Estimating forest above ground biomass in Dak Lak Province, Vietnam

Abstract

Abstract. Bao HD, Huong NTT. 2025. Estimating forest above ground biomass in Dak Lak Province, Vietnam. Asian J For 9: 115-123. Accurately estimating forest Above Ground Biomass (AGB) is essential for assessing carbon stocks, monitoring forest health, and guiding sustainable management practices. This study examined the potential of Landsat 8 satellite imagery for AGB estimation in Dak Lak Province, located in Vietnam's Central Highlands. Field data collected from 415 sample plots across diverse forest types including evergreen broadleaf, semi-deciduous, and dry dipterocarp forests revealed AGB values ranging from 15.16 to 299.33 tons/ha. Multivariate linear regression (MLR) and random forest (RF) models were applied to predict AGB using spectral bands and vegetation indices derived from Landsat 8 imagery. The MLR model demonstrated limited predictive capability (R² = 0.131), indicating that linear relationships between spectral data and AGB were insufficient to capture the system's complexity. In contrast, the RF model exhibited superior predictive performance, achieving an R² of 0.653 and a concordance R² of 0.571 when using spectral bands alone. Incorporating vegetation indices alongside spectral bands further improved the RF model’s accuracy (R² = 0.671, concordance R² = 0.586). Spatial analysis revealed considerable variability in AGB across forest types, with evergreen broadleaf forests exhibiting the highest biomass values. These findings highlight the effectiveness of satellite remote sensing and machine learning for cost-effective biomass estimation. Moreover, they highlight the urgent need for such approaches in forest management and climate change mitigation efforts in tropical regions, including Vietnam.