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

Content Provider	Springer Nature Link
Author	Leyk, Stefan Zimmermann, Niklaus E.
Copyright Year	2006
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.
Starting Page	257
Ending Page	272
Page Count	16
File Format	PDF
ISSN	09212973
Journal	Landscape Ecology
Volume Number	22
Issue Number	2
e-ISSN	15729761
Language	English
Publisher	Springer Netherlands
Publisher Date	2006-10-19
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Predictive uncertainty modelling Fuzzy sets Land cover change analysis Classification bias Correction of survey maps Area estimation Plant Sciences Ecology Forestry Forestry Management Plant Ecology Landscape Ecology
Content Type	Text
Resource Type	Article
Subject	Geography, Planning and Development Ecology Nature and Landscape Conservation

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