Spatio-Temporal Assessment of Vegetation Change for Sustainable Ecosystem Monitoring using Remote Sensing and NDVI-based Change Detection

This paper is a detailed spatio-temporal analysis of vegetation dynamics through the NDVI-based change detection with high-resolution Sentinel-2 data and cloud-based geospatial processing. The study is a comparative study of vegetation conditions in two different time frames to assess the changes in ecological health, landscape productivity and long-term environmental resilience. The patterns of vegetation density were mapped with the help of NDVI classification to determine the areas where degradation or regeneration process was evident. Development of remote sensing and automated processing of Google Earth Engine allows scalable, climate-adaptive monitoring that can be used in the sustainable management of land-use and conservation planning. The results of this research are relevant to the research on vegetation transition that is critical to the protection of biodiversity, sustainability of the ecosystem, and man-responsive resources.