Estimation of aboveground forest biomass in Himalayan region of West Bengal, India using IRS P6 LISS-IV data

Forest aboveground biomass (AGB) measurement is a direct estimator of the live carbon stock of that forest region. Increasing emission and concentration of CO 2 is a global threat as it is a major cause of today’s global warming. The forest AGB is a live carbon sequester that plays a major role by absorbing atmospheric CO 2 . There are field-based measurement methods of AGB, but the main disadvantage is that they are primarily destructive. Several authors have undertaken AGB estimation using different remote sensing data types, but they are mostly not cost-effective for extensive study areas. We have created a cost-effective algorithm for AGB estimation using multispectral (MSS) data. In this study, Indian Remote-Sensing Satellite-P6 (IRS P6) Linear Imaging Self-Scanning Sensor-4 (LISS-IV) MSS data have been used for the analysis. The research has tried to estimate the AGB of different types of forests existing in the study area by using various vegetation indices and the gray-level co-occurrence matrix (GLCM) and created a hybrid methodology combining the vegetation indices and GLCM. Among all vegetation indices, the simple ratio (SR) highly correlates with AGB of pure deciduous and coniferous forests. In a mixed forest region, due to a mixture of two canopy stands, there is a mixture of foliage angle and optical scattering distribution. Therefore, modified simple ratio (MSR) becomes dominant in mixed forest AGB estimation. Previously there was no study to justify this GLCM texture parameter selection. In this study, we have justified the parameter selection of GLCM texture statistics. This parameter selection will help researchers choose the proper GLCM texture parameter for their study. Integration of GLCM textures with vegetation indices enhances the AGB model strength for all forest regions. The deciduous forest map gives validation R 2 of 0.89 with an RMSE of 1.93 ton/pixel. The validation R 2 of the Coniferous Forest map is 0.83 with an RMSE of 1.35 ton/pixel. There is a comparatively identifiable improvement in mixed forest with validation R 2 of 0.96 and RMSE of 0.25 ton/pixel. This study shows AGB storage of deciduous forest has a maximum share over other forest region of Kalimpong forest.