Assessment of flooding risk in Lima, Peru, through change detection based on ERS-1/2 and Sentinel-1 time series

Nancy Alvan Romero,Deodato Tapete,Francesca Cigna

doi:10.3390/iecg2020-08546

Abstract

The increased frequency of floods, landslides and avalanches recorded across Peru in the last decades suggests that the country is one of the most affected by El Niño-Southern Oscillation and its cascading hazards. Catastrophic floods that happened in Lima in 1997–1998 and 2017–2018 caused hundreds of fatalities and significant economic damage. In this paper, we test the hypothesis that information mined from satellite synthetic aperture radar (SAR) images can provide valuable input into the common workflow of flooding hazard assessment, and thus improve current methods for risk assessment in urban areas. The complete SAR image archives collected over the Rímac River basin by the European Space Agency (ESA)’s European Remote-Sensing (ERS-1/2) missions and the European Commission’s Copernicus Sentinel-1 constellation were screened. SAR backscatter color composites and ratio maps were created to identify change patterns occurred prior, during and after the catastrophic flooding events mentioned above. A total of 409 areas (58.50 km2) revealing change were mapped, including 197 changes (32.10 km2) due to flooding-related backscatter variations (flooded areas, increased water flow in the riverbed, and riverbank collapses and damage), and 212 (26.40 km2) due to other processes (e.g., new urban developments, construction of river embankments, other engineering works, vegetation changes). The areas inundated during the flooding events in 1997–1998 and 2017–2018 mostly concentrate along the riverbanks and plain, where low-lying topography and gentle slopes (≤5°), together with the presence of alluvial deposits, also indicate greater susceptibility to flooding. The accuracy in flood area delineation achieved with the proposed change detection method was assessed by comparison with the potential maximum flood extent map that was produced by Copernicus Emergency Management Service (EMS) in the framework of the Risk and Recovery Activation EMSN-038 during the March 2017 flood. All the observed spatial and temporal backscatter change patterns were interpreted through geospatial integration with ancillary data (topography, geology, permanent and seasonal water bodies, urban footprint, new urban development, roads and infrastructures, and population at the district level) and a risk classification map of Lima was produced. The map highlights the sectors of potential concern along the Rímac River, should flooding events of equal severity as those captured by SAR images occur in the future. Compared to published hazard maps made solely based on geological factors, this product has the advantage to embed event-based information and knowledge of the impacts of urbanization. Reference Alvan Romero, N.; Cigna, F.; Tapete, D. ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru. Appl. Sci. 2020, 10, 6598, doi:10.3390/app10186598

Highlights

In recent years, Lima, the Peruvian capital, has experienced severe and catastrophic floods [1].These events became more frequent, especially in the coastal area of the Peruvian mainland, as a consequence of El Niño
These products jointly with three key spatial hazard datasets and ancillary data related to topography, geology, urban footprint, roads and population, allowed us to undertake an integrated evaluation of the hazards and a risk analysis
These changes are distributed throughout the whole extent of the riverbed of the Rímac River and, as expected, include the areas that have been affected by the flooding events occurred in the years 1997–1998 and 2017–2018 (Figure 2)

Summary

Introduction

Lima, the Peruvian capital, has experienced severe and catastrophic floods [1].These events became more frequent, especially in the coastal area of the Peruvian mainland, as a consequence of El Niño. The basic processing workflow in order to achieve this goal consisted in pre and post-processing of SAR data, generation of RGB composites that showed “where” the change patterns occured, and the ratio maps providing the information on the magnitude of such changes. These products jointly with three key spatial hazard datasets (terrain slope, alluvial deposits and land cover) and ancillary data related to topography, geology, urban footprint, roads and population, allowed us to undertake an integrated evaluation of the hazards and a risk analysis

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Conclusion