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Towards Time Series Sensor Data to Accurately Map Flood Hazard and Assess Damages under Climate Change Using Google Earth Engine Cloud Platform and GIS – Case of the Cities of Tetouan and Casablanca (Morocco)

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Języki publikacji
EN
Abstrakty
EN
Climate change poses a major challenge in terms of urban planning management for the sake of a sustainable future. It is affecting the hydrological cycle around the world, leading to extreme weather conditions. Floods rank as the most frequent and widespread disaster in the world, they adversely affect inhabitants in terms of property damage and threat to human safety (and lives, in the worst cases). Uncontrolled urban sprawl also exacerbates floods by expanding impervious surfaces and affecting flow paths. Other factors that trigger flooding (apart from the rainfall intensity) are human involvement in the main waterways, thereby significantly impacting the hydraulic flow characteristics, structural engineering breakdowns, compounded by potential deforestation. For the purpose of monitoring the aftermath of floods experienced by the cities of Casablanca and Tetouan (Morocco) respectively in January and March 2021 and estimating their damages, optical and radar satellite images derived from the Google Earth Engine (GEE) cloud platform were used along with the Geographic Information System (GIS). In this study, a novel technique for extracting flooded areas from high-resolution Synthetic Aperture Radar (SAR) time series images has been developed. A comparison was carried out subsequently between the time-series approach and other traditional approaches including radiometric thresholding method, spectral indices namely Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) as well as Flood Water Index (FWI). Based on the above approach, the water levels were estimated and the damages were assessed and mapped, notably the number of people exposed to flood hazard and the amount of built-up areas and cropland affected. The results demonstrated that Casablanca city has witnessed a higher flood level than Tetouan city, putting a large number of people at risk and affecting a significant area of land use. The findings can also provide local authorities with a comprehensive view of flooding and enable them to make decisions on preparedness, mitigation, and adaptation to flood-related disasters.
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Twórcy
  • GéoTéCa Team, Department of Geology, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University, P.O. Box: 416, Tangier, Morocco
autor
  • GéoTéCa Team, Department of Geology, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University, P.O. Box: 416, Tangier, Morocco
  • GéoTéCa Team, Department of Geology, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University, P.O. Box: 416, Tangier, Morocco
  • GéoTéCa Team, Department of Geology, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University, P.O. Box: 416, Tangier, Morocco
  • GéoTéCa Team, Department of Geology, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University, P.O. Box: 416, Tangier, Morocco
  • GéoTéCa Team, Department of Geology, Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaâdi University, P.O. Box: 416, Tangier, Morocco
Bibliografia
  • 1. Albertini, C., Gioia, A., Iacobellis, V., Manfreda, S. 2022. Detection of surface water and floods with multispectral satellites. Remote Sensing, 14(23), 23.
  • 2. Asmadin, A., Siregar, V., Sofian, I., Jaya, I., Wijanarto, A. 2018. Feature extraction of coastal surface inundation via water index algorithms using multispectral satellite on North Jakarta. IOP Conference Series: Earth and Environmental Science, 176, 012032.
  • 3. Chen, S., Huang, W., Chen, Y., Feng, M. 2021. An adaptive thresholding approach toward rapid flood coverage extraction from Sentinel-1 SAR Imagery. Remote Sensing, 13(23), 4899.
  • 4. Cohen, S., Raney, A., Munasinghe, D., Loftis, D., Molthan, A., Bell, J., Rogers, L., Galantowicz, J., Brakenridge, G.R., Kettner, A.J., Huang, Y.-F., Tsang, Y.-P. 2019. The floodwater depth estimation tool (FwDET v2.0) for improved remote sensing analysis of coastal flooding. Natural Hazards and Earth System Sciences, 19(9)
  • 5. Gholamrezaie, H., Hasanlou, M., Amani, M., Mirmazloumi, S.M. 2022. Automatic mapping of burned areas using Landsat 8 time-series images in Google Earth engine: A case study from Iran. Remote Sensing, 14(24), 24.
  • 6. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017b. Google Earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
  • 7. Jassim, F.A. 2013. Image denoising using interquartile range filter with local averaging. International Journal of Soft Computing and Engineering, 2(6).
  • 8. Juia, R. 2020. Radar vs. Optical: Optimising Satellite Use in Land Cover Classification. Ecology for the Masses.
  • 9. Mehmood, H., Conway, C., Perera, D. 2021. Mapping of flood areas using Landsat with Google Earth engine cloud platform. Atmosphere, 12(7), 7.
  • 10. Nghia, B.P.Q., Pal, I., Chollacoop, N., Mukhopadhyay, A. 2022. Applying Google earth engine for flood mapping and monitoring in the downstream provinces of Mekong river. Progress in Disaster Science, 14, 100235.
  • 11. Raclot, D., Puech, C., Hostache, R. 2007. Caractérisation spatiale de l’aléa inondation à partir d’images satellites RADAR. Cybergeo. https://doi.org/10.4000/cybergeo.7722
  • 12. Roa-Pascuali, L., Demarcq, H., Nieblas, A.-E. 2015. Detection of mesoscale thermal fronts from 4 km data using smoothing techniques: Gradient-based fronts classification and basin scale application. Remote Sensing of Environment, 164, 225–237.
  • 13. Santos, L.A., Ferreira, K.R., Camara, G., Picoli, M.C.A., Simoes, R.E. 2021. Quality control and class noise reduction of satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 75–88.
  • 14. Tuna, C., Merciol, F., Lefevre, S. 2019. Analysis of Min-Trees over Sentinel-1 time series for flood detection. Proc. of 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), 1–4.
  • 15. Uddin, Matin, Meyer. 2019. Operational flood mapping using Multi-Temporal Sentinel-1 SAR Images: A case study from Bangladesh. Remote Sensing, 11(13), 1581.
  • 16. Zhang, Y., Crawford, P. 2020. Automated extraction of visible floodwater in dense urban areas from RGB aerial photos. Remote Sensing, 12(14), 2198.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3a0d8a68-2121-41bc-b434-f8f2b987d0f7
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