Das B1, Deka S1, Bordoloi R1, Pandey P K2, Singha L B1 and Tripathi O P1*
Author Address :
1Department of Forestry, North Eastern Regional Institute of Science and Technology (Deemed University),
Nirjuli-791109, Arunachal Pradesh
2Department of Agriculture Engineering, North Eastern Regional Institute of Science and Technology
(Deemed University) Nirjuli-791109, Arunachal Pradesh
Present study aims to analyze tree composition and above ground biomass (AGB) through geospatial approach using field-based data. Different satellite derived vegetation indices were used for predicting AGB and to select the most appropriate predictive model for spatial mapping. Basal area ranged from 9.1 to 114.7 m2/ha and plot based AGB ranged between 29 and 588.8 t/ha with mean value of 172.36 t/ha. Estimated AGB in mixed dense, plantation and degraded forests are 186 t/ha, 174 t/ha and 81.41 t/ha, respectively. Correlation coefficient (r2) between AGB and vegetation indices such as NDVI, SAVI and ARVI are 0.26, 0.70 and 0.39, respectively. As SAVI resulted greater correlation than the other indices hence was considered best-fit for prediction modeling of AGB. Average predicted AGB for mixed dense forest was 191.16 t/ha followed by plantation (157.61 t/ha) and degraded forest (96.76 t/ha). Predicted AGB of the total forested area of Papum Pare district was 0.072 Pg. Comparative analysis showed that predicted model showed about 27.63 percent greater biomass than the estimated values which could be mainly associated with the number of sampling plots used for spatial modeling. However, present model result good-fit for biomass estimation under limited sampling point conditions.
Landsat-OLI, allometric equation, above ground biomass, regression, modelling
Article Info :
Received : October 14, 2017; Accepted : November 17, 2017.