Carbon Stock Assessment in Natural Forests and Plantations Using Geo-Informatics in Manipur, Northeast India

Abstract

Uncertainties continue to prevail in the potential of natural forests and plantations in carbon stock assessment. The present study was carried out to assess the carbon stock in natural and plantation forests of Manipur using geo-informatics in Imphal East and West districts. The integrated approach of geospatial technology, along with field inventory based data, was used in spatial modeling of biomass carbon of selected natural and plantation forests. The stand density was similar for both LNG and TRS forests (680 individuals ha−1) and lowest for KHP forest (640 individuals ha−1). Paulownia fortunei (770 individuals ha−1) showed highest density among tree species while Tectona grandis (54.07 m2 ha−1) followed by Gmelina arborea (42.18 m2 ha−1) had higher basal area compared to other tree species. The soil moisture content (%) in the natural forest ranged from 19.13 ± 0.47 to 26.9 ± 0.26%. The soil moisture content in the plantation forest ranged from 19.16 ± 0.98 to 25.83 ± 0.06%. The bulk density of natural forests ranged from 1.27 g cm−3 to 1.37 g cm−3 while for plantation forests it ranged from 1.18 g cm−3 to 1.34 g cm−3. Among the studied sites of natural forest, TRS forest had both the highest AGBC value of 132.74 t ha−1 as well as the BGBC value of 38.49 t ha−1. Similarly, among the plantations, T. grandis plantation showed the highest AGBC (193 t ha−1) and BGBC (55.97 t ha−1). On the other hand, Tharosibi forest and T. grandis plantation had the highest total carbon stock for natural and plantation forest with values of 274.824 t ha−1 and 390.88 t ha−1, respectively. The total above-ground carbon stock estimated for the natural forest of KHP, LNG and TRS were 109.60 t ha−1, 79.49 t ha−1 and 132.74 t ha−1, respectively. On the other hand, the estimated total above-ground carbon stock in plantation of GA, PD, PF and TG were 62.93 t ha−1 62.81 t ha−1, 45.85 t ha−1 and 193.82 t ha−1. In the present study, the relationship with the biomass was observed to be better in SAVI compared to NDVI and TVI. The linear regression analysis performed to determine the relationship between the estimated and predicted biomass resulted in a correlation coefficient of R2 = 0.85 for the present study area, which is an indication of a good relationship between the estimated and predicted biomass.