Satellite imagery-based tropical cyclone impact assessment on LULC and vegetation: a case study of cyclone Biparjoy

• Published in: Environmental monitoring and assessment 2024-08, Vol.196 (8), p.748, Article 748
• Main Authors: Patra, Prachee, Das, Umakanta, Agrawal, Sonam
• Format: Article
• Language: English
• Subjects: Agricultural land; Atmospheric Protection/Air Quality Control/Air Pollution; Classification; Coastal inlets; Coastal zone; Cyclone damage; Cyclones; Cyclonic Storms; Damage assessment; Earth and Environmental Science; Ecological effects; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring - methods; Extreme weather; Flooding; Floods; Hurricanes; Image enhancement; Impact assessment; India; Landsat; Mangrove trees; Mangroves; Monitoring/Environmental Analysis; Normalized difference vegetative index; Plants; Rangelands; Remote sensing; Satellite Imagery; Threat evaluation; Tropical cyclones; Vegetation; Vegetation index; Waterlogging
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-024-12902-w

Cyclones pose significant threats to coastal regions, triggering widespread ecological and hydrological changes. This study presents an impact assessment of cyclone Biparjoy, which originated in the Arabian Sea and made landfall on the Gujarat coast of India on June 16, 2023. The research encompasses flood delineation and vegetation impact assessment in the Kachchh and Devbhoomi Dwarka districts of Gujarat, India. Sentinel-1A (VV polarized) imagery is used to precisely map the extent of inundation caused by cyclone Biparjoy. The total flooded area for Kachchh and Devbhoomi Dwarka was calculated to be 6556.73 km 2 and 104.49 km 2 , respectively. The most affected LULC class in Kachchh is found to be bare ground (38.95%) and rangeland (38.94%) which is the major part of the Northeastern Rann region. In Dwarka, most waterlogging has been seen in the cropland (33.04%). The classification of the water and non-water pixels for the pre- and post-images is validated using the ROC curve. The accuracy was 93.2% and 89.5% for pre- and post-images classifications, respectively. Furthermore, vegetation impact was investigated to estimate the cyclone’s ecological consequences. Alterations in vegetation density and overall health were estimated by calculating Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from both pre- and post-cyclone Landsat-8 OLI images. The cyclone-induced damage is further assessed for the mangrove trees in Kori Creek. This work contributes to understanding the ecological repercussions of such extreme weather events.