Machine Learning Approach for Land Use Land Cover Classification of Similipal Reserve Forest

During recent times researchers have shown an increasing interest in Remote Sensing because changes affect the environment's biodiversity. This research aims to analyse remote sensing data using machine learning and Geographic Information System (GIS) on the Similipal Reserve Forest. Similipal Reserve Forest in 2023 is research area which is situated in Odisha. The research area is further classified into six classes such as open forest, dense forest, water bodies, urban areas, crop land, and waste land. In the proposed model remote sensing is used to record the satellite images of a high-quality resolution whereas Machine learning algorithms are used for the classification of these images, and GIS is used to visualization the classified imagery and map Land Use Land Cover (LULC). The present study in this area aims to provide essential comprehension into the machine learning models within the Similipal Reserve Forest. Hence, contributing a broader understanding of the applicability of advanced machine learning technologies in forest regions.