Identifying brown bear habitat by a combined GIS and machine learning method

Identifying brown bear habitat by a combined GIS and machine learning method In this paper we attempt to identify brown bear ( Ursus arctos ) habitat in south-western part of Slovenia, a country lying on the north-western-most edge of the continuous Dinaric-Eastern Alps brown bear population. The knowledge base (in the form of a decision tree) for the expert system for identifying the suitable habitat, was induced by automated machine learning from recorded bear sightings, and then linked to the GIS thematic layers for subsequent habitat/non-habitat classification of the entire study area. The accuracy of the decision tree classifier was 87% (KHAT 73%). The decision tree mostly agreed with the existing domain knowledge. For the study area the main factors considered by the expert system to be important for brown bear habitat were the percentage of forest (positive), proximity to settlements (negative) and elevation above see (positive), however the decision tree did not account for habitat patch size. After filtering out habitat patches smaller than 5000 ha in GIS, the accuracy increased to 89% (KHAT 77%). Whereas 88% of the habitat was within forests, only 33% of all forests were considered suitable as habitat. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Identifying brown bear habitat by a combined GIS and machine learning method

Ecological Modelling, Volume 135 (2) – Dec 5, 2000

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Publisher
Elsevier
Copyright
Copyright © 2000 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(00)00384-7
Publisher site
See Article on Publisher Site

Abstract

In this paper we attempt to identify brown bear ( Ursus arctos ) habitat in south-western part of Slovenia, a country lying on the north-western-most edge of the continuous Dinaric-Eastern Alps brown bear population. The knowledge base (in the form of a decision tree) for the expert system for identifying the suitable habitat, was induced by automated machine learning from recorded bear sightings, and then linked to the GIS thematic layers for subsequent habitat/non-habitat classification of the entire study area. The accuracy of the decision tree classifier was 87% (KHAT 73%). The decision tree mostly agreed with the existing domain knowledge. For the study area the main factors considered by the expert system to be important for brown bear habitat were the percentage of forest (positive), proximity to settlements (negative) and elevation above see (positive), however the decision tree did not account for habitat patch size. After filtering out habitat patches smaller than 5000 ha in GIS, the accuracy increased to 89% (KHAT 77%). Whereas 88% of the habitat was within forests, only 33% of all forests were considered suitable as habitat.

Journal

Ecological ModellingElsevier

Published: Dec 5, 2000

References

  • A regression model for the spatial distribution of red-crown crane in Yancheng biosphere reserve, China
    Li, W.; Wang, Z.; Ma, Z.; Tang, H.
  • Habitat and population modeling of roe deer using an interactive geographic information system
    Radeloff, V.C.; Pidgeon, A.M.; Hostert, P.

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