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Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation

Feilhauer, Hannes; Zlinszky, András; Kania, Adam; Foody, Giles M.; Doktor, Daniel; Lausch, Angela; Schmidtlein, Sebastian; He, Kate [Hrsg.]; Disney, Mat [Hrsg.]

Abstract:
Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature's fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel's class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000129744
Veröffentlicht am 16.02.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2056-3485, 2056-3485
KITopen-ID: 1000129744
Erschienen in Remote sensing in ecology and conservation
Verlag Wiley Open Access
Vorab online veröffentlicht am 26.11.2020
Schlagwörter ecotone, floristic gradient, fuzzy classification, gradual transition, ordination, vegetation mapping
Nachgewiesen in Scopus
Web of Science
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