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AuthorAllen, Nathanieldc.contributor.author
AuthorCooksley, Huwdc.contributor.author
AuthorBuchmann, Carstendc.contributor.author
AuthorSchurr, Frankdc.contributor.author
AuthorPagel, Jörndc.contributor.author
Date of accession2022-11-24T10:51:04Zdc.date.accessioned
Available in OPARU since2022-11-24T10:51:04Zdc.date.available
Date of first publication2022-11-24dc.date.issued
AbstractIn order to understand population and community dynamics, many ecological studies require comprehensive knowledge of the spatial distribution of individual organisms, but obtaining this data is a time and labor-intensive process. In this study we develop a workflow to automatically determine the species of shrubs of the Proteaceae family in South Africa's Fynbos region from drone-based photogrammetric data. We applied deep learning to segment five species of shrub individuals from the background based on spectral and height information. The spectral-height model achieved an average prediction accuracy of 74.4%, compared to 61.6% when using spectral information alone. Despite the challenge in distinguishing sprawling shrubs from the background, which may be overcome with additional training data, the presented workflow holds promise for the efficient mapping of shrub communities.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
Is part ofhttp://dx.doi.org/10.18725/OPARU-46164dc.relation.ispartof
LicenseCC BY 4.0 Internationaldc.rights
Link to license texthttps://creativecommons.org/licenses/by/4.0/dc.rights.uri
Dewey Decimal GroupDDC 000 / Computer science, information & general worksdc.subject.ddc
Dewey Decimal GroupDDC 300 / Social sciencesdc.subject.ddc
LCSHBiodiversitydc.subject.lcsh
LCSHRemote sensingdc.subject.lcsh
TitleAutomated mapping and identification of shrub individuals in South Africa's Fynbos biome using drone imagery and deep learningdc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionpublishedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-46058dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-46134-9dc.identifier.urn
GNDDeep learningdc.subject.gnd
GNDFernerkundungdc.subject.gnd
GNDProteadc.subject.gnd
InstitutionKommunikations- und Informationszentrum (kiz)uulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
Source - Title of sourceProceedings of the 7th bwHPC Symposiumsource.title
Quellenangabe - HerausgeberUniversität Ulmsource.contributor.editor1
Source - PublisherUlmsource.publisherPlace
Source - Volume7source.volume
Source - Year2022source.year
Source - From page11source.fromPage
Source - To page16source.toPage
Source - Article number3source.articleNumber
Source - ISBN978-3-948303-29-7source.identifier.isbn
Conference name7th bwHPC Symposiumuulm.conferenceName
Conference placeUlm University (online event)uulm.conferencePlace
Conference start date2021-11-08uulm.conferenceStartDate
Conference end date2021-11-08uulm.conferenceEndDate
Bibliographyuulmuulm.bibliographie


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