Next Article in Journal
A MODIS-Based Energy Balance to Estimate Evapotranspiration for Clear-Sky Days in Brazilian Tropical Savannas
Next Article in Special Issue
Forest Delineation Based on Airborne LIDAR Data
Previous Article in Journal
The Influence of DEM Quality on Mapping Accuracy of Coniferous- and Deciduous-Dominated Forest Using TerraSAR‑X Images
Previous Article in Special Issue
Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data
Article Menu

Export Article

Open AccessArticle
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)
View Full-Text   |   Download PDF [4061 KB, uploaded 19 June 2014]   |  


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. View Full-Text
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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top