Remote Sensing-Based Vegetation Types Classification Using Landsat and Change Analysis Using Setinel-2a in Google Earth Engine

we present a comprehensive analysis of vegetation types using remote sensing techniques, specifically utilizing Landsat imagery, followed by change analysis through Sentinel-2A data, all conducted within the Google Earth Engine platform. Our research aims to develop a robust methodology for the classification of vegetation types, leveraging the multi-spectral capabilities of Landsat satellites. We employ advanced image processing and machine learning algorithms to accurately categorize various vegetation types across a chosen study area. Furthermore, we utilize Sentinel-2A imagery to track temporal changes in vegetation cover, enabling the identification of land cover alterations over time. By combining Landsat’s spectral richness with Sentinel2A’s temporal consistency, we offer a holis- tic approach for vegetation monitoring and change detection. The results of this study have the potential to significantly enhance our understanding of ecosystem dynamics, support informed land management decisions, and contribute to the conservation of natural habitats in a rapidly changing world.