ESTIMATING ABOVEGROUND BIOMASS OF BAMBOO AND MIXED BAMBOO FOREST IN THUA THIEN-HUE PROVINCE, VIET NAM USING PALSAR-2 AND LANDSAT OLI DATA

Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R²) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R² of 0.60) over the performance of models using only SAR (R² of 0.49) and only Landsat data (R² of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R² of 0.59 and an RMSE of 29.66 tons ha⁻¹. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha⁻¹). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.