Accuracy of Biomass and Structure Estimates from Radar and Lidar

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for estimation of forest parameters from radar instruments in particular, use backscatter intensity, interferometry and polarimetric interferometry. This dissertation analyzes the accuracy of biomass and structure estimates over temperate forests of the North-Eastern United States. An empirical approach is adopted, relying on ground truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry is characterized for the field sites. Full waveform lidar data from two LVIS field campaigns of 2009 over the Harvard and Howland forests is analyzed to assess the accuracy of various lidar-biomass relationships. Radar data from NASA JPL\u27s UAVSAR is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model. The relationship between field biomass and InSAR heights is explored using SRTM elevation and LVIS derived ground topography. Temporal decorrelation, a major factor affecting the accuracy of repeat-pass InSAR observations of forests is analyzed using the SIR-C single-day repeat data from 1994. Finally, PolInSAR inversion of heights over the Harvard and Howland forests is explored using UAVSAR repeat-pass data from the 2009 campaign. These heights are compared with LVIS height estimates and the impact of temporal decorrelation is assessed