Research Papers

Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat

[+] Author Affiliations
Thomas Hilker

NASA Goddard Space Flight Center, Biospheric Sciences Branch Code 618, Greenbelt, Maryland 20771 thomas.hilker@nasa.gov

Nicholas C. Coops

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

Rachel Gaulton

Newcastle University, Department of Civil Engineering and Geosciences, Newcastle upon Tyne, NE1 7RU, United Kingdom

Michael A. Wulder

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

Jerome Cranston, Gordon Stenhouse

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

J. Appl. Remote Sens. 5(1), 053568 (December 01, 2011). doi:10.1117/1.3664342
History: Received June 11, 2011; Revised October 28, 2011; Accepted November 07, 2011; Published December 01, 2011; Online December 01, 2011
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An increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity.

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© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Thomas Hilker ; Nicholas C. Coops ; Rachel Gaulton ; Michael A. Wulder ; Jerome Cranston, et al.
"Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat", J. Appl. Remote Sens. 5(1), 053568 (December 01, 2011). ; http://dx.doi.org/10.1117/1.3664342


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