Zur Kurzanzeige

Remote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphyllus

dc.date.accessioned2021-08-06T13:15:26Z
dc.date.available2021-08-06T13:15:26Z
dc.date.issued2020-09-30
dc.identifierdoi:10.17170/kobra-202108054491
dc.identifier.urihttp://hdl.handle.net/123456789/13082
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kasselger
dc.language.isoengeng
dc.rightsNamensnennung-Nicht-kommerziell 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectabove-ground biomasseng
dc.subjectheterogenous grasslandeng
dc.subjecthyperspectraleng
dc.subjectLiDAReng
dc.subjectsenor fusioneng
dc.subject.ddc500
dc.subject.ddc580
dc.titleRemote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphylluseng
dc.typeAufsatz
dcterms.abstractRemote sensing data fusion is a powerful tool to gain information of quantitative and qualitative vegetation properties on field level. The aim of this study was to develop prediction models from sensor data fusion for fresh and dry matter yield (FMY/DMY) in extensively managed grasslands with variable degree of invasion by Lupinus polyphyllus. Therefore, a terrestrial 3d laser scanner (TLS) and a drone-based hyperspectral camera was used to collect high resolution 3d point clouds and hyperspectral aerial orthomosaics of four extremely heterogenous grasslands. From 3d point clouds multiple features (vegetation height, sum of voxel, point density and surface structure) were extracted and combined with hyperspectral data to develop an optimized biomass model from random forest regression algorithm to predict FMY and DMY (ntrain = 130, ntest = 33). Models from hyperspectral data solitarily had the lowest prediction performance (FMY: R2 = 0.61, nRMSEr = 17.14; DMY: R2 = 0.59, nRMSEr = 19.37). Higher performance was gained by models derived from 3d laser data (FMY: R2 = 0. 76, nRMSEr = 13.3; DMY: R2 = 0. 74, nRMSEr = 15.1). A fusion of both sensor systems increased the FMY prediction performance up to R2 = 0.8; nRMSEr = 12.02 and the DMY prediction performance to R2 = 0.81 and nRMSEr = 12.06. The fusion of complementary sensor systems can increase the power to predict biomass yields of heterogenous and extensively managed grasslands. It is a novel alternative to labour-intensive, traditional biomass prediction methods and to remote sensing methods using only single sensor data.eng
dcterms.accessRightsopen access
dcterms.creatorSchulze-Brüninghoff, Damian
dcterms.creatorWachendorf, Michael
dcterms.creatorAstor, Thomas
dc.relation.doidoi:10.1002/rse2.182
dc.relation.projectidDeutsche Bundesstiftung Umwelt. Grant Number: 32886/01-33/2
dc.subject.swdBiomasseger
dc.subject.swdGrünlandger
dc.subject.swdLidarger
dc.subject.swdFernerkundungger
dc.subject.swdDatenfusionger
dc.subject.swdVielblättrige Lupineger
dc.type.versionpublishedVersion
dcterms.source.identifiereissn:2056-3485
dcterms.source.issueIssue 2
dcterms.source.journalRemote Sensing in Ecology and Conservationeng
dcterms.source.pageinfo198-213
dcterms.source.volumeVolume 7
kup.iskupfalse


Dateien zu dieser Ressource

Thumbnail
Name:
license_rdf
Größe:
914Bytes
Format:
application/rdf+xml
Öffnen
Thumbnail
Name:
rse2_182.pdf
Größe:
1.703Mb
Format:
PDF
Öffnen

Das Dokument erscheint in:

Zur Kurzanzeige

Namensnennung-Nicht-kommerziell 4.0 International
Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: Namensnennung-Nicht-kommerziell 4.0 International