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

2020 ◽  
Vol 10 (18) ◽  
pp. 6598
Author(s):  
Nancy Alvan Romero ◽  
Francesca Cigna ◽  
Deodato Tapete
Keyword(s):  
Urban Areas ◽  
Southern Oscillation ◽  
Flood Hazard ◽  
European Space Agency ◽  
Google Earth ◽  
Sar Images ◽  
Alluvial Deposits ◽  
Enso Events ◽  
Flooding Events ◽  
Flooded Areas

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.

scholarly journals Assessment of the Hydraulic Performance of the Urban Drainage System due to Climate Change using DHI MIKE URBAN

2021 ◽  
Vol 2 (4) ◽  
pp. 261-267
Author(s):  
M Maryam ◽  
R Kumar ◽  
N Thahaby
Keyword(s):  
Hydrodynamic Model ◽  
Urban Areas ◽  
Flood Hazard ◽  
Climatic Variability ◽  
Drainage System ◽  
Urban Drainage ◽  
Urban Flooding ◽  
Present Context ◽  
Flooding Events

Changes in climate, waterlogging hazards and regional floods are more prominent in present context. The paper reviews potential of flood hazard in dense urban areas, using GIS-based 1-D hydrodynamic model (MIKE URBAN). The major factor contributing to the urban waterlogging in recent decades is the climatic variability and thus the long-term variations of precipitation and drainage system of an urban area were evaluated. MIKE URBAN (1-D) hydrodynamic model can be used to comprehensively simulate inundation processes. The model simulates the processes of rainfall and runoff, urban drainage, and flooding. MIKE URBAN can be used to appraise the potential immersion dangers of any planned drainage system. This paper reviews the increasingly urban flooding events expected in the future for the different cities across the globe. Thus, the surface runoff processes of cities need to examine the regional drainage system.

scholarly journals Wind-Forced Delayed Action Oscillator in Tropical Oceans with Satellite Multi-Sensor Observations

2020 ◽  
Vol 12 (6) ◽  
pp. 933
Author(s):  
Jiayi Pan ◽  
Adam T. Devlin ◽  
Hui Lin
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Surface Wind ◽  
European Space Agency ◽  
Sea Surface ◽  
Height Anomaly ◽  
Spatial And Temporal Variability ◽  
Enso Events ◽  
Delayed Action

This study investigates correlations among interannual variabilities of sea surface wind, sea surface temperature (SST), and sea surface height anomaly (SSHA) in the tropical region from latitude 15°S to 15°N. Sea surface winds were derived from the European Space Agency (ESA)’s European Remote-Sensing Satellite (ERS)-1/2 scatterometer and the National Aeronautics and Space Administration (NASA)’s QuickSCAT observations; SST data were obtained from the National Oceanic and Atmospheric Administration (NOAA)’s Advanced Very-High-Resolution Radiometer (AVHRR) missions; and the SSHA data were acquired from the NASA TOPEX/Poseidon and Jason-1 altimeter measurements. All these datasets were resampled into 1° × 1° grids between 15°S and 15°N. The annual cycles were removed from all datasets and an empirical orthogonal function (EOF) analysis was applied to extract the major modes of spatial and temporal variability. The first EOF modes of the wind, SST, and SSHA revealed the interannual variability of each data source, reflecting spatio-temporal signatures related to El Nino Southern Oscillation (ENSO) events. The correlation results suggested that, during the strong El Nino period of 1997–1998, the wind variability led the variability of SST. A wind-forced delayed action oscillator (WDAO) system was proposed and analyzed using the ENSO modes of wind and SST data, covering the period from October 1995 to June 2002. The results show that the delayed SST mechanism is the strongest forcing factor in the WDAO system, and the wind forcing is the second strongest forcing factor. The correlations among SST change rate, the wind, and delayed/un-delayed SST also confirm the WDAO analysis’ results.

