropics.  The ITTO (2009) and FAO (2000, 2006, & 2010) indicated that the new IFs in the tropics were increasing both in individual size and in total area. In particular, the establishment of new plantations has accelerated significantly since the 1990s and there was a remarkable shift from slow-growing, long-rotation plantations to fast-growing, short-rotation ones. Although the has been increasing, the main part of these increases has occurred in only a few key areas, dominated by the Asia-Pacific tropical region. Reports by the FAO (2000, 2006, & 2010) indicated that the total plantation area increased from 100 Mha in 1990, to 140 Mha in 2005, and to 190 Mha in 2010, resulting in an annual mean increase of approximately 4.5 Mha/year. Out of the s total plantation area in 2005, 67.5 Mha were located in tropical countries, of which the Asia-Pacific tropical region contained 54 Mha (80% of the total tropical plantation area). Of this, India held 33 Mha (60% of the total area of the region), followed by Indonesia (9.9 Mha), Thailand (4.9 Mha), Malaysia (1.8 Mha), and Viet Nam (1.7 Mha). Together, these countries accounted for more than 90% of the regional total. Moreover, the mean annual rate of the increase in this region (9.4% per year) was the highest compared to other tropical regions (Africa 8.8%, Latin America and the Caribbean 4.6%) 8  (FAO, 2006; ITTO, 2009). This represented a substantial increase from 24 Mha in 1995 to 54 Mha in 2005. India contributed most of the increase, growing from 14.6 Mha to 32.6 Mha in this period.   The ITTO (2009) and FAO (2006, 2010) also showed that most IFs in the tropics were dominated by relatively few genera including Pinus, Eucalyptus, Acacia, Hevea, and Tectona. Among the tropical IF species, eucalypts (Eucalyptus spp.) and acacias (Acacia spp.) were important tree species, mainly used for pulp and paper industries. Pines (Pinus spp.), rubber (Hevea brasiliensis), and teak (Tectona grandis) were also widely planted and utilized for the production of saw logs, round wood, and panels (e.g., plywood and veneer) (FAO 2006; ITTO, 2009; Asia-Pacific Forestry Outlook Study, 2010). The ITTO (2009) reported that eucalypts were the most widely planted, with the total area estimated at about 8.5 Mha (24% of the total IF area in the tropics), followed by pines (18%), rubbers (18%), teaks (17%), and acacias (9%).   The Asia-Pacific Forestry Outlook Study (2010) also showed that most of the early IFs in the region were vastly dominated by slow-growing and long-rotation species (such as teak) which were destined to produce saw and veneer logs. Recently, however, the area of short-rotation and fast-growing species such as Eucalyptus spp., Pinus spp., and Acacia spp. has significantly increased, leading a big shift from slow-growing species to fast-growing species. The driving forces for this shift involved changes in wood-processing technologies, which had a primary influence on the selection and widespread planting of the fast-growing IF species. In addition, improvements in silvicultural practices, plantation technology, and management, as well as the high demand for these fast-growing species were also important factors. In India, the most widely planted species were Tectona grandis, Eucalyptus spp., Pinus spp. (mainly Pinus roxburghii), Acacia spp. (mainly Acacia nilotica and Acacia mangium) and Hevea brasiliensis. 9  Tectona grandis, Acacia spp., Pinus spp. (especially Pinus merkusii), and Hevea brasiliensis were also were substantially comprised of Acacia, Pinus, and Eucalyptus species, while IFs in Thailand and Malaysia were dominated by rubber, followed by other fast-growing species, such as Eucalyptus (in Thailand) and Acacia (in Malaysia) species (Asia-Pacific Forestry Outlook Study, 2010). IF development has proceeded in the key countries of the Asia-Pacific region in recent decades, including India, Indonesia, Thailand, Viet Nam, and Malaysia.  India is one of the most important players in the establishment of new IFs in the world. Since the 1980s, India has promoted the investment for plantations under different programs, such as agroforestry and social forestry (Ministry of Environment and Forests of India, 2007). The FAO (2000) reported that India had a total of 32.5 Mha of plantations, which accounted for  - only after China - according to the ITTO (2009). Of that, 45% of plantation species were fast-growing species (mostly Eucalyptus spp. and Acacia spp.) and teak (8%). The ITTO (2009) also estimated the total commercial IF plantation area in India in 2000 at 8.2 Mha, including teak (2.6 Mha), eucalypts (2 Mha), acacias (1.6 Mha), pines (0.6 Mha), rubber (0.6 Mha), and other species (0.8 Mha). The India Council of Forestry Research and Education (ICFRE, 2010) also indicated that most of the annual plantation increase in India was established in conjunction with the Twenty Points Program (TPP) for Afforestation, established in 1970 and restructured in 2006, and the National Afforestation Program (NAP), established in 2000, at the rate of 1-2 Mha annually. The area and rate of plantation establishments were different in different states. The ICFRE (2010, 2011) indicated that the largest area and highest rates of tree plantation establishments were found in some key states, such as Andhra Pradesh, Madhya Pradesh, 10  Gujarat, and Maharashtra. Teak IF area in India was also very significant, with most plantations (2 Mha) planted in some key states, such as Maharashtra, Madhya Pradesh, Andhra Pradesh, and Gujarat (ICFRE, 2010). While the majority of rubber plantations (0.7 Mha) were established in Kerala state (90%), the fast-growing species plantations (such as Eucalyptus and Acacia spp.) were mainly developed in the key pulp and paper production centers, such as Andhra Pradesh, Karnataka, Maharashtra, Gujarat, and Orissa states.  Indonesia is also one of the most significant plantation forest countries in the world. The ITTO (2009) estimated the total area of plantations in Indonesia at about 10 Mha in 2005. Of that, the total area of  IF plantations amounted to about 4.9 Mha, with 1.5 Mha of teak, followed by 1 Mha of rubber, 0.8 Mha of pines, 0.7 Mha of acacias, 0.2 Mha of eucalypts, and 0.9 Mha of other species. The area of fast-growing species plantations in Indonesia increased rapidly from 2.2 to 3.4 Mha between 1990 and 2005 (FAO, 2005). Over the same period, the area of rubber plantations also increased from 1.9 to 2.7 Mha. The Indonesia Forestry Statistical Data showed that the total industrial timber plantation area (HTI) had increased from 5.1 Mha in 2001, to 9.4 Mha in 2009, and to 13.1 Mha in 2012. Most of these plantations were located in the East Kalimantan, West Kalimantan, Riau, and South Sumatra provinces. A study by the FAO (2009) also indicated that these provinces were main material sources for pulp and paper industries. Barr (2007) noted that 80% of pulp industrial plantations were Acacia spp., with some Pinus and Eucalyptus spp., and that sawnwood IFs were mainly teak and other broadleaved species. While most of the state-owned teak IFs (1.7 Mha) were planted on Java island, the teak IFs (1 Mha) owned by private companies were developed primarily on the Sumatra and Kalimantan islands (Indonesia Forestry Outlook Study, 2009). Likewise, the private smallholder-owned rubber plantations (3 Mha) were mostly established on 11  the Sumatra and Kalimantan islands. Indonesia also plans to have 9 Mha more of IFs by the end of 2016. Most of the new IF areas will be established in the Papua (1.7 Mha), East Kalimantan (1.5 Mha), West Kalimantan (1 Mha), Riau (1.2 Mha), and South Sumatra (1 Mha) provinces.  total plantation area in 2005 was estimated to be in the range of 4.0-4.9Mha, according to different sources (Blasser et al., 2011; FAO, 2010; ITTO, 2009). The ITTO (2009) estimated that Thailand had a total commercial plantation area of about 4.9 Mha, including rubber (2 Mha), teak (0.8 Mha), pines (0.7 Mha), eucalypts (0.45 Mha), acacias (0.15 Mha), and other species (0.75 Mha). Rubber IFs maintained an important leading position in wood-based industries, were mainly owned by smallholders (93%), and were located mostly in southern Thailand (>80%). Data from the FAO (2010) indicated that the area devoted to rubber plantations in Thailand increased from 2 Mha in 2000 to 2.6 Mha in 2010. However, according to the Rubber Statistics of Thailand (2011), in 2011, Thailand had approximately 3 Mha, an increase of 0.2 Mha from 2009. Pulpwood IFs in Thailand (mainly dominated by Eucalyptus spp. and some Acacia spp.) were principally established by private companies, smallholders, and governmental entities - especially smallholders who held most of the pulpwood plantations in Thailand. Barney (2005a) indicated that most of the Eucalyptus plantations were established in the northeastern area of the country (50%). Teak and Pinus IFs in Thailand were also significant. However, the information on them was scarce. Teak (0.8 Mha) was reported to be mainly established in agrosystems by governmental entities in the Northeast and North. Pinus IFs (0.7 Mha) were predominantly planted in the North, but they tended to be older plantations started in the 1960s (Oberhauser, 1997).   Viet Nam is among a few countries in the world that have significantly accomplished a net gain in forest area since the 2000s12  policies on the expansion of new tree plantations and forest rehabilitation. The FAO (2000) estimated the total plantation area of Viet Nam at about 1.7 Mha including eucalypt plantations (0.45 Mha), followed by rubber (0.3 Mha), pines (0.25 Mha), acacias (0.13 Mha), and other species (0.6 Mha). The FAO (2006) also showed the trend that the IF area used for pulpwood/fiber and sawlogs was 0.56 Mha in 1990, 1.2 Mha in 2000, and 1.5 Mha in 2005. total area of plantation forest is about 3.4 Mha, which is a significant increase from 1.9 Mha in 2002 (MARD, 2012 a&b). Of that, the total IF productive plantation area was 2.5 Mha. The productive plantations were mainly located in the Northeast, North Central, and South Central Coast/Coastal regions of Viet Nam (Viet Nam Forestry Outlook Study, 2009). These regions are considered the main material suppliers of the pulp, paper, artificial board, and chip production industries in Viet Nam. The report of the Ministry of Agriculture and Rural Development (MARD, 2010) also showed the biggest plantation area in 2009 was found in the Northeast (1 Mha), followed by the North Central Coast (0.7 Mha), and South Central Coast (0.4 Mha). Viet Nam is also a significant natural rubber producer. Luan (2013) reported at the end of 2012 that the total rubber area was 0.91 Mha, an increase from 0.41 Mha in 2000. The average area growth rate in the 2000-2012 period was 6.8%/year. Most of the rubber plantations were distributed in the Southeast region and Central Highlands. Pulpwood IFs including Eucalyptus, Acacia, and Pinus spp. were about 1 Mha in 2005 (Barney, 2005b). In addition, the Government of Viet Nam plans to establish approximately 1.4 Mha of new plantation area by 2020.   Along with India, Indonesia, Thailand, and Viet Nam, Malaysia is one of the most important countries for tropical plantations. The development of IFs in Malaysia will be presented in the following section. 13  1.4 The Development of Industrial Forests in Malaysia  Malaysia is one of the key plantation countries in the Asia-Pacific region. The ITTO (2009) IF area around 1.8 Mha in 2005, including Hevea spp. (1.5 Mha), followed by Acacia spp. (0.2 Mha), Pinus spp. (0.06 Mha), Eucalyptus spp. (0.02 Mha), Tectona spp. (0.01 Mha), and other species (0.01 Mha) (Figure 1.1). The FAO (2010) reported that while the total rubber area in 2007 was 1.2 Mha - a significant decrease from 1.8 Mha in 1990 - the area of other plantations was 0.5 Mha. This was a remarkable increase from 0.12 Mha in 1990, especially in Sarawak; there was almost no mention of other industrial timber plantations in 2000, and in 2012, the plantations had increased to more than 0.3 Mha, at the mean annual planting rate of 365%. The distribution of IFs of the country is presented in Figure 1.2.  Figure 1.1. The commercial plantation by species in Malaysia in 2005 (ITTO, 2009).   Figure 1.2. The distribution of plantations (rubber in 2005 & other IFs in 2009) in Malaysia (adapted from (1) Malik et al., 2013); (2) Malaysia Timber Council, 2009). 1,478 180 57 19 12 14 1,750 0 400 800 1,200 1,600 2,000 Rubber Acacias Pines Eucalypt Teak Others Total Area (1000 ha) IF systems IF/Commercial plantation area by species in Malaysia in 2005 0 400 800 1,200 1,600 2,000 Peninsular Malay Sarawak Sabah Area (1000 ha) Regions/States The distribution of IF systems in Malaysia Rubber  (2005) (1) Other IFs (2009) (2) 14   In general, Malaysia has extensive rubber plantations and is one of the most important natural rubber producers in the world. The rubber plantations have been established mostly in private lands under smallholders in the Peninsular Malaysia. Rubberwood represents a sithe Malaysian Ministry of Plantation Industries and Commodities (MPIC) reports a total rubber area of approximately 1.0 Mha in 2013, significantly decreasing from 1.4 Mha in 2000 and 1.2 Mha in 2005 (MPIC, 2013)2.  Pulpwood IFs in Malaysia are mainly Acacia spp. Although, currently, pulp and paper industries are quite underdeveloped (Roda & Rathi, 2006), the Government of Malaysia has identified that the pulp and paper industry is one of priority areas in the new National Economic Development Plan. The Sabah and Sarawak States are the key pulpwood production centers of the country in this plan. To promote the development of this industry, a number of projects have been proposed and implemented. In addition, big companies have been more involved in planting new IFs. For instance, the most significant project was the Planted Forest Pulp and Paper Project in Sarawak. Under this project, it was planned to establish an IF area of 100,000-150,000 ha to fulfill enough raw materials for the mill (Roda & Rathi, 2006).  Besides, Sabah also plans to construct numerous pulp and paper mills and intends to establish significant new pulpwood IF area in the state. As a result, the total timber plantation area (not including rubber) in Sarawak has significantly increased from 7,000 ha in 2000 to 300,000 ha in 2012, with the rate of expansion at 365% or 25,000 ha annually for the period (Figure 1.3)timber plantation area also increased from 150,000 ha in 2000 to 250,000 in 2012. Meanwhile, the area of other plantations in the Peninsular Malaysia only slightly increased from 74,000 ha in 2000 to 110,000 in 2009. Recently, the Federal Government has launched a new plan to establish                                                       2 http://www.kppk.gov.my/statistik_komoditi/Data%20Komoditi/general/planted%20071013.pdf  15  375,000 ha of new forest plantations in the next 15 years, giving priority to rubberwood and Acacia spp. (mainly Acacia mangium and hybrid). The expected annual planting rate is 25,000 ha. In addition, Sabah has also set a target to establish 0.5 Mha of forest plantations by the year 2020, while Sarawak is expected to have a total of 1.2 Mha by 2020 (Malaysia Forestry Outlook Study, 2009). Additionallyntation project also covers another 0.5 Mha. In brief, among the key plantation countries in the Asia-Pacific region, the development of IFs in Malaysia shows a very interesting case. While the rubber area is decreasing, the area of other IFs (especially acacias in Sarawak and Sabah) is increasing at the highest rate of area change in percentage, as compared to the rate of increase for IFs in other countries in the region. Moreover, like Indonesia, plantations in Malaysia are principally dominated by oil palms, which are not included in this study. It is indicated that rubber plantations are being outcompeted by these oil palm plantations (Jagatheswaran et al., 2011; Jagatheswaran et al., 2012), but not by other industrial tree plantations, such as the pulpwood IFs as presented above. As a result, this study will be conducted in Malaysia as a case study to investigate and examine this trend.   Figure 1.3. Industrial plantation development in Sarawak, 1997-2012 (adapted from Sarawak Forestry Department Statistics, 2012)3.                                                       3 http://www.forestry.sarawak.gov.my/modules/web/pages.php?mod=download&id=Annual%20Report&menu_id=0&sub_id=276  0 75,000 150,000 225,000 300,000 375,000 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Area (ha) Year The annual plantation area and total plantation area in Sarawak from 1997 to 2012  Annual planted area Total planted area 16  1.5 Literature Review of the Studies on Industrial Forests 1.5.1 In the Asia-Pacific Region  The purpose of this section is to examine how industrial forests have been studied in the world and the Asia-Pacific region. By doing a very simple search on the Web of Science with the syntax (1) deforestation and forest degradation, and (2) plantations and industrial forests, in the topic, 1,300 papers were found for deforestation and forest degradation, and only 8 papers were found for plantations and industrial forests from 1990 until present day. This implies that most of the past and current research has been focusing on deforestation and forest degradation, and that there are much fewer concerns and interests on the establishments of new IFs. In general, what we know about IFs now is only from general plantation databases made by international entities such as FAO and ITTO, and national forestry statistics in the region. Thus, the next question is how researchers have studied IFs, especially in the key plantation countries, on the LULCC perspectives in the region.  In India, a few studies have been done in plantation systems - in particular, new tree plantations as a new LULCC phenomenon or process. For instance, several studies have been done on the carbon stocks of plantations (e.g., Semwal et al., 2013; Bohre et al., 2013; Devi et al., 2013; Kanime et al., 2013). Other researchers have studied plantations on their ecology domain (e.g., Dey et al., 2014; Gattoo, 2013; Chaudhuri et al., 2013; Rengan et al., 2010; Mandham et al., 2009) or plantation silvicultural practices, technologies, economics, and management (e.g., Pillai et al., 2013; Prasad et al., 2010). Others still have studied plantation sustainability (e.g., Aggarwal, 2014), pulpwood and paperwood demand from plantations (e.g., Kulkarrni, 2013; Prasad et al., 2009), or constraints to the development of plantations in India (e.g., Palm et al., 2013). The study of  Prasad et al. (2009) indicated the potential for the 17  development of short-rotation and fast-growing IFs for pulpwood production from arable lands in India. Likewise, Kulkarrni (2013) studied the pulp and paper industry raw material scenarios in India and concluded that India was facing challenges about forest-based raw material source shortages for pulp and paper industries. He advised that the only strategy feasible to solve these challenges was to promote social and farm forestry plantations. Meanwhile, Palm et al. (2013) showed that there was a possibility of restoring degraded lands based on plantation activities, and this might bring positive environmental, social and economic benefits to the locals; but, in many cases, these new tree plantation establishments were obstructed by various factors, such as financial constraints, relevant soil unavailability, and water scarcity. In general, there have been very few studies on plantations in India, and in particular on the LULCC perspectives and remote sensing-based IF detection and mapping methods development.    In Indonesia, in addition to the above general statistical data, there is the fact that a few studies also have been conducted on IFs in Indonesia, especially viewing them under the LULCC perspective. Though only some researchers were interested in investigating IF ecosystem properties, such as Wilson and John (1982); Hendrien et al. (2007); Erik et al. (2010); Tsukomoto and Sabang (2005). Others conducted their research on nutrient flows and other resources factors of IFs (e.g., Bruijnnzee & Wiersum, 1987; Gunadi & Verhoef, 1993; Otsamo, 2000; Ryota et al., 2008; Naoyuki et al., 2008; Ryota et al., 2010). Several studies mentioned the economic and social aspects of IFs. For instance, Nawir and Santoso (2005) found that there was mutual benefit for both communities and companies when they cooperated in plantation development. Likewise, Ahmad et al. (2013) recognized and emphasized the role of smallholders in IF development in Indonesia. Obidzinski and Dermawan (2012) studied how global wood demands played its role in expanding the pulp production and timber IFs in Indonesia. They 18  found that the pulp and paper industry continued to depend on natural forests for its material supplies. To deal with this situation, Indonesia needs to promote the use of non-forest land for plantations and engage more smallholders in tree-growing programs. In addition, the conversions of IFs from natural forests in peatland also emitted a large amount of CO2 in Indonesia (Jauhiainen et al., 2012). In brief, from the studies researchers have conducted on IFs in Indonesia, it is clear that studies on the rates, extent, and patterns of the new IFs in Indonesia is very necessary to identify and fully quantify their roles, contributions, and impacts as a new LULCC phenomenon in the country. In Thailand, standing on the same mainstream with India and Indonesia, there were also only a few studies done-to-date on IFs. Most of these studies have focused on plantation ecosystem properties and characteristics (e.g., Aratraakorn et al., 2006; Narong et al., 2007; Katsunori et al., 2009; Wangluk et al., 2013; Doi & Ranamukkhaarachchi, 2013; Yasunori et al., 2013). While some researchers were interested in IF silvicultural practices and technologies (e.g., Terwongworakul et al., 2005; Kaewkrom et al., 2005), others were concerned over their impacts on climate change - i.e., carbon emissions and sequestrations from plantations (e.g., Warit et al., 2010; Duangrat et al., 2013). They found that a plantation acted either as a sink or source depending on which ecosystems (natural forests vs. degraded lands) it replaced. Regarding the use of remote sensing (RS) to study IFs, it was interesting that Doi and Ranamukkhaarachchi (2010) showed a possibility of using a Google Earth Image to evaluate how Acacia species helped restore forest land by discriminating canopies of natural forests with Acacia plantation plots. Most notably was the effort of Charat and Wasana (2010) in estimating the total rubber area in the Northeast of Thailand by using an integrated satellite and physical data approach. Another RS application to study rubber was from the Rasamee et al. study (2012). They used 19  Thai Earth Observatory Satellite panchromatic images and were able to identify the different rubber plantation ages. In general, studies on IFs in Thailand are still rare, and the field is lacking more comprehensive studies to fully reflect the processes, dynamics, and patterns of new IFs as a new LULCC.  Likewise, published studies on IFs in Viet Nam are also very rare. For instance, Sikor (2012) researched new IFs, focusing on their processions and land grab problems in Vietnam. Mats et al. (2010) studied the expansion of farm-based IFs by small holders in Viet Nam and found as decisive factors for a LULCC from natural forests, followed by deforestation caused by shifting cultivation practices, to a landscape largely controlled by small holder-based IFs. Conversely, Thulstrup (2014) found it was likely that households became more vulnerable, especially to natural disturbances, as a consequence of establishing new fast-growing species IFs, because this action has bolstered existing inequalities in landholding. Therefore, Pultzel et al. (2012) discussed and sought opportunities to improve likelihoods of small-scale private IF planters from domestic wood industries. In addition to these social studies, several researchers studied IFs on their ecological properties such as Millet et al. (2013); Ermilov and Anichkin (2013); Thinh et al. (2011) or silviculture (e.g., Beadle et al., 2013; Amat et al., 2010).  It is possible that no studies have been done to date with respect to new IFs as a widespread new LULCC phenomenon in the country, or to remote sensing-based methods development to detect and map these IFs.  1.5.2 In Malaysia  Compared to other key countries in the Asia-Pacific region, the studies on IFs in Malaysia were more numerous. However, similar to them, most of the studies were focused on plantation ecosystem properties and characteristics (e.g., Chey et al., 1997; Malmer, 1992, 1994, 1996) and 20  silviculture (e.g., Majid & Paudyal, 1992; Sahri et al., 1993). Several studies on IFs as a LULCC science using remote sensing methods were available (e.g., Aziz et al., 2010; Suratman, 2003, 2007; Suratman et al., 2004). These studies were mainly focused on using Landsat data to quantify rubber area in some areas of interest. Other researchers focused on the production potential of rubberwood in Malaysia on economic perspectives (e.g., Jagatheswaran et al., 2012). They concluded that, although rubberwood was the most important source of wood raw material and has been important for Malaysia (Akira et al., 2011), the steadily declining rubber cultivation area in the country was raising alarms about the future supply of rubberwood. This resulted from the competition of other land use activities (e.g., oil palm). Thus, the future sustainability of rubberwood in Malaysia will remain debatable unless the profitability of rubber growers is ensured by increasing the net value of the wood resource (Jagatheswaran et al., 2011). This raised a demand for more incentives on plantation establishments (Pinso & Vun, 2000). These researchers also argued that although forest plantations were not comparable with natural forests in terms of supplying ecological goods and services, the natural forest depletion taking place necessitated the establishment of plantations, especially on the degraded lands. Notably, the current investment incentives were not financially attractive enough for big players to come. In addition, these studies also warned of the constraints and challenges for the development of forest plantations/IFs in Malaysia consisting of ecology, land, species selection, inadequate supply of quality planting material, labor, mechanization, finance, and private involvement. These may constrain the efforts of the Government in expanding the area of new forest plantations.  21  1.6 Literature Review of the Studies on the Methods Development for Detecting and Mapping Industrial Forests There are a number of studies on the development of remote sensing-based methods to detect and map plantations and industrial forests throughout the world. However, these studies have not yet reached objectives for developing remote sensing-based methods, which can be applied to regional or global detecting and mapping of the expansion of new industrial forests. For instance, Zhai et al. (2012, 2014) developed a remote sensing method for Landsat datasets based solely on visual interpretation and ancillary data in combination with supervised classification to map rubber and pulpwood plantation expansions in Hainan, China. The visual interpretation keys the authors used to map these kinds of plantations were textures, landforms, and land terracing for rubber plantations; and spectral color in combination with ancillary data for pulpwood plantations. Likewise, Yi et al. (2014) and Xiaona et al. (2013) used ancillary data to develop environmental data/variables-based indicators for mapping rubber plantations - including topographical factors - using digital elevation models, climate (precipitation and temperature), and soil conditions. In general, these methods were area-specific and difficult to apply or expand, even regionally.  In addition to creating the Landsat dataset-based methods, Miettinen and Liew (2011) also developed a method using the Advanced Land Observing Satellite with the Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) to detect oil palm, rubber, acacia, and coconut plantations on the island of Borneo. They found that the differences between horizontal transmit and horizontal receive (HH) with horizontal transmit and vertical receive (HV) backscatters were able to separate oil palms from others; and that HV backscatters alone could separate acacia and rubber plantations. The authors also argued that separating these plantation types relied not only 22  on spectral reflectance, but also on contextual indicators such as texture, position, slope, association. In addition, they found that, in this area, pulpwood plantations were mainly acacia and were owned by large-scale industries, while rubber plantations were established in both smallholder and industrial scales. Miettinen and Liew (2011) also suggested that combining ALOS PALSAR with Landsat may help better identify these plantations.  As a result , a number of efforts have been made in developing remote sensing-based industrial forest detection methods by combining Synthetic Aperture Radar and Landsat images. For instance, Kou et al. (2005) studied and mapped deciduous rubber plantations and their ages by using Synthetic Aperture Radar and Landsat images. They found that rubber plantations could be clearly distinguished from natural forests by color in the leaf-off period. However, they were very similar in the leaf-on, or growth, period. They also used the Normalized Difference Vegetation Index (NDVI) to detect the conversion from natural forests to rubber plantations in the study area. Similarly, Dong et al. (2012, 2013) mapped rubber plantations based on both PALSAR and Landsat data. They argued that using Landsat images to map LULCC in general, and rubber plantation in particular, had two constraints: cloud contamination and spectral signal similarity. Moreover, rubber had very similar spectral signals/characteristics with natural secondary forests. These factors presented challenges for mapping rubber plantations. In brief, while these studies suggested that using the combination of Landsat data and ALOS PALSAR to detect and map plantations was a promising method, the data derived from Synthetic Aperture Radar could be spatially and temporally limited.   In another effort to develop an appropriate method to map rubbers, Senf et al. (2013) had used multi-spectral phonological metrics for the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. They also concurrently used TimeSat, a software package 23  for analyzing time-series of satellite sensor data, to extract the phonological metrics from the Enhanced Vegetation Index (EVI) and Shortwave Infrared (SWIR) series. This allowed them to plot time-series vegetation indices data and produce a temporal curve that indicated various stages green vegetation underwent. Li and Fox (2011a, 2011b, & 2012) have developed a method integrating Mahalanobis typicalities with a neural network to map rubber distribution in Southeast Asia by using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data.  By combining nine different bands - including Visible and Near Infrared (VNIR) and Short-Wave Infrared (SWIR)  the Normalized Difference Vegetation Index (NDVI), and Mahalanobis typicalities, the authors found an improvement in mapping rubbers in the study area. They argued that the Mahalanobis distance measured the class relative distance to the class mean, scaled by the class covariance; and it was very useful to determine similarity of an unknown sample to a known group of samples.  Overally, a number of methods have been proposed, developed, and tested to detect and map plantations and industrial forests. However, these methods have various constraints in using remote sensing data and broader regional and global applicability. As a result, this research proposes new remote sensing-based methods development approaches, which could work operationally for monitoring the expansion of new IFs in the region and globe. 1.7 The Significance of this Study: Problems and Rationale  Based on the methods review, it is clear that there is a need to develop new remote sensing methods for detecting, mapping, monitoring, and quantifying new IFs as a new LULCC. There is also a need to conduct more comprehensive studies to better understand the extent and dynamics of this phenomenon from both drivers and monitoring perspectives. Some researchers, (e.g., Skole et al., 2013; Li & Fox, 2012), emphasize the fact that less remote sensing (RS) work has 24  been done on plantations and other types of intensively managed industrial forests in the tropics; although there has been considerable RS research and product development for monitoring closed canopy natural forests in the tropics. Moreover, most of the studies to date have been conducted in the Amazon and other closed canopy forest regions, and that more work needs to be done outside of these regions. As a result, this dissertation research is proposed to be conducted in the Asia-Pacific region, with Malaysia as a case study. It will require some methods development and testing, particularly in the feasibility of deploying the developed methods further into the entire region. This study will also bring insights into the processes that drive LULCC in IF dynamics.   In brief, the above rationale for doing this research come from the fact that new IFs are a very important, newly-emerging LULCC in the tropics as a consequence of rapidly increasing their total area and individual patch size in recent years. However, currently, we poorly understand the processes that drive new IF expansion and dynamics. This is because we are currently lacking comprehensive studies with respect to this widespread LULCC process. Moreover, with methods and tools based on RS, we have until now mainly focused on the closed canopy natural forests. Much less work has been done on IFs and other new LULCC, such as open forests and trees outside of forests. Additionally, by detecting, mapping, and monitoring IFs, we are able to quantify the rates, extent, and patterns to understand the underlying processes/causes and proximate drivers of these new IFs. Thus, this research will also contribute to documenting and enriching the understandings of the patterns and processes of the expansion of this new LULCC. We know that the current data on plantations at international, national, and sub-national scales are poorly documented, very unreliable and unlikely to be updated soon (ITTO, 2009). We do not know exactly what is happening to the new IFs - such as their 25  locations, rates, extent, and scale properties - or what kind of ecosystems they have been replacing (i.e., how much the new IFs were converted from natural forests and how much from degraded land, etc). Lacking the reliable information on plantations has created difficulties and uncertainties for any policy and management on IFs.   Therefore, this study is focused on documenting and understanding the new IF LULCC trend and phenomenon in the tropics, and will contribute to an international and national need for this kind of information. In brief, this study aims to improve the knowledge base and understanding on the extent, characteristics, and drivers of new IFs as a new agent of LULCC, and to develop the methods to detect and quantify IF LULCC patterns and dynamics. Once appropriate methods have been successfully developed and tested in the pilot study sites, they can be applied for the entire region. In other words, this study will involve the development of a continental-scale monitoring method, using a time series of Landsat data that could operationally monitor and quantitatively report on the rate and scale of IF LULCC on a regular basis. Thus, it will formulate a better understanding of drivers and LULCC dynamics associated with emerging IFs in the tropics.   In addition, there are some contributions to advancing research that could come from this study. This research will not only contribute to developing new RS methods, improve documenting, and enrich the understandings of new IFs as a new LULCC, but will also enable researchers to quantify the IFs impacts or contributions on current climate change, the environment, and biodiversity. A new IF can act as a source or sink, depending on what kind of terrestrial ecosystems it is replacing. As a result, we could use them as a sink to sequester carbon dioxide and mitigate climate change. At the same time, we can also use them as a feasible solution to relieve pressures on natural forests and conserve these forests. The information and 26  data derived from this study could be used to better plan and manage IFs in the country, the region, and the tropical world.  1.8 Selection of the Study Area and Industrial Forest Systems  Generally, it is very challenging to draw a panoramic picture about forest plantations or IFs in the Asia-Pacific region in general, and in Malaysia in particular, because data and information on the targeted IFs in the region and the country are very scarce, unreliable, and outdated. However, by doing a literature review based on what is publicly available, I was initially able to (1) stratify Malaysia for IF sources area and (2) consider and assess forest investment and policy targets for key production areas. As a result, it is possible to select two pilot study sites in this country based on the following selection criteria: (1) Selected species: the areas should contain most of the selected species or the targeted plantation/IF systems (i.e., Eucalyptus, Acacia, Pinus, Tectona, & Hevea spp.). (2) Area: the selected sites should show the largest or very significant new IFs area.  (3) Dynamics: the areas should indicate the highest or a very significant rate of change in new IF area, and; (4)  Policy and investment targets:  key production centers and other policy factors should be considered.  The locations selected for this study in Malaysia are the Sarawak and Sabah states (Figure 1.4). This is because these states currently capture the biggest non-rubber selected IFs area and indicate the highest expansion rates, as compared to other states in Malaysia, especially for fast-growing IFs (Table 1.1). Moreover, these regions are also identified as the key production centers for the pulp and paper industry of the country.   27  Industrial Forest Systems Studied:   Plantations of acacias (Acacia spp.), eucalypts (Eucalyptus spp.), pines (Pinus spp.), teak (Tectona spp.), and rubber (Hevea spp.) will be chosen for this study. These IFs are mainly used for the production of wood for pulp and paper, saw logs, and other industrial woods. Focusing on these systems and species accounts for more than 90% of all IFs in the Asia-Pacific tropical region in general, and in Malaysia in particular. Moreover, the development and testing of the new RS-based IF detection methods for these systems are more likely to succeed because all types of IFs are very diverse and cover too many varying LULC characteristics and properties.  Figure 1.4. Map of Malaysia showing the selected study sites (Sarawak & Sabah States).  In brief, for this study, Sarawak and Sabah were selected because (1) these two states show very impressive IF planting rates over the recent years (since 2000), in particular in Sarawak where the IF area (not including rubber) has annually increased 365% on average  from 2000 to 2012; (2) these regions are very notorious for heavy cloud and hazy contamination, therefore it is 28  Table 1.1. A summary of plantation areas and the rate of their change in Malaysia and by state. Species Region/ State Area ( ha) Difference for the period Rate of change  Social & Economic Factors Note Source 1990 (or 2000) 2005 (or 2009) ha %/ year ha/ year Rubber Peninsular Malaysia 2,279,001 1,535,127 -743,874 -2.2 -49,592 Reported as out-competed by oil palms Most rubbers owned by smallholders (80-96%);  Malaysia Rubber Statistics 2011:~ 1,013,000 Mha Malik et al. (2013) Sarawak 152,717 (2000) 209,918 + 57,201 0.9 11,440 Sabah 78,895 (2000) 62,891 -16,004 -4.1 -3,201 Total (other statistics) 1,836,700 1,244,600 (2009) -592,100 -1.7 -31,163 Ratnasingam  et al. (2011)   Other selected IF species (mostly Acacia, some others)   Area in year 2000 Area in year 2012             Peninsular Malaysia 74,000 110,000 (2009) + 36,000 4.1 3,000 Low potentials for IFs   Malaysia Forestry Outlook Study (2009) Sarawak 6,830 306,486 + 299,656 365.6 24,971 Key production centers for pulp & paper industries Sarawak plans to have 1.2 Mha in 2020  Sabah expects to have 0.5 Mha in 2020 Sarawak Forestry Statistics (2012) Sabah 154,640 244,000 + 89,360 4.8 7,447 Sabah  Forestry Statistics (2012) 29  challenging for RS-based methods development. I prefer to choose this area because if my developed methods work in this difficult area, it will be more likely or better work in other regions which have the easier conditions; (3) the area is dominated by oil palm plantations, which are not included in our targeted IF systems but have similar texture and arrangement to them, so that separating these plantations is also very challenging in terms of RS-based methods development; (4) the IF data in Malaysia is quite firm compared to other selected countries, and (5) Malaysia has the most potential among five selected countries to invest and develop industrial forests; it also plans to develop the pulp and paper industries as one of its national priorities.  1.9 Research Questions and Objectives  Research Questions This study aims to improve understanding of the extent, characteristics, and drivers of new IFs as a new agent of LULCC, and to develop the methods to detect and quantify IF LULCC patterns and dynamics. These methods are prototyped and can be applied for the whole region, and can be worked as operational monitoring methods for this LULCC phenomenon. The fundamental questions posed here guide the research: 1. Can we develop and use methods based on RS datasets (i.e., Landsat) that could detect, map, and monitor the area, expansion rate, patterns, and scale of IFs? 2. Are IFs increasing both in individual patch size and the total area in Malaysia? Can we detect and quantify their total extent, expansion rates, and patterns? Is there any shift from fast-growing, short-rotation (e.g., pulpwood) to slow-growing, long-rotation (e.g., sawnwood) IFs? 30  3. If IFs are increasing in Malaysia, what types of natural or managed ecosystems are they replacing? Objectives From the above research questions, the main objectives for this study are as follows: 1. Develop methods based on vegetation/forest fractional cover (fC) and vegetation indices (VIs) analyses for detecting new IFs in a time series of Landsat data. 2. Detect, map, and monitor new IFs in the pilot study sites in Malaysia with more specific aims to measure: a. Expansion rates, sizes, extent, and patterns of the newly established IF systems in the study area, and how they have been changed from 2000 to 2014; b. How much of these new industrial forests were converted from other LULC types (e.g., natural forests and degraded lands), and their consequences in terms of green house gas emissions and biodiversity losses. 