Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

Details

Serval ID
serval:BIB_098835E88B44
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes
Journal
Journal of Applied Ecology
Author(s)
Rutherford G. N., Guisan A., Zimmermann N. E.
ISSN
0021-8901
Publication state
Published
Issued date
2007
Peer-reviewed
Oui
Volume
44
Number
2
Pages
414-424
Language
english
Abstract
The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results.
We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland.
Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results.
Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.
Keywords
land cover change, model accuracy, model selection, multinomial regression, ordinal regression
Web of science
Open Access
Yes
Create date
24/01/2008 19:06
Last modification date
20/08/2019 12:31
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