Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series

Abstract

Aquaculture is one of the fastest growing primary food production sectors in India and ranks second behind China. Due to its growing economic value and global demand, India's aquaculture industry experienced exponential growth for more than one decade. In this study, we extract land-based aquaculture at the pond level for the entire coastal zone of India using large-volume time series Sentinel-1 synthetic-aperture radar (SAR) data at 10-m spatial resolution. Elevation and slope from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) data were used for masking inappropriate areas, whereas a coastline dataset was used to create a land/ocean mask. The pixel-wise temporal median was calculated from all available Sentinel-1 data to significantly reduce the amount of noise in the SAR data and to reduce confusions with temporary inundated rice fields. More than 3000 aquaculture pond vector samples were collected from high-resolution Google Earth imagery and used in an object-based image classification approach to exploit the characteristic shape information of aquaculture ponds. An open-source connected component segmentation algorithm was used for the extraction of the ponds based on the difference in backscatter intensity of inundated surfaces and shape metrics calculated from aquaculture samples as input parameters. This study, for the first time, provides spatial explicit information on aquaculture distribution at the pond level for the entire coastal zone of India. Quantitative spatial analyses were performed to identify the provincial dominance in aquaculture production, such as that revealed in Andhra Pradesh and Gujarat provinces. For accuracy assessment, 2000 random samples were generated based on a stratified random sampling method. The study demonstrates, with an overall accuracy of 0.89, the spatio-temporal transferability of the methodological framework and the high potential for a global-scale application.

Remote sensing of fish-processing in the Sundarbans Reserve Forest, Bangladesh: an insight into the modern slavery-environment nexus in the coastal fringe

“…There are several specific operational benefits of applying remote sensing to investigate the SRF. First, remote sensing is scalable; this is the dominant benefit of applying these data to investigate post-harvest fish-processing, marine capture fisheries, and aquaculture activities (the latter is being analysed using Sentinel-1 Synthetic-Aperture Radar (SAR): see Ottinger et al 2018;Prasad et al 2019) in locations where modern slavery occurrence is high-risk, or has been documented. Second, satellite EO is a relatively inexpensive and costeffective data collection method (Woellert et al 2011;Foody et al 2019).…”

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

“…Ponds have been rapidly expanding during the last decades within fertile coastal zones. SRS can be used to quantitively assess the expansion of the pond's land use and its impact on the coastal environment using satellite images, such as Landsat for long time series analysis [100] and/or Sentinel-1 [146].…”

Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh

“…Another limitation is that cloud covers in monsoon seasons limited the number of images that can be used. Thus, a few previous research works use SAR images instead of optical images for aquaculture pond mapping in South Asia [6,7]. As SAR uses microwave spectrum that can penetrate clouds, and it does not rely on the presence of sun, it can provide significantly better temporal coverage for regions with heavy cloud contaminations.…”