Prediction of forest aboveground biomass using an integrated approach of space-based parameters, and forest inventory data

Abstract

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.

Keywords

• Spectral variables
• regression analysis
• biomass
• Northeast India
• Land use