1.10 Research Methods The initial proposition for this study is that new and needed methods for detecting, mapping, and quantifying IF areas, patterns, and scales in the selected tropical IF systems will be developed. Specifically, I developed methods based on remote sensing (RS) to detect, classify, map, monitor, and analyze changes of Land Use and Land Cover (LULC) for new IFs in the selected tropical country over time. The fundamental principles of these RS-based methods on IFs are that most IFs are monocultures of only a few species, which have similar crown shape, regular spacing, and other typical biophysical characteristics. They greatly differ in form and structure from natural tropical forests and other vegetation covers. This idea exactly fits with the concept of plantation forests of the FAO (2000): that a plantation forest is a forest or a wooded 31  land of introduced species or native species, established through planting or seeding, with a few species at plantation, even age class, and regular spacing. These plantation forests or IFs are typical by their silvicultural rotations or clearing and regrowth cycles, depending on the purpose of using them. For instance, Acacia pulpwood plantations can typically last 5-10 years; a rubber plantation can have a rotation of 25 years; a teak plantation used for producing saw logs can take 25-50 years or more. In other words, based on this information - along with the differences in form, structure, texture, spatial, temporal, patterns, tones, crown shape, and other characteristics and properties of IFs from other vegetation covers, such as natural forests in satellite images - by doing RS analysis, I can detect, classify, and map them. By extracting their areas between and among multi-dated images, I can detect and monitor their changes over time.  In brief, in this study, I developed and tested two method approaches for Landsat data to map the IF extents and patterns in the pilot study sites: (1) Forest or Vegetation Fractional Cover (fC)-based IF detection method, and (2) Vegetation Indices (VIs) analysis in a Time Series to detect IFs for large coverage area. Skole et al. (2013) state that some their recent research results in their lab show strong radiometric signals that can be used in statistical classification methods, as well as other methods, such as forest fractional cover from endmember analysis to detect and map IFs. The approaches and procedures, adapted from Skole et al. (2013), for developing the methods of Forest Fractional Cover (fC) and Vegetation Indices (VIs) analysis in a time series are generally presented (Figure 1.5).  Regarding the detection of the selected/focused species or specific IF stands/systems - such as acacias, eucalypts, pines, teaks, and rubbers (including pure stands and mixed stands) - it is very challenging to detect and map them separately from other species based on RS methods with medium resolution imagery data, like Landsat datasets. Thus, I will differentiate them by 32  (1) using extra spectral and textural analyses; and (2) considering ancillary data in combination with visual interpretation, including their biological, physical, and ecological characteristics, as well as other information sources. For instance, rubber IFs will be planted in some certain soil, elevation, and climate conditions. In Malaysia, they are mostly distributed in Peninsular Malaysia. This type of data may be available or reported by owners, organizations or local governments. Likewise, Acacia IFs were mainly established in the Sarawak and Sabah states. Their locations and areas may be available in reports of investors, timber companies or maps of state governments or research institutions, etc. This kind of information will be combined with information derived from satellite images, such as the silvicultural cycles of clearing and regrowth, textural and spectral analysis, typical green biomass content, and leaf area index, etc., enabling us to map focused and unfocused species, and pure or mixed stands.   In brief, the methods developed and tested in this study will use geographic, ancillary, and visual interpretation information in combination with remote sensing analyses to detect and map the expected IFs, and monitor the IF LULCC in Malaysia in particular and enable us to apply to monitor the IFs in the whole tropical Asia-Pacific region in general. Validation  After the results of the above methods development have been obtained, validation is extremely important to see how these methods work and if they are acceptable. Validation for the developed methods in the Landsat derived pilot area data products was conducted through a stratified random sample design by using the very high-resolution imagery data, such as World View, Quickbird, GeoEye, Ikonos, Pleiades, etc. Both study sites in Malaysia (the Sabah and Sarawak states) was validated by using these very high-resolution imagery data, available through the National Geospatial-Intelligence Agency (NGA) Commercial Data agreement with- 33   Figure 1.5. The general flowchart for the development of forest fractional cover (fC)- and vegetation indices (VIs)-based industrial forest detection methods for Landsat datasets. -NASA or purchased from commercial suppliers such as Apollo Mapping. The validation targets include the accuracy assessments for (1) IF (in general and specific for the selected species) vs. non-IF lands classification and (2) the IF area estimates consisting of individual patch size and total area. The very high-resolution imagery data used to validate the Landsat-derived products was close-to-same date or at least same year data. Error/confusion matrices or contingency tables was computed and reported. 34  In general, there are three approaches typically used to assess the accuracy of research results based on remote sensed imagery data. For LULCC classification (pixel-based, statistical or hard and and omission errors, or Kappa coefficients deriving from an error matrix (also called confusion matrix or contingency table) are used (e.g., Congalton, 1991; Congalton & Green, 2009; & Olofsson et al., 2014). Whereas parameters including the linear regression correlation coefficient (R), the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and System Error (SE) are usually used to evaluate the results obtained from fractional cover  methods (e.g., Dennison & Roberts, 2003; Wang et al., 2005; Jimenez-Munoz et al., 2009, Mei et al., 2010; Lu et al., 2011). Conversely, for Geographic-Object-Based Image Analysis (GEOBIA), usually applied for detecting and delineating individual tree crowns (ITC), the two levels of assessment will generally be used to evaluate the accuracy of the method, namely plot and individual accuracy levels for both detection and delineation results. Normally, pand uaccuracies or overall accuracy are used for tree crown detection; and mean error, absolute error, root mean square error (RMSE) are used for tree crown delineation (e.g., Lamar et al., 2005; Ke et al., 2010; Ke & Quackenbush, 2011). In addition, some researchers (e.g., Pouliot & King, 2005, Huirschmugl et al., 2007; Ke & Quackenbush, 2011) used Accuracy Index to take both the commission and omission errors to assess the accuracy; consequentially Larsen et al. (2011) used a matching score to evaluate the results based on GEOBIA approach derived from very high resolution imagery data.  As clearly stated above, the objectives of this research are to detect, map, and monitor industrial forests in the tropics based on forest/vegetation fractional cover and vegetation indices analysis methods for Landsat datasets. Therefore, IF maps are a type of classification map and 35  the accuracy assessment methods for LULC classification will be used for validating these maps. The principal requirements for the number of samples, their locations, sampling selection methods (random, cluster, systematic, or stratified) is presented in the sampling scheme (Table 1.2), as required for this kind of work. Table 1.2. The principal requirements for a sampling scheme to validate the developed methods. Elements of the scheme Description General requirement The acceptable accuracy level is 85% at the 90% confident level Number of samples 50 samples/LU class for the area less than 0.5 Mha, if the area over 0.5 Mha or has more 12 LU categories, 75-100 samples are needed (Congalton, 1991). Sampling unit 1 or more pixels in field validation or the patch sizes in high-resolution data Location of samples Stratified Random Sampling for each land use type/class in the IF thematic maps or direct the high-resolution data to key areas Reference site/ field survey identification GPS points (predetermined & checked), photos, data sheet for field surveys (date, time, etc), and other data (reports, interviews, etc) Visit plans Predetermined locations, time, vehicle, tools/equipments, accommodation, and cost, etc.  Following that, an error matrix for this validation was produced. In this matrix or table, the classified LULCC types was presented in the rows or column while referenced/verified LULCC types was be located in the columns or rows of the table. As a result, the diagonal line expressed the agreements between the classified and referenced elements/types or classes. The accuracy assessments for three above-stated validation targets include overall accuracy, accuracies of commission for IF maps (Table 1.3).    36  Table 1.3. The ways for assessing accuracy of IF maps derived from Landsat datasets. Accuracy  Description Equation Note Overall  The total number of samples in all types classified correctly divided by the total classified samples (the diagonal elements in table/the classified total) # total correct/total samples Of all of the reference sites, what proportion is classified correctly  The ratio between the number of correctly classified and the row total  # correct/column total Map accuracy from the point of view of a map user or how often the type the map presents should be there really there  The ratio between the number of correctly classified and the column total  # correct/row total Map accuracy from the point of view of map makers or how often real features on the ground correctly shown on the map coefficient/ statistics A measure of how accurate your map is above and beyond the accuracy that would be expected by chance alone (Observed  Expected) / (1  Expected) Observed = Overall accuracy Expected = sum of (row total * column total by class in proportion unit) Taking omission, commission and overall accuracy into account simultaneously Omission Error A type on the ground is not that type on the classified image or the real type is omitted from the classified image   The error of exclusion Commission Error A type on the classified image is not that type on the ground or the type is committed to the classified image  The error of inclusion   37  1.11 The Flowchart of the Study Figures 1.6 and 1.7 present how the study will be developed and conducted.     Figure 1.6. The general flowchart of the study.                Figure 1.7. The system diagram of the study.  IF phenomena:  Recently increasing both in individual patch size and total area in the tropics.  Shifting from slow-growing, long-rotation to fast-growing, short-rotation species.  Appearing a significant geographic shift in the location of new IFs from temperate zones to tropical regions. IF problems:  Limited reliable data on its rates, extent, processes and patterns due to lacking comprehensive studies on new IF LULCC.  Lacking robust tools to detect, map, and monitor it. Study Objectives:  Developing methods based on RS to detect, map, and monitor new IFs.  Quantifying the expansion of IFs. fC and VIs-based Methods Development for:  Landsat data for large areas.  Methods Application to:  Pilot study sites  Literature Review, Data Collection   Policy Assessment and Analysis  Determining Study Sites  Selecting Imagery Datasets IF Maps Validating Results & Methods Investigating:  The extent, rates, processes, and patterns of new IFs. Completion of the study and publications Literature Review, Data Collection, and Policy Assessment and Analysis  Study Area Determination and Imagery Datasets Selection  Methods Validation Land Use Land Cover Changes Assessment  Methods Development and Application Completion of the Study 38  CHAPTER 2     DEVELOPING THE VEGETATION INDICES-BASED INDUSTRIAL FOREST DETECTION METHOD FOR  LANDSAT DATASETS  2.1     Introduction The first approach used in this study to develop a method to detect, map, and monitor new industrial forests in the study area is a vegetation indices change analysis in a time series. Vegetation indices (VIs) analysis is a technique widely used to detect, map, monitor, and analyze vegetation in general, and in forests in particular. The fundamental principles of these VIs-based methods are that vegetation absorbs most of the red band (630-690 nm), while reflecting the near infrared band (760-900 nm).  By analyzing the correlations between them, we can obtain information about the status of vegetation or forests necessary for our studies, as well as for other purposes, such as forest management. The vegetation indices-based methods have proven very useful in studying vegetation in a number of cases (e.g., Basso et al., 2004; Wu, 2014).   In this study, a suite of Vegetation Indices (VIs) will be computed in a time series: the Normalized Difference Vegetation Index (NDVI; Rouse et al., 1974), the Soil-Adjusted Vegetation Index (SAVI; Huete, 1988), the Atmospherically Resistant Vegetation Index (ARVI; Kaufman & Tanre, 1992), the Soil-Adjusted Atmospherically Resistant Vegetation Index (SARVI; Kaufman & Tanre, 1992), the Modified Soil Adjusted Vegetation Index 2 (MSAVI2; Qi et al., 1994), and the Enhanced Vegetation Index (EVI; Huete et al., 2002). NDVI (Rouse et al., 1974) is one of the earliest and most widely used vegetation indices and is very useful in 39  studying vegetation and the environment. Among other uses, it is often used to estimate net primary production, identify eco-regions, monitor phenological patterns of the earth and its vegetative surface, and assess the length of the growing season. However, it is affected by interactions with elements such as soil, atmosphere, and sun-target sensor, and will saturate at the Leaf Area Index (LAI) of 3. To reduce these effects, Huete (1988) transformed this NDVI index and developed it into the Soil-Adjusted Vegetation Index (SAVI) to minimize the soil influences. Nevertheless, this index does not solve the additive atmospheric effect problems on satellite images. As a result, Kaufman and Tanre (1992) developed the Atmospherically Resistant Vegetation Index (ARVI) with the purpose of mitigating the effects of the atmosphere by using a self-correction process on the red channel. This transformation uses the difference in radiance between the blue and red band channels to correct the radiance in the red band. Next, they developed the Soil-Adjusted Atmospherically Resistant Vegetation Index (SARVI) to take both soil and atmospheric effects into account. However, although these indices worked well in many cases and specific areas, they still do not completely eliminate the additive effects in many other cases.   In another effort, Qi et al. (1994) developed the Modified Soil Adjusted Vegetation Index (MSAVI), a new vegetation index better able to handle the soil effects by considering the soil effects as a variable function instead of a constant, as had been done before. This index proved to work well in tropical environments. In 2002, Huete and his colleagues (Huete et al., 2002) developed a vegetation index with global applicability, called the Enhanced Vegetation Index (EVI). This index deals with both soil and atmospheric effect problems. All of these indices have values from -1 to +1.  However, due to the effects of soil and atmospheric conditions mentioned above, specific VIs can perform better than others in the different geographic regions. Therefore, 40  as each index has its own strengths and weaknesses, it is very necessary for us to test and choose the most relevant and best performing indices for the study area.   The idea for using VIs to study IFs is that, with annual Landsat datasets, we can observe their silvicultural clearings and re-growth. These repeated clearings are typical in IFs and could indicate the short or long rotation of IF stands. Moreover, the growth rate of these VI values possibly expresses how fast or slow an IF stand is growing. Therefore, based on this information, we can obtain shorter- versus longer-rotation and faster- versus slower-growing industrial forest stands and are able to analyze both patch size and harvest cycles. Skole et al. (2013) state that by stacking annual VI data sets as a single remote sensing data product where clearings (harvests) and re-growth can be observed, analyzed, and reported for area extent, we can observe individual patch sizes, harvest cycle periods of IFs, as well as their changes over time.  2.2 Acquiring and Preprocessing Images  The multi-temporal Landsat scenes used in this study were freely acquired from historical archives at the EROS Data Center, U.S. Geological Survey, U.S. Department of Interior at http://glovis.usgs.gov/ and the Tropical Rain Forest Information Center at Michigan State University, USA over the past 15 years. The scenes were selected for the years 2000, 2003, 2006, 2009, 2012, and 2014. Aldrich (1975, cited in Coppin & Bauer, 1996; Michener & Houhoulis, 1997;  Coppin et al., 2004) stated that, in most cases, a minimum time interval of three years was required to detect non-forest to forest changes.   In this study, the criteria to select the main scenes were within the time from May to August, cloud cover < 30%, and image quality at least from 7. We know that the study area (Sabah and Sarawak states in Malaysia) is notorious for heavy cloud contamination and haze; therefore, additional scenes were required to fill the gaps created by clouds, cloud shadows, and haze. The 41  criteria to select these extra scenes were as close as possible to the main scenes and at a maximum within one year before and one year after the year of the main scenes chosen. In the case, images close to the date of the main scenes were not available, so better quality images ± one year of the main scenes were selected for the study. Briefly and specifically, details regarding the scenes selected were as follows:  The main scenes were selected for the years 2000, 2003, 2006, 2009, 2012, and 2014, focusing on images from May to August of those years; if not, the best scene in the year was selected;  Additional scenes within ± 1 year were considered. For example, the scenes in 1999 and 2001 could be used for filling the scene of 2000. However, more priority was placed on the scenes of 2000 used to fill the gaps for the selected scene (closer to the original data is better);  The quality of the scenes used to fill the gaps in the selected main scenes was the second priority; and  All errors or no-data of the Enhanced Mapper Plus Scan Line Corrector off (ETM + SLC off), clouds, and cloud shadows had to be removed and filled until the acceptance level.   Sabah covers 8 Landsat scenes consisting of path 116 with rows 56 and 57; path 117 with rows 55, 56, and 57; path 118 with rows 55, 56, and 57. Likewise, Sarawak covers 9 Landsat scenes including path 118 with rows 57, 58, and 59; path 119 with rows 57, 58, and 59; path 120 with rows 58 and 59; and path 121 with row 59. In total, 563 scenes were selected and processed. The full list, quality, and dates of the Landsat scenes used for this study are provided in the Appendices (Tables A.1, A.2, A.3, and A.4). All the Landsat scenes used for this study were pre-processed following a general procedure (Figure 2.1) or they were downloaded from the free 42  online service in which they were already preprocessed. This online service was freely provided by the Earth Resources Observation and Science Center (EROS), U.S. Geological Survey under the U.S. Department of Interior; namely, the Science Processing Architecture on Demand Interface at https://espa.cr.usgs.gov. This is a new service just developed recently. This service provides calibrated images at surface reflectance and processes cloud and cloud shadow by using the Fmask method developed by Zhu and Woodcock (2012); and Zhu et al. (2015).      In general, all images were calibrated by converting the processed data digital numbers (DNs) to the at-sensor-radiance values, and then to exoatmospheric top-of-atmosphere reflectance values. This radiometric correction was conducted by using the calibration coefficient provided in the meta data file in each image or calculated from the coefficients given by Chander et al. (2009). Then, according to Song et al. (2001), and Hadjimitsis et al. (2010), for multi-temporal data to monitor LULCC over time, all images would need to be corrected the atmospheric effects. The method widely used to correct the atmospheric effects was extracted from images i.e., applying the darkest pixel (DP) atmospheric correction method (also called the histogram minimization method) to handle the atmospheric effects on the images. The darkest pixel technique was developed based on the assumption that the lowest DN in each band in each pixel would , and thus its radiometric value represented the atmospheric additive effects. The darkest pixels would be selected based on a DN histogram analysis and image examination. The purpose of these works was to maintain consistency in measurement of surface reflectance among multi-dated datasets, which was needed for multi-temporal data to monitor LULCC over time.  Then, the Fmask method (Zhu & Woodcock, 2012; Zhu et al., 2015) was applied to these images to remove clouds and their shadows. This method was freely available at https://code.google.com/p/fmask/. It was widely applied and proved very effective in masking 43  out clouds and cloud shadows. In addition to using this method to handle cloud contamination in the images, it was also used to remove water bodies, which were not necessary for this study.                       Figure 2.1. The general procedures for preprocessing images. Landsat Data Layer stacking Atmospheric correction Water/Cloud masking  Using Fmask Free-cloud & Atm. corrected Landsat 8:     B 2, 3, 4, 5, 6, 7 LS 4, 5, & 7: B 1, 2, 3, 4, 5, 7 Radiometric calibration Top-of-atmosphere (TOA) Reflectance conversion (TOA reflectance) Using Erdas Models < 2.5%  Cloud/shadow YES OK NO Gap filling 1 Landsat Data 1 Layer stacking 1 Water/Cloud masking 1  Free-cloud; Atm. Corrected 1 Radiometric calibration 1 Landsat Data 2 Layer stacking 2 Atmospheric correction 2 Water/Cloud masking 2  Free-cloud,Atm. Corrected 2 Radiometric calibration 2 < 2.5%  Cloud/shadow YES OK NO Gap filling 2 2.5 %  Cloud/shadow   YES OK Free-Cloud and No- + GLOVIS_Landsat Scenes including TM (LS 4&5), ETM+ (Landsat/LS 7), OLI (Landsat/LS 8) All gaps:  1. Cloud,  2. Cloud shadow, 3. ETM+ SLC off Gaps  with NoData value   must be filled at one by using   ERDAS MosaicPro   Assess the Mosaic Images Atmospheric correction 2 44   In the areas well-known for clouds and haze, such as tropical rainforests in Sabah and Sarawak, Malaysia, we had to use many other images to fill the gaps created by clouds and their shadows. In addition, images obtained from Landsat 7 (ETM+ SLC off) were also known for missing values at the scan lines since 2003. To deal with these problems, a gap-filling technique was used. This technique was done in the ERDAS MosaicPro by using the overlay function until it satisfied the requirements with all gaps filled, in s 2.5%.  Regarding the already preprocessed images, they were downloaded from the EROS Data Center at https://espa.cr.usgs.gov. These images were already calibrated by converting the processed data digital numbers (DNs) to the surface reflectance values. At the same time, these scenes were also processed to remove problems created by clouds. First, their individual bands were chronologically stacked by using the stack function in ERDAS Imagine. Second, they were mosaicked by using the ERDAS MosaicPro as described above until they satisfied the requirements.   Then, all the preprocessed images, due to the heavy haze in the study area, were dehazed by using the TM dehazed model. This model was already built up in ERDAS Imagine. Finally, after the images were preprocessed and dehazed, they were ready for further use and analysis.   2.3 Developing the Method  2.3.1 General Principles   The procedure for developing the Landsat-based IF detection method by using vegetation indices to transform the preprocessed images into final IF maps is described in Figure 2.2. The main assumptions used for developing this method were as follows:  The cycle of increasing and reducing the VI values possibly indicated the silvicultural cycle of clearing and regrowth of vegetation covers, typical for an IF/plantation stand. 45   The time span for a silvicultural cycle could indicate shorter (<=7 years) versus longer (> 7 years) rotation IFs.  The rate of increasing VI values (VI growth rate) may indicate faster-growing versus slower-growing timber plantation species.  The spectral and textural characteristics of an IF in an image may be different from other vegetation covers (e.g., forests) and might differ among different IF species as well. 2.3.2 Results  In this method, after acquiring the already-preprocessed Landsat datasets, a suite of vegetation indices (VIs) was firstly computed: NDVI, EVI, ARVI, SARVI, SAVI, and MSAVI2 as follows: (1) Normalized Difference Vegetation Index (Rouse et al., 1974)  (2) Soil - Adjusted Vegetation Index (Huete, 1988)  (3) Atmospherically Resistant Vegetation Index (Kaufman & Tanre, 1992)  RB = RED   RED) (4) Soil Adjusted - Atmospherically Resistant Vegetation Index (Kaufman & Tanre, 1992)  (5) Modified Soil Adjusted Vegetation Index 2 (Qi et al., 1994)  (6) Enhanced Vegetation Index             (Huete et al., 2002)  46                         Figure 2.2. The flowchart of development of the VIs-based IF detection method. Preprocessed Images VIs Calculations Stacked by VI type Change Detection Clearing & Regrowth Fast vs. Slow Growing IFs Short vs. Long Rotation Fast-Growing, Short-Rotation & Slow-Growing, Long-Rotation IFs Calibrated by Spectral Analysis  Texture (GLCM) Band 4 Band 5 PCA TCA ICA Dissimilarity Mean Homogeneity Visual Visual Interpretation and other ancillary data IF maps Validation Final IF maps Band 4 and Band 5 NOTE: PCA:  Principal Component Analysis TCA:  Tasseled Cap Analysis ICA:  Independent Component Analysis GLCM: Grey Level Co-Occurrence Matrix 47   Where:    NIR:  Landsat Near Infrared Spectrum band (0.76  0.90 µm, band 4)    RED: Landsat Visible Red Spectrum band (0.63  0.69 µm, band 3)    L:  Soil calibration/adjustment factor [0, 1]; its default value is 0.5    RB: Landsat Visible Red (R) and Blue (B) Spectrum bands     The weighting of the Blue band radiance    G: Gain factor, its default value is 2.5 C1&2: Coefficients of the aerosol resistance, the default values for C1 and C2 are 6 and 7.5, respectively. K: Canopy background adjustment factor, its default value is 1. For NDVI, to reduce atmospheric effects to this index, Karnieli et al. (2001) had modified the original version by replacing the red band in the formula with the shortwave infrared band (SWIR) at 2.1 µm, and renamed it the Aerosol Free Vegetation Index (AFRI), as follows:  This is because visible bands in vegetation indices in general, and in NDVI in particular, are very sensitive to the atmospheric effects, especially to smoke and other types of aerosols (Karnieli et al., 2001; Huete et al., 2003; Matricardi et al., 2010). In contrast, shortwave infrared (SWIR) and near infrared bands (NIR) are found to be much less sensitive to the atmospheric conditions. Moreover, under aerosol free atmospheric conditions, they have a very high correlation with visible bands. As a result, these bands were used as an alternative to the most sensitive visible band in vegetation indices. The AFRI or NDVIaf index has been proven to work well (Karnieli et al., 2001; Matricardi et al., 2010). Thus, this modified index was used to obtain vegetation information in the study area instead of using the original NDVI. 48  Likewise, Matricardi et al. (2010) also tested the modified MSAVI under the smoky conditions in the Brazilian Amazon by replacing the red band in the original MSAVI with the shortwave infrared band (SWIR) at 2.1 µm and found improved results compared to the original method. The tropical rainforest conditions in Sabah and Sarawak in Malaysia are very similar to the environmental conditions in the Brazilian Amazon. Therefore, this modified index was also used for the study. This index was named the Modified Soil-Adjusted Vegetation Index Aerosol Resistant (Matricardi et al., 2010) and is presented by the following formulas:  and   L = [ ( NIR  0.5 SWIR )*s + 1 + NIR + 0.5 SWIR ]2  8 *s* ( NIR  0.5 SWIR) Where s = 1.2 (slope of the soil line) For other vegetation indices selected for this study, including ARVI, EVI, SARVI, and SAVI, the original versions were used to calculate the values. These indices were calculated for the preprocessed images, which have been calibrated and atmospherically corrected. The VIs, calculated for the Landsat scenes, were chronologically stacked for better visual change detection recognition. Specifically, these VI images were stacked in ERDAS Imagine and by type (ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI) with the following chronological order rules: the VI image of the year 2000 would be band 1, 2003-band 2, 2006-band 3, 2009-band 4, 2012-band 5, and 2014-band 6. An example of stacking MSAVIaf images in Sabah and Sarawak from 2000 to 2014 is presented (Figure 2.3). This provided an illustration of where the values of the MSAVIaf have changed over time. For instance, the pink areas showed vegetation cover in those areas that was cleared in 2003 and regrown in 2006-2014; likewise, the yellow areas showed vegetation cover that was cleared in 2006 and regrown in 2009-2014; and the blue areas indicated vegetation cover that was cleared in 2000 and regrown in 2003-2014, etc.  49    Figure 2.3. The stacked MSAVIaf images for Sabah and Sarawak, 2000-2014. Sabah Sarawak Pink: vegetation cover was cleared in 2003 & regrown 2006-2014; Yellow: vegetation cover was cleared in 2006 & regrown 2009-2014; Blue: vegetation cover was cleared in 2000 & regrown 2003-2014. 50  The full VI images stacked from 2000 to 2014 by VI type (ARVI, EVI, MSAVIaf, NDVIaf, SARVI, & SAVI) in Sabah and Sarawak are presented in the Appendices (Figures A.1 & A.2). These stacked VI images would be used for further analyses.  Then, the changes of VI values from 2000 to 2014 were detected by using the image differencing method as expressed in the formula [2.1]. The changes were detected for the years 2000-2003, 2003-2006, 2006-2009, 2009-2012, and 2012-2014. The principles for this method, adapted from Cakir et al. (2006), are described in Figure 2.4. The VI value of the later year would be deducted from the VI value of the earlier year. A positive result/number (or the value in the right side of the graph) indicated an increase of the VI value from the earlier year (e.g., 2000) to the later year (e.g., 2003), meaning that there was growth of vegetation cover. Whereas, a negative result/number (or the value in the left side of the graph) indicated a decrease of the VI value from the earlier year to the later year, meaning that there was a decline in or clearing of vegetation cover in the later year. The vegetation cover between two years was not changed when its value approached 0. Cakir et al. (2006) argued that there were 3 regions expressing change or no change in the image differencing method. The first region indicated absolute change which was from a chosen certain figure to 100% change or towards the two tails of the graph. The second region was possibly a change which was expressed in the given value range in the graph (this region could be affected by atmospheric conditions, image quality, etc); and the third region was absolutely no change in which the values approached 0 in the graph.  Therefore, in this method, it was very important for us to determine the change point, or threshold of the change. There are a number of ways to do that. One of the most widely used ways is trial and error experiments. Based on this method, ±15% was found to be good enough for indicating a change in this study because it could effectively mitigate the additive effects or 51  variability of the atmosphere to the images. Thus, this value was selected as the threshold for the vegetation change detection value in this study. The full changes of VI values for the study area from 2000 to 2014 are presented in the Appendices (Figures A.3, A.4, A.5, A.6, A.7, and A.8).  Change = VI (t2)  VI (t1) [2.1]  Where: VI is the value of vegetation index. t2: the after/later image. t1: the before/earlier image.   Figure 2.4. The change detection graph (adapted from Cakir et al., 2006).  Figure 2.5. The changes of MSAVIaf value from 2012 to 2014 in Sabah and Sarawak, Malaysia.        52   The sequence of change (increasing and declining VI values of at least ±15%) or no change of the VI_MSAVIaf values at the location       as the follows:  2000 - VF, 2003 - VF, 2006  NV/NF, 2009 - VF, 2012 - VF, and 2014  NV/NF.             2000   2003   2006   2009   2012   2014 Figure 2.6. The sequence of the VI (MSAVIaf) value changes, 2000-2014, in the study area. For instance, Figure 2.5 presents a VI value change in Sabah and Sarawak from 2012 to 2014. The yellow area indicates an increase of the VI value from at least the threshold of +15%. This represents a vegetation regrowth. Conversely, the red area expresses a decrease of the VI 1 1 1 1 1 1 1 V/F: Full or more vegetation cover NV/NF: None or less vegetation cover 53  value from at least the threshold of -15%. This area indicates a decline in or clearing of vegetation cover. Next, the sequence of VI value changes in study area from 2000 to 2014 was studied. This sequence shows a cycle of the change. It provided initial clues for detecting industrial forests because it could present a silvicultural rotation, which is typical for an industrial forest stand. For instance, Figure 2.6 presents a sequence of MSAVIaf value changes at the threshold of ±15% from 2000 to 2014. The vegetation/forest (V/F) indicates the vegetation cover. It could be the existing vegetation cover as it was or a change from non-vegetation/forest (NV/NF) cover to more or full vegetation cover (regrowth). Conversely, the NV/NF presents none or less vegetation cover (clearing or declining vegetation cover). Additionally, the indication from V/F to NV/NF expresses a reduction in VI values from full or more to none or less vegetation cover (clearing), and the indication from NV/NF to V/F expresses an increase in VI values from none or less to full or more vegetation cover (regrowth). To observe the sequence of the VIs values changes, 30 key locations in each state were chosen to study these VI values changes (Figure 2.7) and the findings of this observation of MSAVIaf are presented (Table 2.1) as an example.   Figure 2.7. The key locations for monitoring the VI value changes in Sabah and Sarawak. SARAWAK, 30 key locations for observing the VI changes SABAH, 30 key locations for observing the VI changes 54  Table 2.1. Sequences of the vegetation cover changes based on the changes of VI values (MSAVIaf) in 30 key areas chosen to observe in Sabah and Sarawak, 2000-2014. ID  SARAWAK 2000 2003 2006 2009 2012 2014 1 V/F NV/NF V/F V/F V/F NV/NF 2 NV/NF V/F V/F V/F V/F N/VF 3 V/F NV/NF V/F V/F NV/NF V/F 4 NV/NF V/F NV/NF V/F V/F V/F 5 V/F NV/NF V/F V/F V/F V/F 6 V/F NV/NF V/F V/F V/F V/F 7 V/F V/F V/F NV/NF V/F V/F 8 V/F NV/NF V/F V/F V/F V/F 9 V/F NV/NF V/F V/F V/F V/F 10 NV/NF V/F V/F V/F V/F V/F 11 V/F NV/NF V/F V/F V/F V/F 12 V/F V/F V/F NV/NF V/F V/F 13 V/F V/F V/F NV/NF V/F V/F 14 V/F V/F V/F V/F NV/NF V/F 15 V/F V/F NV/NF V/F V/F V/F 16 V/F V/F V/F NV/NF V/F V/F 17 V/F V/F V/F V/F NV/NF V/F 18 V/F V/F V/F NV/NF V/F V/F 19 V/F V/F V/F NV/NF V/F V/F 20 V/F NV/NF V/F V/F V/F V/F 21 V/F V/F V/F V/F NV/NF NV/NF 22 NV/NF V/F V/F V/F V/F V/F 23 V/F V/F NV/NF V/F V/F V/F 24 V/F NV/NF V/F V/F V/F V/F 25 V/F V/F V/F NV/NF V/F V/F 26 V/F V/F V/F NV/NF V/F V/F 27 V/F V/F V/F NV/NF V/F V/F 28 NV/NF V/F V/F V/F V/F V/F 29 V/F V/F V/F NV/NF V/F V/F 30 V/F V/F NV/NF V/F V/F V/F  ID SABAH 2000   2003 2006 2009 2012 2014  1 V/F NV/NF V/F V/F V/F V/F 2 V/F V/F NV/NF V/F V/F V/F 3 V/F V/F NV/NF V/F V/F NV/NF 4 V/F V/F NV/NF V/F V/F NV/NF 5 V/F NV/NF V/F V/F V/F NV/NF 6 NV/NF V/F V/F V/F V/F V/F 7 V/F NV/NF V/F V/F V/F V/F 8 NV/NF V/F V/F V/F V/F V/F 9 NV/NF V/F V/F V/F V/F V/F 10 NV/NF V/F V/F V/F V/F V/F 11 V/F V/F V/F V/F NV/NF V/F 12 V/F NV/NF V/F V/F V/F  V/F 13 V/F NV/NF V/F V/F V/F V/F 14 V/F NV/NF V/F V/F V/F V/F 15 V/F NV/NF V/F V/F V/F V/F 16 NV/NF V/F V/F V/F V/F V/F 17 V/F V/F NV/NF V/F V/F V/F 18 NV/NF V/F V/F V/F V/F V/F 19 V/F V/F V/F V/F NV/NF V/F 20 V/F V/F NV/NF V/F V/F V/F 21 NV/NF V/F V/F V/F V/F V/F 22 V/F NV/NF V/F V/F V/F NV/NF 23 NV/NF V/F V/F V/F NV/NF V/F 24 V/F NV/NF V/F V/F V/F V/F 25 V/F NV/NF V/F V/F NV/NF V/F 26 V/F V/F V/F V/F V/F NV/NF 27 V/F V/F V/F NV/NF V/F V/F 28 V/F V/F NV/NF V/F V/F V/F 29 V/F V/F NV/NF V/F V/F V/F  30  NV/NF V/F V/F V/F V/F V/F  Note:    [1] V/F: full or more vegetation cover (regrowth); NV/VF: none or less vegetation (clearing)             [2] From V/F to NV/NF indicating a reduction in VI from full/more to none/less vegetation cover (clearing)           [3] From NV/NF to V/F expressing an increase in VI from none/less to full/more vegetation cover (regrowth)     Considering this table, we can easily realize the changes of vegetation and non-vegetation cover in the observed locations. For instance, for location 1 in Sarawak, two instances of none or less vegetation cover (declining or clearing) at the years of 2003 and 2014 were found, while vegetation cover or its regrowth was observed for 2000, 2006, 2009, and 2012. Based on this information, an algorithm was developed to detect the changes of vegetation cover in the study 55  area from 2000 to 2014 for the VIs datasets (ARVI, EVI, MSAVIaf, NDVIaf, SARVI, & SAVI). The full results of this observation are presented in the Appendices (Figure A.9). An example to illustrate how the changes of vegetation cover in Sarawak were detected and monitored based on the changes of MSAVIaf values is presented (Figure 2.8). In other words, it showed the cycles of clearing and regrowth (rotation) of vegetation cover equal to the increase and decrease of MSAVIaf values from 2000 to 2014 in Sarawak. To minimize salt and peppernoises which may be caused by atmospheric effects, the quality of images, or by other factors, the minimum change detection area of 5 pixels was determined based on trial-and-error experiments.       Figure 2.8. The cycle of rotation (clearing and regrowth) of vegetation cover based on the changes of MSAVIaf values in Sarawak, 2000-2014. SARAWAK, 2000-2014     56   One question that could be posed was what the specific VI values had changed to over time, equaled to the sequence of changes of vegetation cover (i.e., V/F and NV/NF) in the study sites. To answer this question, the values of VIs were observed in 30 key locations in Sabah from 2000 to 2014. The full results are shown in the Appendices (Table A.5). Specifically, an example of how the MSAVIaf values changed for location numbers 3, 7, 17, 23, 25, and 30 in Sabah is provided (Table 2.2 and Figure 2.9). These areas/locations were selected as a example because locations 23 and 30 had the VI values decline (or vegetation cover cleared) in 2000, but the vegetation cover in location 23 was cleared again in 2012 while there was no clearing of vegetation cover in location 30 after 2000. Likewise, locations 3 and 25 had two instances of vegetation clearing (2003, 2012; and 2006, 2014, respectively), while locations 17 and 7 had only one clearing in 2003 and 2006, respectively. Table 2.2. The changes of MSAVIaf values in some key areas in Sabah, 2000-2014.  2000 2003 2006 2009 2012 2014 Location No.23 Sequence                VI value  NV/NF 0.820  V/F 0.922  V/F 0.897  V/F 0.931  NV/NF 0.847  V/F 0.901 Location No.30 Sequence                  VI value  NV/NF 0.736  V/F 0.841  V/F 0.822  V/F 0.864  V/F 0.904  V/F 0.913 Location No.3                Sequence                  VI value  V/F 0.921  NV/NF 0.746  V/F 0.896  V/F 0.907  NV/NF 0.757  V/F 0.901 Location No.17 Sequence                  VI value  V/F 0.908  NV/NF 0.774  V/F 0.866  V/F 0.914  V/F 0.920  V/F 0.909 Location No.25 Sequence                  VI value  V/F 0.936  V/F 0.952  NV/NF 0.788  V/F 0.913  V/F 0.926  NV/NF 0.801 Location No.7 Sequence                  VI value  V/F 0.920  V/F 0.920  NV/NF 0.754  V/F 0.820  V/F 0.881  V/F 0.888   Note:   [1] V/F: full or more vegetation cover (regrowth); NV/VF: none or less vegetation (clearing)             [2] From V/F to NV/NF indicating a reduction in VI from full/more to none/less vegetation cover (clearing)            [3] From NV/NF to V/F expressing an increase in VI from none/less to full/more vegetation cover (regrowth)  57     NOTE: SG/LR: possibly slower-growing/longer rotations. FG/SR: possibly faster-growing/shorter rotation. Figure 2.9.  The changes of the MSAVIaf values at 6 locations (No. 3, 7, 17, 23, 25, & 30) selected as an example in Sabah.  From the results of monitoring the change sequence, or the cycle of clearing and regrowth, of vegetation cover in the study area, it could indicate shorter- versus longer-rotation plantation stands. The shorter-rotation industrial forests normally last about 7 years. They could be fast-growing species (e.g., Acacia or Eucalyptus species) and could be destined for producing pulpwood. A longer-rotation timber plantation may last tens of years (e.g., teaks, rubbers, pines) and could be used for producing saw logs, etc. Thus, if we have annual monitoring data for a long enough time period, we can observe their full harvesting cycles. However, the time span for this study was only 14 years from 2000 to 2014. As such, it is impossible to observe and monitor the full harvesting cycles of saw-log long-rotation plantation stands. Thus, in this study, an assumption was posed. That is, any change of the VI value at or less than 7 years possibly 0.7 0.8 0.9 1 2000 2003 2006 2009 2012 2014 The MSAVIaf value changes in the locations 23 (FG1/SR1) and 30 (SG1/LR1) in Sabah, 2000-2014 FG1/SR1 SG1/LR1 0.7 0.8 0.9 1 2000 2003 2006 2009 2012 2014 The MSAVIaf value changes in the locations 25 (FG2/SR2) and 7 (SG2/LR2)  in Sabah, 2000-2014 FG2/SR2 SG2/LR2 0.7 0.8 0.9 1 2000 2003 2006 2009 2012 2014 MSAVIaf values Year The MSAVIaf value changes in the locations 3 (FG3/SR3)  and 17 (SG3/LR3)  in Sabah, 2000-2014  FG3/SR3 SG3/LR3 58  indicated shorter rotation, and any change of VI value more than 7 years could indicate longer-rotation IFs. The full results of this analysis based on VIs (ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI) are shown in the Appendices (Figures A.10 & A.11). An example showing possibly shorter-rotation ( 7 years) and possibly longer-rotation (> 7 years) plantations based on MSAVIaf value changes from 2000 to 2014 in Sabah is presented (Figure 2.10).  In addition to the time span of the VI changes, which could indicate the silvicultural rotation of an IF stand, another important piece of information that could be drawn from the changes of VIs values was the rate of growth and decline rate of the values. The growth rate of VIs values could indicate how fast or slow a plantation stand grows.   Figure 2.10. Possibly shorter- and longer-rotation plantations based on MSAVIaf, 2000-2014 in Sabah.  In this study, the growth rate of the VIs values was also interesting because it may relate to the fast-growing and slow-growing timber plantations. The faster-growing industrial forests or    59  timber plantations were supposed to develop or grow their canopy/foliar or biomass faster than the slower-growing industrial forests or timber plantations. The growth rate of the VIs was calculated by the following formula [2.2]: growth rate =  (VI(t2)  VI(t1)) / VI(t1) [2.2] Where VI(t1) was the value of VI in the earlier year, or time t1, and VI(t2) was the value of VI in the later year, or time t2. The full results of calculating the growth rates of the VIs in Sabah from 2000 to 2014 are presented in the Appendices (Table A.6). An example to illustrate how the MSAVIaf values have changed or grown in some selected key areas (location numbers 3, 7, 17, 23, 25, and 30) in Sabah is presented (Figure 2.11).     