Characterizing relative degrees of clumping structure in vegetation canopy using waveform LiDAR

With the advances in complexity and resolution in ecological modeling, there is an increasing demand for more detailed characterization of vegetation canopy architecture. Clumping, a characteristic of foliage distribution, is one of the important aspects of canopy structure and significantly influences light penetration through the canopy. Airborne full-waveform LiDAR data contains large amounts of structural information, and presents a powerful avenue for providing detailed foliage distribution information over large areas. In this study, we present a method for describing physical canopy clumping structure for individual trees that can resolve fine scale variations in foliage clumping. We first utilize K-means clustering algorithm to extract structure found within full-waveform LiDAR data. Then we use representative traits for each data cluster to classify them into their natural groups. Based on these traits, we draw conclusions about physical representations of each group and identify groups as containing clumps in vegetation ranging from spares to dense. We formulate a clumping index multiplier based on cluster traits that inform relative degrees of vegetation clumping at the sub-canopy scale. We use the clumping index multiplier to introduce heterogeneity to a modeled plot level clumping index and generate a vertical clumping index profile for a forested plot. This study demonstrates that large amounts of canopy structural information can be extracted from waveform LiDAR data. The fine resolution canopy clumping structure found by the method described in this work can be used as valuable input for ecological models.