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Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

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  • Kocev, Dragi
  • Džeroski, Sašo
  • White, Matt D.
  • Newell, Graeme R.
  • Griffioen, Peter

Abstract

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.

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  • Kocev, Dragi & Džeroski, Sašo & White, Matt D. & Newell, Graeme R. & Griffioen, Peter, 2009. "Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition," Ecological Modelling, Elsevier, vol. 220(8), pages 1159-1168.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:8:p:1159-1168
    DOI: 10.1016/j.ecolmodel.2009.01.037
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    References listed on IDEAS

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    1. R. J. Scholes & R. Biggs, 2005. "A biodiversity intactness index," Nature, Nature, vol. 434(7029), pages 45-49, March.
    2. C. R. Margules & R. L. Pressey, 2000. "Systematic conservation planning," Nature, Nature, vol. 405(6783), pages 243-253, May.
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    2. Kocev, Dragi & Naumoski, Andreja & Mitreski, Kosta & Krstić, Svetislav & Džeroski, Sašo, 2010. "Learning habitat models for the diatom community in Lake Prespa," Ecological Modelling, Elsevier, vol. 221(2), pages 330-337.
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