Abstract
Inland aquaculture in Bangladesh has been growing fast in the last decade. The underlying land use/land cover (LULC) change is an important indicator of socioeconomic and food structure change in Bangladesh, and fishpond mapping is essential to understand such LULC change. Previous research often used water indexes (WI), such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), to enhance water bodies and use shape-based metrics to assist classification of individual water features, such as coastal aquaculture ponds. However, inland fishponds in Bangladesh are generally extremely small, and little research has investigated mapping of such small water objects without high-resolution images. Thus, this research aimed to bridge the knowledge gap by developing and evaluating an automatic fishpond mapping workflow with Sentinel-2 images that is implemented on Google Earth Engine (GEE) platform. The workflow mainly includes two steps: (1) the spectral filtering phase that uses a pixel selection technique and an image segmentation method to automatically identify all-year-inundated water bodies and (2) spatial filtering phase to further classify all-year-inundated water bodies into fishponds and non-fishponds using object-based features (OBF). To evaluate the performance of the workflow, we conducted a case study in the Singra Upazila of Bangladesh, and our method can efficiently map inland fishponds with a precision score of 0.788. Our results also show that the pixel selection technique is essential in identifying inland fishponds that are generally small. As the workflow is implemented on GEE, it can be conveniently applied to other regions.
Highlights
Fishery in Bangladesh has been increasing rapidly in the last few decades as a major source of food and economic growth [1]
In research conducted by Yin et al [37], the Otsu method achieves the best performance among 8 other automatic thresholding methods, and its performance is on par with support vector machine (SVM) and optimal thresholds
The Logistic Regression (LR) model identified in a total of 841 fishponds within the test unions, and the Decision Tree (DT) model identified 789 fishponds
Summary
Fishery in Bangladesh has been increasing rapidly in the last few decades as a major source of food and economic growth [1]. According to the International Food Policy Research Institute (IFPRI), the fish farming market has grown 25 times in all aspects of the aquaculture industry in the last three decades. A great portion of croplands in Bangladesh has been gradually transforming to other land use types, such as fishponds, brickyards, and residential area [1,4]. While Bangladesh Statistical Bureau (BSB) publishes statistical yearbooks every year, it lacks information of the spatial distribution of the land use changes. Such information can better help decision-makers make land use policies for better resource distributions. It is important to investigate the potential of using Sentinel-2 images and GEE platform for fast, timely mapping of inland fishponds in Bangladesh
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