Mapping and Quantitative Assessment of Vegetation Cover Using Sentinel-2 Satellite Data in Hunsur Taluk, Karnataka, India

Trees and green cover support ecological stability, climate regulation, biodiversity, and human well-being, highlighting the need for accurate local-scale vegetation assessment. This study mapped and quantified green cover in Hunsur Taluk, Mysore District, Karnataka, using Sentinel-2 Level-2A multispectral imagery and supervised Random Forest classification. Sentinel-2 surface reflectance images acquired from January to December 2025 were processed in Google Earth Engine through cloud masking, annual median compositing, and clipping to the taluk boundary. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated with spectral bands to improve vegetation discrimination. Classification identified 294.83 km² (32.92%) of vegetated land and 600.57 km² (67.08%) of non-vegetated land. Among non-vegetated classes, barren land occupied 484.43 km² (54.10%), built-up areas 100.90 km² (11.27%), and water bodies 15.24 km² (1.70%). Vegetation was concentrated mainly in southern and southwestern Hunsur, whereas central and northern areas exhibited more extensive barren land, settlements, fallow fields, and fragmented vegetation. Moderate to high NDVI and EVI values were associated with denser vegetation, while low values characterized sparsely vegetated and non-vegetated surfaces. The classification achieved an overall accuracy of 94.73% and a kappa coefficient of 0.9132, indicating strong agreement with reference observations. These findings demonstrate the utility of integrating Sentinel-2 imagery, vegetation indices, and Random Forest classification for taluk-level green-cover mapping. The resulting spatial baseline can support land-use planning, vegetation monitoring, conservation prioritization, and sustainable landscape management in Hunsur Taluk. As an annual median composite was used, the map represents generalized vegetation conditions rather than seasonal extremes or fluctuations.