Automated extraction of aquaculture ponds from Sentinel-2 seasonal imagery – A validated case study in central Thailand

Abstract

The incidence and size of aquaculture ponds are related to a variety of economic, social, environmental, and policy factors, but there is scarce publicly available information. Satellite based aquaculture pond mapping has typically been undertaken by segmentation of single-date images or of temporal composites derived from image time series. In Asia, both aquaculture and rice farming can be undertaken in the same locality and, combined with frequent cloud cover, meaning that segmentations should be derived throughout the year to be able to differentiate aquaculture ponds from rice paddies and to provide spatially-complete pond mapping results. In this paper, a new approach is presented to extract individual aquaculture ponds from seasonal Sentinel-2 10 m images and to combine different sets of pond objects extracted from different images into a single set of pond objects. The approach is demonstrated for the province of Nakhon Pathom (216,800 ha) in central Thailand that is a major shrimp farming area. Aquaculture ponds were extracted from near-cloud-free Sentinel-2 images acquired in January and June 2019 to reduce confusion with rice paddies that are typically vegetation covered in these months. The ponds extracted at different times were then combined using a multi-temporal object combination strategy. Surveys undertaken in 2019 to elucidate farmers’ attitudes and land use practices were used to contextualize the extraction results. Across the province, 22,833 aquaculture ponds were extracted with a total area of 18,066 ha. The mean and median pond sizes were 0.79 ha and 0.60 ha, respectively, which is close to the 0.65 ha mean shrimp pond size in Thailand reported by other researchers via independent surveys. Current methods to map aquaculture ponds have not typically reported object-based validation results and so it is unknown whether object-level information (e.g., number of ponds and pond sizes) are reliable. Therefore, the extraction results were evaluated quantitatively using object-based accuracy metrics. A total of 1733 aquaculture ponds at three validation sites were manually digitized using multi-date GoogleEarth high-resolution images, and compared with the extracted ponds. The evaluation results indicated a robust pond extraction performance, with 87.7% object-based overall accuracy considering all the reference data and the extracted ponds at the three sites. 0.9% of the extracted ponds were over-extracted (commission error), 0.9% were under-split, 2.6% were over-split, and 6.9% of the of the reference ponds were not extracted (omission error). In addition, the mean over-segmentation error of 0.154, mean under-segmentation error of 0.111, and mean F-score (dice coefficient) of 0.858 were obtained. The causes of these errors were examined and discussed with recommended potential research using other sensor data.