Global Flood Mapper: Democratising open EO resources for flood mapping

2021 ◽  
Author(s):  
Pratyush Tripathy ◽  
Teja Malladi
Keyword(s):  
Disaster Response ◽  
Flood Hazard ◽  
European Space Agency ◽  
Google Earth ◽  
Data Availability ◽  
Anthropogenic Factors ◽  
Flood Mapping ◽  
Flood Events ◽  
Space Agency ◽  
The Impact

<p>Climate change has increased the frequency of flood events globally. Floods cause massive loss of life and cause the expenditure of billions of dollars. While it is important to curb floods caused by anthropogenic factors in the first place, it is equally important to reduce the impact in the aftermath of floods. The extent of past flood events is crucial for developing disaster management plans and flood hazard modelling. Due to the lack of capacity and availability of the funds with local officials, many past disasters remain unmapped and the information is just limited to total life loss and damage estimates.</p><p>Satellite data has been widely hailed as an alternative to drone and aerial surveys. And recent advances in open Earth Observation (EO) data availability, for instance, the Sentinel-1 SAR data by the European Space Agency (ESA), and cloud processing platforms such as the Google Earth Engine (GEE) have opened unprecedented opportunities for using EO data for hazard and disaster response efforts. Recent literature in the field of EO is witnessing an increasing number of the Sentinel-1 and GEE combination for flood mapping.</p><p>In the present work, we demonstrate the utility of a recently developed tool, the Global Flood Mapper (GFM), which is an open GEE application for rapid mapping of flood inundation extent using Sentinel-1 data. GFM uses a pre-flood time period to analyse numerous Sentinel-1 scenes of the same study area, this accounts for seasonal variation and has lesser noise as compared to other methods that use just one pre-flood scene. We map a couple of flood events across the globe to demonstrate the scalability and ease of using GFM. In addition, we analyse the flood hazard vulnerability of the state of Bihar in India using flood extent for the year 2018, 2019 and 2020 by delineating frequently flooding areas. This showcases yet another crucial utility of the GFM tool. GFM can support the flood extent mapping of the past events in addition to the rapid flood mapping of the current events, that could aid researchers and disaster managers for better flood preparedness and response. </p><p>We access GFM through the link available on this public repository: https://github.com/PratyushTripathy/global_flood_mapper</p>

scholarly journals SENTINEL-1 DATA TO MAP FLOODED AREAS: THE ROLE OF INSAR COHERENCE AND POLARIMETRIC INFORMATION

2017 ◽  
Vol 50 (3) ◽  
pp. 1730
Author(s):  
A. Papastergios ◽  
M. Chini ◽  
I. Parcharidis
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Satellite System ◽  
Observation Data ◽  
Sar Images ◽  
Flood Detection ◽  
Earth Observation Data ◽  
Coherence Estimation ◽  
Flooded Areas

SAR earth observation data can provide high quality flood maps and information to better assess the flood risk accordingly planning as well as to support civil protection authorities during emergency phase. The scope of this paper is to create flood extent maps from a series of SAR scenes of the Evros basin which represents a transboundary floodplain. The study uses time series SAR images of Sentnel-1 ESA’s Copernicus satellite system covering the period October 2014 to May 2015. The methodology tries to identify the flood that occurs in three main land cover classes, such as urban areas, bare or poorly vegetated soil and vegetated areas, taking advantage of co- and cross-polarized SAR backscattering channels, and the InSAR coherence to better characterize the landscape. Dual-pol SAR data provides the opportunity to have a better understanding and interpretation of flood detection due to way different land cover react to different polarizations. Thus, with the implementation of InSAR coherence estimation we may achieve a better record and knowledge of the flooded areas, over time, in the specific region. 

scholarly journals Brief communication: Hurricane Dorian: automated near-real-time mapping of the unprecedented flooding on the Bahamas using SAR

2019 ◽  
Author(s):  
Diego Cerrai ◽  
Qing Yang ◽  
Xinyi Shen ◽  
Marika Koukoula ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
European Space Agency ◽  
Sar Images ◽  
The Bahamas ◽  
In Situ Data ◽  
Space Agency ◽  
Continental Area ◽  
Flooded Areas

Abstract. Lack of real-time, in situ data on the extent of flooding in many parts of the world can hinder efficient disaster response. With the advent of satellite-based synthetic aperture radar (SAR) sensors, we can deploy techniques to identify flooded areas worldwide while storms are occurring. In this communication, we present an automated near-real-time (NRT) system called RAdar-Produced Inundation Diary (RAPID), applying it to European Space Agency Sentinel-1 SAR images to produce flooding maps for Hurricane Dorian in the northern Bahamas. Images from RAPID released two days after the event show coastal flooding in the Bahamas reached areas located more than 10 km inland, covering more than 3,000 km2 of continental area. RAPID flood estimates from subsequent SAR images show the recession of the flood across the islands.

