ADVANCES IN REMOTE SENSING TECHNIQUES FOR LAND COVER CLASSIFICATION: A COMPREHENSIVE REVIEW

Abstract

Remote sensing techniques have revolutionized land cover classification by providing detailed and comprehensive information about the Earth's surface. This review paper examines the historical development, types, applications, challenges, and future directions of remote sensing in land cover classification. Early techniques such as aerial photography laid the foundation for modern remote sensing technologies, including optical, radar, LiDAR, and hyperspectral remote sensing. These technologies offer unique advantages and limitations, which are crucial to understanding their applications in agriculture, forestry, urban planning, and environmental monitoring. Despite significant advancements, challenges such as data availability, complexity of land cover types, and integration with other data sources remain. Future trends focus on the use of machine learning, integration of multiple sensor systems, and the potential impacts on land cover classification. This review provides insights into the current state and future potential of remote sensing techniques in land cover classification.