Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass

Accurately characterizing carbon stock is vital for reporting emissions from forest ecosystems. We studied the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests Galies region Abbottabad Pakistan. Above-ground (AGB), estimated 60 field plots, was correlated with vegetation indices obtained image-to-map AGB regression models. Furthermore, additional explanatory variables were also associated geo-statistical technique, and kriging interpolation used to predict AGB. The results illustrate that atmospherically resistant index (ARVI) best (R2 =0.67) estimating In spectral reflectance, Band 1(Coastal Aerosol 443 nm) performs better than other bands. Multiple linear models calibrated ARVI, NNIR NDVI yielded = 0.46) lowest RMSE (48.53) MAE (38.42) therefore considered estimation. On hand, distance settlements, ARVI annual precipitation significantly compared others. stepwise method, forward selection resulted a very significant value (less 0.000) ARVI. Therefore, it can be prediction interpolate through kriging. Compared sensing-based performed relatively well. Regarding potential sites REDD+ implementation, temporal analysis Landsat images showed decrease area 8896.23 ha 1988 7692.03 2018. this study concludes state-of-the-art open-source sensor, data, has robust regional acceptable accuracy frequent availability.