scholarly journals Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

2020 ◽  
Vol 12 (7) ◽  
pp. 1182 ◽  
Author(s):  
Andy Hardy ◽  
Gregory Oakes ◽  
Georgina Ettritch
Keyword(s):  
Surface Water ◽  
Flood Risk ◽  
Disease Risk ◽  
Southern Oscillation ◽  
Google Earth ◽  
Hazard Mapping ◽  
Continental Scale ◽  
Enso Events ◽  
Google Earth Engine ◽  
Landsat Satellite

Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of optical Landsat satellite imagery in the Google Earth Engine. The results demonstrate transferability throughout the African continent with a high degree of accuracy (mean 91% accuracy, st. dev 2.6%, n = 10,800). TropWet demonstrated considerable improvements over existing globally available surface water datasets for mapping the extent of important wetlands like the Okavango, Botswana. TropWet was able to provide frequency inundation maps as an indicator of malarial mosquito aquatic habitat extent and persistence in Barotseland, Zambia. TropWet was able to map flood extent comparable to operational flood risk mapping products in the Zambezi Region, Namibia. Finally, TropWet was able to quantify the effects of the El Niño/Southern Oscillation (ENSO) events on the extent of photosynthetic vegetation and wetland extent across Southern Africa. These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.

scholarly journals Use of Sentinel-1 Data in Flood Mapping in the Buna River Area

2020 ◽  
pp. 147-156
Author(s):  
Freskida Abazaj ◽  
Gëzim Hasko
Keyword(s):  
European Space Agency ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Sar Images ◽  
Space Agency ◽  
Optical Images ◽  
River Region ◽  
Speckle Filtering ◽  
Flooded Areas

Floods are one of the disasters that cause many human lives and property. In Albania, most floods are associated with periods of heavy rainfall. In recent years, Synthetic Aperture Radar (SAR) sensors, which provide reliable data in all weather conditions and day and night, have been favored because they eliminate the limitations of optical images. In this study, a flood occurred in the Buna River region in March 2018, was mapped using SAR Sentinel-1 data. The aim of this study is to investigate the potential of flood mapping using SAR images using different methodologies. Sentinel-1A / B SAR images of the study area were obtained from the European Space Agency (ESA). Preprocessing steps, which include trajectory correction, calibration, speckle filtering, and terrain correction, have been applied to the images. RGB composition and the calibrated threshold technique have been applied to SAR images to detect flooded areas and the results are discussed here.

scholarly journals Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

2021 ◽  
Vol 256 ◽  
pp. 112318
Author(s):  
Dong Liang ◽  
Huadong Guo ◽  
Lu Zhang ◽  
Yun Cheng ◽  
Qi Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Sar Images ◽  
Google Earth Engine ◽  
The Antarctic

scholarly journals Urban Flood Modeling Using 2D Shallow-Water Equations in Ouagadougou, Burkina Faso

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2120
Author(s):  
Gnenakantanhan Coulibaly ◽  
Babacar Leye ◽  
Fowe Tazen ◽  
Lawani Adjadi Mounirou ◽  
Harouna Karambiri
Keyword(s):  
Shallow Water ◽  
Shallow Water Equations ◽  
Urban Areas ◽  
Flood Hazard ◽  
Scientific Information ◽  
Roughness Coefficient ◽  
Flood Events ◽  
African Countries ◽  
Numerical Tool ◽  
2D Shallow Water Equations

Appropriate methods and tools accessibility for bi-dimensional flow simulation leads to their weak use for floods assessment and forecasting in West African countries, particularly in urban areas where huge losses of life and property are recorded. To mitigate flood risks or to elaborate flood adaptation strategies, there is a need for scientific information on flood events. This paper focuses on a numerical tool developed for urban inundation extent simulation due to extreme tropical rainfall in Ouagadougou city. Two-dimensional (2D) shallow-water equations are solved using a finite volume method with a Harten, Lax, Van Leer (HLL) numerical fluxes approach. The Digital Elevation Model provided by NASA’s Shuttle Radar Topography Mission (SRTM) was used as the main input of the model. The results have shown the capability of the numerical tool developed to simulate flow depths in natural watercourses. The sensitivity of the model to rainfall intensity and soil roughness coefficient was highlighted through flood spatial extent and water depth at the outlet of the watershed. The performance of the model was assessed through the simulation of two flood events, with satisfactory values of the Nash–Sutcliffe criterion of 0.61 and 0.69. The study is expected to be useful for flood managers and decision makers in assessing flood hazard and vulnerability.

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