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Área de identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Siteplutao.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W/3FCL63N
Repositóriosid.inpe.br/plutao/2013/12.12.15.45.47
Última Atualização2014:03.28.20.00.34 administrator
Metadadossid.inpe.br/plutao/2013/12.12.15.45.48
Última Atualização dos Metadados2018:06.04.23.39.16 administrator
Rótulolattes: 2916855460918534 1 FelgueirasCamaOrtiRenn:2013:UnMoDa
Chave de CitaçãoFelgueirasCamaOrtiRenn:2013:UnMoDa
TítuloUncertainty modeling for data from remote sensing images using copula based indicator approaches
FormatoDVD
Ano2013
Data de Acesso24 fev. 2021
Número de Arquivos1
Tamanho632 KiB
Área de contextualização
Autor1 Felgueiras, Carlos Alberto
2 Camargo, Eduardo Celso Gerbi
3 Ortiz, Jussara de Oliveira
4 Renno, Camilo Daleles
Identificador de Curriculo1 8JMKD3MGP5W/3C9JGQD
2 8JMKD3MGP5W/3C9JGUK
3 8JMKD3MGP5W/3C9JHKL
4 8JMKD3MGP5W/3C9JGN2
Grupo1 DPI-OBT-INPE-MCTI-GOV-BR
2 DPI-OBT-INPE-MCTI-GOV-BR
3 DPI-OBT-INPE-MCTI-GOV-BR
4 DPI-OBT-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 carlos@dpi.inpe.br
2 eduardo@dpi.inpe.br
3 jussara@dpi.inpe.br
4 camilo@dpi.inpe.br
Endereço de e-Mailcarlos@dpi.inpe.br
Nome do EventoSpatial Statistics Conference.
Localização do EventoColumbus, USA
Data2013
Título do LivroProceedings
Tipo SecundárioPRE CI
Tipo TerciárioPoster
Histórico2013-12-12 16:19:21 :: lattes -> administrator :: 2013
2014-04-01 23:00:15 :: administrator -> marcelo.pazos@sid.inpe.br :: 2013
2014-04-04 12:16:09 :: marcelo.pazos@sid.inpe.br -> administrator :: 2013
2018-06-04 23:39:16 :: administrator -> marcelo.pazos@inpe.br :: 2013
Área de conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteudoExternal Contribution
Tipo de Versãopublisher
Palavras-Chaveenvironmental science, forest, geostatistics, interpolation and smoothing, spatial sampling, uncertainty and error.
ResumoIndicator geoestatistic approaches have been used to estimate uncertainty models of environmental information as soil and hydrological properties, atmospheric and weather data, elevations, ... This work explores these approaches, that depend on the variogram (semivariogram) evaluations, to model the uncertainties of remote sensing image information. Bivariate copulas can be used to estimate variances (semivariances), instead of traditional mean variograms, to model the spatial variability of the considered attribute. Unlike traditional semivariograms, copula variograms represent dependence over the whole range of quantiles, including the extremes, and are not sensitive to outliers.
AreaSRE
ArranjoINPE > Produção > DIDPI > Uncertainty modeling for...
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreementnão têm arquivos
Área de condições de acesso e uso
URL dos dadoshttp://urlib.net/rep/8JMKD3MGP3W/3FCL63N
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP3W/3FCL63N
Idiomaen
Arquivo AlvoPoster-CarlosFelgueiras.pdf
Grupo de Usuárioslattes
marcelo.pazos@inpe.br
self-uploading-INPE-MCTI-GOV-BR
Grupo de Leitoresadministrator
marcelo.pazos@inpe.br
Visibilidadeshown
Permissão de Leituraallow from all
Permissão de Atualizaçãonão transferida
Área de fontes relacionadas
Repositório Espelhoiconet.com.br/banon/2006/11.26.21.31
Unidades Imediatamente Superiores8JMKD3MGPCW/3EQCCU5
URL (dados não confiáveis)http://www.spatialstatisticsconference.com/
Acervo Hospedeirodpi.inpe.br/plutao@80/2008/08.19.15.01
Área de notas
NotasInformações Adicionais: Extended Abstract: The objective of this paper is to assess uncertainty models, local or global, for remote sensing image information of a forest region. As the size of remote sensing images is usually very large it is used a random sample set sufficient to represent the spatial variability, or spatial dependence, of the entire image. The sample set is considered as input for assessment of empirical indicator variograms. A set of cutoff values is considered to obtain the empirical variograms using empirical bivariate copulas for various spatial distances values. In this case bivariate distribution of the random field is defined in terms of the bivariate copula. The empirical variograms are fitted by mathematical models in order to be used as input, along with the samples, for indicator geostatistical approaches of kriging estimations and sequential simulations. The main advantage of using copulas is that traditional variograms describe the spatial dependence with mean values, the mean dependence, while the spatial copula describes the dependence over the whole range of quantiles. Moreover copulas are not sensitive to outliers. Indicator approaches, krigings and simulations, has been frequently used to estimate the univariate and multivariate conditional distribution of the random variable or field given a spatial data set. These approaches can be used to perform spatial interpolation at image locations with missed or misleading information caused by clouds and shadows, for example. A case study is presented with China-Brazil Earth Remote Satellite (CBERS) images from the Amazon forest region considering deforested and no deforested areas. The results of the case study is reported along with spatial analyses considering aspects related to performance, goodness of representation, accuracy and uncertainties of the models.
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Campos Vaziosaccessionnumber archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition editor isbn issn lineage mark nextedition numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress rightsholder secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark type volume
Área de controle da descrição
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