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

Aditya Rizky Priatama, Yudi Setiawan, Irdika Mansur, Muhammad Masyhuri


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


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Aditya Rizky Priatama (Primary Contact)
Yudi Setiawan
Irdika Mansur
Muhammad Masyhuri
Author Biography

Yudi Setiawan, Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Ring Road Campus IPB Dramaga, Bogor, Indonesia 16680

Recently, I am a lecturer staff at Faculty of Forestry and Environment, IPB University and researcher at Center for Environmental Research, IPB. I have been working as a remote sensing specialist on land change detection at UNDP-REDD Indonesia, and developed a novel method for the change detection that it should be applicable in the development of a near-real time deforestation detection system for Indonesia.

PriatamaA. R., SetiawanY., MansurI., & MasyhuriM. (2022). Regression Models for Estimating Aboveground Biomass and Stand Volume Using Landsat-Based Indices in Post-Mining Area . Jurnal Manajemen Hutan Tropika, 28(1), 1-14.

Article Details

Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

Muhammad Buce Saleh, Rosima Wati Dewi, Lilik Budi Prasetyo, Nitya Ade Santi
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