Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data

Abstract

Abstract:We present an earth observation based approach to detect aquaculture ponds in coastal areas with dense time series of high spatial resolution Sentinel-1 SAR data. Aquaculture is one of the fastest-growing animal food production sectors worldwide, contributes more than half of the total volume of aquatic foods in human consumption, and offers a great potential for global food security. The key advantages of SAR instruments for aquaculture mapping are their all-weather, day and night imaging capabilities which apply particularly to cloud-prone coastal regions. The different backscatter responses of the pond components (dikes and enclosed water surface) and aquaculture's distinct rectangular structure allow for separation of aquaculture areas from other natural water bodies. We analyzed the large volume of free and open Sentinel-1 data to derive and map aquaculture pond objects for four study sites covering major river deltas in China and Vietnam. SAR image data were processed to obtain temporally smoothed time series. Terrain information derived from DEM data and accurate coastline data were utilized to identify and mask potential aquaculture areas. An open source segmentation algorithm supported the extraction of aquaculture ponds based on backscatter intensity, size and shape features. We were able to efficiently map aquaculture ponds in coastal areas with an overall accuracy of 0.83 for the four study sites. The approach presented is easily transferable in time and space, and thus holds the potential for continental and global mapping.

Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions—A Review

“…In the context of surface water dynamics in river deltas, the rapid expansion of aquaculture ponds is of particular relevance. Ottinger et al [171] used Sentinel-1 SAR time series over a 2-year period to detect aquaculture on the level of individual ponds at a regional scale, based on the method illustrated in Figure 9. Using some of the world's most significant aquaculture production regions as case studies, i.e., the Mekong, Red, Pearl and Yellow Rivers, the method achieved an overall classification accuracy of 0.83.…”

“…This first continental-scale, multi-decadal assessment of annual mangrove distribution showed that mangrove forest extent in Australia peaked in 2010 and has since declined by about 240 km 2 [139]. Several recent studies have used Sentinel-1 SAR time series data to map the distribution of rice paddies and aquaculture ponds in river deltas, taking advantage of the unique seasonal inundation dynamics and microwave backscattering characteristics of these features [171,172,[188][189][190]. Advances in SAR-based mapping of aquaculture ponds are already covered in the previous chapter, and therefore, are not repeated here.…”

“…(Bottom) S1A image (d); related histogram and classification threshold (e); and binary image of water and non-water after application of threshold (f). (Modified from Ottinger et al[171]).…”

Time series sentinel-1 SAR data for the mapping of aquaculture ponds in coastal Asia

“…The different backscatter response of the pond components (dikes and enclosed water surface; see figure 3) and its distinct rectangular structure allow for the separation of aquaculture areas from other surfaces. With dense time series data we are able to distinguish permanent water covered aquaculture ponds from other periodically water filled surfaces, such as paddy rice fields or flooded areas [5]. Figure 3: Radar interaction with ponds: specular, diffuse and corner reflection.…”

“…Figure 3: Radar interaction with ponds: specular, diffuse and corner reflection. Modified according to [5].…”