Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric

Abstract

There have been few approaches developed for the estimation of height using waveform LiDAR data. Unlike any existing methods, we illustrate how the new Moment Distance (MD) framework can characterize the canopy height based on the geometry and return power of the LiDAR waveform without having to go through curve modeling processes. Our approach offers the possibilities of using the raw waveform data to capture vital information from the variety of complex waveform shapes in LiDAR. We assess the relationship of the MD metrics to the key waveform landmarks—such as locations of peaks, power of returns, canopy heights, and height metrics—using synthetic data and real Laser Vegetation Imaging Sensor (LVIS) data. In order to verify the utility of the new approach, we use field measurements obtained through the DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) campaign. Our results reveal that the MDI can capture temporal dynamics of canopy and segregate generations of stands based on curve shapes. The method satisfactorily estimates the canopy height using the synthetic (r2 = 0.40) and the LVIS dataset (r2 = 0.74). The MDI is also comparable with existing RH75 (relative height at 75%) and RH50 (relative height at 50%) height metrics. Furthermore, the MDI shows better correlations with ground-based measurements than relative height metrics. The MDI performs well at any type of waveform shape. This opens the possibility of looking more closely at single-peaked waveforms that usually carries complex extremes.

Keywords

• Moment Distance Index; MD framework; waveform LiDAR; MDI; canopy height; LVIS; shape metric