Abstract:
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 data within Google Earth Engine, we proposed six different strategies for improving fishpond detection as the existing techniques seem unreliable. These techniques include:
(1) identification of the best time 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 known ground truthing fishponds as the metric of success. For machine
learning classifiers, we compared the precision, recall, and F1 score to determine the quality of
results. Among four machine learning methods studied here, the classification and regression trees
performed the best with a precision of 0.738, recall of 0.827, and F1 score of 0.780. Overall, the
proposed strategies enhanced fishpond area detection in all districts within the study area.
