Comparative Analysis of CART and Random Forest Classifiers for LULC Mapping: A case study of Brahmani-Baitarani River Basin India

Abstract

Land Use and Land Cover (LULC) classification is essential for monitoring environmental changes, managing resources, and planning sustainable development. Accurate classification, however, remains a challenge due to the diversity of landscapes and the computational demands of processing large datasets. Among various machine learning (ML) algorithms such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), and Classification and Regression Trees (CART), RF and CART were chosen for this study due to their robustness, simplicity, and efficiency in handling complex LULC classification tasks. This research focuses on the Brahmani-Baitarani River basin, a region known for its environmental significance and susceptibility to land-use changes. Using remote sensing data from Landsat 8, Landsat 9, and Sentinel-2 satellites, a comparative analysis of RF and CART was conducted to evaluate their performance in LULC mapping. The datasets were processed and analyzed on the Google Earth Engine (GEE) platform, utilizing multi-temporal image data and advanced filtering techniques. The results reveal that RF consistently delivers higher classification accuracy compared to CART, making it a reliable choice for LULC studies in dynamic and heterogeneous landscapes. By integrating high-resolution satellite imagery with ML algorithms, this study provides detailed insights into the spatial distribution of land use across the Brahmani-Baitarani basin. These findings have practical applications in urban planning, natural resource management, and environmental conservation, offering valuable information for decision-makers and researchers working to address global environmental challenges.

Keywords: Remote Sensing Imagery, NDVI, NDWI, Supervised Classification, Environmental Monitoring.