A Comparative Analysis for Forty Years of Land Use Land Cover Change (1991–2021) Using Cart and Random Forest Classifiers for Varanasi District (India)

Effective implementation of remote sensing images using classification techniques facilitates us with the extracting spatial and temporal data classification of LULC. Mapping interchanges in LULC pattern is paving a way for investigating the influence of several socio-economic and environmental factors on the surface of Earth. Our case study depicts a comparison analysis between two algorithms using Landsat temporal images to analyze the interchanges in the class of LULC for the Varanasi district of India. We applied Random Forest (RF) Classification and Classification and Regression Tree (CART) classification in our case study, in the Google Earth Engine platform (GEE) extracting images from Landsat 5, Landsat 7 and Landsat 8 since 1991—2021 period. We evaluated the performance metrics with auxiliary data and several spectral indices on our final classification accuracy on both the classifiers. We used multi-spectral Landsat bands’ (Landsat 7– Landsat 8) of spatial resolution from 30 to 15 m. The results shows: 1) values of spectral indices applied Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Moisture Soil Adjusted Vegetation Index (MSAVI), Soil Water Index (SWI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Normalized Burn Ratio2 (NBR2), Urban Index (UI), Visible Red Based Built-up Index (VrNIR_BI), Visible Green Based Built-up Index (VgNIR_BI), 2) Accuracy assessment 98.3%, 97.37%, 96.48%, 95.3% of RF and 87.14%, 91.45%, 89.23%, 88.67% of CART for Training accuracy, training kappa, testing accuracy and testing kappa respectively. Resulting, Random Forest classier outperforms well as compared to CART classifier in our case study.