NOTE: FG1/SR1 is location 23, SG1/LR1 is location 30; FG2/SR2 is location 25, SG2/LR2 is 7; FG3/SR3 is location 3, & SG3/LR3 is location 17. Figure 2.11. The growth rates of the MSAVIaf values in some locations (location numbers 3, 7, 17, 23, 25, & 30) chosen to monitor their value changes in Sabah, 2000-2014. -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 2000-03 2003-06 2006-09 2009-12 2012-14 The growth rate of MSAVIaf of vegetation cover at the locations 23 (FG1/SR1) & 30 (SG1/LR1) in Sabah, 2000-2014 FG1/SR1 SG1/LR1 -0.2 -0.1 0 0.1 0.2 2000-03 2003-06 2006-09 2009-12 2012-14 The growth rate of MSAVIaf of vegetation cover at the locations 25 (FG2/SR2) & 7 (SG2/LR2) in Sabah, 2000-2014 FG2/SR2 SG2/LR2 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 2000-03 2003-06 2006-09 2009-12 2012-14 The growth rate of MSAVIaf of vegetation cover at the locations 3 (FG3/SR3) & 17 (SG3/LR3) in Sabah, 2000-2014 FG3/SR3 SG3/LR3 60   Considering Figure 2.11, we could easily realize that, in general, the growth rate of the MSAVIaf values in locations 3, 23, and 25 (where there were two clearings of vegetation cover) were faster than the growth rate of the MSAVIaf values in locations 7, 17, and 30 (where there was only one clearing of vegetation cover). Based on this information, an assumption was also posed. That is, the faster-growing IFs had the larger VIs growth rates than the slower-growing IFs (< 0.5). The full results of detecting faster-growing and slower-growing IFs in Sabah and Sarawak based on this assumption are presented in the Appendices (Figures A.12 & A.13). An example of the result of possibly faster- versus slower-growing IF detection based on the MSAVIaf values in Sabah from 2000 to 2014 is presented (Figure 2.12).            Figure 2.12. The possibly faster-growing and slower-growing plantations based on MSAVIaf  values  in Sabah, 2000-2014.    61   After obtaining two products of possibly shorter- versus longer-rotation and faster- versus slower-growing plantation stands derived from VIs values changes, an algorithm was developed to detect plantation stands, taking both possibly shorter- versus longer-rotation and faster- versus slower-growing information into account. An example (Figure 2.13) is presented to illustrate this combination (faster-growing, shorter-rotation and slower-growing, longer-rotation plantation stands) in Sabah based on MSAVIaf values from 2000 to 2014. The full result of this analysis is presented in the Appendices (Figures A.14 and A.15). This product was used as an input data for detecting and determining industrial forests in the study area.   Figure 2.13. Possibly faster-growing, shorter-rotation and slower-growing, longer-rotation plantations based on MSAVIaf, 2000-2014 in Sabah.    62   The above analysis provided initial clues for detecting and mapping possibly faster-growing, shorter-rotation and slower-growing, longer-rotation plantations in Sabah and Sarawak from 2000 to 2014. However, this evidence was not enough to know whether they were industrial forests or any specific vegetation covers. Therefore, further analyses were needed to detect and map the targeted IF systems. The additional analyses used to detect industrial forests and calibrate the final results were textural analysis, spectral analysis, and visual interpretation. This task could be simplified by the fact that industrial forests are monoculture of one or a few species. They are usually even-aged and have similar crown shape and regular spacing. Therefore, they will principally differ from other vegetation covers (e.g., natural forests), and these differences can be recognized by using remote sensing methods.  Textural Analysis  One of the promising approaches used to detect IFs in the study area over the study period is textural analysis of vegetation cover. In principal, there are three main ways usually used to analyze texture of an image to identify objects in it i.e., structural, model-based, and frequency-based/feature-based. One of the most-used textural analysis methods is Grey Level Co-Occurrence Matrix (GLCM) consisting of indices: mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation. This method uses variograms and semivariance as a means of classifying images. In this method, it is important for us to determine and design moving window sizes to derive texture variables. In general, using textural analysis to study forests is promising. Thus, many researchers have used it for their studies. For instance, Coburn and Roberts (2004) developed a multiscale texture analysis procedure using variable, variance, mean, mode, and median to improve forest stand classification. They argued that there was only a slight change in the pixel values in relatively homogenous areas. In contrast, coarser 63  texture may contain a lot of abrupt changes. Lu et al. (2014) did a study and stressed the roles of using textural images in improving land cover classification in the Brazilian Amazon. They argued that in medium resolution imagery like Landsat, texture had less capability to distinguish land cover types than spectral signature, but combining it with radiometric data could improve this work. By doing so, they found an improvement of the result at 5.2-13.4% depending on the pixel sizes. They also found that the best combinations for Landsat datasets were red band and near infrared band with dissimilarity index at the moving window size of 9*9 pixels. In their study, texture was used as an extra band in the separability analysis.  In this study, the textural analysis was used as a supporting method to detect IFs. This method was developed based on an assumption that the texture of industrial forests and natural forests was different, and that among different timber plantation species their texture was also different. This is because these vegetation covers differ in form and structure. An example showing how a natural forest is different from a plantation is presented (Figure 2.14). This picture was taken in East of Pekanbaru, Indonesia and retrieved from http://news.mongabay.com/2011/0607-greenpeace_vs_barbie.html.   Figure 2.14. The difference between natural forest and plantation.  Natural forest Plantation 64   The Grey Level Co-occurrence Matrix (GLCM) consisting of indices: mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation were used and tested to select the best indices for detecting IFs. The textural computations were done in the VIs products, and on band 4 and 5 images by using ENVI version 4.8 (Exelis Visual Information Solutions, Boulder, Colorado). Additionally, band 4 and 5 were used for calculating textural indices because they could separate different land covers (bare land, forest, and plantation) compared with other bands in the Landsat scenes, as shown in the Spectral Profiles (Figure 2.20) below. A number of tests were completed with the different moving window sizes from 1*1 pixels to 21*21 pixels for all indices (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation). The following indices: Mean (MEA), Homogeneity (HOM), and Dissimilarity (DIS) worked best with the moving window size at 9*9 pixels for the VIs images, and band 4 and band 5 grey level images.  However, for band 5 images, only MEA index was applied. The formulas for calculating MEA, HOM, and DIS, adapted from Lu et al. (2014), and Coburn and Roberts (2004), are presented as follows:      Where:  N is the number of rows or columns  Vi,j is the value of cell (i,j) (row i and column j) of the moving window.  And   (Adapted from Lu et al., 2014; Coburn & Roberts, 2004)  The values of textural indices of MEA, HOM, and DIS range from 0 to 255. For the DIS index, the lower values express less dissimilarity and the higher values present more dissimilarity among objects or land cover types in the image. Conversely, for the MEA and HOM indices, the 65  higher values indicate more homogenous area, and lower MEA and HOM values will be found in the coarser textural areas, or land cover types which may contain more abrupt changes. An example of how the Mean (MEA) index in GLCM was calculated for an NDVIaf image in 2014 in Sabah and Sarawak is presented (Figure 2.15).  Figure 2.15. The Mean (MEA) index in the GLCM is calculated for an NDVIaf image in 2014 in Sabah and Sarawak.  To identify the values of the indices of Mean (MEA), Homogeneity (HOM), and Dissimilarity (DIS) in VIs (ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI) and band 4 images and to identify the Mean (MEA) for the band 5 images at the grey level, representing    Sabah and Sarawak, 2014, NDVIaf Sabah Sarawak 66  industrial forests or any land use or land cover types, it is necessary for us to know where these land use/land cover types exist and then we will acquire their values by using the area of interest (AOI) function in the ERDAS. These are also known as the training areas. The values of MEA, HOM, and DIS for different land use and land cover types were observed for maximum, minimum, mean and mode (the most distributed value range). Then, these values were used in a model to detect the expected land use and land cover types.    To identify texture values typical or representative of the expected land uses/land covers in the study area, different data sources have been used, including other land use and land cover studies, land cover maps of the State Forestry Departments of Sabah and Sarawak, and the reports of timber companies in the study sites. These sources were confirmed by a check using Google Earth. Finally, five expected land use and land cover types were identified, including acacia plantations, natural forests, oil palm plantations, rubber plantations, and other industrial forests (or other timber plantations; for this other IFs type, it was impossible to recognize it in both the Google Earth check and other studies, although other sources indicated it as timber plantations, thus it was classified as other industrial forests/IFs; Figure 2.16).   The full results of observing the values of MEA, HOM, and DIS for different land use and land cover types in different VIs (ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI) and band 4 images, and the Mean (MEA) for band 5 grey level images in Sabah and Sarawak from 2000 to 2014 are shown in the Appendices (Tables A.7 & A.8). An example presenting the mean values of the MEA, HOM, and DIS for five different land uses/land covers (acacia, forest, oil palm, rubber, and other industrial forests) in Sabah from 2000 to 2014 on the NDVIaf product and band 4 images, and the MEA values for band 5 grey level images is provided (Figure 2.17).  67   These textural values were used as input data for the texture-based industrial forest detection model (Figure 2.18). An example showing the results of using this model to detect industrial forests through the textural values derived from the NDVIaf product, and band 4 and band 5 grey level images in Sabah in 2012 is presented (Figure 2.19).        Figure 2.16. The identification of different land uses/land covers used to acquire the textural values in the study sites. Acacia Forest Oil palm Rubber Other IFs Other IFs 68        Figure 2.17. The values of GLCM_MEA, HOM, and DIS for different Land Uses/Land Covers in the NDVIaf product, band 4, and band 5 grey level images in Sabah, 2000-2014.   100 150 200 250 300 2000 2003 2006 2009 2012 2014 NDVI-based Textural Value of MEAN Year The GLCM_Mean values of  the different LULC  types of the NDVIaf products in Sabah, 2000-2014 Rubber Acacia Other IFs Oil Palm Forest 0 50 100 150 200 250 2000 2003 2006 2009 2012 2014 The GLCM_Homogeneity values of the different LULC type of  the NDVIaf products in Sabah, 2000-2014 Rubber Acacia Other IFs Oil Palm Forest 0 50 100 150 200 250 300 2000 2003 2006 2009 2012 2014 The GLCM_Dissimilarity values of the different LULC types of the NDVIaf product sin Sabah,  2000-2014 Rubber Acacia Other IFs Oil Palm Forest 150 175 200 225 250 275 2000 2003 2006 2009 2012 2014 The GLCM_Mean values of  the different LULC  types of band 4 in Sabah, 2000-2014 Rubber Acacia Other IFs Oil Palm Forest 0 50 100 150 200 250 300 2000 2003 2006 2009 2012 2014 The GLCM_Hommogeneity values of  the different LULC types of band 4 in Sabah, 2000-2014 Rubber Acacia Other IFs Oil Palm Forest 0 50 100 150 200 250 300 2000 2003 2006 2009 2012 2014 The GLCM_Mean values of  the different LULC types of band 5 in Sabah, 2000-2014 Rubber Acacia Other IFs Oil Palm Forest 69    Figure 2.18. The texture-based models for the VI datasets to detect the focused IF systems.   Figure 2.19. Detecting the targeted IF systems based on textural analysis in Sabah, 2012.    SABAH, 2012 70  Spectral Analysis  Spectral analysis was also used as a necessary supporting step in detecting industrial forests based on vegetation indices analysis, as well as forest fractional cover changes analysis method. The spectral analysis was done by first checking the Spectral Profiles for the expected objects on the preprocessed images. The purpose of this check was to see how the spectra of these objects were different. The objects (areas of interest) in the images chosen for this check were bare lands, natural forests, and plantations in general. These plantations could be oil palm plantations, timber plantations, and other plantations, or even agricultural lands. They were selected because it was easy to visually recognize them in the Landsat images/scenes. In other words, these land use and land cover types could be easily identified visually based on their interpretation keys such as color, texture, and arrangements in an image. The result of this spectral profile check for these chosen land uses/land covers is presented (Figure 2.20). Considering this spectral plot, it was clear that the spectra of bare lands were very different from the spectra of vegetation cover (natural forests and plantations). However, the spectra of natural forests and general plantations were very similar, especially for bands 1, 2, 3, and 6. Only two bands, namely band 4 and 5, were able to somewhat differentiate them. This was why conventional statistical land use and land cover classification methods are not able to recognize and classify these land use/land cover types.   Therefore, in this study, band 4 and band 5 in the Landsat images were selected and used for spectral analysis to detect industrial forests. After checking the spectral profiles for the chosen land uses and land covers to select the bands to be used for the analysis, other spectral analysis methods, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Tasseled Cap Analysis (TCA), were also used. These methods are widely applied in 71  anomaly detection, target detection, material mapping and identification, etc. In this study, they were used because it was more interpretable to natural forests, plantations, and bare lands in the images derived from these analyses or transformations. Similar to the above textural analysis, the area of interest (AOI) tool in the ERDAS IMAGINE was also used to identify the values for the land uses/land covers of interest (also known as the training areas). Finally, the results of these spectral analyses were used to support the VIs-based industrial forest detection method.   Figure 2.20. Spectral profiles for bare lands, natural forests, and plantations in the study area.  Principal Component Analysis (PCA) is a conventional feature extraction technique which assumes data in images are of normal or Gaussian distribution. It will transform data based on a correlation and be able to recognize statistical patterns in the images (Jia & Richard, 1999). In this study, three main components were selected and applied for the Landsat scenes. The ERDAS worked to group these three main components by itself. The result of this analysis is presented (Figure 2.21) as an example of applying the PCA to the Landsat data in 2000 for Sabah and Sarawak. Considering this PCA product, we can realize that objects in the image were better separable. 72   Then, like the above textural analysis, five areas of interest consisting of acacias, forests, oil palms, rubbers, and other industrial forests (also known as the training areas) were also identified in Sabah and Sarawak (Figure 2.21). The AOI was created to obtain the values for these land use/land cover types.  The full results of this work are presented in the Appendices (Tables A.9 & A.10). These values were used in a model to detect the expected industrial forests. An example of identifying the mean values of acacias, natural forests, oil palms, rubbers and other industrial forests in Sabah from 2000 to 2014 is presented (Figure 2.22).  Figure 2.21. The Principal Components Analysis for Sabah and Sarawak in 2000.         SARAWAK SABAH  Forest  Oil palm 73      Figure 2.22. The mean values of acacias, natural forests, oil palms, rubbers, and other industrial forests of layer 1, 2, and 3 in the PCA product in Sabah, 2000-2014.  Independent Component Analysis (ICA) is also a feature extraction technique which was just developed recently. The purpose of this technique is to de-correlate the spectral bands to recover the original features in the images. It performs a linear transformation of the spectral bands, such that the resulting components are de-correlated (Shah et al., 2007a; Shah et al., 2007b). Each component will contain information corresponding to a specific feature in the original images. The ICA is used not only to de-correlate the features, but also to make them independent of each other for spectral bands. It can work both in normal distribution data, and skewness or kurtosis data (Comon, 1994). Thus, it is a higher order feature extraction technique compared with the PCA. The ICA is applied in the visual image interpretation because it can improve the recognition of the objects through component color coding. It can be also used in the spectral 100 110 120 130 140 2000 2003 2006 2009 2012 2014 Value Year The principal components values of layer 1 in Sabah, 2000-2014 Acacia1 Forest1 Oil palm1 Rubber1 Other IF1 145 153 161 169 177 2000 2003 2006 2009 2012 2014 The principal components values of layer 2 in Sabah, 2000-2014 Acacia2 Forest2 Oil Palm2 Rubber2 Other IF2 70 80 90 100 110 120 2000 2003 2006 2009 2012 2014 The principal components values of layer 3 in Sabah, 2000-2014 Acacia3 Forest3 Oil Palm3 Rubber3 Other IF3 74  unmixing model, shadow detection, and especially for land use and land cover classification. In LULCC studies, the ICA can further analyze changes and improve land use and land cover classification based on their spectral, textural, and contextual features/information. The ICA is well-suited for the analysis of multi-temporal data because feature-based change detection techniques necessitate extraction of feature with high accuracy. An example of applying the ICA to the Landsat data in 2000 for Sabah and Sarawak is presented (Figure 2.23). Considering this figure, we could also realize that the objects in the image were better separable.  Figure 2.23. The Independent Components Analysis for Landsat data in the study area in 2000.         SARAWAK SABAH  Forest  Oil palm 75   Then, similar to the PCA above, five areas of interest consisting of acacias, forests, oil palms, rubbers, and other industrial forests were also identified in Sabah and Sarawak. The areas located for these land use/land cover types were the same as the areas identified in the PCA above. The full results of obtaining the mean and range values for these land use/land cover types are expressed in the Appendices (Tables A.9 and A.10). These values were used in a model to detect the expected industrial forests. Figure 2.24 presents an example of the mean values of acacias, natural forests, oil palms, rubbers, and other industrial forests in Sabah from 2000 to 2014.     Figure 2.24. The Independent Components mean values (acacias, natural forests, oil palms, rubbers, and other industrial forests) of layer 1, 2, and 3 in Sabah, 2000-2014.  Tasseled Cap Analysis/Transformation (TCA) is often used to study vegetation content in an image through scene brightness, greenness, and wetness and is calculated by using different coefficients for Landsat datasets (Crist & Cicone, 1984; Crist, 1985; Crist &Kauth, 1986; Jensen, 60 70 80 90 100 110 2000 2003 2006 2009 2012 2014 Value Year The independent components values of layer 1 in Sabah, 2000-2014 Acacia1 Forest1 Oil palm1 Rubber1 Other IF1 70 80 90 100 110 2000 2003 2006 2009 2012 2014 The independent components values of layer 2 in Sabah, 2000-2014 Acacia2 Forest2 Oil Palm2 Rubber2 Other IF2 100 120 140 160 180 200 2000 2003 2006 2009 2012 2014 The independent components values of layer 3 in Sabah, 2000-2014 Acacia3 Forest3 Oil Palm3 Rubber3 Other IF3 76  2015). The TCA offers a way to optimize data to study vegetation. This may be helpful in detecting industrial forests. In the TCA, three data structure axes are viewed as a degree of brightness, greenness, and wetness. In that, the brightness component indicates areas of low vegetation and high reflectors, such as bare lands; the greenness component indicates vegetation; and the wetness component reveals water and moisture. In this study, each IF type was supposed to be planted in certain soil, elevation, and climate conditions. Therefore, by using this analysis, IFs in the study area could be detected.  Figure 2.25. The Tasseled Cap Analysis for Landsat data in the study area in 2000.         SARAWAK SABAH  Forest  Oil palm 77   An example showing the result of applying the TCA to the Landsat data in 2000 is presented (Figure 2.25). Then, similar to the PCA and ICA above, five areas of interest consisting of acacias, forests, oil palms, rubbers, and other industrial forests were also identified in Sabah and Sarawak. The full results of obtaining the mean and range values for these land use/land cover types are expressed in the Appendices (Tables A.9 and A.10). These values were also used in a model to detect the expected industrial forests. An example (Figure 2.26) presents the mean Tasseled Cap values of acacias, natural forests, oil palms, rubbers, and other industrial forests in Sabah from 2000 to 2014. Next, in the spectral analysis, as stated above, only band 4 and band 5 could somewhat separate natural forests from plantations in general. The Band 4 is Near Infrared (NIR) in the Landsat 4-5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) with the wavelength range of 0.770.9 µm. In Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), the Near Infrared (NIR) is band 5 with the wavelength of 0.85-0.88 µm.  However, in this study, only the following individual bands (bands 2, 3, 4, 5, 6, and 7) for Landsat 8 (OLI TIRS) were used and stacked. Thus, its band 5 (NIR) would be chronologically stacked and renamed to band 4 to be consistent with data of Landsat 4-5 TM and Landsat 7 ETM+. The NIR (band 4) is well-known and frequently used to study green biomass content of vegetation cover. In addition, band 5 (Short Wave Infrared) in Landsat 4-5 TM and 7 ETM+ with the wavelength of 1.55-1.75 µm; and band 6 in Landsat 8 OLI TIRS, which was renamed to band 5 with 1.57-1.65 µm wavelength, were often used to discriminate moisture content of soil and vegetation. In this step, this study further examined how the mean values of band 4 and band 5 were different among five chosen land use and land cover types (i.e., acacias, natural forests, oil 78  palms, rubbers, and other industrial forests) in the study area. These chosen areas for this examination were the same as the areas selected for textural and spectral analyses above.       Figure 2.26. The Tasseled Cap values (acacias, natural forests, oil palms, rubbers, and other industrial forests) of layer 1, 2, 3, 4, 5, and 6 in Sabah, 2000-2014. 80 90 100 110 120 130 2000 2003 2006 2009 2012 2014 The Tasseled Cap values of layer 1 in Sabah, 2000-2014 Acacia1 Forest1 Oil palm1 Rubber1 Other IF1 130 145 160 175 190 205 220 2000 2003 2006 2009 2012 2014 The Tasseled Cap values of layer 2 in Sabah, 2000-2014 Acacia2 Forest2 Oil Palm2 Rubber2 Other IF2 120 130 140 150 160 170 180 190 2000 2003 2006 2009 2012 2014 The Tasseled Cap values of layer 3 in Sabah, 2000-2014 Acacia3 Forest3 Oil Palm3 Rubber3 Other IF3 210 215 220 225 230 2000 2003 2006 2009 2012 2014 The Tasseled Cap values of layer 4 in Sabah, 2000-2014 Acacia4 Forest4 Oil palm4 Rubber4 Other IF4 180 183 186 189 192 195 2000 2003 2006 2009 2012 2014 The Tasseled Cap values of layer 5 in Sabah, 2000-2014 Acacia5 Forest5 Oil Palm5 Rubber5 Other IF5 100 110 120 130 140 150 160 170 2000 2003 2006 2009 2012 2014 The Tasseled Cap values of layer 6 in Sabah, 2000-2014 Acacia6 Forest6 Oil Palm6 79  The full results of this examination are presented in the Appendices (Tables A.9 & A.10). The results (Figure 2.27) indicated that they could be used as supporting information in the models to detect industrial forests.   Figure 2.27. The mean values of band 4 and band 5 for the different land use/land cover areas of interest in Sabah, 2000-2014.  After doing spectral analyses including Principal Component Analysis, Independent Component Analysis, Tasseled Cap Analysis, and Band 4 and 5 analyses as described above, the results were used in the spectral analysis-based model to detect industrial forests (Figure 2.28). The results are shown in Figure 2.29 as an example of how to detect industrial forests based on the spectral analysis in Sabah in 2012.  Visual Interpretation and Using Other Data  To calibrate the final results for detecting industrial forests, visual interpretation and other data were also used. In addition to the silvicultural rotation, spectral, and textural datasets, a number of land use and land cover studies have been conducted in Sabah and Sarawak. These studies mentioned industrial forests or timber plantations in Malaysia in general, and Sabah and Sarawak in particular, to some degree. A review was done for these studies in combination with visual interpretation to calibrate the final results of detecting industrial forests in the study area.  1000 1500 2000 2500 3000 3500 4000 2000 2003 2006 2009 2012 2014 Reflectance values ( scaled up 10000) Year The mean values of band 4 for different LULC in Sabah, 2000-2014 Acacia Forest Oil palm Rubber Other IF 0 1000 2000 3000 4000 2000 2003 2006 2009 2012 2014 Reflectance value (scaled up 10000) Year The mean values of band 5 for different LULC in Sabah, 2000-2014 Acacia Forest Oil Palm Rubber Other IF 80   Figure 2.28. The spectra-based models for the VI datasets to detect the focused IF systems.  Figure 2.29. The spectral analysis-based industrial forest detection in Sabah, 2012.    81   In addition, the final calibration for the industrial forest maps also used data, reports and documents from the Forestry Departments of Sabah and Sarawak states and local timber companies, such as statistical data and land use/land cover maps.  For Sabah, the following data sources were used (1) Forest Reserves and Other Forest Lands Maps (Sabah Forestry Department, 2012); (2) Forest Resource Management (Sabah Forestry Department 2006, 2009, 2012, 2013); (3) Roda and Rathi (2006); (4) Raynold et al. (2011); (5) Malik et al. (2013); (6) McMorrow and Talip, (2001); (7) Malaysian Timber Council (2009); and (8) maps, reports, and documents from companies: Sabah Softwoods Berhad3, Sabah Forest Industries4, and other smaller timber companies. For Sarawak, the following data sources were used to identify industrial forests in the area: (1) Roda and Rathi (2006); (2) Malik et al. (2013); (3) Annual reports (Sarawak Forestry Department, 2009, 2011, 2012); (4) Ta Ann Plantation 5; (5) Wyn (2011); (6) Bryan et al. (2013), (7) Gunarso et al. (2013), and (8) SarVision (2011). These documents provided a firm foundation for identifying the industrial forests in the study area.  Then, based on visual interpretation keys, the areas and types of industrial forests in Sabah and Sarawak from 2000 to 2014 could be identified. The visual interpretation keys include texture (fractional dimension), position, slope, associations, contextual (spatial dependence), and other environmental factors. For instance, rubber plantations have typical textures, land forms, and landscape terracing, and they were planted both in the smallholder and industrial scale. On the contrary, pulp or acacia plantations also have special color, and they were normally established at the large scale by industries. Oil palm plantations also have special texture. The following figure (Figure 2.30) provides an example of how the different land uses/land covers                                                       3 http://www.softwoods.com.my/;  4 http://www.avanthagroup.com/downloads/Sabah-Forest-Industries-Sdn-Bhd.pdf  5 http://www.taann.com.my/reforestation/  82  were interpreted.  However, in general, because the quality and type of Landsat scenes were very different, it was not easy to interpret the expected land uses and land covers in the study area.         Figure 2.30. An example of how to interpret the different land uses/land covers based on their interpretation keys in Sarawak in 2009.  The visual interpretation for Landsat datasets from 2000 to 2014 was done in the ArcGIS 10.2.2, and then this vector data was converted into raster to be used in the ERDAS for further analysis. The results of visual interpretation and using other data to identify industrial forests in Sabah and Sarawak are presented (Figure 2.31) as an example.   Oil palm Acacia Degraded vegetation cover/forest Rubber Forest 2009 83   Figure 2.31. The visual interpretation-based industrial forest map in Sabah, 2000. Making IF Maps Then, finally, an algorithm based on rules for the shorter- versus longer-rotation (SR vs. LR), faster-growing versus slower-growing (FG vs. SG) IFs, textural analysis, spectral analysis, and visual interpretation as mentioned above was developed.  The algorithm is described in Figure 2.32. In other words, the algorithms could be presented as follows: f(IFs) Texture(IFs)   Spectra(IFs)  FGSR-SGLR(IFs) Visual(IFs)] + [FGSR-SGLR(IFs)  (Texture(IFs) OR/AND  Spectra(IFs) OR/AND Visual(IFs))] + [Visual(IFs)  (Texture(IFs) OR  Spectra(IFs))]).     84  The final results were clumped and the areas smaller than 2 ha were eliminated. The purpose of this work was salt and pepperthat were caused by the atmospheric effects and image quality. This was also due to the fact that the smaller IF patches were more difficult to detect; and because Fox and Castella (2013) indicated smallholders in Southeast Asia commonly owned a land size around 1-4 ha of plantations. Therefore, the minimum land size selected in detecting and mapping new IFs in the study area was 2 ha. The results of detecting industrial forests in Sabah and Sarawak from 2000 to 2014 based on the vegetation indices analysis including ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI are presented (Figures 2.33, 2.34, 2.35, 2.36, 2.37, and 2.38).     Figure 2.32. The final algorithm to identify industrial forest areas and species based on textural analysis, spectral analysis, visual interpretation, and faster-growing, shorter-rotation (FGSR) and slower-growing, longer-rotation (SGLR) IF products. 85        Figure 2.33. The ARVI-based industrial forest maps in Sabah and Sarawak, 2000-2014. ARVI, 2000 ARVI, 2003 ARVI, 2006 ARVI, 2009 ARVI, 2012 ARVI, 2014       86        Figure 2.34. The EVI-based industrial forest maps in Sabah and Sarawak, 2000-2014. EVI, 2000 EVI, 2003 EVI, 2006 EVI, 2009 EVI, 2012 EVI, 2014       87        Figure 2.35. The MSAVIaf-based industrial forest maps in Sabah and Sarawak, 2000-2014. MSAVIaf, 2000       MSAVIaf, 2003 MSAVIaf, 2006 MSAVIaf, 2009 MSAVIaf, 2012 MSAVIaf, 2014 88        Figure 2.36. The NDVIaf-based industrial forest maps in Sabah and Sarawak, 2000-2014. NDVIaf, 2000       NDVIaf, 2003 NDVIaf, 2006 NDVIaf, 2009 NDVIaf, 2012 NDVIaf, 2014 89        Figure 2.37. The SARVI-based industrial forest maps in Sabah and Sarawak, 2000-2014.  SARVI, 2000       SARVI, 2003 SARVI, 2006 SARVI, 2009 SARVI, 2012 SARVI, 2014 90        Figure 2.38. The SAVI-based industrial forest maps in Sabah and Sarawak, 2000-2014.  SAVI, 2000       SAVI, 2003 SAVI, 2006 SAVI, 2009 SAVI, 2012 SAVI, 2014 91  2.4 Validation The validation work for the VIs-based IF detection method in the Landsat datasets was conducted through the use of very high resolution imagery data. Specifically, two high resolution imagery scenes in each state were randomly selected based on the following conditions: (1) the location must contain the significant IF area and various LULC types, (2) the availability of the scenes close to the date or at least in the same year to the Landsat-derived IF maps, (3) the quality of the scene including cloud coverage less than 20% and off-nadir less than 250. Finally, the two scenes in each state were selected (Figure 2.39). The details of the selected scenes are presented in the Appendices (Tables A.12 & A.13). Then, a procedure for the validation was developed as follows:   Figure 2.39. The locations, areas, years, and sensors of the high resolution imagery scenes used to validate the Landsat-derived IF maps in Sabah and Sarawak.  Clipping the Landsat-derived IF maps at the same locations and years as the high resolution imagery data (called the classified IF maps).   Calculating the number of samples based on the area proportion of the IF land versus non IF land. Congalton (1991) recommended taking 50 samples for each LU class for the area Worldview 3, 2014, area of 94 km2 Worldview 2, 2014, area of 166 km2 Sabah, 2014 Quickbird, 2009, area of 78 km2 Worldview 2, 2012, area of 68 km2 Sarawak, 2012  92  less than 0.5 Mha. In this study, the Landsat-derived IF maps were classified into the IF land (including acacia, rubber, and other IFs) and non IF land. Therefore, totally, 200 samples were taken.  Creating the point shapefiles and randomly locating the samples in each class (randomly stratified sampling) in the clipped Landsat-derived IF maps. The sample locations had to be relatively evenly distributed in the class, as presented in the Appendices (Figures A.20, A.21, A.23, A.24, & A.25).  Classifying the high resolution imagery data into the IF maps (called the referenced IF maps) based on the visual interpretation approach.  Converting both the classified and referenced IF maps from vector data into raster data.   Using the combine tool in the ArcGIS software to acquire the accuracy of two maps.  Exporting the data into excel to compute and report the accuracy in the confusion matrices, commission errors, map accuracy, and Kappa coefficient. The accuracy assessment was first conducted for the IF land versus non IF land to see how the developed method and algorithms could separate the lands. Then, it was scaled down to the finer IF classes specific for acacia, rubber, and other IFs. The results of assessing accuracy at the coarser scale indicated that the ARVI-based IF map best separated the IF land versus non IF land, generally followed by the SAVI, SARVI, EVI, NDVIaf and MSAVIaf-based IF maps (Table 2.3). In other words, ARVI worked the best in detecting the IF land in the regions, followed by SAVI, SARVI, EVI, NDVIaf and MSAVIaf. for IFs of the ARVI-based product was 44%, slightly different, compared to 44% for EVI, 41% for SAVI, 39% for SARVI, and 36% for NDVIaf and 34% for MSAVIaf. Consistently93  error was least (56%), followed by EVI (56%), SAVI (59%), SARVI (61%), NDVIaf (64) and MSAVIaf (66%).  Table 2.3. The accuracy assessment results for ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI-based IF land detection methods for Landsat data. ARVI    Classified  Referenced LULC   IFs Non IFs Total IF land 21 27 48 Non IF land 12 140 152 Total 33 167 200 Overall Accuracy   81% User's Accuracy 44% 92%  Producer's Accuracy 64% 84%  Omission Error 36% 16%  Commission Error 56% 8%  Map Accuracy 35% 78%   Kappa Coefficient (moderate agreement) 0.40  EVI    Classified Referenced LULC    IFs  Non IFs Total IF land 17 22 39 Non IF land 21 140 161 Total 38 162 100 Overall Accuracy   79% User's Accuracy 44% 87%  Producer's Accuracy 45% 86%  Omission Error 55% 14%  Commission Error 56% 13%  Map Accuracy 28% 77%  Kappa Coefficient (fair agreement) 0.31   MSAVIaf    Classified  Referenced LULC    IF s Non IFs Total IF land 10 19 29 Non IF land 20 151 171 Total 30 170 200 Overall Accuracy   81% User's Accuracy 34% 88%  Producer's Accuracy 33% 89%  Omission Error 67% 11%  Commission Error 66% 12%  Map Accuracy 20% 76%  Kappa Coefficient (fair agreement) 0.22  NDVIaf     Classified Referenced LULC (ha)   IF s Non IFs Total IF land 9 16 25 Non IF land 19 156 175 Total 28 172 200 Overall Accuracy   83% User's Accuracy 36% 89%  Producer's Accuracy 32% 91%  Omission Error 68% 9%  Commission Error 64% 11%  Map Accuracy 20% 82%  Kappa Coefficient (fair agreement) 0.24   SARVI    Classified Referenced LULC    IF s Non IFs Total IF land 14 22 36 Non IF land 16 148 164 Total 30 170 200 Overall Accuracy   81% User's Accuracy 39% 90%  Producer's Accuracy 47% 87%  Omission Error 53% 13%  Commission Error 61% 10%  Map Accuracy 27% 80%  Kappa Coefficient (fair agreement) 0.31  SAVI    Classified Referenced LULC   IF s Non IFs Total IF land 13 19 32 Non IF land 16 152 168 Total 29 171 200 Overall Accuracy   83% User's Accuracy 41% 90%  Producer's Accuracy 45% 89%  Omission Error 55% 11%  Commission Error 59% 10%  Map Accuracy 27% 81%  Kappa Coefficient (fair agreement) 0.32  94  accuracy, it also showed the highest in the ARVI-based product (64%), followed by SARVI (47%), EVI (45%), SAVI (45%), MSAVIaf (33%) and NDVIaf (32%) to the same was found for the omission error for ARVI (36%), SARVI (53%), EVI (55%), SAVI (55%), MSAVIaf (67%) and NDVIaf (68%). For the map accuracy of IF land and Kappa coefficient - which were more reliable and useful in comparing the accuracy of maps - their values indicated the highest at 35% and 0.4, respectively, in the ARVI-based product, followed by SAVI (27% & 0.32), EVI (28% & 0.31), SARVI (27% & 0.31), NDVIaf (20% & 0.24), and MSAVIaf (20% & 0.22), respectively (Table 2.3). In other words, the value of Kappa coefficient of ARVI (0.4) showed a moderate agreement (0.4-0.6) by chance of the IF land between the classified and referenced IF maps, while the values of this statistic index in the SAVI (0.32), SARVI and EVI (0.31), and NDVIaf (0.24) and MSAVIaf (0.22) indicated a fair agreement (0.2-0.4). For the overall accuracy index, which took both the IF land and non IF land into account and was probably least used in the accuracy assessment, it showed a slight difference between the VIs-based products from 79% for EVI, 81% for ARVI, SARVI, and MSAVIaf to 83% for NDVIaf and SAVI.  Next, considered the accuracy scaled down to the specific IF systems to see how and which VI worked the best in detecting the IF systems in the region. In general, similar to that described above for the detection of the IF land versus non IF land, ARVI continued to work the best, followed by SAVI, SARVI, EVI, NDVIaf and MSAVIaf (Table 2.4). The assessment results showed that the accuracy for each VI index (ARVI, EVI, SARVI, SAVI, NDVIaf and MSAVIaf) in detecting the selected IF systems was different. In general, the accuracy for detecting acacia IFs was larger than that for rubber and other IFs in ARVI, while the accuracy for detecting rubber IFs was larger than that for acacia and other IFs in the remaining VIs. In particular, in all 95  Table 2.4. The accuracy assessment results specific for acacia, rubber, and other IFs for ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI-based IF detection methods for Landsat data. ARVI      Classified  Referenced LULC   Acacia Other IFs Rubber Non IFs Total Acacia 8 0 0 8 16 Other IFs 0 2 0 4 6 Rubber 0 0 11 15 26 Non IFs 3 2 7 140 152 Total 11 4 18 167 200 Overall Accuracy    81% User's Accuracy 50% 33% 42% 92%  Producer's Accuracy 73% 50% 61% 84%  Omission Error 27% 50% 39% 16%  Commission Error 50% 67% 58% 8%  Map Accuracy 42% 25% 33% 78%  Kappa Coefficient (moderate agreement)  0.44  EVI      Classified Referenced LULC   Acacia Other IFs Rubber Non IFs Total Acacia 6 0 0 9 15 Other IFs 0 1 1 4 6 Rubber 0 0 9 13 22 Non IFs 4 2 11 140 157 Total 10 3 21 166 200 Overall Accuracy    78% User's Accuracy 40% 17% 41% 89%  Producer's Accuracy 60% 33% 43% 84%  Omission Error 40% 67% 57% 16%  Commission Error 60% 83% 59% 11%  Map Accuracy 32% 13% 27% 77%  Kappa Coefficient (fair agreement) 0.34   MSAVIaf      Classified  Referenced LULC    Acacia Other IFs Rubber Non IFs Total Acacia 3 0 0 7 10 Other IFs 0 1 0 3 4 Rubber 0 0 6 9 15 Non_IFs 9 2 9 151 171 Total 12 3 15 170 200 Overall Accuracy    81% User's Accuracy 30% 25% 40% 88%  Producer's Accuracy 25% 33% 40% 89%  Omission Error 75% 67% 60% 11%  Commission Error 70% 75% 60% 12%  Map Accuracy 16% 17% 25% 79%  Kappa Coeficient (fair agreement) 0.26  NDVIaf      Classified  Referenced LULC (ha)   Acacia Other IFs Rubber Non IFs Total Acacia 3 0 0 6 9 Other IFs 0 1 0 3 4 Rubber 0 0 5 7 12 Non_IFs 8 3 8 156 175 Total 11 4 13 172 200 Overall Accuracy    83% User's Accuracy 33% 25% 42% 89%  Producer's Accuracy 27% 25% 38% 91%  Omission Error 73% 75% 62% 9%  Commission Error 67% 75% 58% 11%  Map Accuracy 18% 14% 25% 82%  Kappa Coeficient (fair agreement) 0.27   SARVI      Classified  Referenced LULC   Acacia Other IFs Rubber Non_IFs Total Acacia 5 0 0 9 14 Other IFs 0 1 0 5 6 Rubber 0 0 8 8 16 Non_IFs 6 0 10 148 164 Total 11 1 18 170 200 Overall Accuracy    81% User's Accuracy 36% 17% 50% 90%  Producer's Accuracy 45% 100% 44% 87%  Omission Error 55% 0% 56% 13%  Commission Error 64% 83% 50% 10%  Map Accuracy 25% 17% 31% 80%  Kappa Coeficient (fair agreement) 0.35  SAVI      Classified  Referenced LULC    Acacia Other IFs Rubber Non IFs Total Acacia 6 0 0 8 14 Other IFs 0 1 0 3 4 Rubber 0 0 6 8 14 Non_IFs 5 0 11 152 168 Total 11 1 17 171 100 Overall Accuracy    83% User's Accuracy 43% 25% 43% 90%  Producer's Accuracy 55% 100% 35% 89%  Omission Error 45% 0% 65% 11%  Commission Error 57% 75% 57% 10%  Map Accuracy 32% 25% 24% 81%  Kappa Coeficient (fair agreement)   0.36   96  VIs-based products, the accuracy for predicting, detecting, and mapping other IFs was least. For acacia IFs, ARVI showed that it worked and 73%, followed by SAVI at 43% and 55%, EVI at 40% and 60%, SARVI at 36% and 45%, NDVIaf at 33% and 27%, and MSAVIaf at 30% and 25%, respectively. Whereas, for rubber IFs, found the highest in SARVI product at 50% and 44%, followed by 43% and 35% for SAVI, ARVI (42% & 61%), NDVIaf (42% & 38%), EVI (41% and 43%), and MSAVIaf (40% and 40%, respectively; Table 2.4). Consistent with For instance, the commission and omission error for acacia IFs was lowest in the ARVI-based product at 50% and 27%, followed by SAVI (57% & 45%), EVI (60% & 40%), SARVI (64% & 55%), NDVIaf (67% & 73%), and MSAVIaf (70% & 75%, repectively; Table 2.4).   For the map accuracy, it also showed that the accuracy of acacia IFs was better than that of rubber IFs in the ARVI, EVI, and SAVI-based products, and the highest accuracy for acacia was found in the ARVI (42%), followed by EVI and SAVI (32%), SARVI (25%), NDVIaf (18%) and MSAVIaf (16%). The accuracy in detecting and mapping rubber IFs was slightly different: it showed the highest accuracy in ARVI (33%), followed by SARVI (31%), EVI (27%), MSAVIaf and NDVIaf (25%), and SAVI (24%). Meanwhile, for other IFs detection, it reached the highest accuracy for ARVI and SAVI-based products at 25%, followed by SARVI and MSAVIaf at 17%, NDVIaf at 14%, and EVI at 13% (Table 2.4).  Lastly, the Kappa statistical coefficient general for detecting and mapping the specific IF systems was found the highest in the moderate agreement in ARVI (0.44); then SAVI (0.36), SARVI (0.35) and EVI (0.34), and the least was in NDVIaf and MSAVIaf at 0.27 and 0.26, respectively, at the fair agreement (Table 2.4). 