Research Papers

Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics

[+] Author Affiliations
Wiebe Nijland

University of British Columbia, Department of Forest Resources Management, Faculty of Forestry, 2424 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada

Scott E. Nielsen

University of Alberta, Department of Renewable Resources, 751 General Services Building, Edmonton, Alberta T6G 2H1, Canada

Nicholas C. Coops

University of British Columbia, Department of Forest Resources Management, Faculty of Forestry, 2424 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada

Michael A. Wulder

Natural Resources Canada, Canadian Forest Service (Pacific Forestry Centre), 506 West Burnside Road, Victoria, British Columbia V8Z 1M5, Canada

Gordon B. Stenhouse

Foothills Research Institute, Hinton, Alberta T7V 1X6, Canada

J. Appl. Remote Sens. 8(1), 083572 (Aug 11, 2014). doi:10.1117/1.JRS.8.083572
History: Received March 10, 2014; Revised July 3, 2014; Accepted July 9, 2014
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Abstract.  Food and habitat resources are critical components of wildlife management and conservation efforts. The grizzly bear (Ursus arctos) has diverse diets and habitat requirements particularly for understory plant species, which are impacted by human developments and forest management activities. We use light detection and ranging (LiDAR) data to predict the occurrence of 14 understory plant species relevant to bear forage and compare our predictions with more conventional climate- and land cover-based models. We use boosted regression trees to model each of the 14 understory species across 4435km2 using occurrence (presence–absence) data from 1941 field plots. Three sets of models were fitted: climate only, climate and basic land and forest covers from Landsat 30-m imagery, and a climate- and LiDAR-derived model describing both the terrain and forest canopy. Resulting model accuracies varied widely among species. Overall, 8 of 14 species models were improved by including the LiDAR-derived variables. For climate-only models, mean annual precipitation and frost-free periods were the most important variables. With inclusion of LiDAR-derived attributes, depth-to-water table, terrain-intercepted annual radiation, and elevation were most often selected. This suggests that fine-scale terrain conditions affect the distribution of the studied species more than canopy conditions.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Topics

LIDAR ; Landsat

Citation

Wiebe Nijland ; Scott E. Nielsen ; Nicholas C. Coops ; Michael A. Wulder and Gordon B. Stenhouse
"Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics", J. Appl. Remote Sens. 8(1), 083572 (Aug 11, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083572


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