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Remote Sens. 2012, 4(3), 682-702; doi:10.3390/rs4030682

Extracting More Data from LiDAR in Forested Areas by Analyzing Waveform Shape

Metservice, 30 Salamanca Road, Kelburn, Wellington 6012, New Zealand
New Zealand Forest Research Institute Ltd., Private Bag 3020, Rotorua 3046, New Zealand
National Geodetic Survey (NGS), NOAA, JHC-CCOM, 24 Colovos Road, Durham, NH 03824, USA
Author to whom correspondence should be addressed.
Received: 8 January 2012 / Revised: 13 February 2012 / Accepted: 6 March 2012 / Published: 12 March 2012
(This article belongs to the Special Issue Laser Scanning in Forests)
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Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates. In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included.
Keywords: waveform LiDAR; leaf area density; Gaussian fitting; deconvolution; Beer-Lambert law; LAD; Weiner deconvolution; forests waveform LiDAR; leaf area density; Gaussian fitting; deconvolution; Beer-Lambert law; LAD; Weiner deconvolution; forests
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Adams, T.; Beets, P.; Parrish, C. Extracting More Data from LiDAR in Forested Areas by Analyzing Waveform Shape. Remote Sens. 2012, 4, 682-702.

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