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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
27/11/2019 |
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Data da última atualização: |
27/11/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
BISPO, P. da C.; PARDINI, M.; PAPATHANASSIOU, K. P.; KUGLER, F.; BALZTER, H.; RAINS, D.; SANTOS, J. R. dos; RIZAEV, I. G.; TANSEY, K.; SILVA, M. F. da; ARAUJO, L. S. de. |
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Afiliação: |
POLYANNA DA CONCEICAO BISPO, University of Leicester; MATTEO PARDINI, German Aerospace Center; KONSTANTINUS P PAPATHANASSIOU, German Aerospace Center; FLORIAN KUGLER, German Aerospace Center; HEIKO BALTZER, Ghent University; DOMINIK RAINS, University of Leicester; JOAO ROBERTO DOS SANTOS, INPE; IGOR G RIZAEV, University of Bristol; KEVIN TANSEY, University of Leicester; MAIZA FERREIRA DA SILVA, SGE; LUCIANA SPINELLI DE ARAUJO, CNPMA. |
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Título: |
Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Remote Sensing of Environment, v. 232, 2019. Article 111194. |
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DOI: |
https://doi.org/10.1016/j.rse.2019.05.013 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode using InSAR model-based inversion techniques aided by the LiDAR digital terrain model. The validation of the TanDEM-X forest heights with independent LiDAR H100 datasets was carried out in the location of seven field inventory plots (measuring 50?×?50?m, equivalent to 0.25?ha), also allowing for the validation of the LiDAR datasets against the field data. The validation of the estimated heights showed a high correlation (r?=?0.93) and a low uncertainty (RMSE?=?3?m). The information about the successional stages and forest heights from field datasets was used to select training samples in the LiDAR and TanDEM-X forest heights to classify successional stages with a maximum likelihood classifier. The identification of different stages of forest succession based on TanDEM-X forest heights was possible with an overall accuracy of about 80%. MenosAbstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode usi... Mostrar Tudo |
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Palavras-Chave: |
Forest height; TanDEM-X. |
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Thesagro: |
Floresta Tropical; Mapa; Sensoriamento Remoto. |
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Thesaurus Nal: |
Forest succession; Height; Interferometry; synthetic aperture radar; Tropical forests. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/205662/1/ARAUJO-SPINELLI-Mapping-Forest-2019.pdf
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Marc: |
LEADER 03451naa a2200373 a 4500
001 2115303
005 2019-11-27
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.rse.2019.05.013$2DOI
100 1 $aBISPO, P. da C.
245 $aMapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry.$h[electronic resource]
260 $c2019
520 $aAbstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode using InSAR model-based inversion techniques aided by the LiDAR digital terrain model. The validation of the TanDEM-X forest heights with independent LiDAR H100 datasets was carried out in the location of seven field inventory plots (measuring 50?×?50?m, equivalent to 0.25?ha), also allowing for the validation of the LiDAR datasets against the field data. The validation of the estimated heights showed a high correlation (r?=?0.93) and a low uncertainty (RMSE?=?3?m). The information about the successional stages and forest heights from field datasets was used to select training samples in the LiDAR and TanDEM-X forest heights to classify successional stages with a maximum likelihood classifier. The identification of different stages of forest succession based on TanDEM-X forest heights was possible with an overall accuracy of about 80%.
650 $aForest succession
650 $aHeight
650 $aInterferometry
650 $asynthetic aperture radar
650 $aTropical forests
650 $aFloresta Tropical
650 $aMapa
650 $aSensoriamento Remoto
653 $aForest height
653 $aTanDEM-X
700 1 $aPARDINI, M.
700 1 $aPAPATHANASSIOU, K. P.
700 1 $aKUGLER, F.
700 1 $aBALZTER, H.
700 1 $aRAINS, D.
700 1 $aSANTOS, J. R. dos
700 1 $aRIZAEV, I. G.
700 1 $aTANSEY, K.
