Characterizing vegetation structure using waveform LiDAR

The structure of light penetration through the canopy plays an important role in water, carbon, and energy fluxes between the biosphere and the atmosphere. Total foliage and foliage distribution are major aspects of canopy structure that significantly influence light and vegetation interaction. Waveform airborne LiDAR data contains large amounts of vegetation structural information, and is the best tool available for providing detailed physical information for large areas of vegetation. In this thesis, we first provide a complete work flow that extracts and processes waveform LiDAR data for an area of interest. Then we test the feasibility of using waveform LiDAR data to estimate individual tree biomass with limited field samples. We use a voxelization method to generate pseudo-waveforms for individual trees and apply a stepwise regression to find the relationship between pseudo-waveform structural characteristics and biomass estimated by allometric equations using tree survey data. Next, we present a method for describing physical canopy clumping structure for individual trees that provides detailed spatial clumping variations. We utilize the K-means clustering algorithm to extract structure from the large amount of canopy architecture information provided by full-waveform LiDAR. Finally we use representative cluster traits to identify structurally significant clusters