Accurate assessment of carbon stock of trees is essential to model carbon dynamics in the forest ecosystem. Estimation of carbon stock at regional level involves successive quantitative modeling at various scales. While developments in airborne and satellite remote sensing has greatly reduced the uncertainty in up scaling of plot level biomass and carbon stock estimates to regional or national estimates. A substantial amount of uncertainty in the system comes when carbon stock of each tree in a plot is estimated from established allometric equations. In this study, 12 trees were destructively measured for their carbon stock value and the same was estimated using terrestrial laser scanning technique, local allometric equations, and global allometric equations. The carbon content estimates from terrestrial laser scanning method (26.01% RMSE relative to mean) were consistently closer to destructive measurements as compared to local allometric equations (42.58%-101.88% RMSE relative to mean) and global allometric equations (38.8%-50.69% RMSE relative to mean). Field measurement of sample wood density and sample carbon content significantly reduced the uncertainty in local allometric equations. The sources of error and applicability of each technique are discussed in this study.

Since the inception of satellite remote sensing as a technology, characterization of forests has been one of the major application of it. Characterization of forests at Species level is essential for sustainable management of biodiversity. Recent advancements in remote sensing as a technology has enable us to observe not only the reflectance spectra of forests from space, but also their phenology and structure. In this study Earth Observation (EO) datasets were divided into 3 parts namely spectral data, structural and phenological data. Random forest algorithm was applied on the 3 sets of EO data and field inventory-based tree community classes to generate tree community maps of Purna wildlife sanctuary. Classification accuracy achieved from spectral datasets (79.08% to 87.23%) was more than phenological dataset (80.94%) which was more than structural datasets (74.11% to 81.49%). A model with combination of predictors from the 3 datasets increased the classification accuracy to 90.29%. Some salient findings of this study are 1) in general with the current sensors the accuracies achieved for tree community mapping is Spectral datasets> Phenological datasets> Structural datasets 2) Significant increase in accuracy can be achieved by combining the three datasets 3) In case of spectral datasets, the last image before the start of monsoon season gave the best accuracy 4) In the case of spectral datasets, relatively modern spectral bands contributed significantly more to the model as compared to trivial bands.