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1. Identity statement
Reference TypeJournal Article
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Sitemtc-m21c.sid.inpe.br (namespace prefix: upn:3Q3U5H8)
Identifier8JMKD3MGP3W34R/42J6UD5
Repositorysid.inpe.br/mtc-m21c/2020/05.28.14.47   (restricted access)
Last Update2020:05.28.14.47.16 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/05.28.14.47.16
Metadata Last Update2022:01.04.01.35.10 (UTC) administrator
DOI10.1016/j.geomorph.2019.106934
ISSN0169-555X
Citation KeyGuimarãesGaloNarvSilv:2020:CoTeDa
TitleCosmo-SkyMed and TerraSAR-X datasets for geomorphological mapping in the eastern of Marajo Island, Amazon coast
Year2020
MonthFeb.
Access Date2026, Oct. 29
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size6937 KiB
2. Context
Author1 Guimarães, Ulisses Silva
2 Galo, Maria de Lourdes Bueno Trindade
3 Narvaes, Igor da Silva
4 Silva, Arnaldo de Queiroz
Group1
2
3 CRCRA-COCRE-INPE-MCTIC-GOV-BR
Affiliation1 Sistema de Proteção da Amazônia (SIPAM)
2 Universidade Estadual Paulista (UNESP)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Universidade Federal do Pará (UFPA)
Author e-Mail Address1 ulisses.silva@sipam.gov.br
2 trindade.galo@unesp.br
3 igor.narvaes@inpe.br
4 arnaldoq@ufpa.br
JournalGeomorphology
Volume350
PagesUNSP 106934
Secondary MarkA1_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
Host Collectionurlib.net/www/2017/11.22.19.04 upn:3Q3U5H8
History (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. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsSynthetic aperture radar
Amazon coastal environments
Random Forest
AbstractThe 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).
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > CRCRA > Cosmo-SkyMed and TerraSAR-X...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target Fileguimaraes_cosmo.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EUAE4H
Citing Item Listsid.inpe.br/bibdigital/2013/10.03.18.52 - 66
DisseminationWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
6. Notes
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