Integration of Ground Inventory Data with Landsat Imagery to Estimate Aboveground Biomass of Tropical Deciduous Forest in Bago Yoma, Myanmar

Abstract

Even with recently increased awareness of the environmental conservation, the degradation of tropical forests are still becoming the major source for carbon emission to the atmosphere. The aboveground biomass (AGB) of these forests are, therefore, a vital role in global carbon sequestration. As the initial step of the forest conservation in Myanmar, the aboveground biomass of South Zarmani Reserved Forest in Bago Yoma region were estimated using Landsat 8 OLI after the evaluation with 100 sample field inventory plots. Multiple linear regression (MLR) model of band values and their principal component analysis (PCA) model were developed to estimate the AGB using the spectral reflectance from Landsat images and elevation as the input variables. TheMLR model had r2 = 0.43, RMSE = 60.2 tons/ha, relative RMSE = 70.1%, Bias = -9.1 tons/ha, Bias (%) = -10.6%, and p < 0.0001, while the PCA model showed r2 = 0.45, RMSE = 55.1 tons/ha, relative RMSE = 64.1%, Bias = -8.3 tons/ha, Bias (%) = -9.7%, and p < 0.0001. The AGB maps of the study area were generated based on both MLR and PCAmodels. The estimated mean AGB values were 74.74±22.3 tons/ha and 73.04±17.6 tons/ha and the total AGB of the study area are about 5.7 and 5.6 million tons from MLR and PCA, respectively. In conclusion, we were able to generate solid regression models from Landsat 8 OLI image after ground truth and two regression models gave us very similar AGB estimation (less than 2%) of South Zarmani Reserved Forest, Bago Yoma, Myanmar.