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.

The coastal plains of Tamil Nadu, India, are prone to floods, the most common disaster experienced in this region almost every year. This research aims to identify flood risks in the coastal plain region of Tamil Nadu, delineated through a watershed approach with 5020 micro-administrative units covering an area of about 26,000 sq. km. A comprehensive flood risk assessment covering hazard, vulnerability, and exposure parameters was carried out using multiple datasets derived from field surveys, satellite data, and secondary data sources. The flood hazard layer was prepared on a probability scale (0–1) with the help of Sentinel-1 Synthetic Aperture Radar data coupled with GIS-based water rise modelling using Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) and reports of the District Disaster Management Plans of 13 coastal districts. In addition, the National Resources Conservation Service-Curve Number (NRCS-CN) method was adopted to estimate surface runoff potential for identifying low probability flood-prone regions. The vulnerability and exposure of the population to flood hazards were determined using census and household data-based indicators. The different categories of built-up areas were delineated and intersected with the flood hazard layer to estimate elements at flood risk. An exhaustive field survey was conducted at 514 locations of the study area, targeting deprived communities of all major settlements to validate the flood hazard layer and understand the public perceptions. The amalgamation of results shows that very high flood risk prevails in the northern parts of coastal Tamil Nadu, especially the stretch between Chennai and Cuddalore. In addition, to provide baseline datasets for the first time at micro-administrative units for the entire coastal plains of Tamil Nadu, the study offers a pragmatic methodology for determining location-specific flood risks for policy interventions.

Climate-change-induced hazards are negatively affecting the small islands across Indonesia. Sabang Island is one of the most vulnerable small islands due to the rising sea levels and increasing coastal inundation which threaten the low-lying coastal areas with and without coastal defences. However, there is still a lack of studies concerning the long-term trends in climatic variables and, consequently, sea level changes in the region. Accordingly, the current study attempts to comprehensively assess sea level changes and coastal inundation through satellite-derived datasets and model-based products around Sabang Island, Indonesia. The findings of the study show that the temperature (both minimum and maximum) and rainfall of the island are increasing by ~0.01 °C and ~11.5 mm per year, respectively. The trends of temperature and rainfall are closely associated with vegetative growth; an upward trend in the dense forest is noticed through the enhanced vegetation index (EVI). The trend analysis of satellite altimeter datasets shows that the sea level is increasing at a rate of 6.6 mm/year. The DEM-based modelling shows that sea level rise poses the greatest threat to coastal habitations and has significantly increased in recent years, accentuated by urbanisation. The GIS-based model results predict that about half of the coastal settlements (2.5 sq km) will be submerged completely within the next 30 years, provided the same sea level rise continues. The risk of coastal inundation is particularly severe in Sabang, the largest town on the island. The results allow regional, sub-regional, and local comparisons that can assess variations in climate change, sea level rise, coastal inundation, and associated vulnerabilities.