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2023-07-29Zeitschriftenartikel DOI: 10.3390/rs15153779
Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images
dc.contributor.authorWu, Hui
dc.contributor.authorZhuang, Minghao
dc.contributor.authorChen, Yuanchi
dc.contributor.authorMeng, Chen
dc.contributor.authorWu, Caiyan
dc.contributor.authorOuyang, Linke
dc.contributor.authorLiu, Yuhan
dc.contributor.authorShu, Yi
dc.contributor.authorTao, Yuzhong
dc.contributor.authorQiu, Tong
dc.contributor.authorLi, Junxiang
dc.date.accessioned2023-08-17T13:28:22Z
dc.date.available2023-08-17T13:28:22Z
dc.date.issued2023-07-29none
dc.date.updated2023-08-08T16:25:30Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/27779
dc.description.abstractIndividual tree detection for urban forests in subtropical environments remains a great challenge due to the various types of forest structures, high canopy closures, and the mixture of evergreen and deciduous broadleaved trees. Existing treetop detection methods based on the canopy-height model (CHM) from UAV images cannot resolve commission errors in heterogeneous urban forests with multiple trunks or strong lateral branches. In this study, we improved the traditional local-maximum (LM) algorithm using a dual Gaussian filter, variable window size, and local normalized correlation coefficient (NCC). Specifically, we adapted a crown model of maximum/minimum tree-crown radii and an angle strategy to detect treetops. We then removed and merged the pending tree vertices. Our results showed that our improved LM algorithm had an average user accuracy (UA) of 87.3% (SD± 4.6), an average producer accuracy (PA) of 82.8% (SD± 4.1), and an overall accuracy of 93.3% (SD± 3.9) for sample plots with canopy closures less than 0.5. As for the sample plots with canopy closures from 0.5 to 1, the accuracies were 78.6% (SD± 31.5), 73.8% (SD± 10.3), and 68.1% (SD± 12.7), respectively. The tree-height estimation accuracy reached more than 0.96, with an average RMSE of 0.61 m. Our results show that the UAV-image-derived CHM can be used to accurately detect individual trees in mixed forests in subtropical cities like Shanghai, China, to provide vital tree-structure parameters for precise and sustainable forest management.eng
dc.description.sponsorshipNational Key R&D Program of China
dc.description.sponsorshipNational Natural Science Foundation of China
dc.description.sponsorshipChina Postdoctoral Science Foundation
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectunmanned aerial vehicle (UAV)eng
dc.subjectlocal-maximum algorithmeng
dc.subjecturban foresteng
dc.subjecttreetop detectioneng
dc.subjectsubtropical evergreen–deciduous broadleaved mixed foresteng
dc.subject.ddc550 Geowissenschaftennone
dc.titleUrban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Imagesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/27779-3
dc.identifier.doi10.3390/rs15153779none
dc.identifier.doihttp://dx.doi.org/10.18452/27111
dc.type.versionpublishedVersionnone
local.edoc.pages17none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn2072-4292
dcterms.bibliographicCitation.journaltitleRemote sensingnone
dcterms.bibliographicCitation.volume15none
dcterms.bibliographicCitation.issue15none
dcterms.bibliographicCitation.articlenumber3779none
dcterms.bibliographicCitation.originalpublishernameMDPInone
dcterms.bibliographicCitation.originalpublisherplaceBaselnone
bua.departmentMathematisch-Naturwissenschaftliche Fakultätnone

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