97  2.5 Discussions and Conclusions The above study results showed a possibility of using the vegetation indices (specifically ARVI, EVI, MSAVIaf, NDVIaf, SARVI, & SAVI) analysis in a time series to detect and map industrial forests in the tropics, and that the index that worked the best in the region was ARVI. In other words, the most accurate index for detecting the industrial forests in Sabah and Sarawak, Malaysia in this study was ARVI, followed by SAVI, SARVI, EVI, NDVIaf, and MSAVIaf. However, the accuracy assessment results of this method found that their accuracies by using different VIs were at the fair and moderate level. The accuracy of detecting of acacia IFs was the best in ARVI, followed by rubber and other IFs in this index; while the other VIs including SAVI, SARVI, EVI, NDVIaf, and MSAVIaf showed that their ability in detecting rubber plantations was higher than that for detecting acacia and other IFs. For detecting the other IFs, it showed the least accuracy in all VIs. This could be because this kind of IFs was very diverse, including all other types of IFs in the region, such as teak, pine, eucalypt, and other timber species. Therefore, detecting this kind of IFs was extremely challenging and much more difficult than the homogenous acacia and rubber plantations in the regions. Besides, the lower accuracy of the VIs-based method probably came from the following facts, difficulties, and challenges.   The first challenging issue in developing the VIs-based method to detect industrial forests came from the Landsat data itself. The Landsat scenes were very notorious for the effects of cloud contaminations, their shadows, haze, and missing values in the Landsat 7 (ETM+ SLC off). In other words, the quality of the Landsat scenes greatly influenced the ability to detect an IF. The mosaics - or use of a large amount of Landsat scenes to fill the gaps created by cloud problems and the missing values in Landsat 7 in the different times, different sensors, and different quality - may have also resulted in the changes of LULC, rather than the LULCC 98  themselves in reality. This definitely caused difficulties and challenges in detecting IFs in particular and classifying LULC types in general.  The second challenging issue this study faced came from the ideas used to develop the method to detect IFs. As described above, the first assumption used in this study to detect IFs was based on their silvicultural rotations. However, there was the fact that it was impossible to monitor the full cycles of sawlog long-rotation IFs such as teak, rubber, and pine. The rotation of these sawlog IF systems could take tens of years, and we could not take annual Landsat datasets long enough to observe them. Also, clearing was possibly not based on silviculture. Moreover, the silvicultural rotation of an IF system or species also varied greatly depending on the purpose of using it. Even for the same purpose of using it, its rotation might also vary depending on the intention and economic considerations of the owners, and other factors. For instance, in Thailand, a pulpwood eucalyptus stand could last as short as five years or as long as ten years. In the case that the eucalyptus stand is destined for producing saw logs, it could last tens of years. The same thing was also found for the acacia IFs in the study area: they could last 7 years to more than 10 years for pulpwood production. Therefore, using the silvicultural rotation to detect the specific IFs in these cases was challenging. Besides, almost all of the IFs would have been subjected to the silvicultural practices, including thinning and pruning activities. It was possible that we could misclassify these IF stands as a new rotation as well. For the use of the growth rates of VI values to detect IFs, the fact was that we could detect the faster- versus slower-growing IF species or systems. However, the growth rate of an IF system might also depend on the soil and climate conditions, and silvicultural practices. It was possible that a slower-growing IF species planted in a good soil (good site-species matching) and 99  exposed to proper silvicultural practices could grow the stand faster than a fast-growing IF species established in a poor condition. In regard to using the textural analysis as a support step in detecting IFs, although the textures of an IF stand was principally different from other natural vegetations, we could easily realize them in a fine scale image. However, in the medium-resolution satellite imagery data like Landsat, it was also very challenging. How well this analysis worked may be dependent on how well we chose the training areas to be used as the references in classifying IFs in images. In addition, for spectral analysis, the fact was that the spectra were also very similar among different vegetation cover types and different IF systems. Therefore, it was also very challenging to work on this analysis. For example, oil palm - which was one of the most dominating plantations in the region - had very similar spectra and texture to the selected IFs. Consequently, separating them was very difficult. One of the best possible ways we had was to select the training area well enough to represent the typical values for the expected land use and land cover in the region. This may involve dividing the region into the smaller areas and for different kinds of Landsat scenes such as Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Our best option was to build a good spectral library well representative of the different IF systems in the different times, different types of images, and different stages of an IF stand. Lastly, visual interpretation was a very subjective method, and it was dependent on the knowledge and experience of interpreters. It also relied on the quality of the other LULC sources that we would use to identify the IFs in the images. All of these things in combination created difficulties and challenges in detecting IFs in Landsat datasets.   100  In brief, it was possible for us to develop and use a vegetation indices analysis-based method for Landsat datasets that could detect, map, and monitor the area, expansion rate, patterns, and scale of IFs in the tropics. The study results showed that ARVI worked the best in the region, followed by SAVI, SARVI, EVI, NDVIaf, and MSAVIaf.  The accuracy of detecting the acacia IFs was the best, followed by rubber plantations, while the other IFs showed the least accuracy in the method. Although there is still much that can be done to improve the accuracy of this method, it opened a new, innovative, and promising approach in methods development to detect and map new industrial forests in the tropical regions. The development of the VIs-based IF detection method for Landsat data in Sabah and Sarawak was very challenging because these areas are very notorious for cloud contamination and haze. As a result, this method had to process as many as 600 images for 6 points in time from 2000 to 2014 to handle problems created by clouds, their shadows, and haze. Moreover, the most challenging issue this method had to face and deal with was the spectral and textural similarity among different land use and land cover types, as well as the spectral and textural variability in the same land use and land cover class. Additionally, there was the added variable of the rotation and growth rate of an IF normally involved the silvicultural practice activities such as thinning and pruning, and soil condition. These activities and conditions may result in challenges in developing a VIs-based method to detect and map IFs.     101  CHAPTER 3 DEVELOPING THE VEGETATION/FOREST FRACTIONAL COVER-BASED INDUSTRIAL FOREST DETECTION METHOD FOR LANDSAT DATASETS  3.1 Introduction The second approach used in this study to develop a method to detect, map, and monitor new industrial forests in the study area is a vegetation/forest fractional cover changes analysis in a time series. In forestry, remote sensing (RS) tools are most well-known for their applications for studying, quantifying, and monitoring deforestation, and other changes in forest land uses and land covers over a long period of time (e.g., Skole & Tucker, 1993).   Recently, many researchers (e.g., Bateson et al., 2000; Sousa et al., 2005) have successfully developed and applied RS methods in identifying and quantifying forest degradation. These methods, mainly developed based on the continuous-field analysis (also called spectral mixture analysis or spectral endmembers analysis), are very different from the conventional RS methods, in which each pixel of images is assigned one and only one value of a land cover or land use type (e.g., forest or water). Among RS studies on forest degradation, the most remarkable is the Global Observatory Center for Ecosystem Services (the GOES lab/center) at Michigan State University in the USA, which has very successfully developed and published methods for the detection and quantification of selective logging and forest degradation in Amazon tropical forests based on Landsat datasets (e.g., Matricardi et al., 2013; Matricardi et al., 2010; Matricardi et al., 2007; Matricardi et al., 2005; Wang et al., 2005; Skole et al., 2004). These 102  methods were also developed based on a spectral mixture analysis in combination with visual interpretation to quantify the forest fractional cover. In other words, these authors had used spectral endmembers analysis that produced a forest fractional cover dataset. This, in turn, could be used to identify where in forests there has been logging and degrading (Skole et al., 2013). The basic principles of this method are that each pixel can contain one or more land use/land cover types, and that we can extract, analyze, and estimate the proportion and composition of each land cover type in that pixel based on its spectral composition analysis.   This study would also take the same approach as the above-mentioned studies. That is, it would use spectral mixture analysis to estimate the proportion of vegetation fractional cover in each pixel based on its spectral endmembers characteristics. A spectral endmember is a pure spectrum representing a land cover type (e.g., forest) and used as a reference to determine the spectral composition of mixed pixels. As explained by Skole et al. (2013), Landsat data would be processed to present forest fractional cover, fC, a continuous-field algorithm. A threshold value of fC was used to define forest (upper threshold, high fC) and non-forested areas (lower threshold, low fC). Values in fC in the interval between the upper and lower thresholds would be used to detect IFs. This initial detection would be calibrated by using textural analysis, spectral analysis, visual interpretation, and other analyses based on typical characteristics and properties of IFs, as well as ancillary data.  3.2. Acquiring and Preprocessing Images Similar to the above VIs-based IF detection method, this method would also use the same preprocessed Landsat dataset. That is, the Landsat scenes have been already converted from DN to top-of-atmosphere reflectance, calibrated for the atmospheric effects to present surface reflectance, processed clouds and their shadows, filled the gaps of no data, and dehazed. More 103  details on how this Landsat dataset was selected, acquired, and preprocessed were presented in the Section 2.2, Chapter 2 above.  3.3. Developing the Method 3.3.1 General Principles  In general, the approach for this method is similar to the above VIs-based IF detection method. However, it is developed based on the changes of vegetation fractional cover or the silvicultural cycles of clearing and regrowth of vegetation cover, as opposed to being based on the VIs value changes. In other words, it further examines the planting and harvesting cycles of a tree plantation - which are typical for an industrial forest stand - based on how its fractional cover has been changed over time. This vegetation fractional cover analysis method would be generally called the forest fractional cover (fC) method, and it was built based on the following assumptions:  The cycle of increasing and reducing the vegetation coverage fraction (fC) possibly indicates the silvicultural cycle of clearing and regrowth, or the harvesting and planting cycle, which is typical for an IF stand.   The time span for the planting and harvesting cycle of a tree plantation could indicate the shorter vs. longer rotation (> 7 years).   The rate of increasing the coverage (fC value) of an industrial forest stand may be an indicator for faster growing vs. slower growing species.   The different vegetation cover types, in general, and industrial forests in particular, can get the same coverage (or the same fC value), but their green biomass content and leaf area index may be different (e.g., closed forest vs. timber plantation vs. oil palm vs. agricultural land).  The different vegetation covers may have different image texture and spectra.  104  The procedure for developing the Landsat-based IF detection method by using vegetation/forest factional cover analysis to transform the preprocessed images into final IF maps is described (Figure 3.1).                   Figure 3.1. The flowchart of development for the forest factional cover (fC)-based IF detection method. Validation TCA Selecting the VI NOTE: PCA:        Principal Component       Analysis TCA:       Tasseled Cap Analysis ICA:         Independent Component Analysis GLCM:    Grey Level Co-Occurrence Matrix Preprocessed Images Vegetation Indices Images Endmember Identification fC Change Detection Fast vs. Slow Growing IFs Short vs. Long Rotation Fast-Growing Short-Rotation & Slow-Growing Long-Rotation IFs Calibrated by Spectral Analysis  Texture (GLCM) PCA ICA Dissimilarity Mean Homogeneity Visual Visual Interpretation and other ancillary data IF maps Final IF maps fC dataset Spectral Unmixing Model Image Differencing Supervised classification Supervised classification Band 4  Biomass Content Leaf Area Index Validation 105  3.3.2 Results   First, a test was completed for vegetation indices consisting of ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI to see which index was the best for further fC analysis. The results of this test showed that the MSAVIaf performed the best in terms of reducing the atmospheric and soil effects (Figure 3.2). Thus, this MSAVIaf index would be used for producing fC datasets. Moreover, some fC studies (e.g., Matricadi et al., 2010) also found this index worked well in the humid tropic environment and recommended using it. This MSAVIaf index, adapted from Matricadi et al. (2010), would be calculated for all preprocessed Landsat images.   and   L = [ ( NIR  0.5 SWIR )*s + 1 + NIR + 0.5 SWIR ]2  8 *s* ( NIR  0.5 SWIR) Where s = 1.2 (slope of the soil line)  From the MSAVIaf products, two spectral endmembers - namely, bare soil/land and closed canopy forest - would be created and extracted from the images based on image examinations using the AOI (area of interest) tool in the ERDAS (Figure 3.3) and histogram analysis (Somer et al., 2011). The identification of bare soils/lands and closed canopy forests in the images was quite easy based on their texture, color, position, association, etc. For instance, in the dehazed natural color images, closed forests appeared dark green in the large area, normally associated with mountains. While the white and bright areas indicated bare lands or soils. In addition to these visual interpretation keys, other LULC sources were also used to confirm this identification. To calculate the representative value of bare land and fully forested endmembers in the study area, five and six AOIs were created in Sabah and Sarawak respectively for closed forest and bare land to obtain their endmember values. The value of bare land and fully forested endmembers for the areas were mean values of these AOIs. 106           Figure 3.2. A test for different VIs to choose the best index applied to the fC method.   Sabah, 2000, original Original Dehazed ARVI SAVI NDVIaf MSAVIaf SARVI EVI 107   Figure 3.3. An example of choosing the areas for closed forest and bare land endmembers.  The values for closed canopy forest and bare land end-members identified for Sabah and Sarawak from 2000 to 2014 on the MSAVIaf products are presented (Figure 3.4).   Figure 3.4. The endmember values of closed canopy forest and bare soil/land in Sarawak and Sabah, 2000-2014. 0 0.2 0.4 0.6 0.8 1 1997 2000 2003 2006 2009 2012 2015 Endmember values Year MSAVIaf  bare land and closed canopy forest endmember values in Sabah and Sarawak from 2000-2014 Soil EM_Sabah Forest EM_Sabah Soil EM_Sarawak Forest EM_Sarawak The area chosen for a forest endmember The area chosen for a bare land endmember 108  Considering this figure, we could easily realize that the values for closed canopy forest and bare land endmembers in Sarawak and Sabah were very similar and stable from 2000 to 2014. This indicates that these values are highly representative of the areas and have a very high consistency. Then, these two spectral endmembers we-of the two components in the linear spectral un-mixing model.    Where:     VI:   Vegetation index (MSAVIaf value [0-1])    VI (soil): Pure pixel endmember for soil value     VI (forest): Pure pixel endmember for closed canopy forest value   The results of un-mixing two spectral endmembers in each pixel as described above would produce forest/vegetation fractional cover datasets, which were a vegetation continuous field ranging from 0 to 1, or equally from 0 to 100% coverage of vegetation, for Sabah and Sarawak from 2000 to 2014. The full results of this work are presented in the Appendices (Figure A.16). An example (Figure 3.5) presents the vegetation/forest fractional cover map in Sabah and Sawarak in 2014. Considering this figure, we easily realized that the darkest green areas indicated the areas with full coverage or 100% vegetation cover. Conversely, the darkest red areas presented the areas of totally bare land/soil or no vegetation cover.  As illustrated in the above VIs-based industrial forest detection method, the changes of vegetation fractional cover (fC) in the study area would also be detected and analyzed by using the image differencing method (Cakir et al., 2006). The harvesting and planting cycles of an IF stand would indicate the clearing and regrowth of vegetation cover. This cycle would be expressed through an increase and declining of the fC value or the vegetation coverage fraction. 109  Skole et al. (2013) argued that, by doing so, a threshold for forest/non-forest would be identified using a level slice and visual interpretation. Multi-temporal change detection analysis would be done on 1) the full fC datasets, 2) the forest/non-forest datasets, and 3) the fC forest only datasets. Therefore, by using this multi-temporal change analysis, it was possible for us to identify the cycles of clearing and re-growth consistent with IF systems in the study areas.   Figure 3.5. The forest/vegetation fractional cover (fC) map produced from the MSAVIaf products in 2014 for Sarawak and Sabah.      SABAH SARAWAK 2014 110   According to Cakir et al. (2006), the image differencing resulted in three possibilities for an fC change i.e., absolute no change, some possible change, and absolute change. To determine the values for these possibilities, a threshold for fC changes must be identified. Similar to the VIs-based method above, by doing the trial-and-error experiments, a threshold of ± 15% or 0.15 was chosen for identifying an fC change. The value of differencing two dated images was , meaning absolute no change; from > 0 to < +15% or < 0 to > -15%, indicating some possible changes; or > + 15% or < -15%, meaning absolute change. The fC change detection analysis was done for the years 2000-2003, 2003-2006, 2006-2009, 2009-2012, and 2012-2014 (Figure A.17 in the Appendices). An example of fractional cover image differencing to detect the fC change for the years of 2012 and 2014 in Sarawak and Sabah is presented (Figure 3.6). It clearly indicates the areas of absolute change, some possible change, and absolute no change.  Figure 3.6. The fC changes detection for 2012-2014 in Sarawak and Sabah.    The fC changes of 2012-2014 in Sarawak and Sabah SABAH SARAWAK 111   To observe how the fC has been changed in the study area over time, 30 key locations for each state (Sarawak and Sabah) were created to monitor the fC changes (Figure 3.7). These locations were the same locations created in the VIs-based IF detection method. The results of monitoring of the fC changes and the sequences of increasing and reducing the fC values at the threshold of ± 15% for 30 monitored key locations in Sabah from 2000 to 2014 are presented (Table 3.1). The same result for Sarawak is shown in the Appendices (Table A.11). These fC value increasing and declining sequences could indicate or provide initial clues for the silvicultural cycle of planting and harvesting (or clearing and regrowth) of an IF stand.  Figure 3.7. The key locations for monitoring the fC changes in Sabah and Sarawak, 2000-2014. SABAH SARAWAK    112  Table 3.1. The fC value changes in 30 monitored key locations in Sabah, 2000-2014.   SEQUENCES IN INCREASING & REDUCING fC IN KEY AREAS/LOCATIONS IN SABAH, 2000-2014  ID 2000 2003 2006 2009 2012 2014             1 V/F  NV/NF V/F V/F V/F V/F 2 V/F  V/F NV/NF V/F  V/F  V/F  3 V/F  V/F  NV/NF V/F  V/F  NV/NF 4 V/F  V/F  NV/NF V/F  V/F  NV/NF 5 V/F  NV/NF V/F  V/F  V/F  NV/NF 6 NV/NF V/F  V/F  V/F  V/F  V/F  7 V/F  NV/NF V/F  V/F  V/F  V/F  8 NV/NF V/F  V/F  V/F  V/F  V/F  9 NV/NF V/F  V/F  V/F  V/F  V/F  10 NV/NF V/F  V/F  V/F  V/F  V/F  11 V/F  V/F  V/F  V/F  NV/NF V/F  12 V/F  NV/NF V/F  V/F  V/F  V/F  13 V/F  NV/NF V/F  V/F  V/F  V/F  14 V/F  NV/NF V/F  V/F  V/F  V/F  15 V/F  NV/NF V/F  V/F  NV/NF  V/F  16 NV/NF V/F  V/F  V/F  V/F  V/F  17 V/F  V/F  NV/NF V/F  V/F  V/F  18 NV/NF V/F  V/F  V/F  V/F  V/F  19 V/F  V/F  V/F  V/F  NV/NF V/F  20 V/F  V/F  NV/NF V/F  V/F  V/F  21 NV/NF V/F  V/F  V/F  V/F  V/F  22 V/F  NV/NF V/F  V/F  V/F  NV/NF 23 NV/NF V/F  V/F  V/F  NV/NF V/F  24 V/F  NV/NF V/F  V/F  V/F  V/F  25 V/F  NV/NF V/F  V/F  NV/NF V/F  26 V/F  V/F  V/F  V/F  V/F  NV/NF 27 V/F  V/F  V/F  NV/NF V/F  V/F  28 V/F  V/F  NV/NF V/F  V/F  V/F  29 V/F  V/F  NV/NF V/F  V/F  V/F  30 NV/NF V/F  V/F  V/F  V/F  V/F   THEVALUE OF fC IN KEY AREAS/LOCATIONS IN SABAH, 2000-2014  2000 2003 2006 2009 2012 2014             0.987 0.024 0.815 0.755 0.975 0.916 0.991 0.974 0.828 0.924 0.974 0.859 0.990 0.990 0.562 0.945 0.985 0.616 9.800 0.889 0.423 0.983 0.988 0.742 0.971 0.448 0.980 0.938 0.961 0.447 0.248 0.842 0.909 0.890 0.937 0.930 0.935 0.427 0.810 0.951 0.972 0.946 0.067 0.727 0.861 0.953 0.945 0.918 0.548 0.867 0.868 0.951 0.984 0.974 0.088 0.801 0.943 0.943 0.963 0.977 1.000 0.990 0.971 0.997 0.598 0.853 0.998 0.624 0.974 0.853 0.890 0.887 0.992 0.561 0.956 0.918 0.965 0.948 0.561 0.309 0.754 0.893 0.953 0.906 0.690 0.001 0.490 0.431 0.219 0.703 0.509 0.722 0.911 0.811 0.852 0.901 0.965 0.945 0.458 0.625 0.850 0.885 0.181 0.346 0.808 0.891 0.967 0.978 0.877 0.925 0.983 0.957 0.620 0.998 0.901 0.835 0.237 0.960 0.970 0.970 0.317 0.846 0.980 0.929 0.981 0.987 0.960 0.392 0.984 0.969 0.973 0.434 0.683 0.958 0.890 0.988 0.738 0.922 0.967 0.623 0.987 0.996 0.986 0.990 0.970 0.335 0.904 0.931 0.454 0.922 0.988 0.984 0.904 0.945 0.971 0.697 0.951 0.943 0.995 0.026 0.696 0.883 0.955 0.972 0.560 0.775 0.859 0.941 0.986 0.976 0.210 0.897 0.981 0.970 0.444 0.671 0.671 0.780 0.926 0.959  Note:     [1] V/F: full  or more vegetation cover (regrowth); NV/VF: non or less vegetation (clearing)            [2] From V/F to NV/NF indicating a reduction in VI from full/more to none/less vegetation cover (clearing)           [3] From  NV/NF to V/F expressing an increase in VI from none/less to full/more vegetation cover (regrowth)   In other words, the silvicultural cycle of planting and harvesting an IF stand indicated its time span, which could help us detect shorter vs. longer rotation plantation stands. In fact, there is no global standard for how long a shorter vs. longer rotation plantation stand is. The short or long 113  rotation stand is dependent on the purpose of using that plantation stand. In common practice, the short rotation industrial forests last a few years to around 10 years, while a long rotation timber plantation may last tens of years. Thus, in this study, like the above VIs-based method, any change of the fC value at or less than 7 years was assumed to be shorter rotation IFs, and any change of fC value more than 7 years was assumed to be longer rotation IFs. The results of detecting shorter vs. longer rotation plantation stands are presented (Figure 3.8) as an example.   Figure 3.8. The possibly shorter- and longer-rotation industrial forests in Sabah and Sarawak, 2000-2014.  In addition to acquiring the initial clues for detecting industrial forests (i.e., rotation data for plantations derived from analyses of the clearing/harvesting and regrowth/planting cycles of    SABAH SARAWAK 114  industrial forests based on the increasing and declining fC values, as presented above), the next analysis for the growth rate of the vegetation cover (fC value) would also provide an important clue for detecting industrial forests, because the growth rate of the vegetation cover (fC value) may indicate the faster-growing and slower-growing timber plantations.   In this fC method, the faster-growing industrial forests or timber plantations, such as Acacia spp., were hypothesized to have the higher fC growth rate as compared to the slower-growing species, such as teaks or rubbers. The growth rate of the fC was calculated as follows: growth rate =  (fC (t2)  fC (t1)) / fC (t1) where fC (t1) was the value of vegetation/forest fractional cover (fC values) in the earlier year, or time t1, and fC (t2) was the value of vegetation/forest fractional cover (fC values) in the later year, or time t2.   The threshold chosen for identifying the faster-growing industrial forest species and the slower-growing species was 0.5 for both species. This value, like the VIs-based method, was determined based on assumption and the trial-and-error experiments. The growth rate with  fC value larger than 0.5 possibly indicated the faster-growing species, while the growth rate with fC value lower than 0.5 possibly indicated the slower-growing species. The fC growth rate was calculated for the study period. The preliminary results of calculating this fC growth rate in Sarawak and Sabah for the period of 2000-2014 are presented (Figure 3.9).  Then, these two datasets (shorter vs. longer rotation IFs and faster-growing vs. slower-growing IF species) would be combined to create a faster-growing, shorter-rotation and slower-growing, longer-rotation industrial forest dataset (Figure 3.10). This dataset would be used as an input data in the final model to determine the area and species of industrial forests in Sarawak and Sabah.  115   Figure 3.9. The possibly faster-growing and slower-growing industrial forests in Sabah and Sarawak, 2000-2014.  Another important assumption used to develop the fC-based IF detection method was that the different vegetation cover types (e.g., closed forest vs. timber plantation vs. oil palm vs. agricultural land) in general, and industrial forests (e.g., acacias vs. rubbers vs. teaks) in particular, can score the same coverage (or the same fC value), but their green biomass contents may be different. In remote sensing application studies, band 4 (Near Infrared (NIR) in the Landsat 4-5 TM and Landsat 7 ETM+ with the wavelength range of 0.770.9 µm, and band 5   SABAH SARAWAK  116  with the wavelength of 0.85-0.88 µm in Landsat 8 OLI & TIRS) is used to study the green biomass content in vegetation.  Figure 3.10. The possibly faster-growing, shorter-rotation and slower-growing, longer-rotation industrial forests in Sabah and Sarawak.  Thus, this study would also use this band to study green biomass content for different IF systems. Firstly, the values of band 4 were obtained in the same fC areas to examine any difference among them. Its results showed that there were some differences in the band 4 values in the same fC area (Figure 3.11). Then, five different vegetation types including acacia, natural forest, oil palm, rubber, and other industrial forests were also identified as presented in the above   SABAH SARAWAK  117  VIs-based IF detection method. An AOI was created to obtain the band 4 values in these areas and the results are shown (Figure 3.12). Considering this figure, we could realize that the band 4 values for these land use/land cover types were also different.  Figure 3.11. The band 4 values in the same vegetation cover in Sabah.   Figure 3.12. The band 4 values for different vegetation cover types in Sabah, 2000-2014. 0.99 0.99 0.94 0.94 0.945 0.945 0.974 0.975 0.433 0.329 0.372 0.338 0.377 0.344 0.35 0.378 0 0.2 0.4 0.6 0.8 1 1.2 SR LR SR LR SR LR SR LR The band 4 and fC values Short and long rotation The band 4 values  of shorter versus longer rotation stands at the same fC values in Sabah fC B4 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 2000 2003 2006 2009 2012 2014 Band 4 values Year The values of band 4 for different vegetation types in Sabah, 2000-2014 Acacia Rubber Other IFs Oil palm Forest 118   To further examine this assumption, a statistical test was conducted by using non-parametric two-related-samples test (Wilcoxon Signed Ranks Test) for 30 key locations in Sabah and another 30 locations in Sarawak from 2000 to 2014. The results indicated the same fC values and band 4 values were significantly different at p<.0001. This meant that we could use this information for further analysis for detecting industrial forests in the study area.   In addition, among different vegetation cover types in general, and industrial forests in particular, although their fractional cover (or their fC values) are the same, their leaf area index (LAI) may be different. In the other word, this study examines whether the LAI among different IF types in particular, and vegetation types in general, that have the same fC values are different. A number of studies were conducted to investigate the relationship between LAI with the vegetation indices. For instance, Broge and Leblanc (2000), and Haboudane et al. (2004) published studies using hyperspectral vegetation indices to predict green LAI and suggested we should use the following predictive equation based on MSAVI to estimate LAI: LAI = 0.1663exp(4.2731*MSAVI)                         Another study by Boegh et al. (2002) used multispectral data to quantify LAI and found that LAI had a very high correlation with the EVI (Enhanced Vegetation Index), and thus the authors proposed the following formula: LAI = (3.618 * EVI  0.118) > 0 to estimate the leaf area index of vegetation cover.  This index is also used in the ENVI (Exelis Visual Information Solutions, Boulder, Colorado) as a reference. Later, Potirthep et al. (2010) also investigated the relationship between NDVI and EVI with LAI for a deciduous broadleaf forest and found that the LAI-EVI had a better correlation compared with the LAI-NDVI. Likewise, Hassan and Bourque (2010) also found a strong linear correlation between LAI and EVI in the boreal forest region. However, 119  while reviewing this predictive equation for LAI by using EVI, I realized that LAI in this formula/predictive equation was never larger than 4 because of the value range of EVI [-1, 1], while Leigh Jr (1999) showed that the typical LAI for lowland tropical forests normally ranged from 7 to 8 in Pasoh, Malaysia, or even higher. Therefore, using EVI to estimate LAI in the tropical environment may not work. As a result, in this study, MSAVI would be used to investigate LAI of different vegetation cover types in the study area over the study period. However, as presented in the previous section, the modified version (MSAVIaf) was used in this study instead of using the original MSAVI. Thus, to be consistent with the dataset, the MSAVIaf product was used to estimate LAI for different vegetation cover types in the study area. The preliminary results of estimating LAI based on MSAVIaf in Sabah and Sarawak are presented (Figure 3.13).    Figure 3.13. The MSAVIaf-based LAI for different vegetation cover types in Sabah and Sarawak, 2000-2014.  For IF detection based on LAI and band 4, the supervised classification in the ERDAS was used to classify six LULC types, including acacia, rubber, other IFs, oil palm, forest, and other 4 5 6 7 8 9 10 2000 2003 2006 2009 2012 2014 Leaf Area Index Year The MSAVIaf-based LAI in Sabah for different vegetation covers, 2000-2014 Acacia Forest Oil palm Rubber Other IFs 5 6 7 8 9 10 2000 2003 2006 2009 2012 2014 Leaf Area Index Year The MSAVIaf-based LAI in Sarawak for different vegetation covers, 2000-2014 Acacia Forest Oil palm Rubber Other IFs 120  LU/LC types. The locations for the AOI were the same locations identified in the textural analysis and spectral analysis in the VIs-based IF detection method and in this method. Spectral Analysis  Similar to the VIs-based IF detection method described above, this fC-based method also used the spectral analysis consisting of Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Tasseled Cap Analysis (TCA) to support and calibrate the results of using multi-temporal fC dataset analysis, green biomass content, and leaf area index (LAI) analysis to detect industrial forests in the study area (Sabah and Sarawak in Malaysia) over the period of 2000-2014. The datasets used for the spectral analysis in this method were the same datasets used in the VIs-based method. However, there was only one difference; instead of manually identifying the typical values for the expected IFs or other land uses/land covers by examining histograms, the supervised classification function available in the ERDAS IMAGINE was used to statistically classify the expected land uses/land covers. Then, three datasets of the PCA, ICA, and TCA were used to identify and classify the expected land use and land cover types. The full results of this spectral analysis are shown in the Appendices (Figure A.18). An example of classifying land uses/land covers (acacias, forests, oil palms, rubbers, other IFs, and other land uses and land covers/other LULCs) based on the ERDAfunction for Sabah and Sarawak in 2003 is presented (Figure 3.14).   Textural Analysis  This method also used textural analysis, along with spectral analysis, green biomass content, and leaf area index (LAI) analysis as mentioned above, as a supporting method to calibrate the results of detecting industrial forests (IFs) based on multi-temporal forest fractional cover datasets analysis. This method took the same approach with the textural analysis method in the 121  VIs-based IF detection method, but they were different in how they used specific steps and datasets. Specifically, the textural analysis in the VIs-based method applied the Grey Level Co-occurrence Matrix (GLCM) for VIs image datasets including ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI; band 4 and band 5 images. Of that, in the GLCM, the image textural indices consisting of Mean (MEA), Dissimilarity (DIS), and Homogeneity (HOM) were calculated for the VIs images and band 4 datasets, while only the index MEA was applied for band 5 grey level   Figure 3.14. The spectral analysis-based land use/land cover map in Sabah and Sarawak, 2003. images. In this method, the indices in the GLCM comprised of Mean (MEA), Dissimilarity (DIS), and Homogeneity (HOM) would be applied for the fC datasets only. Then, similar to the implementation of the spectral analysis for this method, the supervised classification function in the ERDAS would also be used to classify the expected land use and land cover types for the   SABAH SARAWAK  2003 122  resulting datasets. The expected land use/land cover types classified for the study area (Sabah and Sarawak in Malaysia) from 2000 to 2014 were acacia plantations, natural forests, oil palm plantations, rubber plantations, other industrial forest types, and other land uses and land cover types, such as residential areas, agricultural, and degraded lands.  Then, these datasets were used in a textural analysis-based model to determine the area and type of the expected land uses/land covers in Sabah and Sarawak from 2000 to 2014. The full results of this textural analysis method are expressed in the Appendices (Figure A.19). An example of classifying land uses/land covers types based on textural analysis in Sabah and Sarawak in 2000 is presented (Figure 3.15).  Figure 3.15. The textural analysis-based land use/land cover map in Sabah and Sarawak, 2000. Visual Interpretation and Using Other Data  After obtaining the multi-temporal fC dataset analysis results, LAI, green biomass content, textural analysis, and spectral analysis, visual interpretation - along with other LULCC and    2000 SABAH SARAWAK 123  ancillary data - were used to calibrate the final results for detecting industrial forests. The results of using visual interpretation and other ancillary data were described and acquired from the above VIs-based IF detection method.  Making IF Maps  Then, an algorithm was developed based on rules for the faster-growing, shorter-rotation (FGSR) and slower-growing, longer-rotation (SGLR) datasets, leaf area index, green biomass content, textural analysis, spectral analysis, and visual interpretation. The algorithm is described in Figure 3.16. In other words, the algorithms could be presented as follows: f(IFs) = ([Texture(IFs)   Spectra(IFs)  FGSR-SGLR(IFs) Visual(IFs) Biomass(IFs) LAI(IFs)] + [Texture(IFs)  Spectra(IFs)  FGSR-SGLR(IFs) Biomass(IFs) LAI(IFs)] + [Visual(IFs)  (Texture(IFs) OR/AND  Spectra(IFs) OR/AND FGSR-SGLR(IFs) OR/AND Biomass(IFs) OR/AND LAI(IFs))] + [FGSR-SGLR(IFs) (Texture(IFs) OR/AND  Spectra(IFs) OR/AND Biomass(IFs) OR/AND LAI(IFs))]).   Similar to the above VIs-based IF map product, the final results were also clumped and any area smaller than 2 ha would be eliminated to salt and pepperatmospheric effects and image quality, as well as to adapt with the fact that the smaller IF patches were more difficult to detect and that smallholders in Southeast Asia commonly owned a land size around 1-4 ha of plantations (Fox & Castella, 2013).        124                   Figure 3.16. The simple diagram for developing the final algorithm to detect and map industrial forest areas and species based on the fC dataset analysis.  The results of detecting industrial forests in Sarawak and Sabah for the years 2000, 2003, 2006, 2009, 2012, and 2014 based on the multi-temporal fC dataset analysis are presented (Figures 3.17, 3.18, 3.19, 3.20, 3.21, and 3.22). Texture Spectra Fast-Growing Short-Rotation & Slow-Growing Long-Rotation Visual Conditional Industrial Maps Leaf Area Index Biomass Content Visual Texture Spectra FG-SR & SG-LR Biomass LAI IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs IFs 125   Figure 3.17. The fC-based IF map for Sabah and Sarawak in 2000.  Figure 3.18. The fC-based IF map for Sabah and Sarawak in 2003.     SABAH SARAWAK SABAH SARAWAK  2000  2003 126   Figure 3.19. The fC-based IF map for Sabah and Sarawak in 2006.  Figure 3.20. The fC-based IF map for Sabah and Sarawak in 2009.     SABAH SARAWAK SABAH SARAWAK   2006 2009 127   Figure 3.21. The fC-based IF map for Sabah and Sarawak in 2012.  Figure 3.22. The fC-based IF map for Sabah and Sarawak in 2014.     SABAH SABAH SARAWAK SARAWAK   2012 2014 128  3.4  Validation The validation work for the fC-based IF detection method in the Landsat datasets would also be conducted through the use of very high resolution imagery data. The same high resolution imagery data and the procedure to assess the accuracy of the method were used as in the VIs-based IF detection method. The sample locations were randomly located in each class in the fC-based IF maps and had to be relatively evenly distributed in the class, as presented in the Appendices (Figure A.26). Similar to the VIs-based IF detection method, the accuracy assessment was also first conducted for the IF land versus non IF land to see how the method and algorithms could separate the lands. Then, it would be scaled down to the finer IF classes, specifically for acacia, rubber, and other IFs. The results of the IF land versus non-IF land accuracy assessment showed that t47% and 83% (Table 3.2). The commission and omission error was 53% and 17%, respectively. The map accuracy achieved by the fC-based IF detection method for Landsat data was 43%. At the same time, the Kappa coefficient for detecting and mapping the IF land in this method was 0.46 at the moderate agreement. Table 3.2. The accuracy assessment results for the fC-based IF land detection method. fC    Classified Referenced LULC    IF land Non IF land Total IF land 34   38 72 Non IF land 7 121 128 Total 41 159 200 Overall Accuracy   78% User's Accuracy 47% 95%  Producer's Accuracy 83% 76%  Omission Error 17% 24%  Commission Error 53% 5%  Map Accuracy 43% 73%  Kappa Coefficient (moderate agreement) 0.46 129   Next we would further examine how this method worked for detecting specific IF systems in the regions. The findings of this study showed that it could detect and map acacia plantations better than rubber and other IFs (Table 3.3). For acacia s accuracy and commission error were 50% and 50%, compared to those for the rubber IFs at 47% and 53%, and other IFs at 43% and 57%,  its detection for acacia (82%) higher than that for rubber (81%) but less than that for other IFs (100%). This was also consistent with the omission error for acacia of 18%, rubber of 19%, and other IFs of 0%. The acacia IF detection and mapping also acquired the higher map accuracy (45%) than other IFs (43%) and rubber (42%). The Kappa statistics for detecting the specific IF systems at 0.50 was slightly higher than for detecting the IFs in general at 0.46. This also showed a moderate agreement by chance between the classified and referenced IF maps. Table 3.3. The accuracy assessment results specific for acacia, rubber, and other IFs for the fC-based IF detection method for Landsat data. fC       Classified  Referenced LULC   Acacia Other IFs Rubber Non IF land Total Acacia 9 0 0 9 18 Other IFs 0 3 0 4 7 Rubber 0 0 22 25 47 Non IF land 2 0 5 121 128 Total 11 3 27 159 200 Overall Accuracy     78% User's Accuracy 50% 43% 47% 95%  Producer's Accuracy 82% 100% 81% 76%  Omission Error 18% 0% 19% 24%  Commission Error 50% 57% 53% 5%  Map Accuracy 45% 43% 42% 73%  Kappa Coefficient (moderate agreement)   0.50   130  3.5  Discussions and Conclusions The above study results showed a high possibility of using the vegetation fractional cover (fC) changes analysis method for Landsat datasets in a time series to detect and map industrial forests in the tropics. The accuracy assessment results of this method for both IF land in general, and specific IF systems in particular, were found to be at the acceptable level. The accuracy of detecting and mapping acacia IFs was better than that of rubber and other IFs. Similar to the VIs-based IF detection method, this method least worked in detecting and predicting other IFs. This proved that detecting this kind of IFs was very challenging because of its diversity. In brief, similar to the aforementioned VIs-based IF detection method, the ability of this method to detect and map IFs in the region was confounded by some challenges and difficulties including, the quality and disadvantages of the Landsat data, as well as the rotation and growth rate assumptions used to develop the method, and other textural, spectral, and visual interpretation issues.   For the quality of Landsat scenes, this method used the same data as the VIs-based method. However, instead of directly computing the VI values in the images, this method analyzed the spectral composition and proportion for soil and forest in each pixel based on their spectral endmembers to produce a forest/vegetation fractional cover (fC) dataset. This approach would help reduce the additive effects of image quality to the method. However, it also faced the problem of spectral similarity among different endmembers and spectral variability in an endmember.     