Integration of SAR and Optical Remote Sensing Data for Spatial Estimation of Forest Carbon Stock in Meghalaya, India

Dhruval Bhavsar; Suraj Kumar Swain; Kasturi Chakraborty; H.C. Chaudhary

doi:10.36808/if/2026/v152i6A/171322

Integration of SAR and Optical Remote Sensing Data for Spatial Estimation of Forest Carbon Stock in Meghalaya, India

Authors

Dhruval Bhavsar Forestry and Ecology Division, North Eastern Space Applications Centre (NESAC), Department of Space, Government of India, Umiam – 793103, Meghalaya
Suraj Kumar Swain Forestry and Ecology Division, North Eastern Space Applications Centre (NESAC), Department of Space, Government of India, Umiam – 793103, Meghalaya
Kasturi Chakraborty Forestry and Ecology Division, North Eastern Space Applications Centre (NESAC), Department of Space, Government of India, Umiam – 793103, Meghalaya
H.C. Chaudhary Forests and Environment Department, Government of Meghalaya, Sylvan House, Lower Lachumiere, Shillong – 793001

DOI:

https://doi.org/10.36808/if/2026/v152i6A/171322

Keywords:

Forest carbon stock, Land degradation neutrality, Biomass, Remote Sensing.

Abstract

Land degradation and climate change threaten ecosystem sustainability, particularly in tropical forests. Spatial assessment of forest carbon stock is essential for supporting Land Degradation Neutrality (LDN). This study integrates optical and Synthetic Aperture Radar (SAR) remote sensing data to estimate and map forest carbon stock in reserve and protected forests of Khasi Hills, Meghalaya. Field data from 221 plots were used to derive aboveground biomass (AGB) and estimate total carbon. A multi-linear regression model using vegetation indices and SAR backscatter achieved R² = 0.66 (RMSE = 50.34 Mg ha^-1), while total carbon estimation showed a validation accuracy of R² = 0.67 (RMSE = 26.27 Mg ha^-1). Vegetation carbon was largely concentrated between 100–150 Mg ha^-1 and soil organic carbon between 60–90 Mg ha^-1, leading to total carbon values predominantly in the range of 150–250 Mg ha^-1 with some areas exceeding 250 Mg ha^-1. The study demonstrates the effectiveness of multi-sensor approaches for carbon assessment, supporting ecosystem restoration and LDN objectives.

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