| Precise characterization of vegetation structure and biomass is significant due to current high uncertainty in estimating global terrestrial carbon sink, ranging from 10 to 60% of total fossil fuel emission. LIght Detection And Ranging (LIDAR) remote sensing is an advanced tool developed for this purpose, and recent identified problems in this area include the need of interpreting lidar height on slopes and estimating forest above ground biomass. This research focuses on these two aspects by assessing the feasibility of analytical solutions, as well as investigating alternative physical interpretation of lidar data.;A recently developed slope correction scheme based on a Geometric Optical and Radiative Transfer (GORT) model was used to quantify the topographic impact on lidar measured vegetation height. By using this scheme, data from spaceborne Geoscience Laser Altimeter System (GLAS) were compared to airborne Laser Vegetation Imaging Sensor (LVIS) and small-footprint lidar data, where LVIS data is regarded as less affected by slope, and small-footprint lidar data is regarded as ground truth. Analyses show slope-corrected GLAS vegetation heights match well with both small-footprint lidar (R2 = 0.77, RMSE = 2.2 m) and slope-corrected LVIS heights (R2 = 0.64, RMSE = 3.7 m). Both slope-corrected GLAS and LVIS height biases are independent on slope.;The investigation of the relationship between lidar data and in-situ measured vegetation structure parameters showed that it is scale- and vegetation type- dependant. For dense forest stands, vegetation biomass is more related to lidar height; while for sparse stands, lidar estimated canopy cover can be more important parameters in approximating tree density variation. To better link lidar data with vegetation structure, a lidar biomass index was developed based on height and estimated canopy cover profile to approximate vertical tree density distribution. Analyses in three different types of forests showed high correction (R2=0.75-0.83) and near stable relationships between this index and in-situ measured biomass. This index also helps to explain why some height metrics are optimal based on the vegetation structure and topography.;The results presented in this dissertation suggest that the theoretical development can improve the accuracy and interpretation of lidar data, which, in turn, provide unique remote sensing datasets for studies of vegetation structure and biomass, and ultimately decrease the uncertainty in estimating terrestrial carbon sink. |
