Estimation of tropical forest biophysical attributes with synergistic use of optical and microwave remote sensing techniques (Thailand)

Accurate estimates of tropical forest biophysical attributes provide quantitative information in the assessment of human disturbances and the carbon sequestration in global climate change studies. The research of my dissertation is thus to develop new algorithms to estimate tropical forest biophysical parameters (forest fractional cover, leaf area index, structures, and aboveground biomass) using synergy of optical and radar remote sensing imagery and radiative transfer models. The case study is in Mae Chaem Watershed, ChiangMai, Thailand. Ground data were collected during the field trips sponsored by research projects.; A linear unmixing model in the vegetation index (MSAVI) domain was built to estimate forest fractional cover with Landsat ETM+ image. The forest fractional cover map was validated using both ground measurements and high-resolution IKONOS images. The estimated fractional cover correlated with the ground-measured fractional cover (R2 = 0.76) at 32 study sites and correlated with the IKONOS-calculated fractional cover (R 2 = 0.70) at 400 randomly selected locations. The leaf area index was estimated using a modified Gaussian regression model with forest fractional cover results. The model was examined with a chi2 goodness-of-fit test. The correlation coefficient between the modeled and ground-measured leaf area index values is 0.90.; A microwave/optical synergistic radiative transfer model was built to simulate the radar scattering from the forest components. The leaf scattering and its attenuation to the woody components (branches, trunks) were quantified with the leaf area index derived from optical remote sensing data. The forest structural parameters, such as tree height and stand density, were estimated through model inversion with JERS-1 SAR and VNIR data. The total root-mean-square error (RMSE) of tree height estimation was 4.6 meter and that of stand density estimation was 300 trees/ha. The stand density estimation did not work in tropical evergreen forests because of its saturation at around 500 trees/ha. In accordance with ground measurements, tree height is negatively correlated with stand density in the study area. The model inversion becomes questionable at mountainous areas with high relief and steep slopes. The aboveground forest biomass is also calculated with allometric equations and the modeled forest structural parameters. The total RMSE is 88 ton/ha.; The methods developed in this study could be applied to estimate forest biophysical attributes at regional or global scales. With optical remote sensing imagery, only forest fractional cover and leaf area index could be estimated. When both optical and SAR data are acquired, the forest structural parameters and aboveground biomass can be estimated. These results could provide quantitative information in full carbon accounting in global climate change studies.