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Assessing spatial variability of barley whole crop biomass yield and leaf area index in silvoarable agroforestry systems using UAV-borne remote sensing

dc.date.accessioned2021-07-23T09:07:59Z
dc.date.available2021-07-23T09:07:59Z
dc.date.issued2021-07-13
dc.identifierdoi:10.17170/kobra-202107234399
dc.identifier.urihttp://hdl.handle.net/123456789/13033
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kasselger
dc.language.isoeng
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectUAVeng
dc.subjectagroforestryeng
dc.subjectmultispectraleng
dc.subjectbarleyeng
dc.subjectalley croppingeng
dc.subjectpredictive modellingengeng
dc.subjectSFMeng
dc.subject.ddc630
dc.titleAssessing spatial variability of barley whole crop biomass yield and leaf area index in silvoarable agroforestry systems using UAV-borne remote sensingeng
dc.typeAufsatz
dcterms.abstractAgroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.eng
dcterms.accessRightsopen access
dcterms.creatorWengert, Matthias
dcterms.creatorPiepho, Hans-Peter
dcterms.creatorAstor, Thomas
dcterms.creatorGraß, Rüdiger
dcterms.creatorWijesingha, Jayan
dcterms.creatorWachendorf, Michael
dc.relation.doidoi:10.3390/rs13142751
dc.subject.swdPräzisionslandwirtschaftger
dc.subject.swdAgroforstwirtschaftger
dc.subject.swdFlugkörperger
dc.subject.swdGersteger
dc.subject.swdErnteger
dc.type.versionpublishedVersion
dcterms.source.identifiereissn:2072-4292
dcterms.source.issueIssue 14
dcterms.source.journalRemote sensingeng
dcterms.source.volumeVolume 13
kup.iskupfalse
dcterms.source.articlenumber2751


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