Land Use Land Cover Classification for REL River Using Machine Learning Techniques
Keval H. Jodhani, Keval H. Jodhani, Dhruvesh Patel, N. Madhavan
Abstract
Land Use Land Cover changes plays a significant role in climate change, biodiversity conservation, food security, water resources management and urbanization. This study aims to identify the land use land cover dynamics of Rel river for 2008 using GEE, four ML techniques are used to identify LULC. Accuracy of all techniques was determined using area under curve and kappa coefficient, results show that RF provides best classification for Rel river having area under curve 0.91 and kappa coefficient 0.89. Therefore, the study will decipher the LULC for the REL river which will aid may government agencies, researchers and academicians, also the integrated approach of GEE-ML for LULC will provide a demonstration for investigating large scale land features using medium resolution remote sensing data, and best approach of many ML techniques.