Regression Models for Estimating Aboveground Biomass and Stand Volume Using Landsat-Based Indices in Post-Mining Area

This paper describes the use of remotely sensed data to measure vegetation variables such as basal area, biomass and stand volume. The objective this research was developed regression models estimate area (BA), aboveground (AGB), volume (SV) using Landsat-based indices. examined indices were SAVI, MSAVI, EVI, NBR, NBR2 NDMI. Regression based on least-squared method several forms equation, i.e., linear, exponential, power, logarithm polynomial. Among those models, it recognized that best fit model obtained from exponential model, log (y) = ax + b for estimating BA, AGB & SV. MSAVI had been identified most accurate independent variable estimates with R² 0.70 average verification values 16.39% (4%32.66%); while EVI become (AGB) R2 0.72 18,10% (9%-28.01%); NDMI be 0.69 24.37% (-15%-38.11%).