Title:	Lessonia-1 SAR time-series for identifying flooded áreas.
Authors:	LIMA, S. A. COSTA, F. A. L. BIAS, E. S. SANO, E. E.
Affiliation:	SIDNEY A. LIMA, UNIVERSIDADE DE BRASÍLIA; FELIPE A. L. COSTA, CENTRO DE OPERAÇÕES ESPACIAIS; EDILSON S. BIAS, UNIVERSIDADE DE BRASÍLIA; EDSON EYJI SANO, CPAC.
Date Issued:	2024
Citation:	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 48-3W3, p. 55-62, 2024.
Description:	The catastrophic floods that hit Rio Grande do Sul, Brazil, in May 2024 underscored the urgent necessity for sophisticated flood monitoring and management methods. Notably, Synthetic Aperture Radar (SAR) satellite imagery has proven to be an essential resource for detecting flooded regions and evaluating the extent of flooding, even in challenging weather conditions. The Lessonia-1 SAR project represents a significant advancement in Brazil’s technological capabilities, particularly in enhancing flood management. By providing continuous and precise imagery monitoring, it plays a crucial role in mitigating flood risks. During heavy rains in the southern Brazilian State of Rio Grande do Sul, the Space Operations Center (COPE) was tasked to acquire imagery from Lessonia-1 SAR to support operations aimed at mitigating the impact on residents' lives. The purpose of this study is to assess the feasibility of utilizing Lessonia-1 SAR imagery, operating at X band with VV polarization, using a time-series approach for identifying flooded areas. The results demonstrate that utilizing imagery from October 2022 (before) and May 2024 (after) with normalization between both images enables the identification of flooded areas of interest. Additionally, the study employs false-color composition (R:after, G:before and B:normalization) to visualize the flooding curve in blue.
NAL Thesaurus:	Floods Disasters Synthetic aperture radar
DOI:	https://doi.org/10.5194/isprs-archives-XLVIII-3-W3-2024-55-2024
Notes:	Publicado na Conference Geo-information for Disaster management (Gi4DM) 2024. “Geospatial Intelligence: Bridging AI, Environmental Management, and Disaster Resilience”, 2–3 November 2024, Belém, Brazil.
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CPAC)