A fuzzy logic based method for modeling the spatial distribution of indicators of decomposition in a high mountain environment

DC FieldValueLanguage
dc.contributor.authorHellwig, Niels
dc.contributor.authorAnschlag, Kerstin
dc.contributor.authorBroll, Gabriele
dc.date.accessioned2021-12-23T16:01:03Z-
dc.date.available2021-12-23T16:01:03Z-
dc.date.issued2016
dc.identifier.issn15230430
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/4739-
dc.description.abstractUpscaling of sample data on indicators of decomposition to the landscape scale is often necessary for extensive ecological assessments. The amount of such data is mostly scarce even with high sampling efforts. Moreover, environmental conditions are very heterogeneous in high mountain regions. Therefore, the aim was to find a suitable technique for spatial modeling under these circumstances. A method combining decision tree analysis and the construction of fuzzy membership functions is introduced for a GIS-based mapping of decomposition indicating parameters. It is compared with an approach solely based on decision trees. Within a case study in the Italian Alps the spatial distribution of humus forms, classified by the occurrence of an OH (humified residues) horizon, is examined. There appears to be a strong relationship with elevation and a minor correlation with slope exposition. The fuzzy logic-based approach proves to be suitable for modeling the spatial distribution of indicators of decomposition. Mapping fuzzy values allows for the representation of small-scale variability and uncertainty of data due to a relatively low sample size in a very heterogeneous environment.
dc.description.sponsorshipGerman Research Foundation (DFG)German Research Foundation (DFG) [BR 1106/23-1]; This study was realized in the context of the D.A.CH. project DecAlp and funded by the German Research Foundation (DFG, grant number BR 1106/23-1). The authors thank all colleagues of the project for an excellent cooperation. We are in particular grateful to Dylan Tatti (University of Neuchatel) for sharing his data on humus forms and to Giacomo Sartori (Museo Tridentino di Scienze Naturale) for valuable discussions in the field. We also thank the anonymous reviewers for valuable comments on an earlier version of the manuscript.
dc.language.isoen
dc.publisherINST ARCTIC ALPINE RES
dc.relation.ispartofARCTIC ANTARCTIC AND ALPINE RESEARCH
dc.subjectCLASSIFICATION
dc.subjectDECISION TREE APPROACH
dc.subjectDIGITAL ELEVATION MODELS
dc.subjectEnvironmental Sciences
dc.subjectEnvironmental Sciences & Ecology
dc.subjectEXPERT KNOWLEDGE
dc.subjectFOREST
dc.subjectGeography, Physical
dc.subjectGIS
dc.subjectHUMUS FORMS
dc.subjectPEDOTRANSFER FUNCTIONS
dc.subjectPhysical Geography
dc.subjectREGRESSION TREES
dc.subjectSOIL INFERENCE
dc.titleA fuzzy logic based method for modeling the spatial distribution of indicators of decomposition in a high mountain environment
dc.typejournal article
dc.identifier.doi10.1657/AAAR0015-073
dc.identifier.isiISI:000389317200002
dc.description.volume48
dc.description.issue4
dc.description.startpage623
dc.description.endpage635
dc.contributor.orcid0000-0003-4517-1746
dc.contributor.researcheridK-1322-2019
dc.identifier.eissn19384246
dc.publisher.placeUNIV COLORADO, BOULDER, CO 80309 USA
dcterms.isPartOf.abbreviationArct. Antarct. Alp. Res.
dcterms.oaStatusGreen Published, Bronze
crisitem.author.netidBrGa772-
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