Title
Uncertainty propagation in vegetation distribution models based on ensemble classifiers Uncertainty propagation in vegetation distribution models based on ensemble classifiers
Author
Faculty/Department
Faculty of Sciences. Bioscience Engineering
Publication type
article
Publication
Amsterdam ,
Subject
Biology
Engineering sciences. Technology
Source (journal)
Ecological modelling. - Amsterdam
Volume/pages
220(2009) :6 , p. 791-804
ISSN
0304-3800
ISI
000264645000005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannons entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.
E-info
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