The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W20
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W20, 11–19, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W20-11-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W20, 11–19, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W20-11-2019

  15 Nov 2019

15 Nov 2019

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

T. T. Cat Tuong1,2, H. Tani3, X. F. Wang3, and V.-M. Pham4 T. T. Cat Tuong et al.
  • 1Mientrung Institute for Scientific Research, Vietnam Academy of Science and Technology, 321 Huynh Thuc Khang Street, Thua Thien Hue Province 530000, Vietnam
  • 2Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan
  • 3Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan
  • 4Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Ha Noi, Vietnam

Keywords: aboveground biomass, bamboo, mixed bamboo forest, remote sensing data, multivariate linear regression, Thua Thien Hue province, Vietnam

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.