The second challenging issue this study faced also came from the ideas used to develop the method to detect IFs. That is, the uses of the information about the silvicultural rotation and growth rates of the forest/vegetation covers based on their changes analysis over time also 131  inherited the outstanding issues, which were similar to and argued in the VIs-based method above.  Regarding the use of the textural and spectral analysis as a supportive step in detecting and mapping IFs, this method took the same approach as the above VIs-based method. However, instead of subjectively identifying the spectral and textural values for the expected LULC classes and using them in the built models, this method used the supervised classification function in the ERDAS software to classify the expected LULC classes. Therefore, it could help reduce the subjectivity in identifying the selected IF systems. Besides, this method and the VIs-based method used the same visual interpretation data. Therefore, they would have the same issues.   For other analyses including band 4 value-based green biomass content and MSAVIaf-derived leaf area index that were added to the method to detect and map IFs, the values of band 4 might only represent the green biomass of vegetation canopy instead of representing the whole biomass of the stands. Therefore, using it for biomass content analysis should be carefully considered. Besides, using MSAVIaf-derived leaf area index to identify the IF systems should be also additionally tested in the fields.  In brief, it was possible for us to develop and use a forest/vegetation fractional cover changes analysis-based method for Landsat datasets that could detect, map, and monitor the area, expansion rate, patterns, and scale of IFs in the tropics. The study results showed the accuracy of this method in detecting IFs in the region was better than that of the VIs-based method. Detecting and mapping acacia IFs in this method was better accurate than detecting and mapping rubber plantations, while the other IFs showed the least accuracy in this method. Consequently, like the VIs-based method, there is still much to be done to improve the accuracy of this method in detecting and mapping IFs in tropical regions like Sabah and Sarawak, Malaysia. It also 132  opened a new, innovative, and promising approach in methods development for detecting and mapping new industrial forests in the tropics.  133  CHAPTER 4     ASSESSING THE INDUSTRIAL FOREST LAND USE AND LAND COVER CHANGES, AND THEIR CONSEQUENCES  4.1     Industrial Forest Land Use and Land Cover Changes 4.1.1. The fC-based LULCC The results of the fC-based IF detection method showed that the total IF area (acacia, rubber, and other IFs) in Sabah increased from 102,667 ha in 2000 to 391,214 ha in 2014 at the annual mean rate of 20.1%; in Sarawak, it increased from 54,840 ha to 514,738 ha in the same period at the annual mean rate of 59.9% (Figures 4.1, 4.2a&b).      Figure 4.1. The IF areas in 2000, 2003, 2006, 2009, 2012 and 2014 in Sabah and Sarawak.  -     70,000   140,000   210,000   280,000   350,000   420,000  2000 2003 2006 2009 2012 2014 Area (ha) Year The areas of IFs in Sabah, 2000-2014 Rubber Other IFs Acacia  -     50,000   100,000   150,000   200,000   250,000   300,000  2000 2003 2006 2009 2012 2014 Area (ha) Year The areas of IFs in Sabah, 2000-2014 Acacia Other IFs Rubber  -     100,000   200,000   300,000   400,000   500,000   600,000  2000 2003 2006 2009 2012 2014 Area (ha) Year The IF areas in Sarawak, 2000-2014 Rubber Other IFs Acacia  -     100,000   200,000   300,000   400,000  2000 2003 2006 2009 2012 2014 Area (ha) Year The IF areas in Sarawak, 2000-2014 Acacia Other IFs Rubber 134   The total IF area newly established for the period of 2000-2014 was 288,547 ha in Sabah, and 459,898 ha in Sarawak (Tables 4.1 & 4.2). Specifically, the acacia IF area in Sabah increased 190,353 ha from 47,868 ha in 2000 to 238,221 ha in 2014, with the yearly mean expansion rate for the whole study period at 28.4%, much higher compared to the annual mean increasing rate of rubber plantations (13.7%) and other IFs (10.9%).  In the same period, the rubber area increased 72,274 ha from 37,788 ha in 2000 to 110,062 ha in 2014. Likewise, the area of other IFs in Sabah also slightly increased 25,920 ha from 17,011 ha in 2000 to 42,931 ha in 2014 (Table 4.1 & Figure 4.2a). Compared to the expansion rate of IFs in Sabah, the expansion rate of IFs in Sarawak in the period was much higher and very impressive. Specifically, the total area of acacia IFs has increased from almost nothing (6,864 ha in 2000) to 368,640 ha in 2014 with a net increase of 361,776 ha for 2000-2014 at the annual rate of 376.5% (Table 4.2 & Figure 4.2b). Likewise, the yearly expansion of other IFs in Sarawak was also very impressive, with the annual mean rate of 78.2% in the period, representing a net increase of the area of 63,808 ha from 5,829 ha in 2000 to 69,637 ha in 2014. In contrast, the development of rubber plantations was much lower compared with the development of acacia and other IFs; it only increased at the annual mean rate of 5.8% over the study period. The rubber area had increased 34,314 ha from 42,147 ha in 2000 to 76,461 ha in 2014. The development trend for rubber plantations in Sarawak was similar to the trend of development for rubber plantations in Sabah over the period of 2000-2014 (Table 4.2 & Figure 4.2b).  Breaking the IF expansion area and its rate in Sabah and Sarawak down into the intervals, we realize that the largest newly-expanded IF area (77,538 ha) was found in the period of 2003-2006 in Sabah, followed by 2000-2003 (71,318 ha); after that, the growth slowed down for 2006-2009 (54,089 ha) and 2009-2012 (36,423 ha), and increased again for the period of 2012-2014 (49,179 135  ha) (Table 4.1). A similar trend was also found for the expansion rate of total IFs. The highest rate of change in IF area was also found in 2000-2003 at 23.2% (specific to acacia at 34.3% and other IFs at 21.2%, the highest compared to other periods); then, the IF expansion rate slowed down for 2003-2012, and increased again for 2012-2014 (Figure 4.2a). In Sarawak, the largest new expansion IF area was found in 2012-2014 with an area of 123,572 ha and a growth rate of 15.8%. However, the highest rate of change in IF area was found for 2000-2003 at 29.1%, followed by 2003-2006 (28.9%), 2006-2009 (19.3%), and 2009-2012 (9.7%) (Figure 4.2b).  Table 4.1. The IF area expansion in Sabah, 2000-2014.   Newly expanded IF area in Sabah (ha) Species 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Acacia           49,206            67,944               24,536             16,917          31,750         190,353  Other IFs           10,814  3,800                4,574               4,177            2,555           25,920  Rubber           11,298  5,794 24,979            15,329          14,874           72,274  Total           71,318            77,538               54,089             36,423          49,179         288,547   Table 4.2. The IF area expansion in Sarawak, 2000-2014.   Newly expanded IF area in Sarawak (ha) Species 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Acacia             32,967          73,072             75,452           72,880      107,405         361,776  Other IFs               1,862  9,779            33,440          12,303          6,424           63,808  Rubber             13,026  6,300 2,108            3,137          9,743           34,314  Total             47,855          89,151           111,000          88,320      123,572         459,898      Figure 4.2. The annual rate of change in area in Sabah (a) and Sarawak (b), 2000-2014. 0.0% 7.0% 14.0% 21.0% 28.0% 35.0% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 The rate of annual change (%) Year The annual rate of IF expansion in Sabah, 2000-2014 Acacia Other IFs Rubber Total 0.0% 80.0% 160.0% 240.0% 320.0% 400.0% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 The rate of annual change  (%) Year The annual rate of IF expansion in Sarawak, 2000-2014 Acacia Other IFs Rubber Total a b 136   To further understand the dynamics and processes of the expansion of new IFs in Malaysia, we should consider how IFs at the small- versus large-scale plantations had been expanded. In fact, for the satellite images only-derived LULC map products, it is very challenging or even impossible for us to know which patch is owned by industries or smallholders without any further ownership investigation in the field. However, based on the individual patch size, it is possible for us to assume which patch may belong to smallholders (small-scale) or industries (large-scale). For instance, Bissonnette and De Koninck (2015) find that most countries divide the small-scale and large-scale IFs/plantations based on the land size ranging from 20-40 ha. Lintangah et al. (2010) investigated tree plantation activities among smallholders in Ranau, Sabah and found that, in addition to other tree plantation species, rubbers and acacias were main species, followed by teaks, pines, and eucalypts. For rubber plantations, the average patch size owned by smallholders was about 1.3-1.4 ha. Most of other tree plantations had a size of 0.4 to 2 ha, while some reached 2 to 6.5 ha, and very few were larger than 6.5 ha. This is very likely a common practice in Southeast Asia, where smallholders own a land size around 1-4 ha for perennial cash crops (Fox & Castella, 2013). Therefore, for this reason and conservativeness, the land size used to divide the small-scale versus large-scale IFs in Malaysia in this study was assumed at 40 ha.   In general, based on this assumption, in both states, the total area of small-scale IFs (~30-40%) was found to be much less than the area of large-scale IFs (~60-70%; Figure 4.3; Tables 4.3a&b, & 4.4a&b). Specifically, in Sabah, the large-scale IF area in 2000 was 77,927 ha (76%), while the small-scale area owned by smallholders was only 24,740 ha (24%) for the same year. This increased to 215,910 ha (55%) in 2014 compared to 175,304 ha (45%) under the same ownership (Tables 4.3a&b). The study also found that the percentage of the large-scale IF area in 137  the state declined from 76% in 2000, to 73% in 2003, to 61% in 2006, to 56% in 2009, to 57% in 2012, and to 55 % in 2014. Likewise, in Sarawak, the total large-scale IF area in 2000 was 44,194 ha (81%), compared to 10,646 ha (19%) of smallholdings (small-scale IFs). The large-scale IF area had increased to 348,350 ha (68%) in 2014 compared with an increase to 166,388 ha (32%) for small-scale IFs (Tables 4.4a&b). The trend of change for IF scales in Sarawak over the study period was similar to the trend in Sabah. Specifically, the percentage of small-scale IF area slightly increased from 19% in 2000, to 25 % in 2009, and to 32% in 2014 (Table 4.4b).   In general, most of the absolute expansion of new IFs in the study area was in the large-scale IFs. However, the percentage of total large- versus small-scale IFs also slightly declined over 2000-2014. The percentage specific for different IFs under the large-versus small-scale IFs in different years indicated some differences.  For instance, in Sabah, the percentage of rubber IFs      Figure 4.3. The total large-scale and small-scale IF area in Sabah and Sarawak, 2000-2014. 0 50000 100000 150000 200000 250000 2000 2003 2006 2009 2012 2014 Area (ha) Year The total IF area under the large and small scale in Sabah, 2000-2014 Large scale Small scale  -     50,000   100,000   150,000   200,000   250,000  Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale 2000 2003 2006 2009 2012 2014 Area (ha) Year The acacia, rubber, and other IFs area under the large scale and small scale in Sabah, 2000-2014   Rubber Other IFs Acacia 0 100000 200000 300000 400000 2000 2003 2006 2009 2012 2014 Area (ha) Year The total IF area under the large and small scale in Sarawak, 2000-2014  Large scale Small scale  (50,000)  40,000   130,000   220,000   310,000   400,000  Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale 2000 2003 2006 2009 2012 2014 Area (ha) Year The acacia, rubber, and other IF area under the large and small scale in Sarawak, 2000-2014 Rubber Other IFs Acacia 138  Table 4.3a. The area (in ha) of large-scale and small-scale IFs in Sabah, 2000-2014.  Table 4.3b. The percentage of large-scale and small-scale IFs in Sabah, 2000-2014.  Table 4.4a. The area (in ha) of large-scale and small-scale IFs in Sarawak, 2000-2014.  Table 4.4b. The percentage of large-scale and small-scale IFs in Sarawak, 2000-2014.  under smallholdings (patch size < 40ha) was generally slightly larger than that for acacia and other IFs (Table 4.3b). Conversely, in Sarawak, the percentage of acacia and other small-scale IFs was larger than that for rubber, except for other IFs after 2006. For instance, 74% (2000), 51% (2003), 61% (2006), and 64% (2014) of acacia plantations were large-scale IFs, compared with 83% (2000), 77% (2003), 74% (2006), and 67% (2014) of the large-scale rubber plantations. Likewise, 73% (2000) and 63% (2003) of other IFs were large-scale IFs, compared 139  with 83% and 77% of rubber plantations under the same scale in 2000 and 2003, respectively. More detailed information on the area and percentage of small- and large-scale IFs specific for acacia, rubber, and other IFs from 2000 to 2014 in Sabah and Sarawak are presented (Tables 4.3a&b & 4.4a&b; Figure 4.4).     Figure 4.4. The expansion of the large- and-small-scale IFs in Sabah and Sarawak, 2000-2014.  Likewise, when considering the rate of change in large- and small-scale IF areas for the different periods of 2000-2003, 2003-2006, 2006-2009, 2009-2012, 2012-2014, and 2000-2014 in Sabah and Sarawak, it also presented the same stories. That is, while the total IF area in both states were dominated by large-scale IFs over the study period, the expansion rates for small-scale IFs were also very significant and much higher than that of the large-scale IFs. Specifically, the expansion rate for the total small-scale IF area in Sabah for 2000-2014 (43%) was much higher than the expansion rate for the large-scale IF area (13%; Figure 4.5). It was also similar for the expansion rates of the small-scale acacia (85%), other IFs (13%), and rubber (33%) versus the large-scale acacia (18%), other IFs (10%), and rubber (6%). Similarly, in Sarawak, the rate of small-scale acacia IF area expansion over the period of 2000-2014 (515%) was higher than the rate of large-scale acacia IF area expansion (327%). The same trend was also found for rubber plantations: the expansion rate of the small-scale rubber plantations was 18%, compared 0 40,000 80,000 120,000 160,000 Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Area (ha) Period The new  large-scale and small-scale IF expansion area  in Sabah, 2000-2014 Acacia Other IFs Rubber Total 0 70,000 140,000 210,000 280,000 350,000 Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Area (ha) Period The new large-scale and small-scale IF expansion area  in Sarawak, 2000-2014 Acacia Other IFs Rubber Total 140  to an expansion rate of 3% for the large-scale rubber plantations (Figure 4.5). Further data on the large- and small-scale expansion rates specific for acacia, rubber, and other IFs in the different periods in both states are presented (Figure 4.5).   Figure 4.5. The annual rate of change in large- and small-scale IF area by type in Sabah and Sarawak, 2000-2014. The Pattern Indices for IF LULC Changes  Along with the increase of the total IF area in both Sarawak and Sabah as described above, the number of IF patches and the largest IF patch size also increased (Figure 4.6). Specifically, in Sabah, the total number of IF patches increased from 4,382 in 2000 to 39,327 in 2014. At the same time, the size of the largest patch also increased from 5,475 ha to 9,721 ha. Likewise, in Sarawak, the total number of patches increased from 2,496 in 2000 to 30,413 in 2014. Along with this increase, the largest patch size also increased from 3,759 ha to 15,624 ha (Figure 4.6). In Sabah, the largest IF patch size was found for acacia plantations, with 5,475 ha in 2000 and 9,721 ha in 2014, while the largest patch size for other IFs slightly increased from 948 ha (2000) to 1,280 ha (2014). The largest patch size for rubber was 2,741 ha, and it did not change over the study period (Figure 4.7). Conversely, in Sarawak, the largest patch size found for 2000 was of rubber IFs with 3,759 ha. Then, the largest patch size of acacia increased from 1,224 ha (2000) to 0% 20% 40% 60% 80% 100% Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 The rate of change (%) Year The annual rate of change of IFs by type under the large and small scale in Sabah, 2000-2014 Acacia Other IFs Rubber Total 0% 150% 300% 450% 600% Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale Large scale Small scale 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 The rate of change Year The annual rate of change of IFs by type under the large and small scale in Sarawak, 2000-2014 Acacia Other IFs Rubber Total 141  15,624 ha (2014), while other IFs increased from 1,063 ha (2000) to 11,609 ha (2014; Figure 4.7).   Figure 4.6. The total number of patches and the largest IF patch area (in ha) in Sabah and Sarawak, 2000-2014.    Figure 4.7. The largest patch size of acacia, rubber and other IFs in Sabah and Sarawak, 2000-2014.  Another necessary pattern index for assessing IF LULCC in Sabah and Sarawak is the mean patch size index. Generally, in Sabah, the mean patch size decreased, while the mean IF patch size in Sarawak increased (Figure 4.8). In Sabah, the mean size for all IFs declined from 23 ha in 2000 to 10 ha in 2014. Of which, the acacia mean patch size decreased from 32 ha (2000) to 10  -     10,000   20,000   30,000   40,000  2000 2003 2006 2009 2012 2014 The area of the largest patch (ha) and total number of patches Year The total number of IF patches and the largest patch size (in ha) in Sabah, 2000-2014 Total number of IF patches Largest IF patch size  -     10,000   20,000   30,000   40,000  2000 2003 2006 2009 2012 2014 The area of the largest patch (ha) and total number of patches Year The total number of IF patches and the largest patch size (in ha) in Sarawak, 2000-2014 Total number of IF patches Largest IF patch size  -     2,000   4,000   6,000   8,000   10,000   12,000  2000 2003 2006 2009 2012 2014 Area (ha) Year The largest patch size of acacia, rubber and other IFs in Sabah, 2000-2014 Acacia Other IFs Rubber  -     5,000   10,000   15,000   20,000  2000 2003 2006 2009 2012 2014 Area (ha) Year The largest patch size of acacia, rubber, and other IFs in Sarawak, 2000-2014 Acacia Other IFs Rubber 142  ha (2014). For rubber and other IFs, their mean patch size also decreased from 20 to 8 ha, and from 18 to 15 ha, respectively, in the same period (Figure 4.8). Conversely, in Sarawak, the mean IF patch size for all IFs first declined from 22 ha (2000) to 13 ha (2003 & 2006), and then increased again to 18 ha (2009) and 17 ha (2012 & 2014). Of which, other IF mean patch size increased from 23 ha (2000) to 52 ha (2014), while the rubber mean patch size declined from 24 ha (2000) to 14 ha (2014). For acacia IFs, the mean patch size declined from 14 ha (2000) to 9 ha (2003) and increased again to 11 ha (2006), 15 ha (2012), and 16 ha (2014; Figure 4.8).   Figure 4.8. The mean patch size index of acacia, rubber and other IFs in Sabah and Sarawak, 2000-2014.  To formulate a better understanding of drivers and LULCC dynamics associated with emerging IFs in Malaysia, we should consider how IFs had been developed at the different scales over time.  5 ha, 5-20 ha, 20-40 ha, 40-100 ha, 100- In general, in Sabah, most of IF areas were distributed in the IF patch size class over 200 ha, followed by IF patch size class smaller than 5 ha. In other words, the distribution of area classes based on the patch size classes in general was as follows: A (the total area of the 200-ha-larger-patches class) > A > A5-20ha > A40-100ha > A100-200ha > A20-40ha (Figure 4.9). Specifically, for instance, in 2014, the distribution of the total IF  -     5.0   10.0   15.0   20.0   25.0   30.0   35.0  2000 2003 2006 2009 2012 2014 Area (ha) Year The mean patch size of different IF types in Sabah, 2000-2014 Acacia Other IFs Rubber Total  -     10.0   20.0   30.0   40.0   50.0   60.0  2000 2003 2006 2009 2012 2014 Area (ha) Year The mean patch size of different IF types in Sarawak, 2000-2014 Acacia Other IFs Rubber Total 143  area into class size was as follows: 159,640 ha (A) > 88,044 ha (A) > 65,309 ha (A5-20ha) > 30,017 ha (A40-100ha) > 26,211 ha (A100-200ha) > 22,051 ha (A20-40ha). A similar trend was also found for the remaining years (2000, 2003, 2006, 2009, & 2012), and also for acacia, rubber, and other IFs. Likewise, in Sarawak, the patch size class over 200 ha occupied most of the total IF area in the state, followed by the patch size class smaller 5 ha; the IF area of the patch size class of 20-40 ha was the smallest (Figure 4.10). This trend was similar for all years selected for the study (except for 2014 when A < A5-20ha), and for all IF types in the state (Figure 4.10).     Figure 4.9. IF areas by type and by patch size class in Sabah, 2000-2014.  Figure 4.10. IF areas by type and by patch size class in Sarawak, 2000-2014.  -     30,000   60,000   90,000   120,000   150,000   180,000  < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha 2000 2003 2006 2009 2012 2014 Area (ha) Patch size class and year IF areas by type and by patch size class in Sabah, 2000-2014 Rubber Other IFs Acacia  -     60,000   120,000   180,000   240,000   300,000  < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha < 5 ha 5-20 ha 20-40 ha 40-100 100-200 ha > 200 ha 2000 2003 2006 2009 2012 2014 Area (ha) Patch size class and year IF areas by type and by patch size class in Sarawak, 2000-2014 Rubber Other IFs Acacia 144   Next, we would further examine the total number of the large-scale patches 40 ha,) as classified above and their mean patch size indices in the study area over the period of 2000-2014. The result of studying the pattern indices for the large-scale plantations (industries) showed an increase of the total number of the large-scale IF patches in both Sarawak and Sabah. Specifically, in 2000, Sabah had 308 large-scale patches, which increased to 881 patches in 2014 (Figure 4.11). The acacia IFs contributed to most of this increase. The number of the large-scale acacia patches increased from 90 patches in 2000 to 397 patches in 2014, while the number of the large-scale patches of other IFs and rubber only increased from 61 to 177, and from 157 to 307, respectively, over the same period. Contrary to the increase in the total number of large-scale patches, the mean size of these patches in the state slightly declined from 253 ha in 2000 to 245 ha in 2014 (Figure 4.11). In particular, while the mean patch size for rubber and other IFs under this scale remained stable or slightly increased over the study period, the large-scale mean patch size of acacia IFs remarkably declined from 448 ha in 2000 to 356 ha in 2014 (Figure 4.11). Likewise, in Sarawak, the number of the large-scale IFs patches in the state also noticeably increased (Figure 4.12). Specifically, the total number of large-scale IF patches increased very impressively from 114 in 2000 to 1,006 in 2014. The large-scale acacia IFs substantially contributed to this increase with 583 patches. For rubber and other IFs, each contributed a net increase of 127 and 182 patches, respectively, over the period of 2000-2014. In particular, while the mean size for acacia and other IFs increased, the mean size for rubber in the state decreased (Figure 4.12). The mean large-scale patch size for acacia IFs increased from 337 ha in 2000 to 395 ha in 2014, and the mean size for large-scale other IFs patches also increased from 223 ha to 302 ha in the same period. Conversely, the mean patch size of the large-scale rubber plantations significantly declined from 436 ha in 2000 to 248 ha in 2014 (Figure 4.12).  145   The details of the changes to the mean large-scale patch size and the number of these patches specific for acacia, rubber, and other IFs in the different periods in both Sabah and Sarawak states are presented (Figures 4.11 & 4.12).   Figure 4.11. The total large-scale patch number and the mean patch size of IFs in Sabah, 2000-2014.   Figure 4.12. The total large-scale patch number and the mean patch size of IFs in Sarawak, 2000-2014. 4.1.2. The Vegetation Indices-based LULCC As described above, this method used vegetation indices (VIs) including ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI to detect, map, and monitor the IF expansion in Sabah and 0 200 400 600 800 1000 2000 2003 2006 2009 2012 2014 Number of patches Year The number of the large-scale IF patches in Sabah, 2000-2014 Acacia Other IFs Rubber Total 0 100 200 300 400 500 600 2000 2003 2006 2009 2012 2014 Area (ha) Year The mean large-scale IF patch size in Sabah, 2000-2014 Acacia Other IFs Rubber Average 0 200 400 600 800 1000 1200 2000 2003 2006 2009 2012 2014 Number of patches Year The number of the large-scale IF patches in Sarawak, 2000-2014 Acacia Other IFs Rubber Total 0 80 160 240 320 400 480 2000 2003 2006 2009 2012 2014 Area (ha) Year The mean large-scale IF patch size in Sarawak, 2000-2014 Acacia Other IFs Rubber Average 146  Sarawak from 2000 to 2014. We also know that due to the additive effects of soil and atmospheric conditions as mentioned above, specific VI can perform better than others in the different geographic regions and each index has its own strength and weakness. Therefore, this study would consider and assess how IF area has changed over time in the study area by using the different vegetation indices, as well as which index worked the best in the study area. The total IF areas that were detected in Sabah and Sarawak by using the different VIs were extremely variable. In general, the IF areas detected in both states were as follows: MSAVIaf < NDVIaf < SAVI < SARVI < EVI < ARVI. Specifically, the total IF area detected in 2000 in Sabah was 32,160 ha (by using MSAVIaf), 42,149 ha (NDVIaf), 46,917 ha (SAVI), 56,268 ha (SARVI), 71,475 ha (EVI) to 85,167 ha (ARVI), increasing to 219,743 ha (MSAVIaf), 209,235 ha (NDVIaf), 284,524 ha (SAVI), 309,190 ha (SARVI), 379,428 ha (EVI), and 386,523 ha (ARVI) in 2014, respectively (Figure 4.13). Likewise, in Sarawak, the total IF area in this state had increased from 9,791 ha in 2000 to 148,996 ha in 2014 by using MSAVIaf, 13,804 ha to 184,848 ha (NDVIaf), 13,417 ha to 206,716 ha (SAVI), 18,988 ha to 240,363 ha (SARVI), 22,207 ha to 266,623 ha (EVI), and 28,382 ha in 2000 to 340,816 ha in 2014 by using ARVI.  Not only was the detected total IFs area different by using the different vegetation indices, the IF areas specific for the different species or systems - including acacia, rubber, and other IFs - also varied greatly (Figure 4.13). Specifically, the acacia area detected in Sabah in 2000 had a range from 19,286 ha (MSAVIaf) to 44,683 ha (ARVI), increasing to a range from 163,026 ha (NDVIaf) to 288,583 ha (EVI) in 2014. The rubber area also increased from 8,980-27,380 ha in 2000 to 32,617-88,347 ha in 2014 by using MSAVIaf (smallest) and ARVI (biggest), respectively. For other IFs, the smallest area was also found in the MSAVIaf product with 3,912 ha in 2000, increasing to 12,264 ha in 2014, while the biggest area was found in the ARVI 147  product with 13,104 ha in 2000, expanding to 32,601 ha in 2014 (Figure 4.13). The same findings were also found in Sarawak. The acacia area increased from almost nothing in 2000 -about 3,298 ha (MSAVIaf)-5,806 ha (ARVI) - to 107,298 ha (MSAVIaf)-222,295 ha (ARVI) in 2014 (Figure 4.13). Likewise, the rubber area was also detected in 2000 ranging 4,844-17,682 ha, increasing to 18,334-54,891 ha in 2014 in the MSAVIaf and ARVI products, respectively. For other IFs, it presented a net area increase from 21,716 ha (MSAVIaf) to 58,918 ha (ARVI) over the study period of 2000-2014. The areas specific for the different IF systems over the period of 2000-2014 in Sabah and Sarawak are indicated (Figure 4.13).  Because the detected total IF areas and specific areas for acacia, rubber, and other IFs were different using the different VIs the rates of change in their area were also different and quite variable in the study area over 2000-2014. Specifically, in Sabah, the annual rates of change in total IF area specific for the different VIs from 2000 to 2014 were 25% (ARVI), 28% (NDVIaf), 31% (EVI), 32% (SARVI), 36% (SAVI), and 42% (MSAVIaf; Figure 4.14). Of these, the highest annual IF area change rates were found for the period of 2000-2003 with a range of 23% (ARVI)-41% (MSAVIaf), while the lowest annual rates of change in IF area were 2006-2009 from 2% (EVI, NDVIaf, SARVI, & SAVI) - 3% (ARVI & MSAVIaf). For the different IF species/systems, we found that the annual rates of change in their area also varied among the different VIs usages. Specifically, the annual rates of change in acacia area over 2000-2014 were 36% (ARVI), 40% (NDVIaf), 45% (EVI), 48% (SARVI), 53% (SAVI), and 58% (MSAVIaf). These rates were much higher compared with those for rubber plantations [13% (SARVI), 14% (ARVI & NDVIaf), 15% (SAVI), 16% (EVI), & 19% (MSAVIaf)], and other IFs [8% (EVI, NDVIaf, & SARVI), 9% (SAVI), 11% (ARVI), & 15% (MSAVIaf)]. In particular, acacia and other IFs showed the highest rate of change in its area for 2000-2003 at the range from 40% 148  (ARVI)-61% (MSAVIaf), and from 9% (EVI, SARVI, & SAVI) to 11% (ARVI & MSAVIaf), respectively; meanwhile, rubber plantations were most expanded in 2009-2012 at the range of the expansion rate of 25% (MSAVIaf)-54% (EVI). More details of the annual rates of change in area specific for the different IF systems, years or intervals, and by using the different VIs in the state are presented (Figure 4.14).   Likewise, in Sarawak, the annual rates of change in IF area specific for the different years, IF species/systems, and VIs also varied greatly and were much higher than those in Sabah. The yearly expansion rates in the total IF area ranged from 79% to 103% over the 2000-2014 period (Figure 4.14). The highest rate was found in the SAVI-based IF map product (103%), followed by MSAVIaf (102%), NDVIaf (89%), and SARVI (82%); the lowest rates were found in the ARVI and EVI-based products (79%). This total annual expansion rate over the period of 2000-2014 in this state was most contributed to by the acacia expansion rate. Specifically, the expansion rate of acacia IFs was 225% (MSAVIaf), 239% (NDVIaf), 266% (ARVI), 299% (SARVI), 319% (EVI), and 355% (SAVI). Likewise, the expansion rates for other IFs were found in the range of 76% (lowest by using EVI) to 110% (highest by using SARVI); and for rubber plantations, the area expanded at the annual rate with a range from 10% (EVI & SARVI) to 20% (MSAVIaf; Figure 4.14). Breaking the expansion rates down to the intervals, we found that the acacia IF development rate was highest for the period of 2000-2003 (74%-95%) and 2003-2006, with more than 100%. While the highest rate of rubber was found in 2009-2012 with a range of 11-28%, the highest rate for other IFs (74%-104%) was also over the period of 2003-2006 (Figure 4.14). More details of the annual rates of change in area specific for the different IF systems, years, and by using the different VIs in both states are presented (Figure 4.14). 149          Figure 4.13. The VIs-based IF areas in Sabah and Sarawak, 2000-2014.  -     100,000   200,000   300,000   400,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based total IF areas in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     100,000   200,000   300,000   400,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based total IF areas in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     60,000   120,000   180,000   240,000   300,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based acacia areas in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     50,000   100,000   150,000   200,000   250,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based acacia areas in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     20,000   40,000   60,000   80,000   100,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based rubber areas in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     15,000   30,000   45,000   60,000   75,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based rubber areas in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     10,000   20,000   30,000   40,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based other IF areas in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     15,000   30,000   45,000   60,000   75,000  2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based other IFs areas in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 150          Figure. 4.14. The VIs-based rates of change in IF areas in Sabah and Sarawak, 2000-2014. 0% 10% 20% 30% 40% 50% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VIs-based total IF areas in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 30% 60% 90% 120% 150% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VIs-based total IF areas in Sarawak, 2000-2014  ARVI EVI MSAVI NDVI SARVI SAVI 0% 20% 40% 60% 80% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VIs-based acacia areas in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 100% 200% 300% 400% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VI-based acacia areas in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 10% 20% 30% 40% 50% 60% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VIs-based rubber areas, Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 6% 12% 18% 24% 30% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VIs-based rubber areas in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 4% 8% 12% 16% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VIs-based other IFs areas in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 30% 60% 90% 120% 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in VIs-based other IFs areas in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 151  Similar to the above fC-based LULCC discussions, the VIs-based IF area and its annual rates specific for IF species, time intervals, and the small-scale versus large-scale IF areas were also studied. In general, in Sabah, the total area under smallholdings in all VIs-based IF map products was larger than or almost equal to that of large-scale IFs, while the large-scale IF area in Sarawak was remarkably larger than that of small-scale IFs (Figure 4.15). For instance, in 2014, the total small-scale IF area (patch size < 40 ha) in Sabah that was detected by using VIs was 213,337 ha (ARVI), 227,447 ha (EVI), 139,890 ha (MSAVIaf), 118,557 ha (NDVIaf), 182,210 ha (SARVI), and 181,170 ha (SAVI) - much higher compared with 173,186 ha (ARVI), 151,981 ha (EVI), 79,853 ha (MSAVIaf), 90,678 ha (NDVIaf), 126,980 ha (SARVI), and 102,814 ha (SAVI) for the large-scale IF area (The same trends were also found for other years and specific IF species, especially for rubber plantations (Figure 4.15). Conversely, in Sarawak, the total large-scale IF area was significantly larger than the small-scale IF area. For instance, the total large-scale IF area in 2014 was 249,787 ha (ARVI), 161,704 ha (EVI), 83,373 ha (MSAVIaf), 117,275 (NDVIaf), 154,499 ha (SARVI), and 109,665 ha (SAVI), compared to 91,031 ha (ARVI), 104,919 ha (EVI), 65,623 ha (MSAVIaf), 67,573 ha (NDVIaf), 85,864 ha (SARVI), and 97,051 ha (SAVI) for the small-scale IF area. This trend was also the same for acacia and other IFs, while the small-scale rubber area was bigger than the large-scale rubber area (Figure 4.15). The VIs-based study findings for the rates of change in the total IF area - and specifically for acacia, rubber, and other IF areas - in both Sabah and Sarawak over 2000-2014 also showed that their expansion rates for small-scale IFs was higher compared to those for the large-scale IFs, except the expansion rate for the total IF area in Sarawak, in which the expansion rates for the small-scale IFs was lower than those for the large-scale IFs over the period of 2000-2009 (Figure 4.16). More information on the expansion rates is presented (Figure 4.16). 152          Figure 4.15. The VIs-based large- and small-scale IF areas in Sabah and Sarawak, 2000-2014.  -     50,000   100,000   150,000   200,000   250,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based total IF area under the large scale (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     50,000   100,000   150,000   200,000   250,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based total IF area under the large scale (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     45,000   90,000   135,000   180,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based acacia area under the large scale (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     40,000   80,000   120,000   160,000   200,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based acacia area under the large scale (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     15,000   30,000   45,000   60,000   75,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based rubber area under the large scale (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     10,000   20,000   30,000   40,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based rubber area under the large scale (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     5,000   10,000   15,000   20,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based other IFs area under the large-scale (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI  -     10,000   20,000   30,000   40,000   50,000  Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000 2003 2006 2009 2012 2014 Area (ha) Year VIs-based other IFs area under the large scale (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 153          Figure 4.16. The VIs-based rate of changes in large- and small-scale IF areas in Sabah and Sarawak, 2000-2014. 0% 10% 20% 30% 40% 50% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in total IF area under the large (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 30% 60% 90% 120% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in total IF area under the large (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 100% 200% 300% 400% 500% 600% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in acacia area under the large (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 150% 300% 450% 600% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in acacia area under the large (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 15% 30% 45% 60% 75% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in rubber area under the  large scale (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 9% 18% 27% 36% 45% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in rubber area under the large (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 6% 12% 18% 24% 30% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in other IFs area under the large (Est.) vs. small scale (S.hold) in Sabah, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 0% 45% 90% 135% 180% Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold Est S.hold 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014 2000-2014 Rate of change in area (%) Year Rate of change in other IF area under the large scale (Est.) vs. small scale (S.hold) in Sarawak, 2000-2014 ARVI EVI MSAVI NDVI SARVI SAVI 154  4.2    Assessments of the IF LULC Changes and their Consequences The above findings clearly indicate that the IFs have been increasing in Sabah and Sarawak, Malaysia, both in the individual patch size and the total area, over the study period. The next questions this study clarified were what types and how much area of natural or managed ecosystems these new IFs had replaced. To answer these questions, a procedure to assess the IF LULCC was developed (Figure 4.17).          Figure 4.17. The procedure to assess the IF LULC changes in Sabah and Sarawak, Malaysia. To identify the new IF areas, two IF maps in the consecutive years were first overlaid. Then, the IF areas in the earlier year (e.g., 2000) were used to erase the pre-existing IF areas in the later year (e.g., 2003) by using the ArcGIS analysis tool. The remaining IF areas in the later year (2003) would be the new IF areas, which were expanded between the earlier year (2000) and the later year (2003). Another way to acquire these new areas was to select the attributes by using the following formula_2e.g., 2003 e.g., shapefiles. To know what kinds of natural or The IF area in the earlier year The IF area in the later year The new areas identification Quantifying the area and types of other LULC types were converted to the new IFs Other LULC sources Visual interpretation Assessing the consequences of the IF LULC changes GHG emissions Biodiversity loss 155  managed ecosystems these new IFs had replaced, other LULC sources and visual interpretation would be used to identify what kind and how much area of other LULC types were converted to these new IF areas.  The other LULC sources used in this study were obtained from the Roundtable on Sustainable Palm Oil (RSPO) Organization (Gunarso et al., 2013) for the years of 2000, 2005, and 2010. The LULC was classified into 6 different types, including Undisturbed Forest (UF), Disturbed Forest (DF), Agricultural Land (AL), Oil Palm Land (OP), Waste/Degraded Land (WL), and Residential Land (RL). At the same time, visual interpretation analysis and editing would be also used to identify, include or exclude, and quantify the new IF areas and other LULC types based on the following arguments and assumptions. The study first eliminated the IF areas smaller than 2 ha. This was due to the fact that the smaller IF patches were more difficult to detect; and Fox and Castella (2013) indicated smallholders in Southeast Asia commonly own a land size around 1-4 ha of plantations. Therefore, the minimum land size selected in detecting and mapping new IFs in the study area was 2 ha.  It was also very unlikely that people destroyed their buildings to establish new IFs. Therefore, the new IFs appearing in the built-up or residential area would be eliminated. In addition, various studies (e.g., Jagatheswaran et al., 2012; Jagatheswaran et al., 2011; Akira et al., 2011; Pinso & Vun, 2000) indicated that oil palm plantations were much more profitable than other plantations, so that they outcompeted and replaced other plantations. Therefore, it was impractical to claim that oil palms were converted into the new IFs. Lastly, it was also unlikely that the new IFs would be directly converted from undisturbed forests. This was because much research (e.g., Lawson et al., 2014; Miyamoto et al., 2014; Aziz et al., 2010; Wicke et al. 2008; Suratman, 2007; Grieg-Gran et al., 156  2007) has shown that the deforestation pathway in the region was that primary forests were first converted into disturbed forests and then to other LULC types. By doing so, the study findings showed that, from 2000 to 2014, the total new IF area in Sabah was 288,551 ha including 190,354 ha of acacia, 25,920 ha of other IFs, and 72,277 ha of rubber (Table 4.5). These new IF areas have replaced 237,039 ha (82.1%) of disturbed forest (DF), 51,011 ha (17.7%) of agricultural land (AL), and only 501 ha (0.2%) of degraded/wasteland (WL; Table 4.5 & Figure 4.18). Specifically, 87.5% of new acacia IFs were expanded in DF, 12.47% in AL, and 0.03% in the degraded/waste land (Figure 4.19). Likewise, most new rubber plantations (63.1%) were converted from DF, followed by AL (36.3%), and WL (0.6%). Following the same pattern as new acacia and rubber IFs, 95.9% of new other IFs had replaced DF, and only 4.1% of these IFs were established in AL. There were no new other IFs established in WL. The new IF areas in Sabah specific for the selected IFs, and their replacements for other LULC types are presented (Table 4.5; Figures 4.18 & 4.19).     Table 4.5. The new IF areas and their LULC replacements in Sabah, 2000-2014.  