700 1 $aSILVA, M. F. da
700 1 $aARAUJO, L. S. de
773 $tRemote Sensing of Environment$gv. 232, 2019. Article 111194.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
29/11/2021 |
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Data da última atualização: |
25/03/2022 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
FASIABEN, M. do C. R.; OLIVEIRA, S. R. de M.; MORAES, A. S.; ALMEIDA, M. M. T. B.; OLIVEIRA, O. C. de; EUSÉBIO, G. dos S. |
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Afiliação: |
MARIA DO CARMO RAMOS FASIABEN, CNPTIA; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; ANDRE STEFFENS MORAES, CNPSO; MAXWELL MERÇON TEZOLIN BARROS ALMEIDA, IBGE; OCTÁVIO COSTA DE OLIVEIRA, IBGE; GABRIELA DOS SANTOS EUSÉBIO, UNICAMP. |
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Título: |
Diferenciação e caracterização de municípios produtores de bovinos de corte no bioma Mata Atlântica segundo o Censo Agropecuário 2017. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais... Bagé: Unipampa, 2021. |
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Páginas: |
p. 80-89. |
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Idioma: |
Português |
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Notas: |
Organizado por Ana Paula Lüdtke Ferreira. |
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Conteúdo: |
Resumo. O objetivo deste trabalho é tipificar os municípios produtores de bovinos de corte do bioma Mata Atlântica a partir de dados do Censo Agropecuário 2017. Tabulações especiais do censo foram necessárias para agregar os estabelecimentos agropecuários por município, com ênfase nas características técnicas dos sistemas de produção pecuária. Identificaram-se quatro grupos de municípios segundo os padrões de produção do bioma: dois grupos de alta tecnologia, um deles com destaque para confinamento; um grupo de tecnologia intermediária e outro de baixa tecnologia. O mapeamento dos distintos padrões de produção ao longo do território pode apoiar ações de pesquisa, de transferência de tecnologia e de desenvolvimento mais apropriadas às diferentes situações dos produtores. |
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Palavras-Chave: |
Bioma Mata Atlântica; Bovinos de corte; Censo Agropecuário 2017; Clusters de bovinos de corte. |
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Thesagro: |
Gado de Corte; Mata. |
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Thesaurus NAL: |
Beef cattle. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228161/1/PL-Diferenciacao-caracterizacao-municipios-SBIAgro-2021.pdf
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Marc: |
LEADER 01776nam a2200277 a 4500
001 2136711
005 2022-03-25
008 2021 bl uuuu u01u1 u #d
100 1 $aFASIABEN, M. do C. R.
245 $aDiferenciação e caracterização de municípios produtores de bovinos de corte no bioma Mata Atlântica segundo o Censo Agropecuário 2017.$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais... Bagé: Unipampa$c2021
300 $ap. 80-89.
500 $aOrganizado por Ana Paula Lüdtke Ferreira.
520 $aResumo. O objetivo deste trabalho é tipificar os municípios produtores de bovinos de corte do bioma Mata Atlântica a partir de dados do Censo Agropecuário 2017. Tabulações especiais do censo foram necessárias para agregar os estabelecimentos agropecuários por município, com ênfase nas características técnicas dos sistemas de produção pecuária. Identificaram-se quatro grupos de municípios segundo os padrões de produção do bioma: dois grupos de alta tecnologia, um deles com destaque para confinamento; um grupo de tecnologia intermediária e outro de baixa tecnologia. O mapeamento dos distintos padrões de produção ao longo do território pode apoiar ações de pesquisa, de transferência de tecnologia e de desenvolvimento mais apropriadas às diferentes situações dos produtores.
650 $aBeef cattle
650 $aGado de Corte
650 $aMata
653 $aBioma Mata Atlântica
653 $aBovinos de corte
653 $aCenso Agropecuário 2017
653 $aClusters de bovinos de corte
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aMORAES, A. S.
700 1 $aALMEIDA, M. M. T. B.
700 1 $aOLIVEIRA, O. C. de
700 1 $aEUSÉBIO, G. dos S.
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