Airborne lidar point cloud-based below-canopy line-of-sight visibility estimator

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. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

Probabilistic clutter maps of forested terrain from airborne LiDAR point clouds

“…After finding seed points, at each iteration, a region growing process is applied to associate the lidar points with the seeds. To decide if a seed is a real treetop or a false positive, we applied spin image representation to check the top-left to bottom-right diagonal signatures that result from the spin image process for actual trees [8].…”

“…The resultant point cloud data provides a 3D representation of the vertical forest structure enabling segmentation of individual trees [1][2][3][4] and extracting tree canopy metrics [5,6]. Lidar has also been used to find voids inside forests to estimate microwave signal attenuation [7] and within canopy line-of-sight visibilities [8].…”

Estimation of 2-D Clutter Maps in Complex Under-Canopy Environments From Airborne Discrete-Return Lidar

“…Lidar point cloud data provides a 3D representation of the vertical forest structure enabling identification of individual trees (Lee et al 2010;Jakubowski et al 2013;Strimbu and Strimbu, 2015;Khosravipour et al 2015) and extracting tree canopy parameters (Chen et al 2007;Palminteri et al 2012). Lidar has also been used to find voids inside forests to estimate microwave signal attenuation (Liu et al 2011;Wright et al 2008), intercepted photosynthetically active radiation (Lee et al 2009), and within-canopy line-of-sight visibilities (Lee et al 2013).…”