Combination of remote-sensing spectral indices to classify the areas of land degradation in West Burdwan district, India
2022
Kabiraj, Sabyaschi | Duraisekaran, Elanchezhiyan | Ramaswamy, Malarvizhi
Demarcating the extent of land degradation remains a major challenge worldwide. A combination of remote-sensing spectral indices and Land Degradation Index (LDI) were used for the delineation and classification of land degraded areas in West Burdwan district, West Bengal, India. Mining, brick and other industries were delineated using the maximum likelihood classification (MLC) algorithm of the supervised image classification method. Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil Moisture Index (SMI), night-time LST and Evapotranspiration (ET) were used as indicators of land degradation and were derived from Landsat and Moderate Resolution Imaging Spectroradiometer satellite images for the years from 1990 to 2020. The result indicates an increase in industrial area from 2.06% to 7.62% of the district area from 1990 to 2020. Our pixel-based time-series analysis displays some considerable land degradation over the past 30 years, including (i) increasing daytime LST, (ii) declining vegetation, (iii) decreasing ET. However, no considerable change was found in night-time-LST and SMI. A strong positive correlation (adjusted R² > 0.5 and p < 0.05) was observed between mining area expansion and daytime LST and a negative correlation between mining area expansion and NDVI and ET respectively. LDI shows a total land degradation of 91.01 km² over the entire study region. Mining area expansion was found to be one of the major stressors to local environmental degradation. The results from this study may help the decision-makers and policymakers to provide a better management plan for sustainable land management in future.
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