SNAMP Publication #5: Characterizing spotted owl nesting habitat with lidar
Article Title: LiDAR as a Tool to Characterize Wildlife Habitat: California Spotted Owl Nesting Habitat as an Example
Authors: Celia García-Feced, Douglas J. Tempel, and Maggi Kelly
- Large trees are important for many wildlife species, but they can be difficult to map over large areas.
- We used lidar to map large trees and canopy cover surrounding four confirmed currently used spotted owl nest sites in the Tahoe National Forest.
- We found that all nest sites were surrounded by numerous other large trees, and were located in dense canopies.
- We believe that lidar can help forest managers and scientists in the assessment of wildlife–habitat relationships and conservation of important wildlife species by allowing managers to better identify habitat characteristics on a large scale.
Large trees (greater than 35in or 90 cm in diameter at breast height) are important elements for many wildlife species and often influence the selection of habitat by spotted owls. Unfortunately, it is difficult to identify and map such trees over large areas. We used lidar (defined below) data covering the Last Chance study area in the Tahoe National Forest and mapped all the large trees surrounding four nest sites for spotted owls (Strix occidentalis occidentalis). The four nest trees were located during owl field surveys, and confirmed occupied. We mapped the canopy cover (see below), and the number, density, and pattern of residual trees within 200 m of nest trees.
All nest trees were surrounded by many large trees. The total number of residual trees surrounding each nest tree ranged from 123 to 222 trees, and the density of large trees ranged from 4 to 7 trees per acre (9 to 17 trees per ha). The canopy cover was generally high around all the nest trees, ranging from 50 to 80%.
- We believe that lidar offers great potential to improve the ability of forest managers and scientists to conserve key wildlife species by enhancing habitat assessments.
- Lidar can help by mapping individual trees and canopy cover across large areas.
- However, lidar has important drawbacks. Lidar data can be expensive to acquire, and considerable expertise is required to process the raw data.
- Fortunately, lidar costs per acre decrease substantially as the area sampled increases, and there are many efforts to share acquisition costs among state and federal agencies.
García-Feced, C., D. J. Tempel, and M. Kelly. 2011. LiDAR as a tool to characterize wildlife habitat: California Spotted Owl nesting habitat as an example. Journal of Forestry 108(8): 436-443
The full paper is available here.
For more information about the SNAMP project and the spatial and owl teams, please see the
Spatial Team, and Spatial Team Newsletters:
And the Owl Team and Owl Team Newsletters:
Canopy cover: The percent of an area covered by the crowns of an individual plant species. So canopy cover of 80% means that 80% of the ground is covered by trees; this is considered dense canopy.
Lidar: light detection and ranging. Lidar systems work by “sounding” light against a target in a similar way to sonar or radar. The speed with which a pulse of light returns from a target can be used to measure the target’s height. To learn more about lidar data, check out our lidar FAQs sheet. For more information on lidar data in SNAMP, see the spatial team website: http://snamp.cnr.berkeley.edu/teams/spatial, and our spatial team newsletters that focus on lidar: Vol. 2, No. 3, and Vol. 5, No. 1.