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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/42J6UD5
Repositóriosid.inpe.br/mtc-m21c/2020/05.28.14.47   (acesso restrito)
Última Atualização2020:05.28.14.47.16 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2020/05.28.14.47.16
Última Atualização dos Metadados2022:01.04.01.35.10 (UTC) administrator
DOI10.1016/j.geomorph.2019.106934
ISSN0169-555X
Chave de CitaçãoGuimarãesGaloNarvSilv:2020:CoTeDa
TítuloCosmo-SkyMed and TerraSAR-X datasets for geomorphological mapping in the eastern of Marajo Island, Amazon coast
Ano2020
MêsFeb.
Data de Acesso09 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho6937 KiB
2. Contextualização
Autor1 Guimarães, Ulisses Silva
2 Galo, Maria de Lourdes Bueno Trindade
3 Narvaes, Igor da Silva
4 Silva, Arnaldo de Queiroz
Grupo1
2
3 CRCRA-COCRE-INPE-MCTIC-GOV-BR
Afiliação1 Sistema de Proteção da Amazônia (SIPAM)
2 Universidade Estadual Paulista (UNESP)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Universidade Federal do Pará (UFPA)
Endereço de e-Mail do Autor1 ulisses.silva@sipam.gov.br
2 trindade.galo@unesp.br
3 igor.narvaes@inpe.br
4 arnaldoq@ufpa.br
RevistaGeomorphology
Volume350
PáginasUNSP 106934
Nota SecundáriaA1_INTERDISCIPLINAR A1_GEOGRAFIA A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_AGRÁRIAS_I A2_ENGENHARIAS_III A2_BIODIVERSIDADE B1_CIÊNCIAS_BIOLÓGICAS_I B1_ANTROPOLOGIA_/_ARQUEOLOGIA B2_ASTRONOMIA_/_FÍSICA
Histórico (UTC)2020-05-28 14:47:16 :: simone -> administrator ::
2020-05-28 14:47:17 :: administrator -> simone :: 2020
2020-05-28 14:48:54 :: simone -> administrator :: 2020
2022-01-04 01:35:10 :: administrator -> simone :: 2020
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveSynthetic aperture radar
Amazon coastal environments
Random Forest
ResumoThe Amazon coast is marked by the high discharge of sediments and freshwater, macrotidal influence, a wide continental shelf, extensive flood plains and lowered plateaus which make it unique as a delta and estuary landscape. Further, the tropical climate imposes heavy rains and incessant cloudiness that render microwave systems more suitable for cartography. This study proposed to recognize and map the Amazon coastal environments through the X-band Synthetic Aperture Radar, provided by Cosmo-SkyMed (CSK) and TerraSAR-X (TSX) systems. The SAR datasets consisted of interferometric and stereo pairs, restricted to single-revisit and obtained with small interval (1-11 days), under steeper (theta < 35 degrees) and shallow (theta >= 35 degrees) incidence angles, and during the rainy and dry seasons. From the 4 acquisitions of X-band SAR data, attributes such as the backscattering coefficient, coefficient of variation, texture, coherence, and Digital Surface Model (DSM) were derived, adding each variable in 5 scenarios. These combinations resulted in 20 models, which were submitted individually to the machine learning (ML) classification approach by Random Forest (RF). The backscattering and altimetry described the coastal environments which shared ambiguity and high dispersion, with the lowest separability for vegetated environments such as Mangrove, Vegetated Coastal Plateau and Vegetated Fluvial Marine Terrace. The coherence provided by interferometry was weak (<0.44), even during the dry season, in the other hand, the cross-correlation was strong (>0.60), during the rainy and dry season showing more suitability for radargrammetry on the Amazon coast. The RF models resulted in Kappa coefficient between 0.39 to 0.70, indicating that the use of X-band SAR images at an incidence angle greater than 44 degrees and obtained in the dry season is more appropriated for the morphological mapping. The RF models given by TSX achieved the higher mapping accuracies per scenario of SAR products, in order of 0.48 to 0.63. Despite this, the best classification was carried out by 19 RF model with 0.70, provided by CSK in shallow incidence composed by intensity, texture, coherence and stereo DSM. The CSK and TSX data allowed to map the Amazon coast precisely, based on X-band at single polarization, high spatial resolution and revisit, which has demonstrated the support for detailed cartography scale (1:50,000) and frequent updating (monthly up to yearly).
ÁreaSRE
Arranjourlib.net > BDMCI > Fonds > Produção anterior à 2021 > CRCRA > Cosmo-SkyMed and TerraSAR-X...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvoguimaraes_cosmo.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft24
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3EUAE4H
DivulgaçãoWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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