Project Title: Detection, monitoring, and mapping of sudden oak death using hyperspectral imagery
Principal Investigator: Jon Fridgen, Project Manager,
Institute for Technology Development, Savoy, IL
Collaborators: Terry Shaw, Chief Scientist, WWETAC, USDA-Forest Service, Pacific Northwest Prineville, OR; Alan Kanaskie, Forest Pathologist, Oregon Dept. of Forestry, Salem, OR; Chris Lee, Sudden Oak Death Program Coordinator, University of California Cooperative Extension, Eureka, CA; Rodney McKellip, Project Integration Office, NASA-Stennis Space Center, Stennis Space Center, MS
Key Issues/Problems Addressed:
Sudden Oak Death (SOD) caused by the exotic plant pathogen Phytophthora ramorum is a significant threat to U.S. forests, especially in Western states where it is currently found. Hyperspectral imagery (used in support of a national early warning system) may help detect SOD in early stages and as a result provide a spatial context to aid perimeter mapping of SOD extent/distribution.
Setting and Approach:
Hyperspectral imagery data was collected, analyzed, and investigated as a means to improve pre-visual detection of Sudden Oak Death (SOD) syndrome in forest ecosystems. Two study areas that contain significant amounts of (SOD) were selected: one in Humboldt County, California and the other in Curry County, Oregon. Hyperspectal imagery data were acquired on two occasions and analyzed for its ability to discern SOD from the surrounding forest canopy. Satellite imagery was also obtained from the ASTER and MODIS sensors and analyzed in a multi‐tier/multi‐resolution approach for mapping the extent of SOD.
- Significant differences between healthy and diseased tanoak trees were observed in later stages of disease development. No significant differences were observed between healthy tanoak trees and tanoak trees exhibiting early symptoms of SOD infection.
- Hyperspectral datasets provided positive results for identifying/mapping forest land cover types in the study areas, especially the host species for SOD (i.e., tanoak, California Bay Laurel).
- The ASTER datasets worked well for discriminating between different forest land cover types, especially at the Oregon study site.
- The reduced spatial resolution of the MODIS and ASTER datasets were a limiting factor for accurate land cover mapping in California and the identification of SOD at both study sites.
Hyperspectral datasets are effective in the identification/mapping of forest land cover types and host species for SOD, as well as tree mortality associated with the disease. Alternative datasets with higher spatial resolution and future sensors may improve the ability to detect SOD infection at early stages in the future
Copenhaver, K., Fridgen, J., Hellrung, K. & Venkataraman, S. (2008). Detection, mapping, and monitoring of sudden oak death using hyperspectral imagery. Institute for Technology Development, Final Report, 38 pp. (PDF, 5.2MB)
WWETAC Project ID: FY07TS20