Delineation of management zones with spatial data fusion and belief theory

dc.contributor.authorVallentin, Claudia
dc.contributor.authorDobers, Eike Stefan
dc.contributor.authorItzerott, Sibylle
dc.contributor.authorKleinschmit, Birgit
dc.contributor.authorSpengler, Daniel
dc.date.accessioned2021-03-18T10:01:34Z
dc.date.available2021-03-18T10:01:34Z
dc.date.issued2019-11-22
dc.description.abstractPrecision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.en
dc.identifier.eissn1573-1618
dc.identifier.issn1385-2256
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12866
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11666
dc.language.isoenen
dc.relation.ispartof10.14279/depositonce-11174
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc550 Geowissenschaftende
dc.subject.othermanagement zonesen
dc.subject.otherNDVIen
dc.subject.otherbelief theoryen
dc.subject.othertransferable belief modelen
dc.subject.otherremote sensingen
dc.subject.othersegmentationen
dc.titleDelineation of management zones with spatial data fusion and belief theoryen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1007/s11119-019-09696-0en
dcterms.bibliographicCitation.journaltitlePrecision Agricultureen
dcterms.bibliographicCitation.originalpublishernameSpringeren
dcterms.bibliographicCitation.originalpublisherplaceHeidelbergen
dcterms.bibliographicCitation.pageend830en
dcterms.bibliographicCitation.pagestart802en
dcterms.bibliographicCitation.volume21en
tub.accessrights.dnbfreeen
tub.affiliationFak. 6 Planen Bauen Umwelt>Inst. Landschaftsarchitektur und Umweltplanung>FG Geoinformation in der Umweltplanungde
tub.affiliation.facultyFak. 6 Planen Bauen Umweltde
tub.affiliation.groupFG Geoinformation in der Umweltplanungde
tub.affiliation.instituteInst. Landschaftsarchitektur und Umweltplanungde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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