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

Wiebe Nijland; Scott E. Nielsen; Nicholas C. Coops; Michael A. Wulder; Gordon B. Stenhouse

doi:10.1117/1.JRS.8.083572

11 August 2014 Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics

Wiebe Nijland, Scott E. Nielsen, Nicholas C. Coops, Michael A. Wulder, Gordon B. Stenhouse

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083572 (August 2014). https://doi.org/10.1117/1.JRS.8.083572

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 4435 km² 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.

Citation Download 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," Journal of Applied Remote Sensing 8(1), 083572 (11 August 2014). https://doi.org/10.1117/1.JRS.8.083572

Published: 11 August 2014

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available