Harnessing machine learning techniques for large-scale mapping of inland aquaculture waterbodies in Bangladesh

Aquaculture in Bangladesh has grown dramatically in an unplanned manner in the past few decades, becoming a major contributor to the rural economy in many parts of the country. National systems for the collection of statistics have been unable to keep pace with these rapid changes, and more accurate, up to date information is needed to inform policymakers. Using Sentinel-2 Top of Atmosphere Reflectance images within Google Earth Engine and ArcGIS platforms, we proposed six strategies for improving fishpond detection as the existing techniques seem unreliable. The study area is comprised of seven districts in south-west and south-central Bangladesh. The tested strategies include: 1) identification of the best period for image collection, 2) testing the buffer size for threshold optimization, 3) determining the best combination of image reducer and water-identifying indices, 4) introduction of a convolution filter to enhance edge-detection, 5) evaluating the impact of ground-truthing data on machine learning algorithm training, and 6) identifying the best machine learning classifier. Each enhancement builds on the previous one to develop a comprehensive improvement strategy called the Enhanced Method for fishpond detection. We compared the results of each improvement strategy to the known ground-truthing fishponds as the metric of success. We compared the precision, recall, and F1 score for machine learning classifiers to determine the quality of results. Among the studied methods, the Classification and Regression Trees performed the best. Overall, the proposed strategies enhanced fishpond area detection in all districts within the study area.