Hotspots Analysis and Spatial-temporal Trends of NDVI and Land Use Land Cover Changes in Surface Coal Mining -Affected Regions Using Spatial Autocorrelation Analysis

Surface Coal Mining activities have significant effects on vegetation and land use/land cover (LULC), resulting in environmental degradation and changes in ecosystem services. In this study, geospatial methods, incorporating spatial autocorrelation tools like Moran's I, were adopted to identify and assess notable spatial patterns of Normalized Difference Vegetation Index (NDVI) and LULC alterations in areas impacted by mining for the years 1997 and 2022 in the Damodar River basin, India. Spatial autocorrelation assessments were performed in ArcGIS to identify patterns of clustering and dispersion in NDVI and LULC changes to get an idea about the emerging hot spot and cold spot patterns influenced by surface coal mining in the Damodar River basin, India. The methodology involves calculating Global Moran’s I to analyze overall spatial trends and the Mann Kendall Trend test to analyse the temporal trend. Following this, Hot Spot Analysis (Getis-Ord Gi*) are employed to identify regions undergoing notable vegetation decline or shifts in land use. The spatial weights matrix, an essential element for these evaluations, is configured to reflect spatial relationships, such as contiguity or distance-based interactions.

Initial findings reveal significant clusters of NDVI decline in active mining areas, aligning with widespread deforestation and land cover transformations from natural green cover to mining infrastructure, the mining area shows an increase of 6.89 per cent of the total geographical area of the basin. Hotspot analysis indicates crucial locations that necessitate prompt environmental intervention. The whole basin exhibits a statistically significant temporal trend of high-value aggregation of NDVI. This research underscores the effectiveness of spatial autocorrelation tools in tracking and managing the ecological consequences of mining operations. The results offer valuable information for policymakers and environmental managers to focus restoration efforts and adopt sustainable land use strategies.