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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images

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Autor(es):
Arnesen, Allan S. [1] ; Silva, Thiago S. F. [1] ; Hess, Laura L. [2] ; Novo, Evlyn M. L. M. [1] ; Rudorff, Conrado M. [3] ; Chapman, Bruce D. [4] ; McDonald, Kyle C. [4, 5, 6]
Número total de Autores: 7
Afiliação do(s) autor(es):
[1] Inst Nacl Pesquisas Espaciais, Div Sensoriamento Remoto, BR-12201970 Sao Jose Dos Campos - Brazil
[2] Univ Calif Santa Barbara, Earth Res Inst, Santa Barbara, CA 93106 - USA
[3] Univ Calif Santa Barbara, Bren Sch Environm Sci & Management, Santa Barbara, CA 93106 - USA
[4] CALTECH, Jet Prop Lab, Pasadena, CA 91109 - USA
[5] CUNY City Coll, Dept Earth & Atmospher Sci, CUNY Environm Crossrd Initiat, New York, NY 10031 - USA
[6] CUNY City Coll, CUNY CREST Inst, New York, NY 10031 - USA
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Fonte: REMOTE SENSING OF ENVIRONMENT; v. 130, p. 51-61, MAR 15 2013.
Citações Web of Science: 57
Resumo

The Amazon River floodplain is subject to large seasonal variations in water level and flood extent, due to the large size and low relief of the basin, and the large amount of precipitation in the region. Synthetic Aperture Radar (SAR) data can be used to map flooded area in these wetlands, given its ability to provide continuous information without being heavily affected by cloud cover. As part of JAXA's Kyoto \& Carbon Initiative, extensive wide-swath, multi-temporal SAR coverage of the Amazon basin has been obtained using the ScanSAR mode of ALOS PALSAR This study presents a method for monitoring flood extent variation using ALOS ScanSAR images, tested at the Curuai Lake floodplain, in the lower Amazon River, Brazil. Twelve ScanSAR scenes were acquired between 2006 and 2010, including seven during the 2007 hydrological year. Water level records, field photographs, optical images (Landsat-5/TM and MODIS/Ferra and Aqua) and topographic data were used as auxiliary information. A data mining algorithm allowed the implementation of a hierarchical, object-based classification algorithm, able to map land cover types and flooding status in the study area for all available dates. land cover based on the entire time series (classification levels 1 and 2) had overall accuracies of 90% and 83%, respectively. Level 3 classifications (one map per image date) were validated only for the lowest and highest water stages, with overall accuracies of 76% and 78%, respectively. Total flood extent (Level 4) was mapped with 84% and 94% accuracies, for the low and high water stages, respectively. Regression models were fitted between mapped flooded area and water levels at the Curuai gauge to predict flood extent. A polynomial model had R-2=0.95 (p<0.05) and an overall root mean square error (RMSE) of 241 km(2), while a logistic model had R-2=0.98 (p<0.05) and RMSE = 127 km(2). (C) 2012 Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 10/11269-2 - Modelagem da dinâmica espacial de comunidades de macrófitas na várzea amazônica.
Beneficiário:Thiago Sanna Freire Silva
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 08/07537-1 - Integração de dados multi-sensores, telemétricos, censitários e de campo na avaliação do impacto humano sobre os sistemas aquáticos da várzea do rio Amazonas/Solimões
Beneficiário:Evlyn Márcia Leão de Moraes Novo
Linha de fomento: Auxílio à Pesquisa - Regular