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United States Department of Agriculture

Agricultural Research Service

Research Project: DISTURBANCE ASSESSMENT AND MITIGATION OF GREAT BASIN RANGELAND

Location: Northwest Watershed Management Research

Title: Modeling low-height vegetation with airborne LiDAR

Authors
item Glenn, Nancy -
item Mitchell, Jessica -
item Spaete, Lucas -
item Sankey, Temuulen -
item Shrestha, Rupesh -
item HARDEGREE, STUART

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: September 1, 2010
Publication Date: December 6, 2011
Citation: Glenn, N.F., Mitchell, J., Spaete, L., Sankey, T.T., Shrestha, R., Hardegree, S.P. 2011. Modeling low-height vegetation with airborne LiDAR. In: EOS Transactions, American Geophysical Union, Annual Meeting, Vol 91, December 2010. San Francisco, CA (CD-ROM Abstract).

Interpretive Summary: Low-height vegetation, common in semiarid regions, is difficult to characterize with LiDAR (Light Detection and Ranging) due to similarities, in time and space, of the point returns of vegetation and ground. Other complications may occur due to the low-height vegetation structural characteristics and the effects of terrain slope. LiDAR-derived vegetation height and crown area may be used as input for biomass estimates. This research is focused on modeling methods and error assessment of low-height vegetation in varying terrain. Several methods to best determine vegetation height and 2-d crown area are developed using both the LiDAR point cloud and rasters derived from the point cloud. These methods are tested on varying sloped terrain. Error assessments of bare earth terrain models in low-height vegetation cover types and slopes are also performed. Recommendations for modeling low-height vegetation and/or filtering low-height vegetation from terrain models will be presented, along with open-source algorithms.

Technical Abstract: Low-height vegetation, common in semiarid regions, is difficult to characterize with LiDAR (Light Detection and Ranging) due to similarities, in time and space, of the point returns of vegetation and ground. Other complications may occur due to the low-height vegetation structural characteristics and the effects of terrain slope. LiDAR-derived vegetation height and crown area may be used as input for biomass estimates. This research is focused on modeling methods and error assessment of low-height vegetation in varying terrain. Several methods to best determine vegetation height and 2-d crown area are developed using both the LiDAR point cloud and rasters derived from the point cloud. These methods are tested on varying sloped terrain. Error assessments of bare earth terrain models in low-height vegetation cover types and slopes are also performed. Recommendations for modeling low-height vegetation and/or filtering low-height vegetation from terrain models will be presented, along with open-source algorithms.

Last Modified: 8/19/2014
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