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New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree AnalysisAuthor(s): L.R. Iverson; A.M. Prasad; A. Liaw
Source: In: Smithers, Richard, ed. Landscape ecology of trees and forests, proceedings of the twelfth annual IALE(UK) conference; 2004 June 21-24; Cirencester, UK. [Place of publication unknown]: International Association for Landscape Ecology: 317-320.
Publication Series: Other
Station: Northern Research Station
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DescriptionMore and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in predicting the distributions of four tree species under current and future climate. RTA's single tree was the easicst to interpret but is less accurate compared to BT and RF which use multiple regession trees with resampling and resampling-randomisation respectively. Future estimates of suitable habitat following climate change were also improved with BT and RF, with a slight edge to RF because it better smoothes the outputs in a logical gradient fashion. We recommend widespread use of these tools for CISbased vegetation mapping.
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CitationIverson, L.R.; Prasad, A.M.; Liaw, A. 2004. New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis. In: Smithers, Richard, ed. Landscape ecology of trees and forests, proceedings of the twelfth annual IALE(UK) conference; 2004 June 21-24; Cirencester, UK. [Place of publication unknown]: International Association for Landscape Ecology: 317-320.
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