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Individual snag detection using neighborhood attribute filtered airborne lidar data

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https://ir.library.oregonstate.edu/concern/articles/rb68xd73t

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  • The ability to estimate and monitor standing dead trees (snags) has been difficult due to their irregular and sparse distribution, often requiring intensive sampling methods to obtain statistically significant estimates. This study presents a new method for estimating and monitoring snags using neighborhood attribute filtered airborne discrete-return lidar data. The method first develops and then applies an automated filtering algorithm that utilizes three dimensional neighborhood lidar point-based intensity and density statistics to remove lidar points associated with live trees and retain lidar points associated with snags. A traditional airborne lidar individual-tree detection procedure is then applied to the snag-filtered lidar point cloud, resulting in stem map of identified snags with height estimates. The filtering algorithm was developed using training datasets comprised of four different forest types in wide range of stand conditions, and then applied to independent data to determine successful snag detection rates. Detection rates ranged from 43 to 100%, increasing as the size of snags increased. The overall detection rate for snags with DBH ≥ 25 cm was 56% (± 2.9%) with low commission error rates. The method provides the ability to estimate snag density and stem map a large proportion of snags across the landscape. The resulting information can be used to analyze the spatial distribution of snags, provide a better understanding of wildlife snag use dynamics, assess achievement of stocking standard requirements, and bring more clarity to snag stocking standards.
  • Keywords: Snag detection, Snag density, Airborne lidar, Forestry, Snags, Neighborhood attribute lidar filtering, Lidar filtering
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  • Wing, B. M., Ritchie, M. W., Boston, K., Cohen, W. B., & Olsen, M. J. (2015). Individual snag detection using neighborhood attribute filtered airborne lidar data. Remote Sensing of Environment, 163, 165-179. doi:10.1016/j.rse.2015.03.013
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  • 163
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