Using the "kite" framework for understanding landscape change and improving East African agricultural systems under climate change

The Mount Elgon Ecosystem (MEE), an important hydrological and socio-economic area in East Africa, has exhibited significant landscape changes, driven by both natural factors and human activities, therefore leading to more frequent natural disasters (frequent and extended droughts, floods, and landslides). Yet, few studies have focused on the MEE socio-ecological system; no comprehensive knowledge exists of how humans and nature interact, at multiple scales, to drive ecosystem-wide landscape changes. This dissertation focuses on three interrelated questions: (1.) What is the nature and magnitude of change in MEE greenness for the period 2001-2018, and how is this change related to long-term trends and variability in MEE precipitation? (2.) How is ecological and environmental (eco-environmental) vulnerability distributed across the MEE, and what are the major factors driving these patterns? and (3.) How will the MEE landscape change in the future, and what opportunities exist for streamlining livelihood improvement and environmental conservation efforts?Study 1 characterized comprehensively, over multiple time scales, recent patterns and trends in MEE vegetation greening and browning. The MEE was found to exhibit significant variability in vegetation dynamics and precipitation regimes. There was persistent greening and browning at different time scales and this change was attributed to both natural factors (including changing precipitation) and anthropogenic factors (especially the vegetation-to-cropland conversion). The study also concluded that MEE precipitation had increased substantially in the post-2000 era, which influenced greening and browning patterns observed in the 2006-2010 period. The integration of Mann-Kendall, Sen's slope and bfast (breaks for additive season and trend) proved useful in comprehensively characterizing recent changes in vegetation greenness within the MEE. Study 2 examined eco-environmental vulnerability for the MEE using freely available remote sensing (RS), topographic, and socio-economic data. The study found that the majority of the MEE (comprising savannas, grasslands, and most of the agricultural land in Ugandan MEE) was moderately vulnerable based on the analysis methods and variables used. The eco-environmental vulnerability index (EEVI) showed a marked increase in vulnerability with decrease in elevation. Eco-environmental vulnerability was strongly associated with multi-year variables based on precipitation, temperature, and population density. Moreover, precipitation distribution was changing especially in the wet season, thus adding another layer of risk for agriculture and ultimately for local community livelihoods. Study 3 simulated possible future land use changes in the MEE based on existing RS LULC products and a well-known land use change model. The study projected that agriculture will possibly expand from approximately 58% in 2001 to more than 64% in 2033 if current and future LULC transformation follows rates in 2001-2017. These new croplands will occur mostly around edges of the protected forest and zones of transition between mixed vegetation and existing croplands. Due to the unpredictable LULC transitions in the MEE, simulating forest-to-cropland conversion was less accurate compared to mixed-to-cropland conversion. This research provides a more complete explanation of the underlying complex human-environment interactions shaping the MEE landscape. This is the first study to comprehensively assess landscape dynamics at multiple scales (10-day, 16-day, monthly, seasonal, and household). It is also the first to define and assess at the annual scale, eco-environmental vulnerability as influenced by climate, topographic and socio-economic variables. In addition, by simulating future LULC change, this research provides the opportunity to quantify and anticipate possible LULC changes in the MEE. This research relies on publicly available RS and geospatial datasets and therefore analyses conducted here can easily be translated to other similar regions.