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Improving land change detection based on uncertain survey maps using fuzzy sets


Leyk, Stefan; Zimmermann, Niklaus E (2007). Improving land change detection based on uncertain survey maps using fuzzy sets. Landscape Ecology, 22(2):257-272.

Abstract

In this paper we present a method for correcting inherent classification bias in historical survey maps with which subsequent land cover change analysis can be improved. We linked generalized linear modelling techniques for spatial uncertainty prediction to fuzzy set based operations. The predicted uncertainty information was used to compute fuzzy memberships of forest and non-forest classes at each location. These memberships were used to reclassify the original map based on decision rules, which take into consideration the differences in identification likelihood during the historical mapping. Since the forest area was underestimated in the original mapping, the process allows to correct this bias by favouring forest, especially where uncertainty was high. The analyses were performed in a cross-wise manner between two study areas in order to examine whether the bias correction algorithm would still hold in an independent test area. Our approach resulted in a significant improvement of the original map as indicated by an increase of the Normalized Mutual Information from 0.26 and 0.36 to 0.38 and 0.45 for the cross-wise test against reference maps in Pontresina and St. Moritz, respectively. Consequently subsequent land cover change assessments could be considerably improved by reducing the deviations from a reference change by almost 50 percent. We concluded that the use of logistic regression techniques for uncertainty modelling based on topographic gradients and fuzzy set operations are useful tools for predictively reducing uncertainty in maps and land cover change models. The procedure allows to get more reliable area estimates of crisp classes and it improves the computation of the fuzzy areas of classes. The approach has limitations when the original map shows high initial accuracy

Abstract

In this paper we present a method for correcting inherent classification bias in historical survey maps with which subsequent land cover change analysis can be improved. We linked generalized linear modelling techniques for spatial uncertainty prediction to fuzzy set based operations. The predicted uncertainty information was used to compute fuzzy memberships of forest and non-forest classes at each location. These memberships were used to reclassify the original map based on decision rules, which take into consideration the differences in identification likelihood during the historical mapping. Since the forest area was underestimated in the original mapping, the process allows to correct this bias by favouring forest, especially where uncertainty was high. The analyses were performed in a cross-wise manner between two study areas in order to examine whether the bias correction algorithm would still hold in an independent test area. Our approach resulted in a significant improvement of the original map as indicated by an increase of the Normalized Mutual Information from 0.26 and 0.36 to 0.38 and 0.45 for the cross-wise test against reference maps in Pontresina and St. Moritz, respectively. Consequently subsequent land cover change assessments could be considerably improved by reducing the deviations from a reference change by almost 50 percent. We concluded that the use of logistic regression techniques for uncertainty modelling based on topographic gradients and fuzzy set operations are useful tools for predictively reducing uncertainty in maps and land cover change models. The procedure allows to get more reliable area estimates of crisp classes and it improves the computation of the fuzzy areas of classes. The approach has limitations when the original map shows high initial accuracy

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:National licences > 142-005
Dewey Decimal Classification:Unspecified
Language:English
Date:27 January 2007
Deposited On:14 Dec 2018 18:13
Last Modified:16 Dec 2018 14:13
Publisher:Springer
ISSN:0921-2973
OA Status:Green
Publisher DOI:https://doi.org/10.1007/s10980-006-9021-2
Related URLs:https://www.swissbib.ch/Search/Results?lookfor=nationallicencespringer101007s1098000690212 (Library Catalogue)

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