MAPPING VEGETATION AND MEASURING THE PERFORMANCE OF MACHINE LEARNING ALGORITHM IN LULC CLASSIFICATION IN THE LARGE AREA USING SENTINEL-2 AND LANDSAT-8 DATASETS OF DEHRADUN AS A TEST CASE

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2. Quantifying forest degradation rates and their impact on environmental condition in Dehradun, India A. Srivastava 10.1016/j.scitotenv.2025.179987
3. Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine A. Srivastava 10.3846/gac.2024.20106
4. Spatiotemporal assessment of the nexus between urban sprawl and land surface temperature as microclimatic effect: implications for urban planning A. Shohan et al. 10.1007/s11356-024-33091-6
5. Evaluating the relationship between groundwater quality and land use in an urbanized watershed U. Rasool et al. 10.1007/s11356-023-27775-8
6. Land Use and Land Cover (LULC) Mapping Accuracy Using Single-Date Sentinel-2 MSI Imagery with Random Forest and Classification and Regression Tree Classifiers S. Gülci et al. 10.3390/geomatics5030029
7. Land use land cover classification using Sentinel imagery based on deep learning models S. Sawant & J. Ghosh 10.1007/s12040-024-02298-z
8. Analyzing the Dynamic Spatiotemporal Changes in Urban Extension across Zhejiang Province Using NPP-VIIRS Nighttime Light Data Y. Yan et al. 10.3390/rs14133212