MSc

Mangrove ecosystems are dynamic, and changes in coverage can be subtle. Long-term data (decades or more) is necessary to detect these trends and differentiate natural fluctuations from human-induced changes. Traditional methods for analyzing long-term satellitedatacan be time-consuming and require significant computational resources. Google Earth Engine (GEE) is a powerful cloud-based platform specifically designed for geospatial analysis. Inthis context, this study aims to conduct a comprehensive assessment of selected mangrove vegetation indices in the Godavari, Krishna, and Pichavaram regions from 2001 to 2020, utilizing satellite imagery data through the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index(EVI).Statisticalanalyses,includingKendallTaucorrelation coefficients and Sen's Slope, are applied to discern trends in vegetation health and growth over thetwodecades.Resultsrevealaconsistentimprovementinmangrovevegetationacross the regions, attributed to a combination of effective conservation efforts and adaptive environmental management. Correlation analysis between vegetation indices and climatic variables—temperature, soil moisture, and precipitation—demonstrates the complex interactions that influence mangrove health. Notably, temperature shows amoderatepositive correlation with vegetation indices, whereas other variables indicated a poor relation. This study highlights the value of GEE for analyzing long-term trends in mangrove cover,enabling effective monitoring and management of these vital coastal ecosystems. By integratingremotesensingwithenvironmentaldata,wecantrackchangesinmangrovehealth and further develop strategies to enhance their resilience and long-term sustainability.

Keywords: Mangrove ecosystems, Long-term data, Satellite imagery, Google Earth Engine (GEE), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI)