journal article·2020-09-21Open access

ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru

Nancy Alvan Romero, Francesca Cigna, Deodato Tapete

Applied Sciences· Volume 10· Issue 18

Abstract

The coastline environment and urban areas of Peru overlooking the Pacific Ocean are among the most affected by El Niño-Southern Oscillation (ENSO) events, and its cascading hazards such as floods, landslides and avalanches. In this work, the complete archives of the European Space Agency (ESA)’s European Remote-Sensing (ERS-1/2) missions and European Commission’s Copernicus Sentinel-1 constellation were screened to select synthetic aperture radar (SAR) images covering the most severe and recent ENSO-related flooding events that affected Lima, the capital and largest city of Peru, in 1997–1998 and 2017–2018. Based on SAR backscatter color composites and ratio maps retrieved from a series of pre-, cross- and post-event SAR pairs, flooded areas were delineated within the Rímac River watershed. These are mostly concentrated 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. 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). Urban and landscape changes potentially contributing, either detrimentally or beneficially, to flooding susceptibility were identified and considered in the overall assessment of risk. The extent of built-up areas within the basin was mapped by combining information from the 2011 Global Urban Footprint (GUF) produced by the German Aerospace Center (DLR), the Open Street Map (OSM) accessed from the Quantum GIS (QGIS) service, and 2011–2019 very high-resolution optical imagery from Google Earth. The resulting flooding risk 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.
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Research context

OpenAlex topics: Flood Risk Assessment and Management · Synthetic Aperture Radar (SAR) Applications and Techniques · Tropical and Extratropical Cyclones Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Italian Space Agency (ASI), Via del Politecnico s.n.c., 00133 Rome, Italy

Mathematics Department, University of Rome “Tor Vergata”, Via della Ricerca Scientifica 1, 00133 Rome, Italy

References (90)

Show source-provided references
1. IPCC (2014). Intergovernmental Panel on Climate Change: Climate change 2014: Impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects, Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects.
2. El Niño—The dynamic response of the equatorial Pacific Oceanto atmospheric forcing10.1175/1520-0485(1975)005<0572:ENTDRO>2.0.CO;2 ↗
3. Kundzewicz, Z.W., Szwed, M., and Pińskwar, I. (2019). Climate Variability and Floods—A Global Review. Water, 11.10.3390/w11071399 ↗
4. Influence of El Niño-Southern Oscillation on Global Coastal Flooding10.1029/2018EF000909 ↗
5. Winchester, S. (2017). How the Pacific Ocean changes weather around the world. Our current understanding of the El Niño Southern Oscillation. When the Sky Breaks, Viking.
6. The evolution of El Niño, past and future10.1016/j.epsl.2004.12.003 ↗
7. Heavy rainfall episodes in Ecuador during El Niño events and associated regional atmospheric circulation and SST patterns10.5194/adgeo-6-43-2006 ↗
8. Low-frequency modulation and trend of the relationship between ENSO and precipitation along the northern to centre Peruvian Pacific coast10.1002/hyp.10247 ↗
9. Evaluation of Early Action Mechanisms in Peru Regarding Preparedness for El Niño10.1007/s13753-019-00245-x ↗
10. Effects of El Niño on the Coastal Ecosystems and their Related Services10.4215/rm2017.e16030 ↗
11. Galarza, E., Kámiche, J., Collado, M., and Pacheco, A. (2012). Impactos del Fenómeno El Niño (FEN) en la Economía Regional de Piura, Lambayeque y La Libertad, Cooperacion Alemana al Desarrollo.
12. World-Bank (2012). Disaster Risk Management in Latin America and the Caribbean Region: GFDRR Country Notes (English), World Bank.
13. GFDRR (2017). Inform. Risk Rating: 4.2 Peru, GFDRR: Global Facility for Disaster Reduction and Recovery.
14. Risk perception of people living in vulnerable zones of Lima (Peru) during floods
15. Climate diagnostics of the extreme floods in Peru during early 201710.1007/s00382-019-05038-y ↗
16. Evaluation of a global ensemble flood prediction system in Peru10.1080/02626667.2019.1617868 ↗
17. Riesgo de inundación de la ciudad de Iquitos, Perú
18. Eventos El Niño y lluvias anormales en la costa del Perú: Siglos XVI–XIX
19. CENEPRED (2018). Escenario de Riesgos por Lluvias Intensas—Fenómeno de El Niño, CENEPRED: Centro Nacional de Estimación, Prevención y Reducción del Riesgo de Desastres.
20. Impactos de El Niño y La Niña en las lluvias del Perú (1965–2007)10.1590/S0102-77862014000200003 ↗
21. SENAMHI (2014). El Fenómeno El Niño en el Perú.
22. El Niño occurrences over the past four and a half centuries10.1029/JC092iC13p14449 ↗
23. Forecasts aid flood action in Peru during El Niño
24. Regionalization of rainfall over the Peruvian Pacific slope and coast10.1002/joc.4693 ↗
25. WFP, World Food Programme, Save the Children, and USAID, US Agency for International Development (2018). Fortaleciendo la Respuesta Ante Desastres en el Perú: Lecciones Aprendidas del Fenómeno El Niño Costero 2017 en el Perú.
26. Disaster profile of Peru and institutional approach for risk reduction
27. INGEMMET (1988). Estudio Geodinámico de la Cuenca del río Rímac. Boletín No 8b Serie C.
28. Villacorta Chambi, S.P., Núñez Juárez, S., Vásquez Acuña, J., Pari Pinto, W., Ochoa Zubiate, M.B., Benavente Escobar, C.L., Tatard, L., Luque Poma, G., Rosado Seminario, M., and Fídel Smoll, L. (2015). INGEMMET, Boletín Serie C: Geodinámica e Ingeniería Geológica; Boletín 59: Peligros Geológicos En El Área De Lima Metropolitana Y La Región Callao.
29. CRED, and Guha-Sapir, D. (2018). The Emergency Events Database (EM-DAT), CRED: Centre for Research on the Epidemiology of Disasters.
30. Analysis of extent and effects caused by the flood wave in May and June 2010 in the Vistula and Odra River Valleys
31. Automatic near real-time flood detection using Suomi-NPP/VIIRS data10.1016/j.rse.2017.09.032 ↗
32. Near Real-Time Flood Detection in Urban and Rural Areas Using High-Resolution Synthetic Aperture Radar Images10.1109/TGRS.2011.2178030 ↗
33. Roman-Gonzalez, A., Vargas-Cuentas, N., and Aucapuma, L. (2017, January 25–29). Analysis of Landslide in Chosica Using Satellite Images. Proceedings of the International Astronautical Congress-IAC 2017, Adelaide, Australia.10.26226/morressier.59c106e9d462b80292389cdc ↗
34. Flood Analysis in Peru Using Satellite Image: The Summer 2017 Case
35. Yamada, Y. (2001, January 9–13). Detection of flood-inundated area and relation between the area and micro-geomorphology using SAR and GIS. Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium IGARSS 2001, Sydney, Australia.
36. Santoro, M., and Wegmuller, U. (2012, January 22–27). Multi-temporal SAR metrics applied to map water bodies. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2012, Munich, Germany.10.1109/IGARSS.2012.6352430 ↗
37. Flood Detection in Norway Based on Sentinel-1 SAR Imagery
38. Radoi, A., and Burileanu, C. (2018). Retrieval of Similar Evolution Patterns from Satellite Image Time Series. Appl. Sci., 8.10.3390/app8122435 ↗
39. Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches10.1109/TGRS.2018.2860054 ↗
40. Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., and Matgen, P. (2019). Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and Hurricane Harvey as a test case. Remote Sens., 11.10.3390/rs11020107 ↗
41. Amplitude change detection with ENVISAT ASAR to image the cultural landscape of the Nasca region, Peru10.1002/arp.1451 ↗
42. Mola Revisiting Methods and Potentials of SAR Change Detection
43. Veleda, S., Martínez-Graña, A., Santos-Francés, F., Sánchez-Sanroman, J., and Criado, M. (2017). Analysis of the Hazard, Vulnerability, and Exposure to the Risk of Flooding (Alba de Yeltes, Salamanca, Spain). Appl. Sci., 7.10.3390/app7020157 ↗
44. Copernicus (2017). Emergency Management Service EMSN-038: Post-Disaster Situation Analyses of Flood and Landslides in Lima, Peru. Final Report Summary, Copernicus.
45. SEDAPAL (2008). Servicio de Agua Potable y Alcantarillado de Lima: Río Rímac y Afluentes, Dirección General de Salud Ambiental.
46. Nippon Koei Co., Ltd., and Pacific Consultants International (2002). Chapter 9: Supplemental Investigation on Water Use and Loss in Rimac River Basin. Study on Integrated Water Resources Development in the Cañete River Basin in the Republic of Peru, JICA: Japan International Cooperation Agency.
47. Binnie & Partners Consulting (1970). Los Recursos de Agua para la Gran Lima (Freshwater Resources for Great Lima), Binnie & Partners Consulting.
48. OUPUD (2020, September 10). Crecimiento Urbano en Lima. Expansión Urbana (Urban Growth in Lima. Urban Sprawl). Available online: http://observatoriourbano.org.pe/project/espacio-publico-y-genero-en-lurin/.
49. Zumarán Pineda, J. (2006). Maintenance of River Ecosystems within Urban Areas, Case Study: Lima-Peru, International Institute for Geo-Information Science and Earth Observation.
50. Jara, J. (2014). Un Frágil Ciclo Agua, Energía y Población en Lima, World Wide Fund for Nature-Peru.
51. (2020, September 10). World Urbanization Prospects—Population Division—United Nations. Available online: https://population.un.org/wup/.
52. Kuroiwa, J., Mansen, A., Romero, F., Castro, L., and Vega, R. (2011, January 22–26). Narrowing of the Rimac River Due to Anthropogenic Causes—Partial Engineering Solutions. Proceedings of the World Environmental and Water Resources Congress, Palm Springs, CA, USA.10.1061/41173(414)278 ↗
53. Kapstein, P. (2015, January 23–30). Water Courses and Urban Development. River Banks as Limits: The case of Rimac River (Lima, Peru). Proceedings of the IUFRO Landscape Ecology Working Group (LE WG) Conference, Tartu, Estonia.
54. JUR Junta de Usuarios Rímac: Río Rímac o Cuenca (2018, October 09). Junta Usuarios del Sect. Hidráulico Rímac 2013. Available online: http://jur-nosotros.blogspot.com/p/r.html.
55. SENAMHI (2020, July 02). IDESEP—Infraestructura de Datos Espaciales. Catálogo de Metadatos Cartográficos. Precipitación Anual 2030. Available online: http://idesep.senamhi.gob.pe/geonetwork/srv/eng/catalog.search;jsessionid=782D977A34F2941F2034317F2EE54575#/metadata/4ffdceb0-0df5-41c9-ad9a-6091f45f265e.
56. Bernabé, O.M.A., Pineda, Q.W.J., Alanoca, V.G., and Collantes, M.N. (2010). Estudio Hidrológico y Ubicación de la Red de las Estaciones Hidrométricas en la Cuenca del Río Rímac.
57. SENAMHI (2017). Situación Actual en los Ríos Rímac y Chillón. Aviso N°045.
58. Casareto, M.D., López, S.F., Ortegal, I.A., Perez, P.M., and Romero, C.E. (2016). Rímac: Historia del Río Hablador, PERÚ CUADROS EIRL. [1st ed.].
59. ENFEN (2017). Estudio Nacional del Fenómeno “El Niño”: Informe Técnico Extraordinario N°001—2017/ENFEN El Niño Costero 2017—Resumen Ejecutivo.
60. SENAMHI (2020, July 02). IDESEP—Infraestructura de Datos Espaciales. Catálogo de Metadatos Cartográficos. Anomalía de Precipitación Diciembre Febrero 2016–2017, Available online: http://idesep.senamhi.gob.pe/geonetwork/srv/eng/catalog.search;jsessionid=782D977A34F2941F2034317F2EE54575#/metadata/a154822d-6527-43c9-9b6d-709a71c68411.
61. (2020, September 10). ERS—Earth Online—ESA. Available online: https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers.
62. (2020, May 30). ESA Sentinel-1: ESA’s Radar Observatory Mission for GMES Operational Services. Available online: https://sentinel.esa.int/documents/247904/349449/S1_SP-1322_1.pdf.
63. (2020, May 30). ESA Sentinel Online: Observation Scenario Archive. Available online: https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1/observation-scenario/archive.
64. Massonnet, D., and Souyris, J.-C. (2011). Imaging with Synthetic Aperture Radar, EPFL Press. [1st ed.].
65. UN-SPIDER (2019, April 30). Step-by-Step: Mudslides and Associated Flood Detection Using Sentinel-1 Data. Available online: http://www.un-spider.org/advisory-support/recommended-practices/mudslides-flood-sentinel-1/step-by-step.
66. Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The Shuttle Radar Topography Mission. Rev. Geophys., 45.10.1029/2005RG000183 ↗
67. Shen, X., Wang, D., Mao, K., Anagnostou, E., and Hong, Y. (2019). Inundation Extent Mapping by Synthetic Aperture Radar: A Review. Remote Sens., 11.10.3390/rs11070879 ↗
68. Flood detection in Urban areas using TerraSAR-X10.1109/TGRS.2009.2029236 ↗
69. Review article: Detection of inundation areas due to the 2015 Kanto and Tohoku torrential rain in Japan based on multi-temporal ALOS-2 imagery10.5194/nhess-18-1905-2018 ↗
70. Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering
71. ASF Alaska Satellite Facility (2020, September 10). How to Create an RGB Composite from Multi-Temporal Sentinel-1 Data. Available online: https://asf.alaska.edu/how-to/data-recipes/create-an-rgb-composite-from-multi-temporal-sentinel-1-data/.
72. INGEMMET (2018, October 11). Limite Politico Administrativo, Available online: https://geocatmin.ingemmet.gob.pe/geocatmin/.
73. INGEMMET (2018, October 11). Población, Available online: https://geocatmin.ingemmet.gob.pe/geocatmin/.
74. Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information10.1109/TGRS.2010.2091644 ↗
75. Esch, T., Heldens, W., Hirner, A., Keil, M., Marconcini, M., Roth, A., Zeidler, J., Dech, S., and Strano, E. (2017). Breaking new ground in mapping human settlements from space -The Global Urban Footprint. ISPRS J. Photogramm. Remote Sens., 134.10.1016/j.isprsjprs.2017.10.012 ↗
76. (2018, August 21). MINAM Ministerio del Ambiente: Cuenca Hidrográfica. Available online: http://geoservidor.minam.gob.pe/recursos/intercambio-de-datos/.
77. METI, and NASA (2011). Ministry of Economy, Trade, and Industry (METI), United States National Aeronautics and Space Administration (NASA): Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-Global Digital Elevation Model. (GDEM).
78. (2018, September 27). GEOGPSPERU GEO GPS PERU: Curvas de Nivel. Available online: https://www.geogpsperu.com/.
79. (2018, September 27). GEOGPSPERU GEO GPS PERU: Geología. Available online: https://www.geogpsperu.com/.
80. (2020, September 10). EC-JRC; Google Global Surface Water Explorer. Available online: https://global-surface-water.appspot.com/map.
81. Pekel, J.F., Cottam, A., Gorelick, N., and Belward, A. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540.10.1038/nature20584 ↗
82. OA-CHIRILU (2019). Diagnóstico Inicial Para el Plan. de Gestión de Recursos Hídricos en el Ámbito de las Cuencas Chillón, Rímac, Lurín y Chilca. Observatorio del Agua Chillón Rímac Lurín (OA CHIRILU), ANA: Autoridad Nacional del Agua.
83. Suitability estimation for urban development using multi-hazard assessment map
84. Bathrellos, G., Skilodimou, H., Soukis, K., and Koskeridou, E. (2018). Temporal and Spatial Analysis of Flood Occurrences in the Drainage Basin of Pinios River (Thessaly, Central Greece). Land, 7.10.3390/land7030106 ↗
85. Oliver, C., and Quegan, S. (2004). Understanding Synthetic Aperture Radar Images, SciTech Publishing.
86. Change detection techniques for ERS-1 SAR data10.1109/36.239913 ↗
87. CCRS-Canada Center for Remote Sensing (2014). Fundamentals of Remote Sensing, CCRS.
88. (2020, August 01). Miscelánea-Noticias Perú 2017 Puente Talavera es Destruido en San Juan de Lurigancho, Lima. Available online: https://www.youtube.com/watch?v=h0CrUv9i3Eg.
89. Quinn, J.W. (2020, September 10). Satellite Image Classification & Change Detection. Band Combinations. Available online: http://web.pdx.edu/~nauna/resources/10_BandCombinations.htm.
90. (2020, September 10). COSAPI Proyectos: Jockey Plaza Shopping Center. Available online: https://www.cosapi.com.pe/Site/Index.aspx?aID=345.