Title: Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric
Authors: Eric Ariel L. Salas and Geoffrey M. Henebry
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.
Citation:
Eric Ariel L. Salas, Geoffrey M. Henebry.
Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric.
AIMS Geosciences,
2016,
2(4):
366-390.
doi: 10.3934/geosci.2016.4.366
Download the free PDF copy here.
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