IF Species LULC Type Newly Expanded Area (ha)  Total 2000-03 2003-06 2006-09 2009-12 2012-14 Acacia Disturbed Forest 42,440  62,682  20,132          12,938  28,362  166,554  Agricultural Land 6,729  5,262  4,402            3,980  3,369  23,742  Waste Land 37  0  3  0  18    58  Total 49,026  67,944  24,537         16,918  31,749  190,354  Other IFs Disturbed Forest 10,216  3,659  4,532            3,973  2,483  24,863  Agricultural Land 559  140    42  204  72  1,057  Waste Land 0    0  0                     0    0    0  Total 10,815  3,799  4,574            4,177  2,555  25,920  Rubber Disturbed Forest 6,503  3,382  14,055          10,935  10,747  45,622  Agricultural Land 4,782  2,399  10,834            4,278  3,919  26,212  Waste Land 13  13  90  119  208  443  Total 11,298  5,794  24,979          15,332  14,874  72,277  Total Disturbed Forest 59,159  69,723  38,719          27,846  41,592  237,039  Agricultural Land 12,110  7,801  15,278            8,462  7,360  51,011  Waste Land 50  13  93  119  226  501  Total 71,319 77,537  54,090          36,427  49,178  288,551   157    Figure 4.18. The new IF areas and their other-LULC-types-replacements percentage and area in Sabah, 2000-2014.        Figure 4.19. The percentage of the different LULC types converted to new acacia, rubber, and other IFs in Sabah, 2000-2014. 82.1% 17.7% 0.2% The percentage of other LULC types was changed to the IF land in Sabah, 2000-2014 Disturbed Forest Agricultural Land Waste Land  -     20,000   40,000   60,000   80,000  2000-03 2003-06 2006-09 2009-12 2012-14 Area (ha) Year The different LULC area was changed to the IF land in Sabah, 2000-2014 Waste Land Agricultural Land Disturbed Forest 87.5% 12.47% 0.03% The percentage of the different LULC types was changed to acacia IFs in Sabah, 2000-2014 Disturbed Forest Agricultural Land Waste Land 95.9% 4.1% The percentage of the different LULC types was changed to other IFs in Sabah, 2000-2014 Disturbed Forest Agricultural Land 63.1% 36.3% 0.6% The percentage of the different LULC types was changed to rubber IFs in Sabah, 2000-2014 Disturbed Forest Agricultural Land Waste Land 158   In other words, 70% of the conversion of DF to the new IFs was accounted for by new acacia plantations (166,554 ha), 19% by new rubber plantations (45,622 ha), and 11% by new other IFs (24,863 ha; Figure 4.20). Conversely, the largest part of AL was lost by new rubber IFs (51%; 26, 212 ha), followed by new acacia IFs (47%; 23,742 ha), and new other IFs (2%; 1,057ha). For WL, the total conversion area was 501 ha. Of that, new rubber IFs took 88.4% (443 ha) and acacia IFs 10% (58 ha); there was no conversion to new other IFs (Figure 4.20).      Figure 4.20. The different LULC types area and their percentage converted to new acacia, rubber, and other IFs in Sabah, 2000-2014.  Similar to the IF LULC changes in Sabah, the total new IF area in Sarawak established from 2000 to 2014 was 459,896 ha, including 361,775 ha of acacia, 63,808 ha of other IFs, and 34,313  -     60,000   120,000   180,000   240,000   300,000  Disturbed Forest Agricultural Land Waste Land Total Area (ha) Land Use Land Cover Types The different LULC area was changed to the new IF lands in Sabah, 2000-2014 Rubber Other IFs Acacia 70% 11% 19% The percentage of disturbed forests was changed to the different IF lands in Sabah, 2000-2014 Acacia Other IFs Rubber 47% 2% 51% The percentage of agricultural land was changed to the different IF lands in Sabah, 2000-2014 Acacia Other IFs Rubber 11.6% 88.4% The percentage of degraded/waste land was changed to the different IF lands in Sabah, 2000-2014 Acacia Rubber 159  ha of rubber (Table 4.6). Most of these new IF areas (95.6%; 439,610 ha) were established in the disturbed forest land (DF), 4.38% (20,192 ha) in agricultural land (AL), and only 0.02% (94 ha) in degraded/wasteland (WL) (Table 4.6 & Figure 4.21). Specifically, 96.4% of new acacia IFs were expanded in DF, 3.5% in AL, and only 0.1% in WL (Figure 4.22). Likewise, most new rubber IFs (81%) were converted from DF, followed by AL (19%), and no new establishments in degraded land. For new other IFs, 98.6% were established in DF and only 1.4 % in AL.  Table 4.6. The new IF areas and their LULC replacements in Sarawak, 2000-2014. IF Species LULC Type Newly Expanded Area (ha)  Total 2003 2006 2009 2012 2014 Acacia Disturbed Forest 30,588          72,157  74,466 67,551 104,157 348,919 Agricultural Land 2,363            914  979 5,258 3,248 12,762 Waste Land 17    0  7 70 0 94 Total 32,968         73,071  75,452 72,879 107,405 361,775 Other IFs Disturbed Forest 1,643            9,506  33,241 12,153 6,344 62,887 Agricultural Land 220               273  198 150 80 921 Waste Land 0                    0    0 0 0 0 Total 1,863            9,779  33,439 12,303 6,424 63,808 Rubber Disturbed Forest 10,149            5,339  1,830 2,871 7,615 27,804 Agricultural Land 2,876               961  278 266 2,128 6,509 Waste Land 0                  0    0 0 0 0 Total 13,025            6,300  2,108 3,137 9,743 34,313 Total Disturbed Forest 42,380  87,002 109,537 82,575 118,116 439,610 Agricultural Land 5,459  2,148 1,455 5,674 5,456 20,192 Waste Land 17  0 7 70 0 94 Total 47,856  89,150 110,999 88,319 123,572 459,896           Figure 4.21. The new IF areas and their other-LULC-types-replacements percentage and area in Sarawak, 2000-2014. 95.6% 4.38% 0.02% The percentage of other LULC types was changed to the IF land in Sarawak,  2000-2014 Disturbed Forest Agricultural Land Waste Land  -     40,000   80,000   120,000   160,000  2000-03 2003-06 2006-09 2009-12 2012-14 Area (ha) Year The different LULC area was changed to the IF land in Sarawak, 2000-2014 Disturbed Forest Agricultural Land Waste Land 160      Figure 4.22. The percentage of the different LULC types converted to new acacia, rubber, and other IFs in Sarawak, 2000-2014.  In other words, 80% of the loss of disturbed forests were caused by the new acacia IFs (348,919 ha), 14% by the new other IFs (62,887 ha), and 6% by the new rubber plantations (27,804 ha; Figure 4.23). Likewise, the largest part of agricultural land was also lost by the new acacia IFs (63%; 12,762 ha), followed by the new rubber plantations (32%; 6,509 ha), and the new other IFs (5%; 921 ha). Finally, 100% of the degraded land (94 ha) was converted to the new acacia IFs, and no new rubber and other IFs were established in this kind of land (Figure 4.23). 96.4% 3.5% 0.1% The percentage of the different LULC types was changed to acacia IFs in Sarawak, 2000-2014 Disturbed Forest Agricultural Land Waste Land 98.6% 1.4% The percentage of the different LULC types was changed to other IFs in Sarawak, 2000-2014 Disturbed Forest Agricultural Land 81% 19% The percentage of the different LULC types was changed rubber plantations in Sarawak, 2000-2014 Disturbed Forest Agricultural Land 161      Figure 4.23. The different LULC area and percentage converted to new acacia, rubber and other IFs in Sarawak, 2000-2014. The Consequences of the IF LULC Changes   In LULCC studies, quantifying the consequences of a LULCC is very important in understanding and assessing its contributions and impacts to humans and nature. As we know, LULCC influences the global climate, the carbon cycle, water, energy balance, biodiversity, and other environmental and resource factors. However, the comprehensive, adequate, and accurate quantification of these impacts are very challenging. Therefore, this study would only grossly estimate the contributions and impacts of changes from managed and natural ecosystems to the new IF land, in terms of carbon emissions and biodiversity loss, based on literature review and general approaches.   -     100,000   200,000   300,000   400,000   500,000  Disturbed Forest Agricultural Land Waste Land Total Area (ha) Land Use Land Cover Types The different LULC area was changed to the new IF lands in Sarawak, 2000-2014 Rubber Other IFs Acacia 80% 14% 6% The percentage of disturbed forests was changed to the different IF lands in Sarawak, 2000-2014 Acacia Other IFs Rubber 63% 5% 32% The percentage of agricultural land was changed to the different IF lands in Sarawak, 2000-2014 Acacia Other IFs Rubber 100% The percentage of waste land was changed to the different IF lands in Sabah, 2000-2014 Acacia 162   CO2 Emissions: To estimate the net carbon emissions caused by the IF LULCC in the study area over the period of 2000-2014, the approach of the United Nations Intergovernmental Panel on Climate Change (IPCC, 2006) would be used as follows:  Emission = Activity Data * Emission Factor  Where activity data is the area of specific LULC changes, and emission factor is the changes in carbon stock of a LULC type. In the previous part, the IF LULCC quantitative assessments specific for acacia, rubber, and other IFs over the period of 2000-2014 have been conducted.   For the emission factors, Agus et al. (2013a), and Agus et al. (2013b) did a comprehensive literature review for C stocks for the different LULC types in Malaysia, Indonesia, and Papua New Guinea including Sabah and Sarawak, and found that the above ground biomass (AGB) or C stocks (in tonne of carbon per ha, tC ha-1 or Mg C ha-1) for the different LULC types in the study area were the following: (1) undisturbed forests/UF (189 ± 87 tC ha-1 for upland, 162 ± 51 tC ha-1 for swamp, & 148 ± 43 tC ha-1 for mangrove), (2) disturbed forest/DF (104 ± 59 tC ha-1 for upland, 84 ± 42 tC ha-1 for swamp, & 101 ± 15 tC ha-1 for mangrove), (3) 58 tC ha-1 for rubber plantation, (4) 36 ± 11 tC ha-1 for oil palm plantation/OP, (5) 44 ± 14 tC ha-1 for timber plantation, (6) 54 ± 24 tC ha-1 for mixed tree crop, (7) 7 ± 3 tC ha-1 for settlement/residential land/RL, (8) 36 tC ha-1 for bare soil and 3-30 tC ha-1 for degraded non-forest land/WL, (9) 8-12.5 tC ha-1 for agricultural land/AL, and (10) 0-36 tC ha-1 for other LULC types. The Roundtable for Sustainable Palm Oil (RSPO, 2014) also recommends using the following default AGB carbon stock values: 268 Mg C ha-1 for undisturbed forest, 128 Mg C ha-1 for disturbed forest, 46 Mg C ha-1 for shrub land, 75 Mg C ha-1 for tree crops, 50 Mg C ha-1 for oil palm, and 8.5 Mg C ha-1 for annual/food crop or agricultural land for Sabah and Sarawak. As a result, this study would take those values to quantify C stocks and their changes in the classified LULC types (Table 4.7). 163  Table 4.7. The above ground carbon stock values (tC ha-1/MgC ha-1) for the classified LULC types in Sabah and Sarawak (adapted from Agus et al., 2013a; Agus et al., 2013b; RSPO, 2014). LULC Type Mean AGB Mg C ha-1 Range (Mg C ha-1) for above ground c stock (AGB) Lowest Highest Disturbed Forest (DF) 104 33 250 Undisturbed Forest (UF) 189 66 399 Agricultural Land (AL) 11 8 12.5 Oil Palm Land (OP) 36 22 60 Waste Land (WL) 19 3 36 Residential Land (RL) 7 4 10 Acacia 44 29 70 Rubber 55 31 89 Other IFs 54 33 77    The results of the study found that the total AGB stock in Sabah has declined -11,472,205 Mg C (tC) from 2000 to 2014 as a consequence of the LULCC caused by the expansion of new IFs (Figure 4.24 & Table 4.8). This change is equal to a total release of about 42,064,752 Mg of CO2 into the atmosphere over the period. Of that, the new acacia IFs contributed most of the C stock change and emissions (81%), and other IF systems (rubber and other IFs) contributed the remaining part (19%; Figure 4.24). The majority of the carbon stock change caused by the IF LULCC over 2000-2014 in Sabah had mainly occurred in the disturbed forests/DF (98%); only 2% occurred in the other LULC types (AL & WL; Figure 4.24). The C stocks in the new IFs, their replacements, and estimates of CO2 emissions for specific years are presented (Table 4.8). Table 4.8. Comparisons of AGB stocks (Mg) of new IFs and their LULC replacements in Sabah.  LU/LC type (Mg C) 2000-03 2003-06 2006-09 2009-12 2012-14 Total Disturbed Forest  6,152,536 7,251,192 4,026,776 2,895,984 4,325,568 24,652,056 Agricultural Land 133,210 85,811 168,058 93,082 80,960 561,121 Waste Land 950 247 1,767 2,261 4,294 9,519 Total other LULC types 6,286,696 7,337,250 4,196,601 2,991,327 4,410,822 25,222,696 Total new IFs  3,370,464 3,507,558 2,700,469 1,813,210 2,352,996 13,750,491 Difference -2,916,232 -3,829,692 -1,496,132 -1,178,117 -2,057,826 -11,472,205 CO2 Emissions (Mg) 10,692,851 14,042,204 5,485,817 4,319,762 7,545,362 42,064,752 164      Figure 4.24. The AGB stock changes as a consequence of the IF LULCC in Sabah, 2000-2014.  Likewise, the total AGB stock in Sarawak over the study period of 2000-2014 also declined 24,692,391 MgC/tC as a consequence of IF LULCC (Figure 4.25 & Table 4.9). This change contributed an emission of 90,538,767 tCO2 into the atmosphere over the study period. Most of  Table 4.9. Comparisons of C stocks (Mg) of new IFs and their LULC replacements in Sarawak.   LU/LC type  2000-03 2003-06 2006-09 2009-12 2012-14 Total Disturbed Forest 4,407,520 9,048,208 11,391,848 8,587,800 12,284,064 45,719,440 Agricultural Land 60,049 23,628 16,005 62,414 60,016 222,112 Waste Land 323 0 133 1,330 0 1,786 Total other LULC 4,467,892 9,071,836 11,407,986 8,651,544 12,344,080 45,943,338 Total IFs 2,267,569 4,089,690 5,241,534 4,043,573 5,608,581 21,250,947 Difference -2,200,323 -4,982,146 -6,166,452 -4,607,971 -6,735,499 -24,692,391 CO2 Emissions 8,067,851 18,267,869 22,610,324 16,895894 24,696,830 90,538,767        -15,000,000 -10,000,000 -5,000,000 0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 -15,000,000 -10,000,000 -5,000,000 0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 2000-03 2003-06 2006-09 2009-12 2012-14 Total C stock (tC or Mg C) Year The above ground C stocks of other LULC types prior to converting to IFs and IFs; and the C stock difference of LULCC in Sabah, 2000-2014 Disturbed Forest Agricultural Land Waste Land Difference Total IFs -10,000,000 -6,000,000 -2,000,000 2,000,000 6,000,000 10,000,000 14,000,000 18,000,000 -10,000,000 -6,000,000 -2,000,000 2,000,000 6,000,000 10,000,000 14,000,000 18,000,000 2000-2014 C stock (tC or Mg C) The above ground C stocks of acacia IFs and the LULC types prior to converting to the acacia IFs; and their C stock difference in Sabah, 2000-2014 Waste Land Agricultural Land Disturbed Forest Difference Acacia IFs -1,200,000 -800,000 -400,000 0 400,000 800,000 1,200,000 1,600,000 2,000,000 2,400,000 2,800,000 -1,200,000 -800,000 -400,000 0 400,000 800,000 1,200,000 1,600,000 2,000,000 2,400,000 2,800,000 2000-2014 C stock (tC or Mg C) The above ground C stocks of other IFs and the LULC types prior to converting to the other IFs; and their C stock difference in Sabah, 2000-2014 Agricultural Land Disturbed Forest Difference Other IFs -2,000,000 -1,000,000 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 -2,000,000 -1,000,000 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 2000-2014 C stock (tC or Mg C) The above ground C stocks of rubber IFs and the LULC types prior to converting to the rubber IFs; and their C stock difference in Sabah, 2000-2014 Waste Land Agricultural Land Disturbed Forest Difference Rubber IFs 165      Figure 4.25. The C stock changes as a consequence of the IF LULCC in Sarawak, 2000-2014. the above ground C stock changes and CO2 emissions also occurred in the disturbed forests (99.5%) and was accounted for by the new acacia IFs (~83%), followed by new other IFs (13%), and new rubber plantations (4%). The above ground C stock changes in agricultural land and degraded land negligibly contributed to the total C budget change in the region over the period of 2000-2014. The above ground C stocks in the new IFs, their replacements, and estimates of CO2 emissions caused by the IF LULCC specific for years are presented (Table 4.9 & Figure 4.25).  In summary, the changes of LULC in Sabah and Sarawak caused by the expansion of new IFs from 2000 to 2014 released a significant amount of CO2 into the atmosphere (Figures 4.24, 4.25 & 4.26; Tables 4.8 & 4.9).  -30,000,000 -20,000,000 -10,000,000 0 10,000,000 20,000,000 30,000,000 40,000,000 50,000,000 -30,000,000 -20,000,000 -10,000,000 0 10,000,000 20,000,000 30,000,000 40,000,000 50,000,000 2000-03 2003-06 2006-09 2009-12 2012-14 Total C stock (tC or Mg C) The above ground C stocks of other LULC types prior to converting to IFs; and the C stock difference of LULCC in Sarawak, 2000-2014 Disturbed Forest Agricultural Land Waste Land Difference Total IFs -24,000,000 -16,000,000 -8,000,000 0 8,000,000 16,000,000 24,000,000 32,000,000 40,000,000 -24,000,000 -16,000,000 -8,000,000 0 8,000,000 16,000,000 24,000,000 32,000,000 40,000,000 2000-2014 C stock (tC or Mg C) The above ground C stocks of acacia IFs and the LULC types prior to converting to the acacia; and their C stock difference in Sarawak, 2000-2014 Waste Land Agricultural Land Disturbed Forest Difference Acacia IFs -4,000,000 -3,000,000 -2,000,000 -1,000,000 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 -4,000,000 -3,000,000 -2,000,000 -1,000,000 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 2000-2014 C stock (tC or Mg C) The above ground C stocks of other IFs and the LULC types prior to converting to the other IFs; and their C stock difference in Sarawak, 2000-2014 Agricultural Land Disturbed Forest Difference Other IFs -1,500,000 -1,000,000 -500,000 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 -1,500,000 -1,000,000 -500,000 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 2000-2014 C stock (tC or Mg C) The above ground C stocks of rubber IFs and the LULC types prior to converting to the rubber ; and their C stock difference in Sarawak, 2000-2014 Agricultural Land Disturbed Forest Difference Rubber IFs 166   Figure 4.26. CO2 emissions caused by the IF LULCC in Sabah and Sarawak, 2000-2014. Biodiversity loss: In addition to concerns over carbon stock changes and carbon emissions in the IF LULCC, another issue that also received much concern in this LULCC was biodiversity loss. In general, adequately and accurately quantifying biodiversity loss in a LULCC is notoriously difficult because of the lack of reliable data. One of the most widely-accepted models used to quantify the biodiversity loss or impacts by a LULCC is the species-area relationship model (Brook et al., 2003; Desmet & Cowling, 2004; Triantis et al., 2008; Sodhi, 2009; Koh & Ghazoul, 2009; Koh et al., 2010; He & Hubbell, 2011; He & Hubbell, 2013; Matthews et al., 2014; Chaudhary et al., 2015).  A literature review was also conducted for biodiversity for the different LULC types in the study area and other tropical regions. Various studies have been done on the quantification and comparison of biodiversity in the different land use and land cover types in Malaysia in particular, and,  in general, in other tropical regions that have environmental conditions similar to Sabah and Sarawak (e.g., Chung et al., 2000; Hammer et al., 2003; Dumbrell & Hill, 2005; Peh et al., 2005; Peh et al., 2006; Gardner et al., 2007; Barlow et al., 2007a; Barlow et al., 2007b; 0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000 140,000,000 2000-03 2003-06 2006-09 2009-12 2012-14 Total Mg CO2 (tCO2) Year CO2 emissions caused by the IF LULCC in Sabah and Sarawak, 2000-2014 Sabah Sarawak Total Annual Average 167  Barlow et al., 2007c; Koh, 2007; Fitzherbert et al., 2008; Koh, 2008; Koh & Wilcove, 2008; Wilcove & Koh, 2010; Yule, 2010; Koh et al., 2011). Most of these studies focused on biodiversity quantification in primary (or undisturbed) forest, secondary (or disturbed) forest, plantation forests (rubber, acacia, eucalyptus plantations; here they were generally called IFs), and oil palm plantations, while very few studies were conducted on biodiversity for other LULC types, such as agricultural land, residential land or degraded land.  A summary for these studies was synthesized and is presented (Table 4.10 & Figure 4.27). It indicated that, in general, for forest birds, forest butterflies, beetles, trees and lianas, amphibians, reptiles, and mammals, primary/undisturbed forests were found the most diverse, followed by disturbed forests, IFs, and oil palm plantations. Conversely, there was little difference among UF, DF and IFs for fruit flies; and between DF and IFs for bat species. In particular, for orchid bees, moths, and grasshoppers, the number of species in IFs was higher than those in forestland, including UF and DF (Table 4.10; Figure 4.27). Briefly, biodiversity in IF land was more diverse compared with oil palm plantations, but less diverse than forestlands. Table 4.10. The number of species in the different LULC types in the study area. Number of Species Undisturbed Forest Disturbed Forest IFs Oil Palm Forest Birds 159 127 43 37 Forest Butterflies 68 58 N/A 11 Beetles  79 66 24 18 Trees and Lianas  200 80 1 1 Amphibians 96 61 22 N/A Reptiles 81 48 45 N/A Mammals 49 43 15 N/A Bats 45 32 30 N/A Fruit Flies 28 28 25 N/A Orchid Bees 15 18 16 N/A Moths 145 140 200 N/A Grasshoppers 25 20 27 N/A 168   Figure 4.27. A number of species for different ecosystems (UF, DF, IF, and OP) in the study area.  Considering the above literature review on biodiversity for different LULC types, we could conclude that the conversion of forestlands into IF lands would lead to a reduction of biodiversity for most species in the region (Table 4.11; Figure 4.28). For instance, forest bird species in the UF and DF would reduce 73% and 66%, respectively. A monoculture IF with one or a few tree species would replace forestlands with hundreds of tree species (Figure 4.28).  Table 4.11. The percentage of declining or increasing number of species if UF, DF and OP lands were converted into IF land. Number of Species Undisturbed Forest/UF Disturbed Forest/DF(**) Oil Palm/OP Forest Birds -73% -66% 16% Forest Butterflies N/A N/A N/A Beetles  -70% -64% 33% Trees and Lianas  -100% -99% 0% Amphibians -77% -64% N/A Reptiles -44% -6% N/A Mammals -69% -65% N/A Bats -33% -6% N/A Fruit Flies -11% -11% N/A Orchid Bees 7% -11% N/A Moths 38% 43% N/A Grasshoppers 8% 35% N/A (**) Note that in this study, only disturbed forests were converted into new industrial forestlands 0 50 100 150 200 250 Forest Birds Forest Butterflies Beetles  Trees and Lianas  Amphibians Reptiles Mammals Bats Fruit Flies Orchid Bees Moths Grasshoppers Number of species Relative comparison of number of species for different LULC types in the study area Unidirturbed Forest Disturbed Forest IFs Oil Palm 169    Figure 4.28. The percentage of change in number of species in IFs compared with other LULC types in the study area.   Conversely, the conversion of oil palm lands to IF lands could increase biodiversity in the region (e.g., increasing 16% for forest birds and 33% for beetle species). In addition, for some species, such as moths and grasshoppers, the conversion of forestlands to IF lands could increase their richness and abundance. For instance, the number of species of moths and grasshoppers would increase 43% and 35%, respectively, if disturbed forests were changed to IF lands (Table 4.11; Figure 4.28).  It is clear that, in general, the conversion of forestlands to IF lands indicated a reduction of biodiversity (number of species) for most species. The next question was how much biodiversity had been lost as a consequence of the expansion of new IFs in Sarawak and Sabah over the study period from 2000 to 2014. To answer this question, the species-area relationship model was used. The model was expressed as follows: S = k(A)z, where S was species, A was area, and k and z were constants. The model could be written S = k(Anew/Aoriginal)z. The value of z = 0.25 was usually used as a default value for Southeast Asia (Brook et al., 1999; May & Stumpf, 2000). -100% -80% -60% -40% -20% 0% 20% 40% Forest Birds Beetles  Trees and Lianas  Percentage of change in number of species Percentage of change in number of species in IFs compared with forest lands and oil palm Unidirturbed Forest Disturbed Forest Oil Palm -80% -60% -40% -20% 0% 20% 40% 60% Percentage of change in number of species Percentage of change in number of species in IFs compared with forest lands Unidirturbed Forest Disturbed Forest 170  However, later, Brook et al. (2003) proposed new z values for the different species in Southeast Asia (Table 4.12).  Table 4.12. Estimating the biodiversity loss caused by the expansion of the new IFs in the study area from 2000 to 2014 (adapted from Brook et al., 2003). Study area Species Forest area z value Biodiversity loss (%) Total in 2000 (1000 ha)  Total area lost by new IFs (1000 ha)  Sabah Average 5034 237 0.11 0.71 Trees 5034 237 0.1 0.74 Butterflies 5034 237 0.15 0.63 Amphibians 5034 237 0.02 0.94 Reptiles 5034 237 0.02 0.94 Birds 5034 237 0.13 0.67 Mammals 5034 237 0.17 0.59 Average (other studies*) 5034 237 0.25 0.47 Sarawak Average 9719 440 0.11 0.71 Trees 9719 440 0.1 0.73 Butterflies 9719 440 0.15 0.63 Amphibians 9719 440 0.02 0.94 Reptiles 9719 440 0.02 0.94 Birds 9719 440 0.13 0.67 Mammals 9719 440 0.17 0.59 Average (other studies*) 9719 440 0.25 0.46 * the value z = 0.25 was derived from (Brook et al., 1999; May & Stumpf, 2000).  The results of this study showed that the expansion of new IFs could cause a biodiversity loss. In other words, biodiversity in the region most likely faced a probability of extinction due to the expansion of new IFs between 2.79% and 4.98% in Sabah and between 2.77% and 4.96% in Sarawak. Specifically, amphibians and reptiles in both Sabah and Sarawak faced the highest loss at 0.94%, followed by tree species at 0.74% and 0.73% in Sabah and Sarawak, respectively. Birds, butterflies, and mammals also faced a loss ranging from 0.59% to 0.67% in these areas as a consequence of the expansion of new IFs in the area (Table 4.12).   171  4.3    Discussions and Conclusions The above findings clearly indicate that the selected IFs (acacia, rubber, and other IFs) were increasing in Sarawak and Sabah over the study period of 2000-2014. In other words, the selected IFs have been expanding in these areas. However, the extent of expansion, the expansion rate, and expansion pattern specific for the different selected IF systems and years in Sabah and Sarawak were different. In general, this study found that the total extent and expansion rate of fast-growing, short-rotation IFs, such as acacia plantations, were much higher than those of slow-growing, long-rotation IF systems, such as rubber and other IFs. For the development of IFs in Sabah, which is known as an old area for IFs because the IFs were established there a very long time ago, the new IFs continued to expand significantly in this area, although their expansion rates were much lower than those in Sarawak, where new IFs just emerged as a new LULCC recently.  The IF area detected in this study was consistent and inconsistent with various other research results and data, depending on the sources. Specifically, in Sabah, the total IF area, including acacia and other IFs, that was detected in 2000 was 64,879 ha lower than the IF statistical data from the Sabah Forestry Statistics in 2000 (154,640 ha) and from FAO (2002), with 117,000 ha detected in 2001. This detected IF area increased to 225,753 ha in 2009 and 246,874 ha in 2012, with the annual mean expansion rate of 23.8%, compared with 244,000 ha in 2012 from Sabah Forestry Statistics, with the annual expansion rate of 4.8%. This number was also relatively consistent with the plantation forest data reported by Malaysian Timber Council (2009) in 2009, with 200,000 ha, and the study of Reynolds et al. (2011), with the total timber plantation area of 122,000 ha in 1990 and 244,700 ha in 2010. The Malaysia Forestry Outlook Study (2009) also reported that in Sabah, over a 20-year period from 1985 to 2005, the area of forest plantations 172  had increased from a low of 50,000 ha to 200,000 ha - an increase of 150,000ha at an average annual rate of 14.9%. In addition, this study also presented relatively consistent IF area data compared with FAO (2010), which found that Sabah had 90,000 ha of plantation forests (including 56,000 ha of acacia and 34,000 ha of other species, not including rubber) in 2001, increasing to 244,000 ha in 2010.  In Sarawak, the development of IFs, including acacia and other IFs, showed a different trajectory from Sabah. These IFs were a relatively new LULC in the area. FAO (2010) indicated that in 2001, Sarawak had merely 4,000 ha of acacia and 9,000 ha of other IFs. These areas increased to 221,000 ha of acacia and 82,000 of other IFs in 2010. The IF statistical data from Sarawak Forestry Department also presented that the total IF area (acacia and other IFs, not including rubber) in this state had increased impressively from 6,830 ha in 2000 to 141,050 ha in 2006 and 306,486 ha in 2012. However, some studies did not find the same results as the statistical figures mentioned previously. For instance, Miettienen et al. (2010) reported there were no pulp and other industrial plantations in Sarawak in 2010. In general, the data from FAO (2010) and Sarawak Forestry Department (2012) were relatively consistent with the findings in this study. This study found that the IF area (not including rubber) in 2000 was 12,693 ha (including 6,864 ha of acacia and 5,829 ha of other IFs), increasing to 130,373 ha (112,903 ha of acacia, and 17,470 ha of other IFs) in 2006 and 438,277 ha (368,640 ha of acacia, and 69,637 ha of other IFs) in 2014.  In contrast, the development of rubber plantations from 2000-2014 presented some differences between this study and various data sources. For instance, in Sabah, the detected rubber area in this study in 2000 was 37,788 ha, increasing to 110,062 ha in 2014, with the annual expansion rate of 13.7%. This was significantly different from the findings of Malik et al. 173  (2013), in which the total rubber area reported in 2000 was 78,895 ha, declining to 62,891 ha in 2005 at the annual reduction rate of 4.1%. The statistical data from Malaysia Rubber Board (2010) also reported that, in 2000, Sabah had a total of 87,400 ha (including 2,400 ha under estates and 85,000 ha under smallholdings), shrinking to 64,400 ha in 2003 and increasing again to 71,100 ha in 2009. Likewise, the development of rubber plantations in Sarawak that were detected in this study also revealed different pathways than other data sources. For instance, Malik et al. (2013) indicated the rubber plantation area in Sarawak increased at the rate of 7.49% per annum from 153,000 ha in 2000 to 210,000 ha in 2005. Meanwhile, the statistical data from Malaysia Rubber Board (2010) reported that, in 2000, Sarawak had a total of 160,100 ha, shrinking to 155,610 ha in 2006 and slightly increasing again to 157,160 ha in 2009. The rubber plantation area detected in this study was inconsistent with the data mentioned previously. The total detected rubber area in 2000 was 42,147 ha, increasing to 61,473 ha in 2006 and 63,581 ha in 2009.  One of the important findings in this study is that the development of IFs in both states was largely dominated and promoted by the large-scale IFs, as opposed to the small-scale IFs. The smallholders in Sabah captured about 24-45% of the total IF area, and those in Sarawak captured from 19-34%. This ratio gradually increased over time from 2000 to 2014.  FAO (2010) also showed that, of 90,000 ha of forest plantations in Sabah in 2000, 62% belonged to private companies and 38% belonged to state government agencies. However, it was possible that the elimination of IF areas smaller than 2 ha in this study would underestimate the area of small-scale IFs. For instance, Lintangah et al. (2010) investigated tree plantation activities among smallholders in Ranau, Sabah and found that the average rubber patch size owned by smallholders was about 1.3-1.4 ha.  Besides, quantifying the small- vs. large-scale areas by using 174  remote sensing (RS) tools may not truly reflect the reality. A large IF patch in the RS-based product may be created by many smaller patches gathering together, or it is also possible that a large patch could be divided into many smaller patches due to the harvesting and planting activities on the ground.  However, this study found that the increase in percentage of the small-scale IFs over time proved that the small-scale IFs played a more and more important role in developing the IFs in the regions. In other words, the dynamics and processes of LULCC in the regions associated with the emerging new IFs were dominated by both the large-scale and small-scale. The increasing roles of the small-scale IFs in the regions were also indicated by an increase of the number of IF patches and a decrease of the mean IF patch size index in both states. Moreover, the study findings from investigating the IF area distribution based on the IF patch size classes presented that the IF areas were mainly distributed in the IF patch size classes over 200 ha and less than 5 ha. This was consistent with the study finding of Lintangah et al. (2010) that most smallholders owned a plantation area less than 6.5 ha. An investigation into the scales of rubber plantations in this study showed that most rubber plantations (~45%-71%) were large-scale plantations (with the patch size > 40 ha), and this figure gradually reduced from 2000 to 2014 in both states, whereas Malaysia Rubber Board (2010) reported that almost all rubber plantations in Sabah and Sarawak were under smallholdings. This difference may derive from the difference between this study and the source in defining the small- and large-scale areas. Besides, this study also found that the increase in the number of large-scale IF patches presented a consistency with the increase of the large-scale IF area in both states. However, the mean patch size for all selected IF systems in Sabah was declining. This could also indicate a reduction in establishing large-scale IFs. Conversely, in Sarawak, both the number of large-scale IF patches and their 175  mean size was increasing. This likely revealed a more dynamic IF development in this state as opposed to Sabah because the IF land use just emerged recently in the state, while the IFs in Sabah had been established a long time ago. For the VIs-based IF detection, VIs were well-known for additive effects caused by soil and atmospheric conditions. Therefore, using them to detect IFs varied greatly depending on the atmospheric and soil conditions at the time the satellite images were taken. Generally, ARVI worked the best in the regions, followed by SAVI, SARVI, EVI, NDVIaf, and MSAVIaf. In Sabah, the IF area detection results by using VIs showed that the small-scale IF area was larger than the large-scale IF area. This differed from the results of the fC-based IF detection method presented above. In contrast, the large-scale and small-scale IF area detected using these vegetation indices in Sarawak were consistent with the results of the fC-based method. In general, the expansion of new IFs in Sabah and Sarawak over the study period of 2000-2014 significantly contributed to LULC change in the regions. Most of new IFs in Sarawak and Sabah replaced disturbed forest (81-95%), followed by agricultural land (4-18%), and waste land (less than 0.5%). This finding was also consistent with other study findings. For instance, Malaysian Timber Council (2009) also indicated that plantations in Sarawak were mostly located inside permanent reserved forests.  Grieg-Gran et al. (2007), Koh and Wilcove (2008), and Wicke et al. (2008) argued that pulpwood plantations in Malaysia accounted for 17% of the forest loss. In particular, since 2000, the conversion of forests to industrial timber plantations in Sabah and Sarawak has been an important driver of deforestation. Other analyses by SarVision (2011) and Lawson et al. (2014) indicated that, from 2006-2010, Sarawak lost 0.9 Mha of forests and that 43% of this loss was accounted for by the expansion of oil palm, while new timber plantations contributed 21%. Malik et al. (2013) also found that, from 2000-2005, 29,000 ha of 176  rubber plantations displaced natural forests in Sarawak. In brief, this study found that new IFs were a significant deforestation driver, possibly only after oil palm plantations, in Sabah and Sarawak over the period of 2000-2014. The conversion of forestland into industrial forestland has led a significant reduction of the above ground C stock in the regions. This change released a remarkable amount of CO2 into the atmosphere over the 2000-2014 period in Sabah and Sarawak. However, this CO2 emission only accounted for the balance of the above ground C stocks in the area; it did not include the C stocks regarding how much this IF wood helped reduce the wood extraction from natural forests for the industries in the regions. Moreover, the C stocks that were taken into account in this study were only for above living biomass by using the default values. This did not include below ground C stock, soil C stock and nonliving biomass in the area. Therefore, the calculations of C stocks and CO2 emissions did not truly reflect the all of reality; it only provided a gross estimate about the living C stocks partially captured and lost by new IFs in the area.       This significant forestland conversion into new IFs also noticeably reduced biodiversity in the area. The biodiversity faced a loss or reduction of 2-5%. However, the biodiversity loss in this study only accounted for forest birds, butterflies, beetles, trees and lianas, amphibians, reptiles, mammals, bats, fruit flies, orchid bees, moths, and grasshoppers. Thus, this did not fully reflect biodiversity loss or impacts caused by the expansion of new IFs in the areas; it can only provide us a general quantitative estimate of how much the expansion of new IFs would influence the biodiversity. Besides, the quantification of the impact of new IFs in this study did not include assessments of their impacts on natural resources, such as water, soil and energy balance.  177   In brief, the following conclusions could be drawn from this study: The total IF area in both Sabah and Sarawak increased over the study period. This increase was accounted for by all of the selected IF species/systems (acacia, rubber, and other IFs). The increase of acacia IFs, both in the total area and in its rate of change in area, were larger than that of rubber and other IFs. Additionally, the increase of the total IF area and the rate of change in area in Sarawak (known as a newly emerging IF area) was larger than that in Sabah (known as an old IF area).  The development of IFs in Sabah and Sarawak were generally dominated by the large-scale IFs with a patch size larger than 40 ha, and the percentage of large-scale IF area in Sarawak (68-81%) was larger than that in Sabah (55-76%). However, in both states, the percentage of large-scale IFs decreased, while the percentage of small-scale IFs increased over the study period. The same trend was also found for all selected IF systems (acacia, rubber, and other IFs). This fact was proven through evidence that the expansion rate of the small-scale IFs was much higher than that of the large-scale IFs. This indicated that small-scale IFs played a more and more important role in developing new IFs in the regions.  Along with the increase of the total IF area, the number of IF patches and the largest patch size in both Sabah and Sarawak also increased. The increase in the number of acacia IF patches and the largest acacia IF patch size were both larger than those for rubber and other IFs. However, the mean IF patch size index generally decreased, especially in Sabah, except in Sarawak, where the mean patch size index of acacia and other IFs increased. The study results of the IF area class distribution based on the IF patch size classes showed that most of the IF area was distributed in the 200-ha-or-larger-patch-size class (industrial scale), followed by the-5-ha-or-smaller-patch-size class (smallholdings), with the least in the transitional patch size class of 20-40 ha.  178   Likewise, along with the increase of the large-scale IF area in both states, the number of the large-scale IF patches also increased. This increase was accounted for by both the total IFs and by the specifically selected IF systems. However, the increase in the number of large-scale acacia patches was larger than that of rubber and other IFs. Besides, the study findings indicated that while the mean large-scale patch size in Sarawak - particularly for acacia and other IFs - increased, the mean large-scale patch size in Sabah decreased.  The VIs-based IF detection study results also presented the same trend as the fC-based study findings. That is, the total IF area and the area for the specifically selected IF systems increased in both states over the study period of 2000-2014. Of that, most of this increase was contributed to acacia IFs. However, the detected IF area and the rate of change in area using different VIs were different and variable. In general, the IF area detected by using ARVI was the largest, followed by EVI, SARVI, SAVI, NDVIaf, and MSAVIaf. Conversely, the rates of change in the VIs-based IF areas were generally highest using SAVI, followed by MSAVIaf, NDVIaf, SARVI, EVI, and ARVI, depending on specific areas, years, and IF systems. The study results for the large-scale versus small-scale IF area showed that the small-scale IF area in Sabah was larger than the large-scale IF area, while the opposite was true in Sarawak, where the large-scale IF area was larger than the small-scale IF area.  The emerging and expansion of new IFs in Sabah and Sarawak over the study period of 2000-2014 made a significant land use and land cover change in the regions. Specifically, most of these new IF expansions have replaced disturbed forests (82.1% in Sabah, 95.5% in Sarawak), followed by agricultural land (17.7% in Sabah and 3.5% in Sarawak). Only a very insignificant amount of degraded or waste land (0.2% in Sabah and 0.02% in Sarawak) was converted into IF land. Acacia IFs contributed most of this change. Specifically, they contributed to the conversion 179  of forestland into IF land at a rate of 70% in Sabah and 80% in Sarawak. This was followed by other IFs at 11 % in Sabah and 14% in Sarawak. Rubber IFs contributed to only 4% of forest loss in Sarawak, but to 19% in Sabah. Most of the agricultural land was converted into rubber plantations (51% in Sabah and 32 % in Sarawak), followed by acacia IFs (47% in Sabah and 63% in Sarawak).  The changes of land use and land cover caused by the expansion of new IFs in Sabah and Sarawak from 2000 to 2014 have significantly decreased the living C stock in the regions. Specifically, in total, Sabah lost 11.5 Tg C and Sarawak lost 24.7 Tg C over the study period. This contributed remarkably to an emission of 42.1 Tg CO2 in Sabah and 90.5 Tg CO2 in Sarawak into the atmosphere. Most of this C stock change happened in the disturbed forests (98% in Sabah and 99.5% in Sarawak) and was mainly caused by new acacia IFs (81% in Sabah and 83% in Sarawak), followed by other IFs (10% in Sabah and 13% in Sarawak), and new rubber plantations (9% in Sabah and 4% in Sarawak).  The expansion of new IFs in Sabah and Sarawak also placed a threat to biodiversity in the regions. It led to a reduction of biodiversity estimated in Sabah at 2.79-4.98% and in Sarawak at 2.77-4.96%. Of that, amphibians and reptiles faced the highest loss at 0.94%, followed by other species (tree, forest birds, butterflies, and mammals) from a loss of 0.59% to 0.74%.       180  CHAPTER 5 SYTHESIS  5.1 Introduction This chapter will explain and discuss the shortcoming and applicability of this study. How the developed methods in this research should be improved for the future works and how the methods are possibly applied to other parts of the works will also be discussed. 5.2 Shortcoming In the process of the new remote-sensing method development for Landsat datasets based on vegetation/forest fractional cover (fC) and vegetation indices (VIs) analyses in time series to detect, map, and monitor new IFs in the tropic with the selection of Sarawak and Sabah states of Malaysia as a case study, this study faced with some facts, difficulties, and challenges, which led to the shortcomings of the developed methods. These shortcomings have resulted in the moderate and fair accuracy in detecting IF land and specific for acacia, rubber, and other IFs in both methods.   