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Forest ecosystems play a vital role in maintaining ecological balance, biodiversity, and climate regulation. However, monitoring forest canopy dynamics in protected areas like the Periyar Tiger Reserve (PTR), Kerala, remains underexplored, especially in terms of Fractional Vegetation Cover (FVC), a key indicator of vegetation health. Remote sensing-based environmental assessment is essential for understanding forest structure and implementing conservation strategies. This research aims to estimate and analyse the spatial variability of FVC in PTR to understand vegetation distribution and canopy status.
The primary objectives of the study are 1) to estimate FVC using selected vegetation indices NDVI, NDMI, SAVI, and GCI, from Sentinel-2 imagery; 2) to conduct a comparative analysis of these indices based on their sensitivity to vegetation greenness, moisture, and chlorophyll content; and 3) to identify the most suitable index for FVC estimation in dense forest ecosystems using statistical rescaling methods and spatial modelling techniques.
For this purpose, Sentinel-2 data were utilized within the Google Earth Engine (GEE) cloudcomputing platform to perform preprocessing, index calculation, and FVC modelling. Supporting tools like ArcGIS and QGIS were used for spatial visualization and analysis.
By integrating geospatial tools and spectral vegetation information, the study provides a costeffective and scalable approach for forest canopy monitoring. Results reveal index-specific variations in FVC performance, with NDMI and GCI showing stronger correlations in moistureand chlorophyll-rich zones of PTR. These insights can support long-term ecological monitoring and sustainable forest management in protected areas.
Keywords: Fractional Vegetation Cover (FVC), Periyar Tiger Reserve, Vegetation Indices, GEE, Sentinel-2, Forest Canopy Monitoring.
