A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic...

Canopy metrics from ICESat/GLAS lidar waveforms were applied to estimate stemwood volume for a mixed temperate forest. Gaussian decomposition from product GLA14 was used to infer a ground surface return from within the waveform. The region of the waveform returned from the vegetation was subsequently taken to be between this position and the beginning of the waveform signal. The heights of vege...

Modeling sub-canopy elevation is an important step in the processing of waveform lidar data to measure three dimensional forest structure. Here, we present a methodology based on high resolution discrete-return lidar (DRL) to correct the ground elevation derived from large-footprint Laser Vegetation Imaging Sensor (LVIS) and to improve measurement of forest structure. We use data acquired over ...

Model-based forest height inversion from Pol-InSAR data relies on the realistic parameterization of underlying (vertical) radar reflectivity function. In context interferometric TanDEM-X measurements - especially in global single pol DEM mode this is not possible due to limited dimensionality observation space. order overcome this, use lidar waveforms directly approximate proposed. This allows ...