The first shortcoming in developing the VIs- and fC-based methods to detect industrial forests came from the quality of the Landsat scenes, which was notoriously affected by cloud contaminations, their shadows, haze, and missing values in the Landsat 7 (ETM+ SLC off). To deal with these problems, gap filling techniques were used by the mosaics or use of a large amount of other Landsat scenes to fill the gaps created by cloud problems and the missing values in Landsat 7 in the selected scenes. The use of many Landsat scenes in the different times, different sensors, and different quality definitely greatly influenced the ability of the developed algorithms in the VIs- and fC-based methods  to detect an IF because it may result in the changes 181  of LULC due to the scene quality rather than the LULC changes by themselves in reality. In other words, this could lead to the interpretation and detection in the changes of LULC due to the differences between and among Landsat scenes as the clearing and regrowth of a predicted IF stand. Moreover, to reduce the influences of seasonal factors, this study selected the Landsat datasets from May to August. However, the scenes in this time period were not always available. Consequently, the scenes in other times were also selected. This possibly also led to the changes of vegetation covers due to seasonal influences in the reality in the study sites rather than the changes by the silvicultural cycles. Therefore, it will be definitely easier for developing the Landsat data-based IF detection methods by using fC and VIs analyses in time series in the places where less cloud contamination.  The second shortcoming came from the ideas using the silvicultural rotation and growth rate of IFs to develop the methods to detect them. As described above, it was impossible to annually monitor the full cycles of sawlog long-rotation IFs such as teak, rubber, and pine destined for producing saw logs because their rotations can last tens of years. It is impractical for us take annual Landsat datasets long enough to observe them. Also, many clearing activities were possibly not based on silviculture. Moreover, the silvicultural rotation of an IF system or species also varied greatly depending on the purpose of using it. Even for the same purpose of using it, its rotation might also vary depending on the intention and economic considerations of the rotation to detect the specific IFs in these cases was challenging. Besides, almost all of the IFs would have been subjected to the silvicultural practices, including thinning and pruning activities. It was possible that we could misclassify these IF stands as a new rotation as well. In this study, choosing the threshold detect the changes of VIs and fC at ± 15% was only based on 182  -and- or silvicultural observations due to the lacks of this kind of data to check the validity of these threshold values. For the use of the growth rates of VI or fC values to detect IFs, the fact was that we could detect the faster- versus slower-growing IF species or systems. However, the growth rate of an IF system might also depend on the soil and climate conditions, and silvicultural practices. It was possible that a slower-growing IF species planted in a good soil (good site-species matching) and exposed to proper silvicultural practices could grow the stand faster than a fast-growing IF species established in a poor condition. Therefore, it is important for the next studies in detecting IFs based on their silvicultural cycles that investigators need to acquire the adequate and reliable silvicultural data in their study sites. In regard to using the textural and spectral analysis as a support step in detecting IFs in both the VIs- and fC-based methods, although, in fact, their textures were principally different from other natural vegetations; and these differences were easily recognized in the very high resolution imagery data, it was very difficult to realize them in the coarser or medium-resolution satellite imagery data like Landsat, especially in the small patches. The fact is that the smaller patch size is more difficult to detect. To identify their typical textural values in the Grey Level Co-occurrence Matrix, how well this analysis worked may be dependent on how well we chose the training areas to be used as the references in classifying IFs in images. The textural values must represent for the different development periods or ages of a given IF system in different types of Landsat scenes.   In addition, for spectral analysis, the fact was that the spectra were also very similar among different vegetation cover types and different IF systems in the Landsat datasets. Therefore, it was also very challenging to work on this analysis. For example, oil palm - which was one of the 183  most dominating plantations in the region - had very similar spectra and texture to the selected IFs. Consequently, separating them was very difficult. One of the best possible ways we had was to select the training area well enough to represent the typical values for the expected land use and land cover in the region. This may involve dividing the region into the smaller areas and for different kinds of Landsat scenes such as Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). Our best option was to build a good spectral library well representative of the different IF systems in the different times, different types of images, and different stages of an IF stand. In other words, the most challenging issue leading to the biggest shortcoming of the study was the spectral and textural similarity among different land use and land cover types including IF systems, as well as the spectral and textural variability in the same land use and land cover class comprising of IF systems. The future studies must handle this challenge to better detect, map, and monitor the IFs in the tropic. For other analyses used in the fC-based IF detection method development including band 4 value-based green biomass content and MSAVIaf-derived leaf area index that were added to the method to detect and map IFs, the values of band 4 might only represent the green biomass of vegetation canopy instead of representing the whole biomass of the stands. Therefore, using it for biomass content analysis should be carefully considered, specific for IF systems and their stand ages in the different Landsat types. Besides, using MSAVIaf-derived leaf area index to identify the IF systems should be also additionally tested in the fields specific for the selected IF systems or species at the different stages of an IF stand or system. 184   Lastly, visual interpretation was a very subjective method, and it was dependent on the knowledge and experience of interpreters. It also relied on the quality of the other LULC sources that we would use to identify the IFs in the images.  In brief, for both fC- and VIs-based IF detection methods, their ability in detecting and map IFs in the region was confounded by some challenges and difficulties including, the quality and disadvantages of the Landsat data, as well as the rotation and growth rate assumptions used to develop the method, and other textural, spectral, and visual interpretation issues.   5.3 Applicability  The developed methods have a potential to apply in areas, where have similar conditions as in the study sites (Sarawak and Sabah of Malaysia). They could be in the island of Borneo. For other regions in the tropics which have different environmental conditions or IF systems, the algorithms must be modified to better reflect the reality in those regions. For instance, if they are applied in Thailand, the sivilcultural cycles, textural and spectral values, and others (e.g., green biomass content and leaf area index) for eucalypts, teak, and pines (these IF systems/species are prevalent or dominated in the country) must be considered and identified while acacia IFs were more dominated in the study sites of this research. Likewise, production plantations (or IFs) in Vietnam are usually mixed of some IF species (e.g., acacia with eucalypt), it must be also considered carefully.  In addition, these methods need to be radically modified to work in the temperate zones providing that we have enough data on the silvicultural cycles, textural and spectral data, and other typical characteristics for the IFs in the regions. 185                         APPENDIX 186  Table A.1. The full list of Landsat scenes used for the study in Sarawak, Malaysia, 2000-2014. 2000 118-57 LT51180572000190DKI00 LE71180572001056SGS00 LE71180571999355EDC00 LE71180572001248SGS00 LE71180572001296SGS01 LT51180571998328DKI00      118-58 LT51180582000190DKI00 LE71180582001056SGS00 LE71180582001232EDC00 LE71180581999355EDC00 LT51180581998328DKI00 LT51180581998024DKI00    118-59 LE71180592000246SGS00 LE71180592000198EDC00 LT51180591997213DKI00       119-57 LT51190572000069DKI00 LT51190572000101DKI00 LT51190572000117DKI00 LE71190572001191SGS00 LE71190572001095SGS00 LE71190571999330SGS00    119-58 LT51190582000101DKI00 LT51190582000197DKI00 LE71190582001191SGS00 E71190582001095SGS00      119-59 LT51190592000197DKI00 LT51190592000101DKI00 LE71190592001175EDC00 LE71190592001143SGS00 LE71190592001191SGS00 LE71190592001351EDC00    120-58 LE71200582000132SGS00 LE71200582001166SGS02 LE71200582000244EDC01 LE71200582000052EDC00 LT51200582000092DKI00 LT51200581999233DKI00 LE71200582001182SGS00   120-59 LE71200592000244EDC01 LE71200592000132SGS00 LE71200592001006SGS00 LE71200592001182SGS00      121-59 LE71210592000139SGS00 LT51210592000131DKI00 LT51210592000163DKI00 LE71210591999312SGS00 LE71210592001189SGS00 LE71210592001077SGS00   2003 118-57 LE71180572003142EDC00 LE71180572002139SGS00 LE71180572002235EDC00 LT51180572004169BKT01 LT51180572004185BKT01 LE71180572004193SGS01 LE71180572004081EDC02 LE71180572002091SGS00  118-58 LE71180582003142EDC00 LT51180582004185BKT01 LT51180582004137BKT02 LE71180582004145EDC01 LE71180582002139SGS00 LE71180582002347EDC00    118-59 LE71180592003142EDC00 LE71180592003110EDC00 LE71180592002347EDC00 LE71180592002187SGS00      119-57 LE71190572003213EDC03 LE71190572002178SGS00 LE71190572002098EDC00 LE71190572004248EDC02 LT51190572004224BKT00 LT51190572004080BKT00    119-58 LE71190582003213EDC03 LE71190582002098EDC00 LE71190582003101ASN00 LT51190582004224BKT00 LE71190582004248EDC02 LE71190582001191SGS00    119-59 LE71190592003149EDC00 LE71200592003316ASN01 LE71190592004232EDC02 LE71190592004104EDC01 LE71190592002098EDC00 LT51190592004256BKT00 LT51190592005162BKT01   120-58 LE71200582003316ASN01 LE71200582003108ASN00 LE71200582002217DKI00 LE71200582002153SGS00 LE71200582004239DKI01 LE71200582004223EDC01 LE71200582004143EDC03   120-59 LE71200592003268ASN01 LE71200592002233SGS00 LE71200592002201SGS00 LE71200592002153SGS00 LE71200592002217DKI00 LE71200592004143EDC03 LE71200592004223EDC01   121-59 LT51210592003315BKT00 LE71210592003051DKI00 LE71210592002176SGS00 LT51210592004174BKT01 LT51210592004094BKT00 LE71210592004182DKI00   2006 118-57 LE71180572006070EDC00 LT51180572006190BKT00 LT51180572006158BKT01 LT51180572006094BKT00 LT51180572007177BKT00 LE71180572005211EDC00 LE71180572005259EDC00    118-58 LT51180582006094BKT00 LT51180582006110BKT00 LE71180582006214EDC00 LT51180582007177BKT00 LE71180582007185EDC00 LE71180582005211EDC00    118-59 LE71180592006278EDC00 LE71180592005211EDC00 LT51180592007177BKT00 LE71180592007217EDC00 LE71180592007185EDC00     119-57 LE71190572006237EDC00 LE71190572006157EDC00 LT51190572005210BKT00 LT51190572005162BKT01 LE71190572007240EDC00 LT51190572007184BKT00    119-58 LE71190582006157EDC00 LT51190582006165BKT00 LT51190582005226BKT01 LT51190582005258BKT00 LT51190582007200BKT00 LT51190582007184BKT00 LE71190582007240EDC00   119-59 LE71190592006189EDC00 LT51190592005226BKT01 LT51190592005162BKT01 LT51190592005258BKT00 LT51190592007216BKT00 LT51190592007184BKT00 LE71190592007128EDC00   120-58 LE71200582006164EDC00 LE71200582006068EDC00 LE71200582006116DKI00 LT51200582006044BKT00 LT51200582006140BKT00 LT51200582005249BKT00 LE71200582007231EDC00 LE71200582007103EDC00  120-59 LE71200592006228EDC00 LE71200592006164EDC00 LE71200592006180EDC00 LT51200592006268BKT01 LE71200592005017EDC00 LE71200592005065EDC00    121-59 LE71210592006155EDC00 LT51210592006099BKT00 LT51210592006179BKT00 LT51210592005224BKT01 LE71210592005216EDC00 LE71210592007174EDC00   2009 118-57 LE71180572009062EDC00 LT51180572008148BKT00 LT51180572009230BKT00 LT51180572009166BKT01 LT51180572009246BKT00 LE71180572009174EDC00 LE71180572008268EDC00    118-58 LT51180582009310BKT00 LT51180582009246BKT00 LE71180582009062EDC00 LE71180582010241EDC00 LE71180582010225EDC00 LE71180582008268EDC00    118-59 LE71180592009062EDC00 LE71180592009222EDC00 LE71180592008140EDC00 LT51180592010041BKT00      119-57 LE71190572009117EDC00 LT51190572009157BKT00 LE71190572008163EDC00 LE71190572008179EDC00 LT51190572008315BKT00 LT51190572008331BKT00    119-58 LE71190582009213EDC01 LT51190582009221BKT00 LT51190582009157BKT00 LE71190582009357EDC00 LT51190582009253BKT00 LT51190582010224BKT00    119-59 LE71190592009357EDC00 LT51190592009253BKT00 LE71190592009213EDC01 LE71190592010040EDC01 LE71190592008163EDC00 LT51190592008123BKT00 LT51190592008283BKT00 LT51190592010224BKT00  120-58 LE71200582009332EDC00 LE71200582009204EDC00 LT51200582009212BKT00 LT51200582009164BKT00 LE71200582010287EDC00 LE71200582010239EDC00    120-59 LE71200592009220EDC00 LE71200592009284EDC00 LT51200592009212BKT00 LT51200592008146BKT00      121-59 LE71210592009291EDC00 LE71210592009211SGS00 LT51210592009235BKT00 LE71210592008017EDC01 LE71210592008305EDC00 LE71210592010134EDC00   2012 118-57 LE71180572012167EDC01 LT51180572011220BKT00 LE71180572012215PFS00 LC81180572013241LGN00 LE71180572012087EDC00 LE71180572011196EDC00 LT51180572011172BKT00    118-58 LE71180582012215PFS00 LE71180582012167EDC01 LE71180582012055EDC00       118-59 LE71180592012055EDC00 LE71180592012215PFS00 LE71180592012167EDC01       119-57 LE71190572012014EDC00 LE71190572012126EDC00 LE71190572012222EDC00 LE71190572011171EDC00 LT51190572011227BKT00 LC81190572013168LGN00    119-58 LE71190582012222EDC00 LC81190582013328LGN00 LC81190582013168LGN00 LE71190582012126EDC00 LT51190582011227BKT00 LE71190582011251EDC00 LE71190582011171EDC00   119-59 LE71190592012222EDC00 LE71190592012126EDC00 LE71190592013288EDC00 LC81190592013216LGN00 LE71190592011219EDC00 LE71190592011171EDC00 LE71190592013160EDC00   120-58 LE71200582012277EDC00 LC81200582013159LGN00 LE71200582012245EDC00 LE71200582012165EDC00     187  Table A  120-59 LE71200592012229EDC00 LE71200592012277EDC00 LE71200592012341EDC00 LT51200592011170BKT00 LE71200592011322EDC00 LE71200592013167EDC00    121-59 LT51210592011273BKT00 LE71210592011313EDC00 LE71210592013142DKI00 LE71210592013110EDC00     2014 118-57 LC81180572014260LGN00 LC81180572014100LGN00 LC81180572014292LGN00 LE71180572014124EDC00 LE71180572014028EDC00 LC81180572014116LGN00      118-58 LE71180582014252EDC00 LC81180582014292LGN00 LC81180582014036LGN00 LC81180582014116LGN00 LC81180582014260LGN00     118-59 LC81180592014036LGN00 LC81180592014116LGN00 LC81180592014260LGN00       119-57 LE71190572014035EDC00 LC81190572014187LGN00 LC81190572014075LGN00 LC81190572014123LGN00 LC81190572014347LGN00 LC81190572013328LGN00    119-58 LC81190582014075LGN00 LE71190582013160EDC00 LC81190582014347LGN00 LC81190582014235LGN00 LC81190582014267LGN00 LC81190582014347LGN00 LC81190582014075LGN00   119-59 LC81190592014075LGN00 LC81190592014219LGN00 LC81190592013168LGN00 LC81190592013152LGN00 LC81190592014075LGN00 LC81190592013168LGN00    120-58 LC81200582014178LGN00 LC81200582014146LGN00 LC81200582014130LGN00 LC81200582014114LGN00 LE71200582014122EDC00 LE71200582013167EDC00 LC81200582013287LGN00   120-59 LC81200592014338LGN00 LC81200592014114LGN00 LC81200592014178LGN00 LE71200592014282EDC00 LE71200592013167EDC00 LC81200592013175LGN00    121-59 LE71210592014257EDC00 LE71210592014273EDC00 LC81210592014233LGN00 LC81210592014201LGN00 LC81210592014089LGN00 LC81210592014217LGN00    Table A.2.  The date, type, and cloud coverage of Landsat scenes used for the study in Sarawak, Malaysia, 2000-2014. 2000 118-57 7/8/00,TM,10% 12/21/00,ETM+,14% 2/25/01,ETM+,13% 9/5/01,ETM+,18% 10/23/01,ETM+,21% 11/24/98,TM,15%     118-58 7/8/00,TM,19% 2/25/01,ETM,22% 8/20/01,ETM+,29% 12/21/99,ETM,29% 11/24/98,TM,30% 1/24/98,TM,14%    118-59 7/16/00,ETM+,16% 9/2/00,ETM+,27% 8/1/97,TM,24%       119-57 4/26/00,TM,19% 4/5/01,EMT+,2% 4/10/00,TM, 13% 3/9/00,TM,11% 11/26/99,ETM+,7% 7/10/01,ETM+,0%    119-58 4/10/00,TM,9% 7/10/01,ETM+,0% 7/15/00,TM,21% 4/5/01,ETM+,20%      119-59 7/15/00,TM,14% 4/10/00,TM,19% 5/23/01,ETM+,14% 6/24/01,ETM+,11% 7/10/01,ETM+,8% 4/8/02,ETM+,17% 12/17/01,ETM+,30%   120-58 5/11/00,ETM+,7% 6/15/01,ETM+,11% 8/31/00,ETM+,7% 2/21/00,ETM+,14% 8/21/99,tm,25% 7/1/01,ETM+,6% 4/1/00,TM,27%   120-59 8/31/00,ETM+,13% 5/11/00,ETM+,19% 1/6/01,ETM+,17% 7/1/01,ETM+,12% 6/2/02,ETM+,7%     121-59 6/11/00,TM,2% 3/18/01,ETM+,11% 7/8/01,ETM+,8% 5/18/00,ETM+,0% 5/10/00,TM,11% 11/8/99,ETM+,7%   2003 118-57 5/22/03,ETM+,13% 6/17/04,TM,9% 5/19/02,ETM+,8% 8/23/02,ETM+,28% 3/7/04,TM,22% 3/21/04,ETM+,28% 4/1/04,ETM+,ETM+,20% 5/22/03,ETM+,13%  118-58 5/22/03/ETM+,25% 5/19/02,ETM+,21% 12/13/ETM+,24% 5/16/04,TM,16% 7/3/04,TM,25% 5/25/04,ETM,28%    118-59 5/22/03,ETM+,28% 4/20/03/ETM+,27% 12/13/02,ETM+,24% 7/6/02,ETM+,26%      119-57 8/1/03,ETM+,13% 3/20/04,TM,13% 8/11/04,TM,23% 9/4/04,ETM+,1% 6/7/02,ETM+,1% 4/8/02,ETM+,5%    119-58 8/1/03,ETM-off,8% 4/8/02,ETM+,9% 8/11/04,TM,18% 4/11/03,ETM+,30% 9/4/04,ETM-off,27% 7/10/01,ETM-off,0%    119-59 5/29/03,ETM+,29% 5/26/02,ETM+,19% 4/13/04,ETM+,26% 8/19/04,ETM+,3% 4/8/02,ETM+,17% 9/12/01,TM,27% 6/11/05,TM,10%   120-58 11/12/03,ETM-off,4% 4/18/03,ETM+,13% 8/5/02,ETM+,6% 6/2/02,ETM+,5% 5/22/04,ETM+,7% 8/10/04,ETM+,0% 8/26/04,ETM+,5%   120-59 9/25/03,ETM-off,17% 11/12/03,ETM-off,23% 8/21/02,ETM+,15% 7/20/02,ETM+,12% 8/5/02,ETM+,26% 5/22/04,ETM+,16% 8/10/04,ETM+,5%   121-59 11/11/03,TM,20% 4/3/04,TM,8% 6/22/04,TM,1% 6/25/02,ETM+,15% 2/20/03,ETM+,25% 6/30/04,ETM+,4%   2006 118-57 3/11/06,ETM+,19% 4/4/06,TM,16% 6/7/06,TM,12% 7.9.06,TM,22% 9/16/05,ETM-off,15% 7/30/05,ETM-off,9% 6/26/07,TM,7%    118-58 4/20/06,TM,24% 4/4/06,TM,15% 8/2/06,ETM-off,27% 7/30/05,ETM,18% 6/26/07,TM,11% 7/4/07,ETM,8%    118-59 10/5/06,ETM-off,5% 7/20/05,ETM+,30% 6/20/07,TM,28% 7/4/07,ETM,24% 8/5/07,ETM-off,30%     119-57 6/6/06, ETM+,5% 8/25/06/ETM-off,12% 7/3/07,TM,0% 8/28/07,ETM+,7% 7/29/05,TM,0% 6/11/05/TM,0%    119-58 6/6/06,ETM-off,21% 9/15/05,TM,105 8/14/05,TM,15% 6/14/06,TM,19% 7/3/07,TM,9% 8/28/07,ETM-off,15% 7/19/07,TM,18%   119-59 7/8/06,ETM+,19% 9/15/05,TM,9% 8/14/05,TM,8% 6/11/05,TM,10% 7/3/07,TM,8% 8/4/07,TM,11% 5/8/07,ETM+,30%   120-58 6/13/06,ETM-off,6% 12/13/06,TM,19% 3/9/06,ETM-off,15% 4/26/06,ETM-off,27% 5/20/06,TM,19% 6/9/05,TM,18% 4/13/07,ETM+,6% 8/19/07,ETM-off,9%  120-59 8/16/06,ETM+,2% 6/13/06,ETM-off,13% 6/5/06,TM,21% 6/29/06,ETM-off,27% 3/6/05,ETM+,18% 1/17/05,ETM+,8% 9/25/06,TM,1%   121-59 6/4/06, ETM+,12% 6/28/06,TM,5% 6/23/07,ETM+,2% 4/9/06,TM,7% 8/12/05,TM,4% 8/4/05,ETM+,22%   2009 118-57 3/3/09,ETM-off,16% 6/15/09,TM,12% 8/18/09,TM,23% 5/27/08,TM,14% 9/24/08,ETM-off,14% 9/3/09,TM,13% 6/23/09,ETM-off,19%    118-58 9/3/09,TM,9% 3/3/09,ETM-off,10% 9/24/08,ETM,15% 11/6/09,TM,26% 6/26/07,TM,11% 8/29/10,ETM,18%   188  Table A  118-59 8/10/09,ETM+,26% 3/3/09,ETM+,30% 5/19/08,ETM+,9% 10/30/10,TM,30%      119-57 6/609,TM,10% 4/27/09,ETM-off,9% 11/26/08,TM,14% 11/10,08,TM,15% 6/27/08/ETM+,0% 6/11/00,ETM+,4%    119-58 8/1/09,ETM-off,1% 6/6/09,TM,14% 8/9/09,TM,14% 12/23/09,ETM-off,14% 9/10/09,TM,6% 8/12/09,TM,9%    119-59 12/23/09,ETM+,11% 8/1/09,ETM+,8% 9/10/09,TM,18% 2/9/10,ETM+,15% 5/2/08,TM,20% 6/11/08,ETM+,19% 10/9/08,TM,24% 8/12/10,TM,17%  120-58 11/28/09,ETM+,10% 7/31/09,TM,15% 7/23/09,ETM-off,15% 6/13/09,TM,15% 8/27/10,ETM+,20% 10/14/10,ETM+,15%    120-59 8/8/09,ETM+,6% 7/31/09,TM,3% 5/20/09,ETM_off,13% 10/11/09,ETM-off,27% 5/25/08,TM,17%     121-59 7/30/09,ETM+,21% 10/18/09,ETM+,14% 5/14/10,ETM+,8% 1/17/08,ETM+,13% 10/31/08,TM,14% 8/23/09,TM,17%   2012 118-57 6/15/12,ETM-off,8% 8/2/12,ETM-off,11% 8/8/11,TM,17% 8/29/13,OLI,24% 6/21/11,TM,13% 3/27/12,ETM-off,21% 7/15/11,ETM-off,23% 6/15/12,ETM-off,8%  118-58 8/2/12,ETM,10% 6/15/12/ETM,9% 2/24/12,ETM,23%       118-59 6/15/12,ETM+,18% 8/2/12,ETM+,23% 2/24/12,ETM,19%       119-57 8/9/12,ETM,9% 8/15/2011,TM,0% 6/20/11,ETM+,5% 5/5/12,ETM+,18% 1/14/12,ETM+,13% 6/17/13,OLI,14%    119-58 8/9/12,ETM-off,8% 6/9/13,ETM-off,6% 6/17/13,OLI,16% 5/5/12,ETM-off,20% 6/20/11,ETM+,8% 8/9/11,ETM+,24% 8/15/11,TM,29%   119-59 8/9/12,ETM-off,11% 6/1/13,OLI,15% 5/5/12,ETM-off,15% 6/17/13,OLI,19% 7/8/11,ETM+,24% 6/20/11,ETM+,22% 6/9/13/ETM+,17%   120-58 10/3/12,ETM+,0% 9/1/12,ETM-off,13% 6/13/12,ETM-off,16% 6/6/13,OLI,17%      120-59 8/16/12,ETM-off,13% 6/19/11,TM,14% 10/3/12,ETM-off,23% 12/6/12,ETM-off,24% 11/18/11,ETM+,19% 6/16/13,ETM+,8%    121-59 5/19/12,ETM+,26% 4/20/13,ETM+,1% 11/9/11,ETM+,22% 9/30/11,TM,5% 5/22/12,ETM+,9% 6/15/13,ETM+,5%   2014 118-57 9/17/14,OLI,9% 1/23/15/OLI,16% 10/19/14,OLI,19% 4/10/14,OLI,20% 1/28/14,ETM-off,15% 4/26/14,OLI,24%      118-58 9/17/14,OLI,12% 9/9/14,ETM+,28% 4/26/14,OLI,22% 2/5/14,OLI,24% 1/23/15,OLI,24% 10/19/14,OLI,30%    118-59 9/17/14,OLI,12% 4/26/14,OLI,14% 2/5/14,OLI,14%       119-57 5/3/14,OLI,7% 12/13/14,OLI,8% 2/4/14,OLI,11% 11/24/13,OLI,14% 3/16/14,OLI,15% 7/6/14,OLI,18%    119-58 3/16/14,OLI,12% 11/24/13,OLI,13% 8/23/14,OLI,27% 12/13/14,OLI,25% 3/16/14,OLI,12% 13/12/14,OLI,25% 9/24/14,OLI,25%   119-59 3/16/14,OLI,17% 8/4/13,OLI,18% 8/7/14,OLI,29% 10/15/13,ETM-off,30% 3/16/14,OLI,17% 6/9/13,ETM,17% 6/17/13,OLI,17%   120-58 6/27/14,OLI,16% 5/26/14,OLI,17% 5/10/14,OLI,17% 4/24/14,OLI,12% 10/14/13,OLI,14% 5/2/14,OLI,25% 6/16/13,ETM+,8%   120-59 12/4/14,OLI,16% 4/24/14,OLI,15% 6/27/14,OLI,26% 10/9/14,OLI,23% 6/24/13,OLI,16% 6/16/13,ETM+,8%    121-59 5/8/14,OLI,10% 30/9/14,ETM+,11% 7/20/14,OLI,13% 3/30/14,OLI,17% 8/21/14,OLI,21% 9/14/14,ETM-off,14%    Table A.3. The full list of Landsat scenes used for the study in Sabah, Malaysia, 2000-2014. 2000 116-56 LT51160562000128DKI00 LE71160562000312SGS00 LE71160562000344EDC00 LT51160561999317DKI00         116-57 LE71160572000312SGS00 LE71160572001266AGS00 LE71160572001250DKI00 LE71160572001138DKI01 LE71160571999261SGS00 LE71160572001106DKI01 LE71160572000312SGS00   117-55 LT51170552000199DKI00 LT51170552000119DKI00 LT51170552000071DKI00 LT51170551999196DKI00 LE71170552000127SGS00 LE71170551999252SGS00    117-56 LE71170562000191EDC00 LE71170562000127SGS00 LE71170561999252SGS00 LT51170562000135DKI00 LE71170562001241DKI00 LE71170562001177EDC00    117-57 LE71170572000191EDC00 LE71170572000127SGS00 LE71170572001177EDC00 LE71170571999252SGS00 LT51170572000199DKI00 LE71170572001353EDC00 LE71170571999364EDC00   118-55 LT51180552000158DKI00 LT51180551999235DKI00 LE71180552000118EDC00 LE71180552001104SGS00      118-56 LT51180562000190DKI00 LT51180562000014DKI00 LT51180562000158DKI00 LE71180561999275SGS00 LE71180562001248SGS00 LE71180562001152DKI01 LE71180562001200SGS00 LT51180561999251DKI00  118-57 LT51180572000190DKI00 LE71180572001056SGS00 LE71180571999355EDC00 LE71180572001248SGS00 LE71180572001296SGS01 LT51180571998328DKI00   2003 116-56 LE71160562003112BKT01 LE71160562002205BKT00 LE71160562002237EDC00 LE71160562004099EDC02         116-57 LE71160572003112BKT01 LE71160572002365BKT00 LE71160572004099EDC02 LE71160572004051EDC02 LE71160572002269SGS00 LE71160572002205EDC00    117-55 LT51170552003319BKT00 LE71170552003119BKT00 LT51170552004098BKT00 LE71170552003279EDC01 LE71170552004058EDC02 LE71170552003103EDC00    117-56 LE71170562003279EDC01 LE71170562002148EDC00 LT51170562003319BKT00 LT51170562004082BKT00 LT51170562004146BKT00 LT51170562004098BKT00    117-57 LE71170572003023EDC00 LE71170572004010EDC01 LT51170572004146BKT00 LT51170572004178BKT00 LT51170572004306BKT00 LE71170572004218PFS01 LE71170572002340EDC00   118-55 LE71180552003126BKT00 LE71180552002203EDC00 LE71180552002235EDC00      189  Table A  118-56 LE71180562003318DKI00 LE71180562003046SGS00 LE71180562003126DKI00 LE71180562003014EDC00 LE71180562004081EDC02 LE71180562002091DKI00 LE71180562002139SGS00   118-57 LE71180572003142EDC00 LE71180572002139SGS00 LE71180572002235EDC00 LT51180572004169BKT01 LT51180572004185BKT01 LE71180572004193SGS01 LE71180572004081EDC02 LE71180572002091SGS00 2006 116-56 LT51160562006208BKT01 LE71160562006296EDC00 LT51160562005221BKT00 LT51160562005141BKT00         116-57 LT51160572006064BKT01 LE71160572006328EDC LE71160572006296EDC00 LT51160572005221BKT00 LT51160572005141BKT00 LT51160572005061BKT00 LT51160572006064BKT01 LE71160572006296EDC00 LE71160572006328EDC00  117-55 LT51170552006215BKT01 LE71170552006287EDC00 LE71170552006255EDC00 LT51170552007106BKT00 LT51170552007138BKT00 LT51170552007074BKT00    117-56 LT51170562006231BKT00 LT51170562006167BKT00 LT51170562006215BKT01 LE71170562006159EDC00 LT51170562005212BKT00 LT51170562007074BKT00    117-57 LE71170572006159EDC00 LT51170572005260BKT01 LE71170572006319EDC00 LE71170572007066EDC00 LT51170572005164BKT00 LT51170572005020BKT00 LT51170572007074BKT00   118-55 LT51180552006286BKT00 LT51180552006158BKT01 LT51180552006126BKT00       118-56 LT51180562006158BKT01 LT51180562006350BKT00 LT51180562006318BKT00 LT51180562006286BKT00 LT51180562007065BKT00 LE71180562005211EDC00 LE71180562005259EDC00   118-57 LE71180572006070EDC00 LT51180572006190BKT00 LT51180572006158BKT01 LT51180572006094BKT00 LT51180572007177BKT00 LE71180572005211EDC00 LE71180572005259EDC00  2009 116-56 LT51160562009216BKT01 LT51160562009200BKT01 LT51160562009232BKT00           116-57 LT51160572009344BKT00 LT51160572009008BKT00 LT51160572009216BKT01 LE71160572009128EDC00 LE71160572008142EDC00 LT51160572008278BKT00    117-55 LT51170552009255BKT00 LE71170552009087EDC00 LE71170552009327EDC00 LE71170552009295EDC00 LE71170552009231EDC00 LT51170552008285BKT00    117-56 LE71170562009231EDC00 LE71170562009087EDC00 LT51170562009255BKT00 LT51170562009223BKT01 LT51170562010242BKT00 LE71170562009295EDC00    117-57 LE71170572009215EDC00 LT51170572009303BKT00 LT51170572009223BKT01 LE71170572009295EDC00 LT51170572010034BKT00 LT51170572010242BKT00 LE71170572008213EDC00   118-55 LT51180552009134BKT00 LT51180552009310BKT00 LE71180552009222EDC00       118-56 LT51180562009118BKT01 LT51180562009310BKT00 LT51180562009134BKT00 LE71180562009222EDC00 LT51180562009230BKT00 LE71180562009078EDC00 LT51180562008292BKT00   118-57 LE71180572009062EDC00 LT51180572008148BKT00 LT51180572009230BKT00 LT51180572009166BKT01 LT51180572009246BKT00 LE71180572009174EDC00 LE71180572008268EDC00  2012 116-56 LE71160562012265EDC00 LE71160562012233EDC00 LE71160562011246EDC00 LC81160562013115LGN01         116-57 LE71160572012233EDC00 LE71160572011246EDC00 LE71160572012121EDC00 LE71160572012361DKI00 LC81160572013115LGN01 LE71160572011246EDC00 LE71160572012121EDC00 LC81160572013179LGN01 LC81160572013147LG N00  117-55 LE71170552012240EDC00 LE71170552012224EDC00 LE71170552011173EDC00 LE71170552013178EDC00 LE71170552013146EDC00 LC81170552013154LGN00    117-56 LE71170562012240EDC00 LE71170562012224EDC00 LC81170562013170LGN00 LC81170562013154LGN00 LE71170562013178EDC00 LE71170562011173EDC00    117-57 LE71170572012240EDC00 LE71170572011221EDC00 LC81170572013154LGN00 LC81170572013170LGN00 LE71170572011349EDC00 LC81170572013314LGN00 LC81170572013122LGN01   118-55 LE71180552012343EDC00 LE71180552012055EDC00 LE71180552011292EDC00       118-56 LE71180562012215PFS00 LE71180562012055EDC00 LC81180562013113LGN01 LE71180562012359EDC00 LC81180562013241LGN00 LE71180562013057EDC00    118-57 LE71180572012167EDC01 LT51180572011220BKT00 LE71180572012215PFS00 LC81180572013241LGN00 LE71180572012087EDC00 LE71180572011196EDC00 LT51180572011172BKT00  2014 116-56 LE71160562014174EDC00 LE71160562014206EDC00 LC81160562014246LGN00 LC81160562014294LGN00         116-57 LE71160572014110EDC01 LC81160572014342LGN00 LC81160572014262LGN00 LC81160572014182LGN00 LC81160572014134LGN00 LC81160572014102LGN00    117-55 LE71170552014037EDC00 LE71170552014053EDC00 LC81170552014061LGN00 LC81170552014109LGN00 LC81170552014157LGN00 LC81170552014221LGN00 LC81170552014237LGN00   117-56 LC81170562014237LGN00 LC81170562014157LGN00 LC81170562014221LGN00 LE71170562014149EDC00 LC81170562014125LGN00 LC81170562014061LGN00 LC81170562014029LGN00   117-57 LE71170572014069EDC00 LE71170572014053EDC00 LC81170572014029LGN00 LC81170572014253LGN00 LE71170572014293EDC00 LC81170572014157LGN00 LC81170572014365LGN00   118-55 LC81180552014116LGN00 LC81180552014180LGN00 LC81180552014292LGN00       118-56 LC81180562014260LGN00 LC81180562014180LGN00 LC81180562014292LGN00 LC81180562014116LGN00 LC81180562014052LGN00 LC81180562014068LGN00 LC81180562014100LGN00   118-57 LC81180572014260LGN00 LC81180572014100LGN00 LC81180572014292LGN00 LE71180572014124EDC00 LE71180572014028EDC00 LC81180572014116LGN00    Table A.4.  The date, type, and cloud coverage of Landsat scenes used for the study in Sabah, Malaysia, 2000-2014. 2000 116-56 5/7/2000, TM, 29% 11/7/2000, ETM+, 14% 12/7/2000, ETM+, 2%  11/13/1999, TM, 6%         116-57 7/11/00, ETM+,5% 9/18/99, ETM+, 4% 4/16/01, ETM+, 13% 5/18/01, ETM+, 12% 9/7/01, ETM+, 15% 9/23/01, ETM, 13%    117-55 5/6/00, ETM,4% 3/11/00,TM,12% 9/9/99,ETM,6% 7/15/99,TM, 7% 4/28/00,TM, 16% 7/17/00,TM,4%   190  Table A  117-56 7/9/00,ETM+,15% 9/9/99,ETM+,8% 5/14/00,TM,23% 5/6/00,ETM+,18% 6/26/01,ETM+,12% 8/29/01,ETM+,11%    117-57 9/7/00,ETM+,23% 9/9/99,ETM+, 14% 6/26/01,ETM+,10% 5/6/00,ETM,21% 12/30/99,ETM+,15% 12/19/01,ETM+,22% 7/17/00,TM,23%   118-55 6/6/00,TM,15% 4/22/00,ETM+,18% 8/23/99,TM,17% 4/14/01,ETM+,9%      118-56 7/8/00,TM,15% 6/6/00,TM,15% 1/14/00,TM,13% 10/2/99,ETM+, 14% 9/5/01,ETM+,18% 7/19/01,ETM+,24% 6/1/01,ETM+,25%, 9/8/99,TM,21%  118-57 7/8/00,TM,10% 12/21/00,ETM+,14% 2/25/01,ETM+,13% 9/5/01,ETM+,18% 10/23/01,ETM+,21% 11/24/98,TM,15%   2003 116-56 4/22/2003, ETM+, 10% 8/25/2002,ETM+, 9%,  7/24/2002, ETM+, 29% 4/8/2004, ETM+, 10%         116-57 4/22/03, ETM, 25% 7/24/02, ETM+, 27% 9/26/02, ETM+,11% 12/31/02, ETM, 15% 2/20/04, ETM-off,8% 4/8/04,ETM-off,9%    117-55 4/13/03,ETM,8% 4/29/03,ETM,11% 10/6/03,ETM,15% 2/27/04,ETM,0% 4/7/02,TM,4% 11/15/03,TM,22%    117-56 10/6/03,ETM-off,19% 5/28/02,ETM+,12% 11/1503,TM,30% 3/22/04,TM,17% 4/7/04,TM,16% 5/25/01,TM,14%    117-57 1/23/03,ETM+,26% 1/10/04,ETM-off,25% 5/25/04,TM,21% 6/26/04,TM,27% 12/6/02,ETM+,29% 8/5/04,ETM-off,25% 11/1/04,TM,26%   118-55 5/6/03,ETM+, 13% 8/23/02,ETM+,1% 7/22/02,ETM+,2%       118-56 11/14/03,ETM-off,16% 1/14/03,ETM+,15% 5/6/03,ETM+,20% 2/15/03,ETM+,24% 10/26/02,ETM+,13% 5/19/02,ETM+,5% 4/1/02,ETM+,8% 3/21/04,ETM-off,5%  118-57 5/22/03,ETM+,13% 6/17/04,TM,9% 5/19/02,ETM+,8% 8/23/02,ETM+,28% 3/7/04,TM,22% 3/21/04,ETM+,28% 4/1/04,ETM+,ETM+,20% 7/11/04,ETM-off,22% 2006 116-56 7/27/06, ETM+, 29% 10/23/06, ETM+, 9% 8/9/05,TM, 24% 5/21/05,TM,3%         116-57 5/3/06, TM, 3% 10/23/06,ETM-off,5% 11/24/06, ETM-off,11% 3/2/05,TM,8% 5/21/05,TM,5% 8/9/05,TM,8%    117-55 8/3/06,TM,7% 9/12/06,ETM+,14% 3/15/07,TM,7% 5/18/07,TM,5% 4/16/07,TM,3% 10/14/06, ETM+,28%    117-56 8/19/06,TM,10% 8/3/06,TM,18% 6/16/06,TM,27% 6/8/06,ETM+,24% 5/31/05,TM,9% 3/15/07,TM,10%    117-57 6/8/06,ETM-off,19% 3/7/07,ETM+,17% 9/17/05,TM,26% 11/15/06,ETM+,23% 3/15/07,TM,15% 1/20/05,TM,21% 6/13/05,TM,22%   118-55 5/6/06,TM,8% 6/7/06,TM,0% 10/13/06,TM,7%       118-56 6/7/06,TM,5% 10/13/06,TM,15% 11/14/06,TM,11% 12/16/06,TM,12% 5/16/05,ETM-off,5% 7/30/05,ETM-off,7% 6/3/07,TM,8%   118-57 3/11/06,ETM+,19% 4/4/06,TM,16% 6/7/06,TM,12% 7.9.06,TM,22% 9/16/05,ETM-off,15% 7/30/05,ETM-off,9% 6/26/07,TM,7%  2009 116-56 8/20/09,TM,9% 7/19/09, TM,9% 8/4/09, TM, 8%           116-57 5/8/09,ETM-off,4% 8/4/09,TM,10% 1/8/09,TM,17% 10/4/08,TM,10% 8/21/09,ETM-off,4% 12/10/09/TM,4%    117-55 8/19/09,ETM-off,5% 9/12/09,TM,27% 10/22/09,ETM-off,7% 11/23/09,ETM-off,3% 3/28/09,ETM-off,3% 10/11/08,TM,4%    117-56 8/19/09,ETM-off,8% 8/11/09,TM,6% 9/12/09,TM,11% 3/28/09,ETM+,16% 10/22/09,ETM-off,9% 8/30/10,TM,7%    117-57 8/3/09,ETM-off,8% 8/11/09,TM,21% 10/22/09,ETM-off,22% 10/30/09,TM,29% 8/30/10,TM,19% 2/3/10,TM,26% 7/3/08,ETM-off,26%   118-55 8/10/09,ETM-off,0% 11/6/09,TM,2% 5/14/09,TM,21%       118-56 4/28/09,TM,2% 5/14/09,TM,9% 8/10/09,ETM-off,4% 11/6/09,TM,7% 3/19/09,ETM-off,13% 8/10/08,TM,12% 8/18/09,TM,12%   118-57 3/3/09,ETM-off,16% 6/15/09,TM,12% 8/18/09,TM,23% 5/27/08,TM,14% 9/24/08,ETM-off,14% 9/3/09,TM,13% 6/23/09,ETM-off,19%  2012 116-56 8/20/12, ETM-off, 19% 21/9/12, ETM+, 20% 9/3/11, ETM+, 13% 5/24/13/OLI, 15%         116-57 8/20/12,ETM-off,23% 12/26/12,ETM-off,27% 4/30/12,ETM-off,10% 9/3/12,etm-OFF,12% 4/25/13, oli,13% 4/30/12,etm-OFF,16%    117-55 8/11/12,ETM-off,7% 6/22/11,ETM-off,16% 8/27/12,ETM,18% 3/6/13,OLI,18% 5/26/13, ETM,22% 6/27/13,ETM+,30%    117-56 8/27/12,ETM-off,9% 6/3/13,OLI,9% 8/11/12,ETM+,11% 6/19/13,OLI,11% 6/26/11,ETM-off,9% 6/27/13,ETM-off,5%    117-57 8/27/12,ETM-off,23% 8/9/11,ETM,15% 6/3/13,OLI,20% 6/19/13,OLI,20% 12/15/11,ETM-off,22% 5/2/12/OLI,29% 11/10/13,OLI,24%   118-55 2/24/12,ETM+,13% 10/19/11,ETM+,8% 12/8/12,ETM+,4%       118-56 8/2/12,ETM-off,9% 7/1/12,ETM-off,12% 4/23/13,OLI,12% 2/24/12,ETM-off,7% 12/24/12,ETM-off,20% 2/26/13,ETM-off,18% 9/29/13,OLI,18%   118-57 6/15/12,ETM-off,8% 8/2/12,ETM-off,11% 8/8/11,TM,17% 8/29/13,OLI,24% 6/21/11,TM,13% 3/27/12,ETM-off,21% 7/15/11,ETM-off,23%  2014 116-56 6/23/14, ETM-off,0% 7/25/2014, ETM-off,6% 9/3/14, OLI, 20%  10/21/14, OLI, 9%        116-57 4/20/14,ETM,5% 4/12/14,OLI,14% 5/14/14,OLI,12% 7/1/14,OLI,12% 9/19/14,OLI,14% 12/8/14,OLI,17%    117-55 8/25/15,OLI,3% 8/9/14,OLI,7% 6/6/14, OLI,9% 4/19/14, OLI,11% 3/2/14,OLI,9% 2/22/14,ETM+,8% 2/6/14,ETM-off,12%   117-56 8/25/14,OLI,5% 11/29/14,OLI,16% 8/9/14,OLI,11% 6/6/14,OLI,14% 5/29/14,ETM-off,11% 1/29/14,OLI,19% 2/3/14,OLI,19% 5/5/14,OLI,13% 191  Table A  117-57 3/10/14,ETM-off,9% 1/29/14,OLI,9% 2/22/14,ETM-off,11% 9/10/14,OLI,17% 6/6/14,OLI,18% 10/20/14,ETM+,17% 12/31/14,OLI,22%   118-55 6/29/14,OLI,5% 10/19/14,OLI,5% 4/26/14,OLI,5%       118-56 9/17/14,OLI,6% 10/19/14,OLI,10% 6/29/14,OLI,12% 4/26/14,OLI,9% 2/21/14,OLI,12% 4/10/14,OLI,11% 3/9/14,12%   118-57 9/17/14,OLI,9% 1/23/15/OLI,16% 10/19/14,OLI,19% 4/10/14,OLI,20% 1/28/14,ETM-off,15% 4/26/14,OLI,24% 5/4/14,ETM-off,27%  192        Figure A.1. The stacked VI images by type for Sabah, 2000-2014.   Sabah, ARVI, 2000-2014 Sabah, EVI, 2000-2014 Sabah, NDVIaf, 2000-2014 Sabah, MSAVIaf, 2000-2014 Sabah, SARVI, 2000-2014 Sabah, SAVI, 2000-2014 a b c d e f 193        Figure A.2. The stacked VI images by type for Sarawak, 2000-2014.                                                                                         Sarawak, ARVI, 2000-2014 a Sarawak, EVI, 2000-2014 Sarawak, NDVIaf, 2000-2014 Sarawak, MSAVIaf, 2000-2014 Sarawak, SARVI, 2000-2014 Sarawak, SAVI, 2000-2014 b c d e f 194           Figure A.3. The changes of EVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia. a b c d e EVI, 2000-2003 EVI, 2003-2006 EVI, 2006-2009 EVI, 2009-2012 EVI, 2012-2014 SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SABAH SABAH SABAH SABAH SABAH 195           Figure A.4. The changes of MSAVIaf values from 2000 to 2014 in Sabah and Sarawak, Malaysia. a b c d e MSAVIaf, 2000-2003 MSAVIaf, 2003-2006 MSAVIaf, 2006-2009 MSAVIaf, 2009-2012 MSAVIaf, 2012-2014 SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SABAH SABAH SABAH SABAH SABAH 196          Figure A.5. The changes of NDVIaf values from 2000 to 2014 in Sabah and Sarawak, Malaysia. a b c d e NDVIaf, 2000-2003 NDVIaf, 2003-2006 NDVIaf, 2006-2009 NDVIaf, 2009-2012 NDVIaf, 2012-2014 SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SABAH SABAH SABAH SABAH SABAH 197            Figure A.6. The changes of SARVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia. a b c d e SARVI, 2000-2003 SARVI, 2003-2006 SARVI, 2006-2009 SARVI, 2009-2012 SARVI, 2012-2014 SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SABAH SABAH SABAH SABAH SABAH 198            Figure A.7. The changes of SAVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia.  a b c d e SAVI, 2000-2003 SAVI, 2003-2006 SAVI, 2006-2009 SAVI, 2009-2012 SAVI, 2012-2014 SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SABAH SABAH SABAH SABAH SABAH 199            Figure A.8. The changes of ARVI values from 2000 to 2014 in Sabah and Sarawak, Malaysia. a b c d e ARVI, 2000-2003 ARVI, 2003-2006 ARVI, 2006-2009 ARVI, 2009-2012 ARVI, 2012-2014 SABAH SABAH SABAH SABAH SABAH SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK 200        Figure A.9. The clearing and regrowth cycle (rotation) of vegetation cover based on the changes of ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in Sabah and Sarawak, 2000-2014.    a b c d e ARVI, 2000-2014 EVI, 2000-2014  MSAVIaf, 2000-2014 NDVIaf, 2000-2014 SARVI, 2000-2014 SAVI, 2000-2014 f SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SABAH SABAH SABAH SABAH SABAH SABAH 201  Table A.5. The changes of ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in 30 key areas in Sabah, 2000-2014. SABAH   THE VI VALUES IN KEY AREAS/LOCATIONS IN SABAH, 2000-2014  ID VI 2000 2003 2006 2009 2012 2014             1 ARVI 0.887 0.359 0.711 0.799 0.821 0.764 EVI 0.603 0.19 0.575 0.601 0.622 0.61 MSAVIaf 0.929 0.587 0.868 0.857 0.92 0.899 NDVIaf 0.868 0.418 0.768 0.752 0.852 0.817 SAVI 0.538 0.185 0.527 0.497 0.517 0.561 SARVI 0.545 0.039 0.491 0.481 0.554 0.539 2 ARVI 0.858 0.85 0.729 0.844 0.797 0.752 EVI 0.572 0.511 0.488 0.611 0.615 0.483 MSAVIaf 0.931 0.927 0.872 0.906 0.919 0.88 NDVIaf 0.871 0.864 0.776 0.829 0.85 0.788 SAVI 0.526 0.482 0.46 0.533 0.568 0.456 SARVI 0.533 0.487 0.44 0.538 0.563 0.441 3 ARVI 0.873 0.908 0.536 0.926 0.831 0.569 EVI 0.716 0.57 0.38 0.646 0.583 0.476 MSAVIaf 0.936 0.952 0.788 0.913 0.926 0.801 NDVIaf 0.881 0.908 0.652 0.841 0.862 0.763 SAVI 0.63 0.532 0.378 0.535 0.541 0.45 SARVI 0.625 0.535 0.327 0.539 0.537 0.409 4 ARVI 0.816 0.826 0.464 0.905 0.83 0.643 EVI 0.585 0.609 0.348 0.624 0.564 0.599 MSAVIaf 0.924 0.902 0.734 0.929 0.927 0.842 NDVIaf 0.859 0.824 0.587 0.868 0.864 0.732 SAVI 0.545 0.538 0.348 0.545 0.529 0.546 SARVI 0.543 0.533 0.272 0.555 0.526 0.503 5 ARVI 0.835 0.542 0.852 0.856 0.809 0.479 EVI 0.475 0.407 0.508 0.54 0.524 0.404 MSAVIaf 0.912 0.779 0.926 0.915 0.916 0.746 NDVIaf 0.838 0.642 0.863 0.844 0.846 0.6 SAVI 0.449 0.397 0.485 0.495 0.497 0.39 SARVI 0.445 0.328 0.468 0.49 0.488 0.327 6 ARVI 0.373 0.796 0.791 0.764 0.824 0.817 EVI 0.338 0.673 0.614 0.601 0.679 0.618 MSAVIaf 0.65 0.889 0.897 0.896 0.908 0.903 NDVI_af 0.489 0.801 0.815 0.813 0.832 0.825 SAVI 0.314 0.574 0.549 0.551 0.586 0.546 SARVI 0.275 0.564 0.537 0.532 0.587 0.544 7 ARVI 0.871 0.583 0.812 0.831 0.856 0.85 EVI 0.621 0.363 0.666 0.668 0.662 0.626 MSAVIaf 0.908 0.774 0.866 0.914 0.92 0.909 NDVIaf 0.832 0.633 0.768 0.842 0.853 0.833 SAVI 0.535 0.357 0.541 0.585 0.58 0.548 202  Table A.5.   