Monitoring Flood Driven Changes in Land Use Land Cover with Satellite Imagery

Abstract

Flood events significantly change the geographic distribution and extent of land use and land cover (LULC), with impacts on agriculture, barren land, built-up area, vegetation and water bodies. This study assesses LULC changes in the Krishna River basin, near Sangli district, Maharashtra, before and after the 2019 flood events using Landsat 8 OLI imagery for May 2019 (pre-flood) and October 2019 (post flood). LULC classification was carried out using the supervised classification approach using Maximum Likelihood technique, which ensures high precision in distinguishing between pre-flood and post-flood land cover types by using spectral information derived from training samples to assign each pixel to the most likely class. The accuracy of classified images was validated using ground truth data, accuracy assessment, including kappa statistics. The result indicates class- wise changes agricultural land expanded from 49.06% to 71% driven by rapid replanting, sediment deposition, enhanced soil moisture and barren land decreased from 12 % to 4%, while built-up areas reduced from 28% to 17% due to inundation. Vegetation showed a slight increase from 9% to 10% and water bodies remained constant at 1%, indicating that most floodwaters had receded. These changes illustrate both the destructive effects of inundation on built-up areas and the regenerative impacts on agriculture and vegetation. Results indicate substantial changes in LULC classes. The study demonstrates the effectiveness of integrating multi-temporal remote sensing with GIS tools to measure flood induced changes in land use and land cover. This approach offers significant contributions to disaster impact assessment, sustainable land management and rehabilitation in areas that are vulnerable to flooding.