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Geospatial Mapping and Analysis of the 2019 Flood Disaster Extent and Impact in the City of Ghat in Southwestern Libya Using Google Earth Engine and Deep Learning Technique

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Environmental Applications of Remote Sensing and GIS in Libya

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Abstract

Flooding impacts from heavy rainfall, thunderstorm, and other natural hazards are a significant concern in many areas of the world. The objectives of this study were to: (1) develop a framework to identify flood-affected areas after storm impact; (2) map the flooded areas caused by the heavy rainfall and thunderstorm in the region; and (3) assess the major effect of the storm on the land cover during the flood period. The flood extent extraction analysis results indicated that approximately 2255.67 hectares of the study area were flooded during the wave of the heavy rainfall and thunderstorm event in June 2019, causing flooding and damage in several locations around the city. During this event, 70% of urban areas and roads were affected by floods, followed by half of the shrubs area inundated. About 30% of agriculture, tree canopy, and barren land were flooded overall land cover classes, while the sand dunes had less area affected. These results not only indicate flood risk on the land cover but also demonstrate the advantage of utilizing Google Earth Engine and the public archive database in its platform to track and monitor the natural hazards over time.

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References

  • Abdel-Fattah M, Kantoush S, Sumi T (2015) Integrated management of flash flood in wadi system of Egypt: disaster prevention and water harvesting. B= Disaster Prev Res Inst Ann B 58(B):485–496

    Google Scholar 

  • Ashour MM (2002) Flashfloods in Egypt (a case study of Drunka village–Upper Egypt). Bull Soc Gèog Ègypte 75:101–114

    Google Scholar 

  • Banerjee A, Bhavnani R, Burtonboy CH, Hamad O, Linares-Rivas Barandiaran A, Safaie S et al (2014) Natural disasters in the middle East and North Africa: a regional overview, vol No. 81658. The World Bank, pp 1–114

    Google Scholar 

  • Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35:1798–1828

    Article  Google Scholar 

  • Billa L, Mansor S, Mahmud AR, Ghazali AH (2005) AVHRR data for real-time operational flood forecasting in Malaysia. In: Geo-information for disaster management. Springer, Berlin/Heidelberg, pp 1357–1379

    Chapter  Google Scholar 

  • Campos-Taberner M, García-Haro FJ, Martínez B, Izquierdo-Verdiguier E, Atzberger C, Camps-Valls G, Gilabert MA (2020) Understanding deep learning in land use classification based on Sentinel-2 time series. Sci Rep 10(1):1–12

    Article  Google Scholar 

  • Chang H, Franczyk J, Kim C (2009) What is responsible for increasing flood risks? The case of Gangwon Province. Korea Nat Haz 48(3):339–354

    Article  Google Scholar 

  • Colombo A, Hervás J, Vetere Arellano AL (2002) Guidelines on flash flood prevention and mitigation. European Commission Joint Research Centre (JRC)

    Google Scholar 

  • Dolman AJ, Gash JHC, Goutorbe JP, Kerr Y, Lebel T, Prince SD, Stricker JNM (1997) The role of the land surface in Sahelian climate: HAPEX-Sahel results and future research needs. J Hydrol 188:1067–1079

    Article  Google Scholar 

  • Elfadli K (2019) GHAT floods; 1–6 June 2019. Government report, submitted on JUL 15, 2019

    Google Scholar 

  • ESRI—Environmental Systems Research Institute (n.d.) ArcGIS Pro 2.9. Available online: https://pro.arcgis.com/en/pro-app/latest/get-started/arcgis-pro-system-requirements.htm Accessed 13 Dec 2021

  • Haq M, Akhtar M, Muhammad S, Paras S, Rahmatullah J (2012) Techniques of remote sensing and GIS for flood monitoring and damage assessment: a case study of Sindh province, Pakistan. Egypt J Remote Sens Space Sci 15(2):135–141

    Google Scholar 

  • Ilyas I, Jaelani LM, Syariz MA, Hidayat H (2021) World view-2 satellite image classification using U-net deep learning model. J Appl Geospatial Inf 5(2):502–509

    Article  Google Scholar 

  • IRIN (2013) Preparing for floods in West Africa. Available from: http://reliefweb.int/report/nigeria/-preparing-floods-west-africa

  • Keller E, DeVecchio D (2015) Natural hazards: earth’s processes as hazards, disasters, and catastrophes. Pearson Higher Education, Upper Saddle River

    Google Scholar 

  • Korichi K, Hazzab A, Atallah M (2016) Flash floods risk analysis in ephemeral streams: a case study on Wadi Mekerra (northwestern Algeria). Arab J Geosci 9(11):1–11

    Article  Google Scholar 

  • Langill JC, Abizaid C (2019) What is a bad flood? Local perspectives of extreme floods in the Peruvian Amazon. Ambio:1–14

