Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping

• Published in: International journal of remote sensing 2002-01, Vol.23 (21), p.4633-4652
• Main Authors: Franklin, S. E., Peddle, D. R., Dechka, J. A., Stenhouse, G. B.
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Pollution, environment geology; Teledetection and vegetation maps
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160110113971

Multisource data consisting of satellite imagery, topographic descriptors derived from DEMs, and GIS inventory information have been used with a detailed, field-based landcover classification scheme to support a quantitative analysis of the spatial distribution and configuration of grizzly bear ( Ursus arctos horribilis ) habitat within the Alberta Yellowhead Ecosystem study area. The map is needed to determine if bear movement and habitat use patterns are affected by changing landscape conditions and human activities. We compared a multisource Evidential Reasoning (ER) classification algorithm, capable of handling this large and diverse data set, to a more conventional maximum likelihood decision rule which could only use a subset of the available data. The ER classifier provided an acceptable level of accuracy (ranging to 85% over 21 habitat classes) for a level 3 product, compared to 71% using a maximum likelihood classifier.