Flood Extent Delineation and Exposure Assessment in Senegal Using the Google Earth Engine: The 2022 Event

被引:0
|
作者
Sy, Bocar [1 ]
Bah, Fatoumata Bintou [1 ]
Dao, Hy [2 ,3 ]
机构
[1] Univ Amadou Mahtar Mbow, Dept Geosci & Environm, Lab Appl Geomatic LAG, Polytech Diamniadio, Rue 21 20,2eme Arrondissement, Dakar 15258, Senegal
[2] Univ Geneva, Geneva Sch Social Sci, Dept Geog & Environm, 66 Blvd Carl Vogt, CH-1205 Geneva, Switzerland
[3] Univ Geneva, Inst Environm Sci, Blvd Carl Vogt 66, CH-1205 Geneva, Switzerland
关键词
flood extent mapping; flood exposition assessment; remote sensing; Google Earth Engine; Sentinel-1; hydrological and hydraulic modeling; SENTINEL-1; RISK; INUNDATION;
D O I
10.3390/w16152201
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from the Sentinel-1 SAR, Global Surface Water, HydroSHEDS, the Global Human Settlement Layer, and MODIS land cover type, our primary objective is to delineate the extent of flooding and compare this with flooding for a one-in-a-hundred-year flood event, offering a comprehensive assessment of exposure during the period from July to October 2022 across Senegal's 14 regions. The findings underscore a total inundation area of 2951 square kilometers, impacting 782,681 people, 238 square kilometers of urbanized area, and 21 square kilometers of farmland. Notably, August witnessed the largest flood extent, reaching 780 square kilometers, accounting for 0.40% of the country's land area. Other regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experienced varying extents of flooding, with the data for August showing a 1.34% overlap with flooding for a one-in-a-hundred-year flood event derived from hydrological and hydraulic modeling. This low percentage reveals the distinct purpose and nature of the two approaches (remote sensing and modeling), as well as their complementarity. In terms of flood exposure, October emerges as the most critical month, affecting 281,406 people (1.56% of the population). The Dakar, Diourbel, Thi & egrave;s, and Saint-Louis regions bore substantial impacts, affecting 437,025; 171,537; 115,552; and 77,501 people, respectively. These findings emphasize the imperative for comprehensive disaster preparation and mitigation efforts. This study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Automated delineation of salt-affected lands and their progress in coastal India using Google Earth Engine and machine learning techniques
    Sabyasachi Kabiraj
    Marappan Jayanthi
    Muthusamy Samynathan
    Selvasekar Thirumurthy
    [J]. Environmental Monitoring and Assessment, 2023, 195
  • [42] Automated delineation of salt-affected lands and their progress in coastal India using Google Earth Engine and machine learning techniques
    Kabiraj, Sabyasachi
    Jayanthi, Marappan
    Samynathan, Muthusamy
    Thirumurthy, Selvasekar
    [J]. ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (03)
  • [43] Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
    Johary, Rosa
    Revillion, Christophe
    Catry, Thibault
    Alexandre, Cyprien
    Mouquet, Pascal
    Rakotoniaina, Solofoarisoa
    Pennober, Gwenaelle
    Rakotondraompiana, Solofo
    [J]. REMOTE SENSING, 2023, 15 (22)
  • [44] Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing
    Elnashar, Abdelrazek
    Zeng, Hongwei
    Wu, Bingfang
    Zhang, Ning
    Tian, Fuyou
    Zhang, Miao
    Zhu, Weiwei
    Yan, Nana
    Chen, Zegiang
    Sun, Zhiyu
    Wu, Xinghua
    Li, Yuan
    [J]. REMOTE SENSING, 2020, 12 (23) : 1 - 22
  • [45] Cemetery Detection Using Satellite Images in Google Earth Engine
    Rodrigo Suarez, Ranyart
    Villasenor, Elio
    [J]. PROCEEDINGS OF THE 2021 XXIII IEEE INTERNATIONAL AUTUMN MEETING ON POWER, ELECTRONICS AND COMPUTING (ROPEC 2021), 2021,
  • [46] Rapid multispectral data sampling using Google Earth Engine
    Brooke, Sam A. S.
    D'Arcy, Mitch
    Mason, Philippa J.
    Whittaker, Alexander C.
    [J]. COMPUTERS & GEOSCIENCES, 2020, 135
  • [47] Analysis of changes in rivers planforms using google earth engine
    Tobon-Marin, Alejandro
    Canon Barriga, Julio
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2020, 41 (22) : 8654 - 8681
  • [48] Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine
    Jodhani, Keval H.
    Patel, Dhruvesh
    Madhavan, N.
    Gupta, Nitesh
    Singh, Sudhir Kumar
    Rathnayake, Upaka
    [J]. RESULTS IN ENGINEERING, 2024, 24
  • [49] Google Earth Engine-Based Identification of Flood Extent and Flood-Affected Paddy Rice Fields Using Sentinel-2 MSI and Sentinel-1 SAR Data in Bihar State, India
    Kumar, Himanshu
    Karwariya, Sateesh Kumar
    Kumar, Rohan
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2022, 50 (05) : 791 - 803
  • [50] Google Earth Engine-Based Identification of Flood Extent and Flood-Affected Paddy Rice Fields Using Sentinel-2 MSI and Sentinel-1 SAR Data in Bihar State, India
    Himanshu Kumar
    Sateesh Kumar Karwariya
    Rohan Kumar
    [J]. Journal of the Indian Society of Remote Sensing, 2022, 50 : 791 - 803