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2017-01-19Zeitschriftenartikel DOI: 10.18452/20346
sgdm: An R Package for Performing Sparse Generalized Dissimilarity Modelling with Tools for gdm
dc.contributor.authorLeitão, Pedro
dc.contributor.authorSchwieder, Marcel
dc.contributor.authorSenf, Cornelius
dc.date.accessioned2019-08-01T12:06:30Z
dc.date.available2019-08-01T12:06:30Z
dc.date.issued2017-01-19none
dc.date.updated2019-07-29T16:11:55Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/21104
dc.description.abstractGlobal biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed to deal with high dimensional datasets, such as time series or hyperspectral remote sensing data. In this manuscript we present sgdm, an R package for performing sparse generalized dissimilarity modelling (SGDM). The package includes some general tools that add functionality to both generalized dissimilarity modelling and sparse generalized dissimilarity modelling. It also includes an exemplary dataset that allows for the application of SGDM for mapping the spatial patterns of tree communities in a region of natural vegetation in the Brazilian Cerrado.eng
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.subjectCerrado treeseng
dc.subjectcommunity turnovereng
dc.subjecthigh-dimensional dataeng
dc.subjecthyperspectral remote sensingeng
dc.subjectgeneralized dissimilarity modellingeng
dc.subjectsparse canonical component analysiseng
dc.subjectR packageeng
dc.subject.ddc550 Geowissenschaftennone
dc.titlesgdm: An R Package for Performing Sparse Generalized Dissimilarity Modelling with Tools for gdmnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/21104-4
dc.identifier.doihttp://dx.doi.org/10.18452/20346
dc.type.versionpublishedVersionnone
local.edoc.pages12none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn2220-9964
dcterms.bibliographicCitation.doi10.3390/ijgi6010023none
dcterms.bibliographicCitation.journaltitleISPRS International Journal of Geo-Informationnone
dcterms.bibliographicCitation.volume6none
dcterms.bibliographicCitation.issue1none
dcterms.bibliographicCitation.originalpublishernameMDPInone
dcterms.bibliographicCitation.originalpublisherplaceBaselnone
dcterms.bibliographicCitation.pagestart23/1none
dcterms.bibliographicCitation.pageend23/12none
bua.import.affiliationLeitão, Pedro; Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany,none
bua.import.affiliationSchwieder, Marcel; Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany,none
bua.import.affiliationSenf, Cornelius; Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany,none
bua.departmentMathematisch-Naturwissenschaftliche Fakultätnone

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