Applying LiDAR technology for tree measurements in burned landscapes
Michael G. Wing A B , Aaron Eklund A and John Sessions AA Peavy Hall 204, Forest Engineering, Resources, and Management, Oregon State University, Corvallis, OR 97331, USA.
B Corresponding author. Email: michael.wing@oregonstate.edu
International Journal of Wildland Fire 19(1) 104-114 https://doi.org/10.1071/WF08170
Submitted: 3 October 2008 Accepted: 22 June 2009 Published: 5 February 2010
Abstract
Wildfires burn several million hectares in the United States annually. Time is critical in gathering information from burned landscapes for post-fire recovery planning. A technology to obtain spatial vegetation information across landscapes is Light Detecting and Ranging (LiDAR). We compared tree positional and height measurements, primarily from Douglas-fir (Pseudotsuga menziesii) and ponderosa pine (Pinus ponderosa), between field-based and LiDAR-derived measurements at three south-western Oregon (USA) sites. The sites represented a range of tree mortality from minimal to extensive. Our primary objective was to determine whether significant differences existed between field and LiDAR tree measurements in burned landscapes. Secondary objectives were to examine whether LiDAR pulse intensities in burned landscapes could differentiate coniferous from deciduous trees, discern fire-killed from live trees, and whether other tree measurement parameters were related to pulse intensities. No significant differences were detected between field-based and LiDAR-derived horizontal positions. Tree height differences between field-based and LiDAR measurements were significant at one site likely owing to dense canopy and measurement biases. Mean and maximum LiDAR intensities were significantly different between live and dead (fire-killed) trees in two of three sites. Additionally, crown diameter and tree sweep were significant in explaining variation in maximum LiDAR intensities at all sites.
Additional keywords: geospatial, GPS, wildfire.
Acknowledgements
We gratefully acknowledge the University of Washington Precision Forestry Cooperative for providing LiDAR data and assistance with this study.
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