5 September 2013 Airborne lidar point cloud-based below-canopy line-of-sight visibility estimator
Heezin Lee, S. Bruce Blundell, Michael J. Starek, John G. Harris
Author Affiliations +
Abstract
Point cloud data collected by small-footprint lidar scanning systems have proven effective in modeling the forest canopy for extraction of tree parameters. Although line-of-sight visibility (LOSV) in complex forests may be important for military planning and search-and-rescue operations, the ability to estimate LOSV from lidar scanners is not well developed. A new estimator of below-canopy LOSV (BC-LOSV) by addressing the problem of estimation of lidar under-sampling of the forest understory is created. Airborne and terrestrial lidar scanning data were acquired for two forested sites in order to test a probabilistic model for BC-LOSV estimation solely from airborne lidar data. Individual crowns were segmented, and allometric projections of the probability model into the lower canopy and stem regions allowed the estimation of the likelihood of the presence of vision-blocking elements for any given LOSV vector. Using terrestrial lidar scans as ground truth, we found an approximate average absolute difference of 20% between BC-LOSV estimates from the airborne and terrestrial point clouds, with minimal bias for either over- or underestimates. The model shows the usefulness of a data-driven approach to BC-LOSV estimation that depends only on small-footprint airborne lidar point cloud and physical knowledge of tree phenology.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2013/$25.00 © 2013 SPIE
Heezin Lee, S. Bruce Blundell, Michael J. Starek, and John G. Harris "Airborne lidar point cloud-based below-canopy line-of-sight visibility estimator," Journal of Applied Remote Sensing 7(1), 073510 (5 September 2013). https://doi.org/10.1117/1.JRS.7.073510
Published: 5 September 2013
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
LIDAR

Clouds

Visibility

Data modeling

Vegetation

Visual process modeling

Image segmentation

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