The land-use sector needs special importance owing to its ability to store and emit carbon back to the atmosphere. Land-use changes and their correlations could elucidate conditions that put forests at risk of conversion to other land uses. The impact of rapid forest cover change tends to reduce the percentage of forest cover, thereby reducing the potential for carbon storage in woody biomass. The study was conducted in the state of Arunachal Pradesh to estimate the above-ground biomass, carbon pool and sequestration potential of major land-use sectors. The above-ground biomass of selected land-uses viz. dense forest, moderately dense forest, open forest, plantations, Jhum >5 years, Jhum <5 years and current jhum are 332.28 t ha⁻¹, 246.63 t ha⁻¹, 145.36 t ha⁻¹, 179.31 t ha⁻¹, 149.63 t ha⁻¹, 55.40 t ha⁻¹, 16.84 t ha⁻¹ respectively. The developed model is derived from Soil Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index (ARVI) for prediction of above-ground biomass (AGB) (R² = 0.85, p < 0.05). The resultant R² value of 85% predicts that 79% of accuracy could be assumed by the model. The RMSE of the model was 53.21 t ha⁻¹, having no multicollinearity problem keeping tolerance (0.49) and VIF (2.80). The spatial AGB density map was predicted using a step-wise linear regression model which used AGB, SAVI and ARVI of the corresponding sample plot location. The implication of the land-use change revealed that about 84% carbon will be lost from dense forest once it is converted to Jhum (Jhum is the alternate name of Shifting cultivation in India) followed by Jhum <5 years (65%). It is pertinent to mention here that, the present analysis will help the policymakers in visualizing a proper developmental goal to regulate land-use changes for achieving the higher carbon stock and maintaining balance in the global climate scenario.

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.

Forests contribute significantly in mitigating the effects of climate change by sequestering atmospheric carbon in biomass and soil. To comprehend carbon stock and sequestration, forest degradation, and climate change mitigation, precise calculation biomass is needed. The present study in Arunachal Pradesh, Northeast India, used Landsat OLI spectral variables, land surface temperature, and field inventory data to estimate aboveground biomass (AGB) and carbon stock in selected forest types. The stepwise multilinear regression model was developed using Landsat-derived spectral data, land surface temperature, and soil moisture The stand density varies from 122 individual ha⁻¹ to 833 individual ha⁻¹ . The estimated AGB density varies from 2.64 t ha⁻¹ to 534.21 t ha⁻¹ among the sample plots. The mean land surface temperature was 14.41°C. The predictive integrated model showed that the mean AGB ranged from 9.45 t ha⁻¹ to 330 t ha⁻¹ and dense forest recorded the maximum mean AGB (239.34 t ha⁻¹). Akaike information criteria (AIC) and Bayesian information criteria (BIC) were utilised to evaluate the predictive model, and the AIC (176.13) and BIC (184.58) were omparatively lower than other regression models.To evaluate the accuracy of the integrated model (IM), the linear regression was performed between the predicted and observed AGB. The findings of the study will be useful in formulating site-specific suitable management plan.

Comprehending the prominence of forest carbon in climate change, this study was piloted in different land use of West Kameng district, Arunachal Pradesh, India to record the floristic composition, community characteristics, and above ground biomass (AGB) carbon using random sampling and geospatial approach. Preliminary field survey was done in 2016. Altogether 45 quadrats (0.1 ha each) were laid. Total tree richness recorded was 164 species from 49 families. Dominance and frequency distribution pattern of species revealed heterogeneity in composition with majority species showing clumped distribution. Plantations showed highest tree density while mixed dense forest showed maximum basal area (58.89 m² ha⁻¹). Estimated AGB were 218.21 ton/ha for mixed dense forest, 84.94 ton/ha for abandoned forest, and 105.09 ton/ha for plantations. Total estimated carbon stocks were 120.01, 46.17, and 57.80 ton/ha for mixed dense, abandoned forest, and plantations, respectively. Predicted average AGB using Geographic Information System (GIS) techniques was 163.25 ton/ha. Field-based AGB was slightly greater than the values observed from satellite data. Findings of the study may be useful for calculating total biomass and carbon stored in the major land cover of the district in particular and region in general. It will also support in future studies for calculating the long-term data on biomass carbon sequestration.