SARVI 0.536 0.239 0.524 0.583 0.578 0.539 8 ARVI 0.273 0.732 0.768 0.81 0.793 0.823 EVI 0.201 0.506 0.56 0.643 0.632 0.606 MSAVIaf 0.566 0.857 0.882 0.914 0.91 0.899 NDVIaf 0.398 0.752 0.791 0.842 0.835 0.818 SAVI 0.205 0.466 0.512 0.584 0.577 0.539 SARVI 0.071 0.42 0.476 0.559 0.556 0.516 9 ARVI 0.536 0.783 0.872 0.837 0.838 0.826 EVI 0.408 0.575 0.556 0.631 0.637 0.631 MSAVIaf 0.772 0.896 0.884 0.915 0.927 0.92 NDVIaf 0.63 0.812 0.793 0.844 0.865 0.852 SAVI 0.393 0.528 0.473 0.563 0.579 0.574 SARVI 0.327 0.499 0.459 0.554 0.572 0.559 10 ARVI 0.27 0.779 0.807 0.819 0.841 0.849 EVI 0.244 0.635 0.595 0.633 0.67 0.636 MSAVIaf 0.574 0.878 0.908 0.914 0.919 0.922 NDVIaf 0.408 0.784 0.833 0.844 0.852 0.856 SAVI 0.244 0.546 0.546 0.571 0.594 0.571 SARVI 0.127 0.53 0.525 0.558 0.581 0.561 11 ARVI 0.955 0.948 0.853 0.9 0.709 0.741 EVI 0.549 0.586 0.463 0.503 0.466 0.645 MSAVIaf 0.951 0.945 0.918 0.934 0.805 0.88 NDVIaf 0.908 0.897 0.849 0.877 0.68 0.789 SAVI 0.508 0.525 0.443 0.474 0.403 0.573 SARVI 0.506 0.534 0.432 0.467 0.367 0.548 12 ARVI 0.911 0.652 0.826 0.74 0.841 0.725 EVI 0.505 0.562 0.618 0.537 0.613 0.599 MSAVIaf 0.937 0.826 0.919 0.871 0.893 0.889 NDVIaf 0.882 0.711 0.85 0.788 0.809 0.803 SAVI 0.476 0.503 0.563 0.5 0.527 0.559 SARVI 0.468 0.46 0.553 0.482 0.516 0.534 13 ARVI 0.827 0.621 0.814 0.772 0.863 0.786 EVI 0.624 0.532 0.623 0.605 0.672 0.631 MSAVIaf 0.932 0.811 0.914 0.905 0.916 0.909 NDVIaf 0.873 0.684 0.843 0.827 0.846 0.834 SAVI 0.565 0.478 0.566 0.559 0.578 0.574 SARVI 0.558 0.432 0.553 0.543 0.578 0.565 14 ARVI 0.658 0.482 0.687 0.77 0.796 0.782 EVI 0.347 0.365 0.529 0.533 0.61 0.571 MSAVIaf 0.777 0.734 0.848 0.897 0.915 0.895 NDVIaf 0.64 0.586 0.738 0.813 0.843 0.811 SAVI 0.321 0.354 0.481 0.502 0.563 0.525 SARVI 0.264 0.288 0.459 0.48 0.551 0.501 15 ARVI 0.628 0.256 0.496 0.496 0.375 0.671 EVI 0.371 0.201 0.353 0.353 0.276 0.388 MSAVIaf 0.822 0.577 0.765 0.765 0.681 0.83 NDVIaf 0.7 0.407 0.622 0.622 0.524 0.711 203  Table A  SAVI 0.368 0.208 0.356 0.356 0.283 0.374 SARVI 0.321 0.131 0.296 0.296 0.224 0.339 16 ARVI 0.617 0.701 0.81 0.752 0.802 0.829 EVI 0.276 0.52 0.546 0.521 0.58 0.55 MSAVIaf 0.759 0.856 0.898 0.873 0.882 0.894 NDVIaf 0.613 0.75 0.816 0.777 0.79 0.81 SAVI 0.275 0.483 0.504 0.482 0.507 0.495 SARVI 0.153 0.441 0.469 0.445 0.495 0.47 17 ARVI 0.895 0.838 0.503 0.627 0.801 0.845 EVI 0.514 0.578 0.372 0.452 0.562 0.588 MSAVIaf 0.92 0.92 0.754 0.82 0.881 0.888 NDVIaf 0.852 0.854 0.609 0.697 0.789 0.8 SAVI 0.47 0.53 0.367 0.432 0.497 0.508 SARVI 0.466 0.529 0.289 0.374 0.479 0.486 18 ARVI 0.422 0.566 0.757 0.777 0.838 0.837 EVI 0.178 0.385 0.484 0.553 0.651 0.643 MSAVIaf 0.642 0.75 0.865 0.896 0.921 0.924 NDVIaf 0.475 0.604 0.765 0.813 0.854 0.86 SAVI 0.181 0.354 0.443 0.514 0.581 0.583 SARVI 0.089 0.282 0.418 0.494 0.578 0.57 19 ARVI 0.791 0.797 0.808 0.813 0.655 0.876 EVI 0.497 0.599 0.582 0.606 0.451 0.694 MSAVIaf 0.888 0.912 0.921 0.915 0.812 0.937 NDVIaf 0.799 0.838 0.854 0.843 0.693 0.881 SAVI 0.463 0.552 0.548 0.556 0.414 0.618 SARVI 0.44 0.539 0.528 0.54 0.383 0.608 20 ARVI 0.924 0.752 0.362 0.83 0.85 0.819 EVI 0.436 0.476 0.265 0.538 0.581 0.583 MSAVIaf 0.896 0.887 0.645 0.917 0.918 0.917 NDVIaf 0.812 0.799 0.495 0.847 0.848 0.847 SAVI 0.392 0.456 0.264 0.505 0.525 0.539 SARVI 0.393 0.438 0.215 0.49 0.525 0.53 21 ARVI 0.418 0.738 0.831 0.788 0.836 0.901 EVI 0.235 0.632 0.634 0.581 0.655 0.679 MSAVIaf 0.691 0.89 0.922 0.907 0.923 0.924 NDVIaf 0.531 0.804 0.856 0.83 0.857 0.858 SAVI 0.253 0.578 0.581 0.543 0.59 0.58 SARVI 0.104 0.549 0.554 0.512 0.576 0.575 22 ARVI 0.819 0.529 0.862 0.902 0.902 0.546 EVI 0.531 0.424 0.642 0.582 0.582 0.381 MSAVIaf 0.917 0.752 0.931 0.919 0.919 0.739 NDVIaf 0.847 0.615 0.872 0.851 0.851 0.602 SAVI 0.5 0.394 0.581 0.512 0.512 0.353 SARVI 0.492 0.339 0.57 0.515 0.515 0.286 23 ARVI 0.621 0.849 0.827 0.853 0.685 0.758 204  Table A  EVI 0.453 0.615 0.597 0.687 0.559 0.609 MSAVIaf 0.82 0.922 0.897 0.931 0.841 0.901 NDVIaf 0.695 0.856 0.804 0.87 0.737 0.819 SAVI 0.431 0.559 0.521 0.544 0.506 0.565 SARVI 0.383 0.543 0.504 0.537 0.469 0.54 24 ARVI 0.81 0.733 0.836 0.865 0.839 0.809 EVI 0.491 0.447 0.579 0.612 0.6 0.522 MSAVIaf 0.92 0.828 0.925 0.933 0.925 0.928 NDVI_swir 0.851 0.71 0.86 0.874 0.861 0.866 SAVI 0.482 0.402 0.543 0.561 0.555 0.51 SARVI 0.451 0.36 0.517 0.551 0.537 0.491 25 ARVI 0.845 0.507 0.815 0.805 0.533 0.728 EVI 0.455 0.286 0.597 0.558 0.375 0.603 MSAVIaf 0.921 0.746 0.896 0.907 0.757 0.901 NDVIaf 0.854 0.599 0.812 0.831 0.623 0.821 SAVI 0.441 0.292 0.529 0.516 0.365 0.573 SARVI 0.428 0.22 0.513 0.504 0.289 0.543 26 ARVI 0.84 0.898 0.858 0.814 0.848 0.678 EVI 0.559 0.566 0.581 0.62 0.665 0.504 MSAVIaf 0.928 0.93 0.896 0.911 0.918 0.828 NDVIaf 0.866 0.87 0.813 0.837 0.848 0.708 SAVI 0.521 0.506 0.5 0.554 0.579 0.454 SARVI 0.525 0.529 0.501 0.559 0.583 0.413 27 ARVI 0.9 0.885 0.899 0.331 0.647 0.804 EVI 0.538 0.549 0.532 0.158 0.491 0.648 MSAVIaf 0.914 0.918 0.929 0.622 0.835 0.888 NDVIaf 0.841 0.849 0.867 0.453 0.719 0.799 SAVI 0.479 0.492 0.488 0.173 0.46 0.554 SARVI 0.48 0.497 0.49 0.04 0.417 0.545 28 ARVI 0.842 0.838 0.535 0.698 0.766 0.77 EVI 0.511 0.502 0.257 0.507 0.561 0.587 MSAVIaf 0.916 0.927 0.787 0.863 0.884 0.907 NDVIaf 0.846 0.863 0.654 0.762 0.793 0.83 SAVI 0.479 0.477 0.267 0.48 0.508 0.549 SARVI 0.467 0.48 0.225 0.444 0.494 0.531 29 ARVI 0.867 0.867 0.379 0.83 0.794 0.782 EVI 0.637 0.637 0.319 0.699 0.633 0.673 MSAVIaf 0.927 0.927 0.674 0.898 0.921 0.916 NDVIaf 0.864 0.864 0.51 0.815 0.853 0.846 SAVI 0.564 0.564 0.317 0.585 0.586 0.615 SARVI 0.527 0.572 0.222 0.582 0.576 0.601 30 ARVI 0.555 0.721 0.649 0.713 0.818 0.799 EVI 0.262 0.47 0.444 0.495 0.574 0.604 MSAVIaf 0.736 0.841 0.822 0.864 0.904 0.913 NDVIaf 0.586 0.729 0.701 0.763 0.826 0.84 SAVI 0.259 0.427 0.418 0.466 0.519 0.558 SARVI 0.185 0.402 0.378 0.436 0.508 0.541    205        Figure A.10. Possibly shorter- and longer-rotation plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI in Sabah, 2000-2014. a b c d e ARVI, 2000-2014 EVI, 2000-2014  MSAVIaf, 2000-2014 NDVIaf, 2000-2014 SARVI, 2000-2014 SAVI, 2000-2014 f    206        Figure A.11. Possibly shorter- and longer-rotation plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI in Sarawak, 2000-2014. b c d e ARVI, 2000-2014 EVI, 2000-2014  MSAVIaf, 2000-2014 NDVIaf, 2000-2014 SARVI, 2000-2014 SAVI, 2000-2014 f    a 207  Table A.6. The result for calculating the growth rate of VIs in Sabah, 2000-2014. ID VI 2000-03 2003-06 2006-09 2009-12 2012-14 ID VI 2000-03 2003-06 2006-09 2009-12 2012-14 The growth rate of different vegetation represented though the rate of VI changes The growth rate of different vegetation represented though the rate of VI changes 1 ARVI -0.60 0.98 0.12 0.03 -0.07 16 ARVI 0.14 0.16 -0.07 0.07 0.03 EVI -0.68 2.03 0.05 0.03 -0.02 EVI 0.88 0.05 -0.05 0.11 -0.05 MSAVIaf -0.37 0.48 -0.01 0.07 -0.02 MSAVIaf 0.13 0.05 -0.03 0.01 0.01 NDVIaf -0.52 0.84 -0.02 0.13 -0.04 NDVIaf 0.22 0.09 -0.05 0.02 0.03 SAVI -0.66 1.85 -0.06 0.04 0.09 SAVI 0.76 0.04 -0.04 0.05 -0.02 SARVI -0.93 11.59 -0.02 0.15 -0.03 SARVI 1.88 0.06 -0.05 0.11 -0.05 2 ARVI -0.01 -0.14 0.16 -0.06 -0.06 17 ARVI -0.06 -0.40 0.25 0.28 0.05 EVI -0.11 -0.05 0.25 0.01 -0.21 EVI 0.12 -0.36 0.22 0.24 0.05 MSAVIaf 0.00 -0.06 0.04 0.01 -0.04 MSAVIaf 0.00 -0.18 0.09 0.07 0.01 NDVIaf -0.01 -0.10 0.07 0.03 -0.07 NDVIaf 0.00 -0.29 0.14 0.13 0.01 SAVI -0.08 -0.05 0.16 0.07 -0.20 SAVI 0.13 -0.31 0.18 0.15 0.02 SARVI -0.09 -0.10 0.22 0.05 -0.22 SARVI 0.14 -0.45 0.29 0.28 0.01 3 ARVI 0.04 -0.41 0.73 -0.10 -0.32 18 ARVI 0.34 0.34 0.03 0.08 0.00 EVI -0.20 -0.33 0.70 -0.10 -0.18 EVI 1.16 0.26 0.14 0.18 -0.01 MSAVIaf 0.02 -0.17 0.16 0.01 -0.13 MSAVIaf 0.17 0.15 0.04 0.03 0.00 NDVIaf 0.03 -0.28 0.29 0.02 -0.11 NDVIaf 0.27 0.27 0.06 0.05 0.01 SAVI -0.16 -0.29 0.42 0.01 -0.17 SAVI 0.96 0.25 0.16 0.13 0.00 SARVI -0.14 -0.39 0.65 0.00 -0.24 SARVI 2.17 0.48 0.18 0.17 -0.01 4 ARVI 0.01 -0.44 0.95 -0.08 -0.23 19 ARVI 0.01 0.01 0.01 -0.19 0.34 EVI 0.04 -0.43 0.79 -0.10 0.06 EVI 0.21 -0.03 0.04 -0.26 0.54 MSAVIaf -0.02 -0.19 0.27 0.00 -0.09 MSAVIaf 0.03 0.01 -0.01 -0.11 0.15 NDVIaf -0.04 -0.29 0.48 0.00 -0.15 NDVIaf 0.05 0.02 -0.01 -0.18 0.27 SAVI -0.01 -0.35 0.57 -0.03 0.03 SAVI 0.19 -0.01 0.01 -0.26 0.49 SARVI -0.02 -0.49 1.04 -0.05 -0.04 SARVI 0.23 -0.02 0.02 -0.29 0.59 5 ARVI -0.35 0.57 0.00 -0.05 -0.41 20 ARVI -0.19 -0.52 1.29 0.02 -0.04 EVI -0.14 0.25 0.06 -0.03 -0.23 EVI 0.09 -0.44 1.03 0.08 0.00 MSAVIaf -0.15 0.19 -0.01 0.00 -0.19 MSAVIaf -0.01 -0.27 0.42 0.00 0.00 NDVIaf -0.23 0.34 -0.02 0.00 -0.29 NDVIaf -0.02 -0.38 0.71 0.00 0.00 SAVI -0.12 0.22 0.02 0.00 -0.22 SAVI 0.16 -0.42 0.91 0.04 0.03 SARVI -0.26 0.43 0.05 0.00 -0.33 SARVI 0.11 -0.51 1.28 0.07 0.01 6 ARVI 1.13 -0.01 -0.03 0.08 -0.01 21 ARVI 0.77 0.13 -0.05 0.06 0.08 EVI 0.99 -0.09 -0.02 0.13 -0.09 EVI 1.69 0.00 -0.08 0.13 0.04 MSAVIaf 0.37 0.01 0.00 0.01 -0.01 MSAVIaf 0.29 0.04 -0.02 0.02 0.00 NDVIaf 0.64 0.02 0.00 0.02 -0.01 NDVIaf 0.51 0.06 -0.03 0.03 0.00 SAVI 0.83 -0.04 0.00 0.06 -0.07 SAVI 1.28 0.01 -0.07 0.09 -0.02 SARVI 1.05 -0.05 -0.01 0.10 -0.07 SARVI 4.28 0.01 -0.08 0.13 0.00 7 ARVI -0.33 0.39 0.02 0.03 -0.01 22 ARVI -0.35 0.63 0.05 0.00 -0.39 EVI -0.42 0.83 0.00 -0.01 -0.05 EVI -0.20 0.51 -0.09 0.00 -0.35 MSAVIaf -0.15 0.12 0.06 0.01 -0.01 MSAVIaf -0.18 0.24 -0.01 0.00 -0.20 NDVIaf -0.24 0.21 0.10 0.01 -0.02 NDVIaf -0.27 0.42 -0.02 0.00 -0.29 SAVI -0.33 0.52 0.08 -0.01 -0.06 SAVI -0.21 0.47 -0.12 0.00 -0.31 SARVI -0.55 1.19 0.11 -0.01 -0.07 SARVI -0.31 0.68 -0.10 0.00 -0.44 8 ARVI 1.68 0.05 0.05 -0.02 0.04 23 ARVI 0.37 -0.03 0.03 -0.20 0.11 EVI 1.52 0.11 0.15 -0.02 -0.04 EVI 0.36 -0.03 0.15 -0.19 0.09 MSAVIaf 0.51 0.03 0.04 0.00 -0.01 MSAVIaf 0.12 -0.03 0.04 -0.10 0.07 NDVIaf 0.89 0.05 0.06 -0.01 -0.02 NDVIaf 0.23 -0.06 0.08 -0.15 0.11 208  Table A        SAVI 1.27 0.10 0.14 -0.01 -0.07 SAVI 0.30 -0.07 0.04 -0.07 0.12 SARVI 4.92 0.13 0.17 -0.01 -0.07 SARVI 0.42 -0.07 0.07 -0.13 0.15 9 ARVI 0.46 0.11 -0.04 0.00 -0.01 24 ARVI -0.10 0.14 0.03 -0.03 -0.04 EVI 0.41 -0.03 0.13 0.01 -0.01 EVI -0.09 0.30 0.06 -0.02 -0.13 MSAVIaf 0.16 -0.01 0.04 0.01 -0.01 MSAVIaf -0.10 0.12 0.01 -0.01 0.00 NDVIaf 0.29 -0.02 0.06 0.02 -0.02 NDVIaf -0.17 0.21 0.02 -0.01 0.01 SAVI 0.34 -0.10 0.19 0.03 -0.01 SAVI -0.17 0.35 0.03 -0.01 -0.08 SARVI 0.53 -0.08 0.21 0.03 -0.02 SARVI -0.20 0.44 0.07 -0.03 -0.09 10 ARVI 1.89 0.04 0.01 0.03 0.01 25 ARVI -0.40 0.61 -0.01 -0.34 0.37 EVI 1.60 -0.06 0.06 0.06 -0.05 EVI -0.37 1.09 -0.07 -0.33 0.61 MSAVIaf 0.53 0.03 0.01 0.01 0.00 MSAVIaf -0.19 0.20 0.01 -0.17 0.19 NDVIaf 0.92 0.06 0.01 0.01 0.00 NDVIaf -0.30 0.36 0.02 -0.25 0.32 SAVI 1.24 0.00 0.05 0.04 -0.04 SAVI -0.34 0.81 -0.02 -0.29 0.57 SARVI 3.17 -0.01 0.06 0.04 -0.03 SARVI -0.49 1.33 -0.02 -0.43 0.88 11 ARVI -0.01 -0.10 0.06 -0.21 0.05 26 ARVI 0.07 -0.04 -0.05 0.04 -0.20 EVI 0.07 -0.21 0.09 -0.07 0.38 EVI 0.01 0.03 0.07 0.07 -0.24 MSAVIaf -0.01 -0.03 0.02 -0.14 0.09 MSAVIaf 0.00 -0.04 0.02 0.01 -0.10 NDVIaf -0.01 -0.05 0.03 -0.22 0.16 NDVIaf 0.00 -0.07 0.03 0.01 -0.17 SAVI 0.03 -0.16 0.07 -0.15 0.42 SAVI -0.03 -0.01 0.11 0.05 -0.22 SARVI 0.06 -0.19 0.08 -0.21 0.49 SARVI 0.01 -0.05 0.12 0.04 -0.29 12 ARVI -0.28 0.27 -0.10 0.14 -0.14 27 ARVI -0.02 0.02 -0.63 0.95 0.24 EVI 0.11 0.10 -0.13 0.14 -0.02 EVI 0.02 -0.03 -0.70 2.11 0.32 MSAVIaf -0.12 0.11 -0.05 0.03 0.00 MSAVIaf 0.00 0.01 -0.33 0.34 0.06 NDVIaf -0.19 0.20 -0.07 0.03 -0.01 NDVIaf 0.01 0.02 -0.48 0.59 0.11 SAVI 0.06 0.12 -0.11 0.05 0.06 SAVI 0.03 -0.01 -0.65 1.66 0.20 SARVI -0.02 0.20 -0.13 0.07 0.03 SARVI 0.04 -0.01 -0.92 9.43 0.31 13 ARVI -0.25 0.31 -0.05 0.12 -0.09 28 ARVI 0.00 -0.36 0.30 0.10 0.01 EVI -0.15 0.17 -0.03 0.11 -0.06 EVI -0.02 -0.49 0.97 0.11 0.05 MSAVIaf -0.13 0.13 -0.01 0.01 -0.01 MSAVIaf 0.01 -0.15 0.10 0.02 0.03 NDVIaf -0.22 0.23 -0.02 0.02 -0.01 NDVIaf 0.02 -0.24 0.17 0.04 0.05 SAVI -0.15 0.18 -0.01 0.03 -0.01 SAVI 0.00 -0.44 0.80 0.06 0.08 SARVI -0.23 0.28 -0.02 0.06 -0.02 SARVI 0.03 -0.53 0.97 0.11 0.07 14 ARVI -0.27 0.43 0.12 0.03 -0.02 29 ARVI 0.00 -0.56 1.19 -0.04 -0.02 EVI 0.05 0.45 0.01 0.14 -0.06 EVI 0.00 -0.50 1.19 -0.09 0.06 MSAVIaf -0.06 0.16 0.06 0.02 -0.02 MSAVIaf 0.00 -0.27 0.33 0.03 -0.01 NDVIaf -0.08 0.26 0.10 0.04 -0.04 NDVIaf 0.00 -0.41 0.60 0.05 -0.01 SAVI 0.10 0.36 0.04 0.12 -0.07 SAVI 0.00 -0.44 0.85 0.00 0.05 SARVI 0.09 0.59 0.05 0.15 -0.09 SARVI 0.09 -0.61 1.62 -0.01 0.04 15 ARVI -0.59 0.94 0.00 -0.24 0.79 30 ARVI 0.30 -0.10 0.10 0.15 -0.02 EVI -0.46 0.76 0.00 -0.22 0.41 EVI 0.79 -0.06 0.11 0.16 0.05 MSAVIaf -0.30 0.33 0.00 -0.11 0.22 MSAVIaf 0.14 -0.02 0.05 0.05 0.01 NDVIaf -0.42 0.53 0.00 -0.16 0.36 NDVIaf 0.24 -0.04 0.09 0.08 0.02 SAVI -0.43 0.71 0.00 -0.21 0.32 SAVI 0.65 -0.02 0.11 0.11 0.08 SARVI -0.59 1.26 0.00 -0.24 0.51 SARVI 1.17 -0.06 0.15 0.17 0.06      209        Figure A.12. The possibly faster-growing and slower-growing plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in Sabah, 2000-2014. a b c d e ARVI, 2000-2014 EVI, 2000-2014  MSAVIaf, 2000-2014 NDVIaf, 2000-2014 SARVI, 2000-2014 SAVI, 2000-2014 f    210        Figure A.13. The possibly faster-growing and slower-growing plantations based on ARVI, EVI, MSAVIaf, NDVIaf, SARVI, and SAVI values in Sarawak, 2000- 2014. a b c d e ARVI, 2000-2014 EVI, 2000-2014  MSAVIaf, 2000-2014 NDVIaf, 2000-2014 SARVI, 2000-2014 SAVI, 2000-2014 f    211        Figure A.14. Possibly faster-growing, shorter-rotation and slower-growing, longer-rotation plantations based on VIs values in Sabah, 2000-2014. a b c d e ARVI, 2000-2014 EVI, 2000-2014  MSAVIaf, 2000-2014 NDVIaf, 2000-2014 SARVI, 2000-2014 SAVI, 2000-2014 f    212         Figure A.15. Possibly faster-growing, shorter-rotation and slower-growing, longer-rotation plantations based on VIs values in Sarawak, 2000-2014. a b c d e ARVI, 2000-2014 EVI, 2000-2014  MSAVIaf, 2000-2014 NDVIaf, 2000-2014 SARVI, 2000-2014 SAVI, 2000-2014 f    213  Table A.7. The GLCM_MEA, DIS, and HOM values for different LULC types in VIs, band 4 and 5 images in Sarawak, 2000-2014.   TYPE Value SARAWAK_GLCM_MEAN_ARVI SARAWAK_GLCM_HOM_ARVI SARAWAK_GLCM_DIS_ARVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 240 235 238 233 214 236 152 138 142 171 140 122 61 82 89 51 70 96 Mode 249-251 248-252 250-255 247-250 227-233 253-255 188-195 149-167 179-189 190-219 179-207 199-221 26-32 33-49 35-42 17-38 21-38 20-40 Acacia Mean 166 195 220 219 245 140 29 66 110 173 88 43 205 156 97 47 134 177 Mode 177-181 197-228 220-233 221-231 249-255 210-242 0-27 0-63 113-136 180-198 100-119 0-109 197-201 79-112 50-75 22-41 51-128 151-218 Other IFs Mean 223 246 241 233 248 239 222 187 211 157 117 180 99 41 29 69 81 45 Mode 223-228 253-255 243-246 241-245 252-255 244-249 232-237 217-223 200-213 200-219 113-143 201-223 16-21 23-27 30-39 25-38 41-52 14-26 Oil Palm Mean 223 248 243 234 223 215 201 160 179 159 100 112 30 55 39 58 94 83 Mode 215-239 230-255 239-249 227-225 220-255 241-245 168-255 106-221 167-255 114-246 22-186 50-189 0-61 18-94 10-75 9-126 34-116 57-63 Forest Mean 250 216 231 224 221 234 174 169 193 160 167 201 40 43 37 55 37 26 Mode 247-255 210-227 222-239 199-249 214-230 235-238 129-227 116-236 146-252 109-241 125-216 160-255 6--72 24-66 15-62 10-109 22-61 16-28  TYPE Value SARAWAK_GLCM_MEAN_EVI SARAWAK_GLCM_HOM_EVI SARAWAK_GLCM_DIS_EVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 249 243 239 240 229 238 148 106 117 133 113 87 86 129 114 90 112 126 Mode 253-255 253-255 247-254 241-248 232-247 250-252 181-189 101-133 135-169 131-166 113-149 89-109 51-58 68-93 50-85 58-79 55-93 77-88 Acacia Mean 141 189 169 173 204 124 74 67 112 171 140 106 166 163 118 63 93 128 Mode 143-157 167-183 155-183 163-203 215-237 150-179 76-83 51-81 88-109 161-198 130-157 80-133 106-111 147-159 111-133 33-59 61-93 83-128 Other IFs Mean 218 242 237 242 208 202 139 167 156 112 123 111 82 62 72 107 100 95 Mode 211-213 247-251 223-231 244-252 201-216 202-209 167-171 171-199 162-178 121-149 140-169 104-121 57-61 48-70 51-66 73-99 73-97 74-96 Oil Palm Mean 247 254 254 250 225 200 198 206 200 165 159 183 45 41 45 67 85 54 Mode 228-255 241-255 249-255 224-255 222-255 160-255 150-255 186-255 164-244 99-247 130-225   6--80 6--76 7--86 21-117 34-91 6--68 Forest Mean 184 189 176 189 169 186 160 166 141 98 152 155 67 64 83 121 73 59 Mode 166-207 161-221 155-211 156-233 155-187 154-212 80-239 105-234 80-218 16-179 92-234 95-223 24-117 19-121 37-132 63-192 39-117 27-90  TYPE Value SARAWAK_GLCM_MEAN_MSAVIaf SARAWAK_GLCM_HOM_MSAVIaf SARAWAK_GLCM_DIS_MSAVIaf 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 228 234 232 239 238 231 146 144 149 155 143 119 88 82 91 69 83 91 Mode 241-247 245-250 247-251 253-254 253-255 247-251 173-189 137-177 203-211 201-208 191-198 142-175 25-36 21-51 45-49 27-33 35-39 31-54 Acacia Mean 197 233 246 252 251 164 189 109 135 178 148 55 184 106 88 47 73 175 Mode 221-227 229-243 241-244 253-255 253-255 247-254 0-47 59-131 121-163 188-195 164-186 0-149 119-123 21-73 62-78 31-41 44-53 49-89 Other IFs Mean 234 246 253 250 254 246 208 212 214 163 148 162 34 33 42 63 62 52 Mode 233-235 245-247 251-153 254-255 252-254 250-253 197-202 200-205 223-233 208-213 152-169 168-199 34-37 35-41 31-37 19-27 47-58 30-47 Oil Palm Mean 232 243 248 248 230 222 208 196 194 184 135 147 35 43 52 42 85 68 Mode 218-250 239-252 244-255 242-255 239-251 231-255 188-244 162-255 184-236 138-251 97-213 107-229 16-52 16-75 30-72 11-71 30-110 46-53 Forest Mean 254 253 253 250 254 253 207 188 195 180 193 209 35 43 49 45 39 27 Mode 250-255 250-255 248-255 246-255 253-255 251-255 169-255 148-236 157-243 144-237 149-236 167-255 12--63 27-62 35-66 17-62 26-56 2-56   214  Table A  TYPE Value SARAWAK_GLCM_MEAN_NDVIaf SARAWAK_GLCM_HOM_NDVIaf SARAWAK_GLCM_DIS_NDVIaf 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 223 237 235 236 238 235 153 142 144 149 139 123 86 92 106 84 97 97 Mode 240-242 250-253 251-254 251-253 249-252 247-250 227-232 203-210 182-191 199-209 179-189 139-184 26-38 41-49 63-75 39-47 54-63 30-67 Acacia Mean 197 237 244 250 246 164 39 102 123 180 166 62 182 120 114 59 79 174 Mode 211-217 221-241 246-249 248-252 248-253 241-251 0-38 73-131 158-188 178-204 195-213 26-147 161-165 53-87 72-80 45-51 46-58 93-148 Other IFs Mean 234 249 252 246 252 247 189 201 200 165 150 168 48 46 60 74 77 55 Mode 233-235 252-254 252-254 251-252 253-254 251-254 187-192 197-211 210-219 210-227 157-178 179-191 43-46 39-51 46-62 28-38 51-67 37-45 Oil Palm Mean 233 247 248 238 229 232 201 164 199 183 136 116 42 68 219 55 98 94 Mode 218-251 235-255 244-253 233-250 235-251 220-255 146-254 130-221 144-255 143-255 99-213 81-178 12--73 39-92 35-97 19-83 46-120 34-129 Forest Mean 254 250 254 248 254 253 200 188 180 172 183 199 43 53 70 64 57 36 Mode 248-255 242-255 248-255 225-255 247-255 246-255 132-255 132-246 136-238 126-244 133-241 165-255 16-78 27-80 47-95 29-83 32-84 10-56  TYPE Value SARAWAK_GLCM_MEAN_SARVI SARAWAK_GLCM_HOM_SARVI SARAWAK_GLCM_DIS_SARVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 249 244 241 242 232 242 171 121 134 152 137 99 70 110 103 77 97 114 Mode 254-255 254-255 241-248 249-254 241-249 250-253 204-211 151-161 140-159 160-227 171-196 68-179 36 -39 59-79 59-84 40-88 48-74 32-89 Acacia Mean 154 199 188 192 217 131 54 66 125 175 157 86 181 162 102 60 80 147 Mode 147-153 187-209 187-205 182-219 218-239 170-225 47-62 47-112 125-150 191-215 194-219 80-129 136-141 171-196 79-93 30-52 40-62 103-132 Other IFs Mean 230 247 241 241 225 217 173 184 184 134 150 128 58 54 55 93 81 80 Mode 223-226 252-254 244-249 245-252 230-240 211-229 183-190 170-212 190-219 131-178 169-190 117-135 41-45 53-87 37-50 22-67 52-81 57-76 Oil Palm Mean 249 254 254 247 230 208 206 199 215 175 149 167 41 47 40 61 91 65 Mode 233-255 249-255 251-255 233-255 230-255 186-255 158-255 142-255 180-255 133-250 189-240 114-250 9--68 11--83 6--73 11-103 32-119 10-101 Forest Mean 209 199 199 205 233 205 176 180 160 108 169 170 55 55 69 105 63 48 Mode 185-244 180-223 117-229 169-245 172-213 179-224 124-243 124-240 94-222 37-189 117-237 118-235 22-103 27-94 41-101 40-163 31-96 20-84  TYPE Value SARAWAK_GLCM_MEAN_SAVI SARAWAK_GLCM_HOM_SAVI SARAWAK_GLCM_DIS_SAVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 247 243 241 242 241 243 177 113 131 147 120 97 74 132 112 87 103 122 Mode 248-250 254-255 250-254 251-254 253-255 253-255 183-195 161-171 141-179 134-205 118-145 110-149 42-49 69-88 61-107 45-94 73-94 82-114 Acacia Mean 153 201 176 181 201 125 69 69 113 169 160 108 177 168 122 70 72 132 Mode 165-171 199-207 188-200 169-201 190-214 161-193 66-86 50-81 73-103 150-181 142-189 72-129 105-130 159-187 110-136 52-86 56-78 91-128 Other IFs Mean 232 244 231 232 219 212 152 172 174 135 127 117 83 76 64 94 97 97 Mode 211-239 231-237 231-239 237-241 220-233 212-229 73-138 161-197 168-193 144-175 139-158 105-133 45-89 67-93 41-62 60-88 66-89 69-111 Oil Palm Mean 252 254 250 237 236 208 209 208 223 184 160 180 47 51 35 63   62 Mode 241-255 246-255 244-255 230-255 236-255 178-255 165-255 155-255 186-255 150-255 113-241 145-255 21-62 20-76 5--71 21-91 23-96 9-101 Forest Mean 198 186 191 198 195 199 165 171 141 88 138 159 75 72 87 132 80 64 Mode 172-238 163-214 163-223 157-245 172-218 172-221 95-233 124-227 75-212 21-179 76-201 93-224 36-123 36-115 42-133 62-212 42-118 32-102 215  Table A  TYPE Value GLCM_MEAN_Band 4 GLCM_HOM_Band 4 GLCM_DIS_Band 4 GLCM_MEAN_Band 5 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 253.4 253 252.6 252.6 254 252 167.7 92 120.6 127 109 92 83 164 132 113 135 149 254 230 221 219 251 221 Mode 249-255 245-255 249-255 249-255 251-255 249-255 96-255 0-173 45-223 63-200 66-174 36-169 22-154 83-234 57-210 62-160 85-175 62-222 251-255 203-255 197-242 195-227 248-255 195-255 Acacia Mean 239.8 245 240 239 240 233 98.5 88.5 124 161.6 173 154 152 166 130 84 79 92 251 171 166 140 243 197 Mode 237-245 241-255 233-252 235-242 235-244 219-239 39-187 0-208 38-235 128-234 144-255 88-222 70-202 82-213 38-130 35-150 13-135 26-138 247-255 135-216 137-207 128-132 240-247 110-222 Other IFs Mean 249.8 251 250 250 244 242 119 164.7 166 129 108 101 126.7 88 85.5 111 138 135 253 200 195 191 246 159 Mode 246-255 248-255 246-255 248-255 242-251 239-248 38-218 111-242 109-235 61-196 68-176 36-160 58-206 46-124 43-134 69-170 80-166 79-181 252-255 161-236 194-201 170-218 244-250 128-191 Oil palm Mean 254.8 254.3 254.5 252.6 253 249 216 232 235 184 197 212 39 26 23 67 58 39 254 244 201 208 250 192 Mode 252-255 253-255 253-255 243-255 251-255 231-255   181-255 196-255 141-255 150-255 158 0-76 0-89 0-74 18-97 0-91 0-70 253-255   174-206 212-225 249-253 110-121, 190-222 Forest Mean 240.7 243 241 242 241 239 170 172 140 79 141 158 82 81 106.6 162 104 80 245 138 173 135 244 129 Mode 237-248 238-249 239-247 237-250 238-247 235-245 139-245 122-236 83-216 22-170 108-192 115-216 38-122 42-129 53-152 96-220 68-132 43-120 245-248 136-139 157-194 128-137 240-247 110-152   Table A.8. The GLCM_MEA, DIS, and HOM values for different LULC types in VIs, band 4 and 5 images in Sabah, 2000-2014. TYPE Value SABAH_GLCM_MEAN_MSAVIaf SABAH_GLCM_HOM_MSAVIaf SABAH_GLCM_DIS_MSAVIaf 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 237 241 232 227 238 230 162 178 127 127 149 138 79 65 93 78 80 82 Mode 237-243 240-249 230-253 230-238 237-242 231-255 164-188 168-235 129-193 129-153 162-186 139-206 52-85 29-65 44-81 36-107 40-85 18-108 Acacia Mean 254 254 245 255 254 113 178 190 153 228 132 2 67 50 63 26 106 248 Mode 242-244 248-255 248-253 255 250-255 93-170 162-206 212-252 136-183 226-255 157-231 0-71 43-73 21-61 45-81 12--44 27-75 109-255 Other IFs Mean 254 254 250 250 246 247 165 186 183 187 152 190 73 53 53 59 82 46 Mode 251-255 217-255 248-255 252-255 249-255 143-255 150-197 158-246 170-239 181-246 159-185 178-255 58-76 23-93 34-50 21-58 35-70 3-48 Oil palm Mean 245 245 248 246 245 247 172 183 165 184 150 158 69 52 64 52 65 58 Mode 244-254 246-252 240-255 247-255 239-254 139-255 138-243 146-243 173-255 163-255 113-188 96-231 40-85 31-76 4-90 17-77 38-94 Jan-95 Forest Mean 250 252 253 254 254 245 186 209 176 192 178 182 61 40 55 47 53 47 Mode 250-255 251-255 251-255 252-255 250-255 247-255 160-218 187-245 152-225 161-227 146-226 215-255 51-72 20-60 19-50 26-74 29-77 1-42      216  Table A  TYPE Value SABAH_GLCM_MEAN_NDVIaf SABAH_GLCM_HOM_NDVIaf SABAH_GLCM_DIS_NDVIaf 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Rubber Mean 236 240 232 225 238 231 157 176 127 147 138 135 89 80 102 85 92 92 Mode 237-242 249-250 234 228-235 238-241 240-244 159-179 200-226 158-168 158-171 133-163 176-186 71-93 46-68 78-80 64-75 68-77 49-65 Acacia Mean 254 254 245 255 206 111 159 174 164 218 135 3 84 74 71 34 105 246                    Mode 251-254 254-255 248--250 255 221-232 120-160 171-185 167-176 188-203 205-215 204-231 1--4 70-91 66-77 50-63 23-47 29-51 240-250 Other IFs Mean 254 252 249 248 248 249 145 168 180 172 137 179 93 82 62 75 99 63 Mode 253-255 249-250 251-253 247-255 254-255 253-254 160-181 186-209 214-230 190-208 147-165 188-196 74-90 55-72 32-46 46-59 67-77 41-49 Oil palm Mean 245 244 246 246 245 245 169 198 154 180 175 147 77 54 82 61 59 73 Mode 248-252 245-250 254-255 250-255 247-251 247-251 139-215 234-253 163-206 193-209 148-166 135-185 43-98 32-59 51-72 39-59 48-80 52-79 Forest Mean 251 251 253 254 253 253 199 189 162 162 160 171 62 64 72 67 68 65 Mode 252-255 251-254 254-255 252-255 253-255 253-255 192-224 181-215 158-197 155-179 132-156 193-244 48-74 50-72 55-78 59-78 62-86 20-52  TYPE Value SABAH_GLCM_MEAN_ARVI SABAH_GLCM_HOM_ARVI SABAH_GLCM_DIS_ARVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Acacia Mean 236 248 228 238 165 106 184 182 166 199 164 8 37 35 46 30 67 220 Mode 238-242 252-255 225-236 233-243 160-241 110-205 222-228 203-215 145-170 181-213 225-254 1--106 21-36 15-40 35-55 11--35 7--49 190-235 Forest Mean 238 243 237 240 237 222 157 169 145 134 123 190 48 33 61 66 74 51 Mode 235-247 236-255 236-247 231-251 237-255 228-240 125-202 129-202 145-192 70-194 103-123 186-255 27-64 15-55 39-50 32-112 59-81 2--52 Oil palm Mean 248 246 244 240 232 244 129 170 157 159 142 120 91 33 64 56 65 84 Mode 248-255 245-253 245-255 245-254 228-242 245-255 104-180 140-218 188-211 158-189 109-171 88-167 25-108 20-43 27-45 25-61 38-82 46-98 Rubber Mean 221 241 229 214 239 227 178 188 139 162 115 144 43 33 82 60 81 84 Mode 221-224 235-251 241-247 212-227 235-245 240-252 181-204 201-222 195-212 161-183 118-137 161-189 29-39 10--15 27-41 31-43 60-73 18-49 Other IFs Mean 234 248 246 229 242 233 165 166 207 186 111 187 49 36 33 51 94 53 Mode 240-243 245-253 246-252 231-241 241-249 227-248 156-172 165-185 215-235 201-229 131-146 215-239 33-44 20-35 17-39 24-33 51-75 22-46           217  Table A  TYPE Value SABAH_GLCM_MEAN_EVI SABAH_GLCM_HOM_EVI SABAH_GLCM_DIS_EVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Acacia Mean 199 208 167 196 170 142 221 224 200 235 219 36 25 37 33 19 34 185 Mode 191-195 205-245 150-195 181-227 196-237 173-200 243-255 200-234 170-277 204-238 119-255 0-82 9-15 0-34 15-39 4--39 0-30 126-161 Forest Mean 171 173 173 178 183 193 231 212 216 199 183 189 25 40 26 44 58 36 Mode 165-181 155-197 162-182 169-192 174-196 149-190 222-255 184-250 229-253 162-255 133-248 137-255 6--41 5--80 0-46 9--80 15-77 5--72 Oil palm Mean 238 246 245 241 238 244 184 213 163 184 172 140 56 35 76 55 59 81 Mode 226-251 236-255 229-255 227-255 231-251 216-255 163-241 173-255 149-249 159-240 124-229 88-210 21-90 10-56 Oct-65 9--85 29-86 15-129 Rubber Mean 201 225 213 189 235 226 128 152 99 158 129 97 93 83 126 66 95 112 Mode 196-206 215-235 210-240 173-207 231-246 231-248 102-139 155-177 80-125 129-168 141-159 101-127 83-91 59-101 60-87 49-67 51-69 61-89 Other IFs Mean 196 200 215 199 200 190 114 131 128 101 87 90 106 87 80 117 124 105 Mode 190-198 185-200 215-235 201-227 195-225 178-229 139-153 86-111 100-133 72-96 71-98 52-99 61-80 92-124 70-92 93-118 91-113 61-99  TYPE Value SABAH_GLCM_MEAN_SARVI SABAH_GLCM_HOM_SARVI SABAH_GLCM_DIS_SARVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Acacia Mean 215 226 188 214 165 144 224 233 200 238 213 22 30 30 43 20 43 208 Mode 205-225 218-242 175-206 188-229 161-205 127-205 217-241 182-240 190-225 221-249 177-255 0-68 9--51 0-37 15-61 0-45 0-36 175-208 Forest Mean 195 198 193 200 198 191 231 217 214 191 186 196 28 36 36 48 58 43 Mode 191-204 185-218 180-202 192-217 177-216 180-214 216-255 169-255 165-253 158-236 148-239 163-255 14-42 0-73 4--80 9--82 14-88 Jun-57 Oil palm Mean 246 251 246 245 239 247 181 206 169 184 165 137 61 40 80 57 67 88 Mode 236-255 242-255 223-255 241-255 233-252 230-255 139-255 177-253 199-254 159-250 144-200 99-224 20-92 14-61 7-142 17-67 29-116 25-120 Rubber Mean 213 238 219 203 239 232 148 166 105 161 153 113 84 73 125 68 83 106 Mode 210-220 205-248 215-240 194-217 229-245 240-253 116-178 160-195 87-125 174-199 141-175 127-162 50-75 10--48 67-99 34-53 30-61 65-79 Other IFs Mean 213 221 227 215 216 213 131 141 159 124 108 120 95 77 69 98 111 93 Mode 195-214 219-235 230--242 207-231 213-232 211-231 154-184 107-135 169-180 108-127 111-130 130-153 40-66 80-94 50-69 76-98 79-102 52-77  TYPE Value SABAH_GLCM_MEAN_SAVI SABAH_GLCM_HOM_SAVI SABAH_GLCM_DIS_SAVI 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Acacia Mean 210 223 186 210 159 158 225 229 207 235 221 28 24 34 37 28 35 195 Mode 200-235 222-242 173-205 197-231 150-203 159-181 230-255 184-221 175-215 221-255 132-255 0-83 0-42 0-72 11--59 0-52 0-35 123-203 Forest Mean 187 191 186 193 188 174 222 207 209 193 180 181 28 47 37 57 68 50 Mode 174-197 178-212 174-200 173-218 164-207 161-200 184-255 175-246 158-255 123-255 122-245 144-255 1--60 15-81 0-104 6-112 12-164 11--59 Oil palm Mean 240 251 244 246 239 244 195 226 172 189 157 139 51 33 79 63 73 87 Mode 231-254 242-255 224-255 238-255 227-252 221-255 175-255 202-255 126-255 176-255 108-205 88-241 12--86 10--53 0-107 10-104 41-111 26-101 Rubber Mean 214 241 217 207 237 227 137 157 93 156 153 103 92 84 137 78 86 113 Mode 205-220 218-241 192-248 203-219 231-242 241-251 120-134 140-170 77-119 161-183 175-199 123-158 80-97 45-75 67-98 52-74 41-61 60-98 Other IFs Mean 212 220 221 217 211 207 118 130 140 98 92 93 106 92 81 127 125 108 Mode 190-214 212-238 221-239 211-239 201-223 201-225 140-165 82-121 102-139 78-99 71-107 84-112 50-75 75-107 63-89 110-129 101-119 89-109 218  Table A  TYPE Value GLCM_MEAN_Band 4 GLCM_HOM_Band 4 GLCM_DIS_Band 4 GLCM_MEAN_Band 5 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 2000 2003 2006 2009 2012 2014 Acacia Mean 240 240 235 240 191 246 246 238 240 232 156 87 9 20 18 26 91 152 243 133 182 209 166 254 Mode 239-241 241-243 234-236 240-242 188-191 247-251 247-248 249-250 245-246 234-237 155-167 144-157 0-19 0-21 0-18 0-34 107-119 77-115 243-246 125-127 182-184 209 130-135   Forest Mean 233 233 234 236 179 226 229 205 218 179 156 201 26 50 39 75 93 42 243 148 178 208 128 161 Mode 232-235 231-237 232-238 234-239 174-187   223-249 194-240 228-230 136-196 132-223 174-246 0-46 0-86 0-75 27-123 39-138 13-72 243-246 135-145 177-184   127-137 154-166 Oil palm Mean 248 252 252 252 239 251 220 219 180 179 203 170 34 36 80 77 51 79 251 204 205 248 233 220 Mode 246-250 250-252 249-255 250-255 233-245 242-255 213-249 190-240 215-240 144-238 198-255 173-221 1--55 0-50 0-63 50-109 0-90 38-62 251-254 203-205 192-196 253-255 233-235 200-234 Rubber Mean 243 248 246 243 235 247 121 143 91 148 134 88 119 108 160 102 116 146 250 215 213 237 227 223 Mode 243-246 247-249 246-247 242-244 234-237 249-252 80-103 143-150 93-101 149-141 160-172 106-109 100-120 101-107 140-143 108-110 68-113 122-136 251-253 203-206 204-212 230-232 231-236 222-234 Other IFs Mean 241 240 244 243 199 234 117 126 121 81 82 92 127 120 121 165 159 128 245 166 183 216 161 166 Mode 240-242 239-241 246-249 246-248 205-211 234-237 210-225 124-131 115-125 53-66 104-107 76-89 157-169 138-141 114-130 172-181 166-169 106-155 244-246 164-167 182-183 209-211 130-133 157-167 219  Table A.9. The PCA, ICA, TCA, band 4 and band 5 values for different Land Use Land Cover (LULC) types in Sabah, 2000-2014.  YEAR  ICA_ VALUES PCA_VALUES TCA_VALUES B4 B5  LU L1 L2 L3 L1 L2 L3 L1 L2 L3 L4 L5 L6     2000                Acacia    mean 99 94 116 133 155 109 121 153 164 222 192 120 2923 864 range 98-99 94-97 114-120 133-134 155-157 109-111 123-124 156-157 164-167 222-224 192-196 120-123 2699-3192 755-953 Forest mean 98 96 111 132 154 108 121 150 161 223 193 119 2547 1015 range 97-100 96-99 110-115 131-135 153-158 108-111 121-124 149-154 161-164 222-226 193-197 119-122 2236-2906 862-1167 Oil Palms mean 96 96 118 137 154 107 124 157 161 223 192 120 3405 1490 range 96-98 96-98 116-123 136-141 154-159 106-110 123-128 155-162 160-165 223-226 191-196 119-123 2927-4011 1307-1845 Rubber mean 97 97 115 136 153 107 123 154 160 223 192 119 3144 1460 range 97-99 93-99 112-122 133-141 153-156 107-111 121-129 149-162 160-164 223-226 191-196 119-123 2616-3674 972-1996 Other IFs mean 98 95 115 134 154 109 122 153 163 222 192 120 2973 1047 range 98-101 94-98 110-124 132-139 154-158 106-112 121-127 149-161 161-167 222-226 191-197 120-123 2344-3634 712-1481 2003 Acacia mean 80 95 112 125 160 87 115 160 143 220 190 161 3042 956 range 79-84 94-99 111-117 124-129 159-165 86-91 115-118 160-165 142-147 219-224 190-194 161-164 2879-3285 844-1108 Forest     mean 79 97 107 124 159 86 114 157 142 220 191 161 2615 1036 range 79-84 96-100 104-113 122-129 158-163 86-89 114-118 154-163 141-146 219-224 191-195 160-165 2144-2993 887-1209 Oil palms mean 79 97 116 130 159 85 118 164 142 220 189 161 3698 1571 range 79-82 97-101 114-122 129-133 158-163 85-88 117-121 162-169 141-145 220-224 189-192 160-164 3419-3988 1437-1703 Rubber mean 79 98 114 129 159 85 117 162 141 221 189 161 3471 1560 range 78-82 97-101 110-122 126-134 158-162 85-89 116-122 159-169 140-145 221-224 188-194 160-164 2795-4126 1124-2061 Other IFs mean 79 96 111 126 160 86 115 160 142 220 190 161 3009 1108 range 79-83 95-100 106-121 86-89 158-163 86-89 114-120 157-168 142-146 220-224 189-194 160-165 2503-2756 751-1626 2006 Acacia mean 101 80 109 128 151 109 119 150 183 213 194 113 2606 1060 range 100-105 79-84 107-114 127-132 150-154 108-112 119-122 149-155 182-187 212-217 193-197 112-117 2344-2812 926-1250 Forest mean 101 79 109 127 151 109 119 151 184 212 194 112 2614 1004 range 101-104 79-83 107-114 126-132 151-156 109-113 118-122 149-155 184-187 212-216 194-198 112-115 2273-2912 839-1165 Oil palms mean 100 80 118 133 151 109 122 158 184 213 191 113 3691 1501 range 100-104 79-84 115-126 132-138 151-155 108-113 122-126 156-164 183-188 212-216 191-195 112-116 3334-4276 1330-1814 Rubber mean  101 81 114 132 150 109 121 154 183 212 192 112 3269 1497 range 100-104 79-87 109-124 129-139 149-154 107-113 120-127 151-162 181-187 212-217 191-196 111-116 2854-4009 1016-2107 Other IFs mean 101 79 114 130 152 109 120 154 184 213 192 112 3136 1199 range 100-104 79-84 107-124 127-137 151-155 108-113 119-125 149-163 184-189 212-216 191-197 112-116 2388-4082 756-1682 2009 Acacia mean 94 99 111 113 153 108 112 154 168 222 193 113 2951 915 range 94-99 99-103 109-116 113-117 153-158 108-111 111-116 153-158 168-171 222-226 192-196 113-116 2708-3213 823-1021 Forest mean 94 101 108 112 153 107 111 152 166 222 194 113 2675 987 range 93-97 100-104 105-114 111-117 152-156 107-110 111-115 150-158 166-170 222-225 193-197 112-116 2317-3110 861-1194 Oil palms mean 93 102 116 117 152 106 114 159 166 223 192 113 3632 1544 range 93-96 102-105 112-122 116-121 151-156 106-109 111-118 156-164 165-169 222-226 191-195 113-117 3261-4067  Rubber mean 94 103 110 115 151 106 114 154 164 223 193 112 3085 1516 range 93-98 102-106 106-117 113-121 150-155 106-108 113-117 152-159 163-168 223-226 192-196 112-116 2554-3636  Other IFs mean 94 101 111 115 152 107 113 155 166 223 192 113 3108 1199 range 93-97 100-105 105-124 112-122 151-156 106-110 111-118 151-164 166-170 222-226 191-196 113-116 2325-4184 821-1724    220  Table A   2012 Acacia mean 84 85 178 108 157 93 106 177 162 216 184 163 2911 1231 range 83-87 83-87 177-184 107-109 153-161 89-97 105-116 176-181 163-167 216-219 184-187 162-167 2706-3114 902-1381                Forest mean 82 84 179 106 159 92 104 178 163 219 186 164 2749 1003 range 82-86 83-88 176-187 105-112 158-162 91-96 104-108 177-184 162-167 217-223 186-190 163-167 2405-3167 814-1256 Oil palms mean 82 86 190 111 158 91 107 185 162 219 184 163 3671 2497 range 82-84 85-90 184-197 110-114 157-162 91-94 107-111 183-190 161-166 219-223 184-187 163-167 3290-4015 1468-1704 Rubber mean  81 86 188 111 158 91 107 185 161 220 184 164 3586 1564 range 80-85 85-89 182-203 108-117 158-162 90-94 106-112 182-193 161-165 220-223 183-188 164-167 2931-4373 1150-2025 Other IFs mean 82 85 182 108 159 91 105 181 162 220 185 164 3019 1171 range 81-85 83-88 173-201 105-114 159-162 91-95 104-110 176-191 162-166 219-223 184-190 164-167 2207-4248 744-1682 2014 Acacia mean 70 89 167 120 167 76 105 200 126 227 186 106 3647 3097 range 65-76 77-108 146-190 117-128 156-179 70-83 102-113 185-221 112-140 224-231 184-190 104-110 2700-4486  Forest mean 75 76 174 108 177 81 95 201 137 226 191 107 2810 1056 range 74-76 75-78 170-180 105-113 171-181 81-83 94-97 198-207 136-141 214-229 191-195 101-111 2312-3268 870-1130 Oil palms mean 73 76 183 115 178 79 99 212 137 227 189 108 3887 1591 range 72-75 75-79 178-192 111-121 177-182 79-81 98-104 207-221 136-141 226-231 188-193 108-112 3205-4517  Rubber mean 73 78 180 114 176 79 99 209 136 227 189 107 3655 1661 range 71-76 76-80 175-189 111-121 176-181 78-85 97-105 205-219 135-140 227-231 188-193 106-111 2929-4560  Other IFs mean 74 76 177 110 177 80 96 205 137 227 190 108 3112 1171 range 73-77 75-78 172-186 106-117 177-181 80-84 95-101 200-215 137-141 227-230 189-194 108-111 2551-4008 820-1586  Table A.10. The PCA, ICA, TCA, band 4 and band 5 values for different Land Use Land Cover (LULC) types in Sarawak, 2000-2014. YEAR ICA_ VALUES PCA_VALUES TC_VALUES B4 B5  LU L1 L2 L3 L1 L2 L3 L1 L2 L3 L4 L5 L6     2000                               Acacia mean 97 86 105 120 156 106 111 149 164 216 167 160 2756 1426 range 97-98 86-87 102-110 119-120 159-160 107-108 111-112 150-151 165-166 217-218 169-170 160-161 2035-3513 696-2080 Forest mean 97 83 107 118 159 106 109 152 167 216 168 160 2723 939 range 96-99 82-85 104-114 115-124 159-162 106-108 109-114 149-156 166-170 216-220 167-171 159-163 2093-3432 629-1310 Oil palms mean 96 85 114 123 152 105 113 157 165 217 166 161 3612 1582 range 95-98 84-88 112-118 123-126 158-161 105-107 113-116 156-161 164-169 217-222 165-169 161-164 3270-3950 1410-1773 Rubber mean 95 85 113 123 158 105 113 156 165 217 166 160 3503 1586 range 94-97 84-89 111-119 121-127 157-162 104-106 111-117 152-163 163-169 215-219 166-170 160-163 2982-4153 1073-2126 Other IFs mean 96 85 111 121 158 106 112 154 165 217 167 160 3253 1401 range 96 -100 84-88 105-120 118-128 158-161 105-109 110-118 150-162 162-169 216-221 164-170 160-165 2581-3920 1058-1714            221  Table A   2003 Acacia mean 78 97 113 125 158 97 114 156 164 215 166 128 3039 1197 range 78-79 97-98 110-112 125-126 158-159 98-99 114-115 154-156 164-165 217-218 167-168 129-130 2278-4182 734-1863 Forest mean 78 96 112 123 159 97 113 155 165 214 167 128 2784 949 range  77-84 94-101 107-119 122-128 158-162 97-100 112-117 153-161 164-169 214-218 166-170 128-131 2161-3434 694-1249 Oil palms mean 77 98 118 127 159 95 115 161 164 216 166 128 3513 1449 range 76-81 97-102 116-123 127-131 157-163 95-99 115-119 160-165 162-168 214-220 165-169 127-132 3216-3844 1302-1628 Rubber mean 77 98 118 128 158 95 116 161 163 216 165 128 3603 1562 range 75-81 97-103 109-125 121-132 156-162 94-100 113-117 152-170 161-170 213-220 164-171 126-133 2777-4644 927-2308 Other IFs mean 77 98 116 126 159 96 115 159 164 216 166 128 3292 1308 range 77-85 96-104 108-126 122-133 157-160 93-99 113-120 155-166 163-172 213-221 165-172 125-131 2750-4149 987-1655 2006 Acacia mean 83 102 106 124 158 99 114 153 162 217 163 123 2619 935 range 83-84 102-103 105-107 125-126 158-159 100-101 115-116 153-154 163-164 218-219 164-165 124-125 1703-3621 544-1317 Forest mean 83 102 106 124 158 99 114 153 162 218 164 124 2680 974 range 83-86 102-105 104-111 123-128 158-161 99-102 115-118 151-159 162-166 218-222 163-168 114-119 1933-3959 678-1301 Oil palms mean 82 103 114 129 158 98 117 159 162 218 163 123 3495 1436 range 82-85 103-106 112-117 128-131 157-161 98-101 116-120 158-163 161-163 217-219 162-164 123-127 3220-3858 1268-1631 Rubber mean 82 104 113 129 157 97 117 158 161 219 162 123 3445 1552 range 81-86 102-108 110-120 131-132 160-162 97-101 120-126 147-153 158-161 216-219 161-163 120-121 2668-4266 1004-1989 Other IFs mean 82 103 111 127 158 98 116 157 162 218 163 123 3210 1252 range 82-87 102-107 103-121 130-133 156-160 97-100 114-120 162-165 165-167 217-221 162-167 122-125 2704-3774 1009-1532 2009 Acacia mean 84 109 112 122 159 91 113 154 157 212 162 154 2599 962 range  84-85 109-110 112-113 122-123 160-161 92-93 113-114 154-155 157-158 213-214 163-164 154-155 2013-3050 627-1267 Forest mean 84 109 113 123 159 91 114 155 157 212 162 154 2785 1028 range  85-86 108-111 110-119 120-127 158-162 91-94 114-118 153-158 158-160 214-216 161-165 155-156 1994-3719 696-1398 Oil palms mean 84 110 118 126 159 91 116 159 156 212 161 153 3412 1445 range  87-88 110-113 116-123 125-130 157-163 90-96 118-121 159-163 155-158 212-213 160-162 153-154 2982-3947 1153-1729 Rubber mean 83 111 118 126 158 90 116 158 155 213 161 154 3371 1553 range 83-84 115-117 109-124 132-133 156-163 88-89 120-123 163-165 153-156 215-217 159-161 152-153 2712-4729 1032-1934 Other IFs mean 84 109 117 125 159 91 115 158 157 212 161 153 3235 2755 range 82-89 108-114 120-125 122-131 154-163 90-91 112-113 152-164 151-154 211-214 159-166 156-157 2748-3840 2206-3307 2012 Acacia mean 72 118 161 122 160 81 110 166 144 219 163 133 2755 981 range 73-74 118-119 160-162 122-123 160-161 81-82 110-111 166-167 145-146 218-219 163-164 133-134 2206-3334 596-1403 Forest mean 73 117 160 122 159 81 110 165 144 220 163 133 2723 1002 range 73-76 117-120 157-165 121-126 159-162 81-85 109-112 164-168 144-147 220-223 163-167 133-136 2063-3389 720-1323 Oil palms mean 72 120 169 127 158 80 133 170 142 220 161 133 3517 1713 range 72-74 119-123 166-172 126-129 158-163 80-86 112-115 168-172 142-144 221-224 161-163 132-137 3197-3923 1372-1864 Rubber mean 73 119 172 128 158 81 114 171 143 220 161 132 3688 1646 range 77-78 123-125 172-176 130-132 154-158 83-87 116-117 173-174 149-150 217-220 160-162 131-133 2987-403 1008-2242 Other IFs mean 72 118 164 123 160 81 110 167 144 220 163 133 2937 1085 range 72-76 117-122 153-157 116-123 158-163 80-86 105-109 159-164 147-148 215-218 167-168 132-136 2417-3600 677-1589    222   Table A   2014 Acacia mean 95 109 178 137 148 85 126 171 146 223 188 159 2546 1614 range 95-96 105-106 161-181 136-138 149-152 86-87 126-127 162-170 151-153 223-225 189-190 160-161 1458-3721 648- 1533 Forest mean 94 104 190 136 152 85 125 178 151 224 189 160 2857 1069 range 94-97 104-108 185-195 134-141 152-155 85-89 124-128 175-182 150-154 224-228 188-192 159-162 2209-3615 808-1363 Oil palms mean  94 107 191 140 150 84 127 179 148 225 187 160 3249 1611 range  93-98 106-109 195-200 139-145 151-156 84-88 124-131 174-184 148-153 222-229 183-192 158-163 2357-4209 922-1988 Rubber mean 93 107 198 142 151 83 129 184 149 226 186 160 3717 1750 range 92-99 105-111 200-205 145-148 147-149 82-84 129-133 185-190 144-149 225-230 182-193 158-164 2799-4678 1065-2655 Other IFs mean 94 105 191 137 152 85 126 179 150 225 188 160 3042 1225 range 91-98 103-109 181-186 132-147 149-157 84-91 123-126 171-177 148-154 223-228 186-193 159-163 2460-3831 827-1807   223         Figure A.16. Vegetation/forest fractional cover maps of 2000, 2003, 2006, 2009, 2012, and 2014 in Sarawak and Sabah. 2000 2003 2006 2009 2012 2014    SABAH SABAH SABAH SABAH SABAH SABAH SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK 224        Figure A.17. Vegetation/forest cover change detection for 2000-2014 in Sarawak and Sabah. 2000-2003 2003-2006 2006-2009 2009-2012 2012-2014   SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SABAH SABAH SABAH SABAH SABAH 225   Table A.11. The fC value changes and its change sequence in 30 monitored key locations in Sarawak, 2000-2014.    SEQUENCES IN INCREASING & REDUCING fC IN KEY AREAS/LOCATIONS IN SARAWAK, 2000-2014  ID 2000 2003 2006 2009 2012 2014             1 V/F NV/NF V/F V/F V/F NV/NF 2 NV/NF V/F V/F V/F V/F NV/VF 3 V/F NV/NF V/F V/F NV/NF V/F 4 NV/NF V/F NV/NF V/F V/F V/F 5 V/F NV/NF V/F V/F V/F V/F 6 V/F NV/NF V/F V/F V/F V/F 7 V/F V/F V/F NV/NF V/F V/F 8 V/F NV/NF V/F V/F V/F V/F 9 V/F NV/NF V/F V/F V/F V/F 10 NV/NF V/F V/F V/F V/F V/F 11 V/F NV/NF V/F V/F V/F V/F 12 V/F V/F V/F NV/NF V/F V/F 13 V/F V/F V/F NV/NF V/F V/F 14 V/F V/F V/F V/F NV/NF V/F 15 V/F V/F NV/NF V/F V/F V/F 16 V/F V/F V/F NV/NF V/F V/F 17 V/F V/F V/F V/F NV/NF V/F 18 V/F V/F V/F NV/NF V/F V/F 19 V/F V/F V/F NV/NF V/F V/F 20 V/F NV/NF V/F V/F V/F V/F 21 V/F V/F V/F V/F NV/NF NV/NF 22 NV/NF V/F V/F V/F V/F V/F 23 V/F V/F NV/NF V/F V/F V/F 24 V/F NV/NF V/F V/F V/F V/F 25 V/F V/F V/F NV/NF V/F V/F 26 V/F V/F V/F NV/NF V/F V/F 27 V/F V/F V/F NV/NF V/F V/F 28 NV/NF V/F V/F V/F V/F V/F 29 V/F V/F V/F NV/NF V/F V/F 30 V/F V/F NV/NF V/F V/F V/F  THE VALUES OF fC IN KEY AREAS/LOCATIONS IN SARAWAK,  2000-2014  2000 2003 2006 2009 2012 2014             1 0.45 0.96 1 1 0.1 0 1 1 0.98 0.99 0 1 0.52 1 0.94 0 0.68 0.55 0.97 0.39 0.97 0.93 0.96 0.98 0 0.95 1 1 0.99 1 0.73 1 0.97 1 1 1 1 1 0.34 1 1 0.97 0.5 1 0.99 1 0.99 0.77 0.28 0.76 0.89 1 0.97 0 0.26 0.46 0.89 0.93 1 1 0 0.44 0.54 0.86 0.83 0.97 0.92 0.97 0 0.76 0.91 0.98 1 1 0.18 0.83 0.81 1 1 1 1 0 0.6 0.92 0.95 0.15 0.78 0.96 1 1 1 1 0.3 0.96 1 1 1 1 1 0 0.3 0.97 1 1 0.33 0.68 0.97 1 0.98 1 0.59 0.65 0.93 1 0 0.37 0.83 0.88 0.93 1 1 1 1 0.79 0 0.4 0.77 0.85 0.96 0.89 0.94 0.97 1 0 0.36 0.71 0.9 1 0 0.53 0.69 0.74 0.92 1 1 1 0 0.71 0.92 1 1 1 0 0.8 0.94 1 1 1 0 0.84 0.68 0 0.54 0.8 0.93 0.95 0.87 1 1 1 0.58 1 1 1 1 0 0.97 0.98 1    226          Figure A.18. The spectral analysis-based LULC maps in Sabah and Sarawak, 2000-2014. 2000 2003 2006 2009 2014 2012    SABAH SABAH SABAH SABAH SABAH SABAH SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK 227          Figure A.19. The textural analysis-based LULC maps in Sabah and Sarawak, 2000-2014.  2000 2003 2006 2009 2014 2012    SABAH SABAH SABAH SABAH SABAH SABAH SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK SARAWAK  228  Table A.12. Details of the high resolution imagery data used for the validation in Sabah. LULC Types #ID Sensor Resolution Acquisition Date Off-Nadir Sun Azimuth Sun Elevation Cloud (%) Cost /km2 Area (km2)  Acacia, Rubber, Other IFs, Non IFs 1040010004CF5900 World View 3 (WV3) 31 cm 11.29.2014 12 143.1 56.4 1 23 94 Rubber, Acacia, Non IFs 1030010033109F00 World View 2 (WV2) 50 cm 07.26.2014 23 330 68.6 1.2 12.25 166  Table A.13. Details of the high resolution imagery data used for the validation in Sarawak. LULC Types #ID Sensor Resolution Acquisition Date Off-Nadir Sun Azimuth Sun Elevation Cloud (%) Cost /km2 Area (km2) Acacia,  Other IFs, Non IFs 103001001A506000 World View 2 (WV2) 50 cm 08.13.2012 24 51.9 71.6 5 12.25 68 Rubber, Non IFs 1010010009B8F700 Quickbird (QB) 60 cm 06.05.2009 8 40.6 61.6 0 12.25 78  229        Figure A.20. The location and distribution of the samples in the ARVI-based IF maps in Sarawak and Sabah.       Sabah, 2014 World View 3, area of 94 km2  Quickbird, area of 78 km2  Sarawak, 2009  ARVI  ARVI  ARVI  Sarawak, 2012  Sabah, 2014, Worldview 2, area of 166 km2  World View 2, area of 68 km2   230       Figure A.21. The location and distribution of the samples in the EVI-based IF maps in Sarawak and Sabah.      Sabah, 2014 World View 3, area of 94 km2  Quickbird, area of 78 km2  Sarawak, 2009  EVI  EVI  EVI  Sarawak, 2012  World View 2, area of 68 km2  Sabah, 2014, World View 2, area of 166 km2   231        Figure A.22. The location and distribution of the samples in the MSAVIaf-based IF maps in Sarawak and Sabah.      Sabah, 2014 Worldview 3, area of 94 km2  Quickbird, area of 78 km2  Sarawak, 2009  MSAVIaf  MSAVIaf  MSAVIaf  Sarawak, 2012  World View 2, area of 68 km2  Sabah, 2014, World View 2, area of 166 km2   232       Figure A.23. The location and distribution of the samples in the NDVIaf-based IF maps in Sarawak and Sabah.      Sabah, 2014 Worldview 3, area of 94 km2  Quickbird, area of 78 km2  Sarawak, 2009  NDVIaf  NDVIaf  NDVIaf  Sarawak, 2012  World View 2, area of 68 km2  Sabah, 2014, World View 2, area of 166 km2   233       Figure A.24. The location and distribution of the samples in the SARVI-based IF maps in Sarawak and Sabah       Sabah, 2014 Worldview 3, area of 94 km2  Quickbird, area of 78 km2  Sarawak, 2009  SARVI  SARVI  Sarawak, 2012  SARVI  World View 2, area of 68 km2  Sabah, 2014, World View 2, area of 166 km2   234        Figure A.25. The location and distribution of the samples in the SAVI-based IF maps in Sarawak and Sabah       Sabah, 2014 Worldview 3, area of 94 km2  Quickbird, area of 78 km2  Sarawak, 2009  SAVI  SAVI  Sarawak, 2012  SAVI  World View 2, area of 68 km2  Sabah, 2014, World View 2, area of 166 km2   235       Figure A.26. The location and distribution of the samples in the fC-based IF maps in Sarawak and Sabah.     Sabah, 2014 Worldview 3, area of 94 km2  Sarawak, 2009  Quickbird, area of 78 km2  fC  Sarawak, 2012  fC  fC  World View 2, area of 68 km2  Sabah, 2014, Worldview 2, area of 166 km2  236                         REFERENCES 237  REFERENCES  ABARE-Jaakko Pöyry Consulting. (1999). Global outlook for plantations. In Research Report No. 99.9. Australian Bureau of Agricultural and Resource Economics, Canberra, Australia.  Aggarwal, A. (2014). How sustainable are forestry clean development mechanism projects?-A review of the selected projects from India. 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