An RS-GIS-Based Comprehensive Impact Assessment of Floods-A Case Study in Madeira River, Western Brazilian Amazon

Author(s)

Santos, Leonardo B. L. ; Carvalho, Tiago ; Anderson, Liana O. ; Rudorff, Conrado M. ; Marchezini, Victor ; Londe, Luciana R. ; Saito, Silvia M.

• Document type: Journal article
• Source: IEEE Geoscience and Remote Sensing Letters; v. 14, n. 9, p. 1614-1617, SEP 2017.
• Web of Science Citations: 2

Abstract

Geographical information systems-based methods can be handled as powerful tools in assessing and quantifying impacts and, thus, supporting strategies for disaster risk reduction (DRR). This is particularly relevant on scenarios of global climate change and intensified increased human interventions on riverine systems. The Madeira River in Porto Velho city (Brazilian Amazon) is a good example of susceptible area to both of these factors. We take advantage of the 2014 flood, the largest recorded for this region, for combining remote sensing and geographic information system with socio, health, and infrastructure data to quantify spatially the flood impacts. Using high resolution airborne images, we applied a machine learning classification algorithm for detecting urban areas. Our results show that at the flood extent related to the highest river level at least 0.65 km(2) of urban area, 87 km of urban streets, four public schools, and two public health units were affected. More than 16 800 people suffered the impacts directly, and children represented 29.7% of them. Based on registered data, it was quantified that the city registered more than 20 cases of leptospirosis and the truck flow on the region decreased up to 92%. The spatially-explicit results of this letter are potential to guide strategies aiming to support decision-making for DRR. (AU)

• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support Opportunities: Research Projects - Thematic Grants