    Google Scholar 

  • Lee J, Han D, Shin M, Im J, Lee J, Quackenbush LJ (2020) Different spectral domain transformation for land cover classification using convolutional neural networks with multi-temporal satellite imagery. Remote Sens 12(7):1097

    Article  Google Scholar 

  • Lin X (1999) Flash floods in arid and semi-arid zones. Technical documents in hydrology. UNESCO, Paris

    Google Scholar 

  • Llasat MC, Llasat-Botija M, Prat MA, Porcu F, Price C, Mugnai A et al (2010) High-impact floods and flash floods in Mediterranean countries: the FLASH preliminary database. Adv Geosci 23:47–55

    Article  Google Scholar 

  • Malik K, Robertson C, Braun D, Greig C (2021) U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales. International Journal of Applied Earth Observation and Geoinformation, 104, p.102510

    Google Scholar 

  • Markus K, Jurgen G, Christphoph B, Bruno R, Franz R (2006) World map of the Koppen- Geiger climate classification updated. Meteorologishe Zeitschrift 15(3):259–263

    Article  Google Scholar 

  • Martinis S, Twele A, Voigt S (2009) Towards operational near real-time flood detection 563 using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat Hazards Earth Syst Sci 9:303–314

    Article  Google Scholar 

  • Moawad MB (2013) Analysis of the flash flood occurred on 18 January 2010 in Wadi El Arish, Egypt (a case study). Geomat Nat Haz Risk 4(3):254–274

    Article  Google Scholar 

  • Moawad MB, Abdel Aziz AO, Mamtimin B (2016) Flash floods in the Sahara: a case study for the 28 January 2013 flood in Qena. Egypt Geom Nat Hazards sRisk 7(1):215–236

    Article  Google Scholar 

  • Morris J, Brewin P (2014) The impact of seasonal flooding on agriculture: the spring 2012 floods in Somerset, England. J Flood Risk Manage 7:128–114

    Article  Google Scholar 

  • OCHA (Office for the Coordination of Humanitarian Affairs) (2012) Mali complex emergency situation report no. 15. 11 September 2012. United Nations. Available from: http://www.un.org/maintenance/

  • OCHA (Office for the Coordination of Humanitarian Affairs) (2019) LIBYA: Floods in the South – West FLASH UPDATE 2 As of 11 June 2019. United Nations

    Google Scholar 

  • Podlaha A, Bowen S, Darbinyan C, Lörinc M (2019) Global catastrophe recap-June 2019. Aon Benfield Analytics

    Google Scholar 

  • Reid I, Powell M, Laronne JB, Garcia C (1994) Flash floods in desert rivers: studying the unexpected. EOS Trans Am Geophys Union 75(39):452–452

    Article  Google Scholar 

  • Robinson C, Hohman F, Dilkina, B (2017) A deep learning approach for population estimation from satellite imagery. In: Proceedings of the 1st ACM SIGSPATIAL Workshop on Geospatial Humanities, (pp 47–54)

    Google Scholar 

  • Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 234–241

    Google Scholar 

  • Sene K (2012) Flash floods: forecasting and warning. Springer Science & Business Media

    Google Scholar 

  • Stanley M, Hirst, Abu Md. Ibrahim (1996) Effects of flood protection on soil fertility in a Riverine floodplain area in Bangladesh, Communications in Soil Science and Plant Analysis, 27:1–2, 119–156. https://doi.org/10.1080/00103629609369549

  • UNICEF (2019) UNICEF Libya Humanitarian Situation Report, January – June 2019 (Issue June)

    Google Scholar 

  • United nation (2021) Country intervention on drought- Libya. department of economic and social affairs sustainable development: Access on 21 Dec 2021. Available at: https://sdgs.un.org/documents/country-intervention-drought-libya-19926

  • United Nations Office for the Coordination of Humanitarian Affairs (OCHA) (2019) Floods in the South – West of Libya (As of 5 June 2019) (Issue June)

    Google Scholar 

  • Warner TT (2004) Desert meteorology. Cambridge university press, Edinburgh, p 612

    Book  Google Scholar 

  • Waske B, Fauvel M, Benediktsson JA Chanussot J (2009) Machine learning techniques in remote sensing data analysis. Kernel methods for remote sensing data analysis, pp 3–24

    Google Scholar 

  • Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033

    Article  Google Scholar 

  • Yang L, Jin S, Danielson P, Homer C, Gass L, Bender SM, Case A, Costello C, Dewitz J, Fry J, Funk M (2018) A new generation of the United States National Land Cover Database: requirements, research priorities, design, and implementation strategies. ISPRS J Photogramm Remote Sens 146:108–123

    Article  Google Scholar 

  • Yousif M, Hussien HM (2020) Flash floods mitigation and assessment of groundwater possibilities using remote sensing and GIS applications: Sharm El Sheikh, South Sinai, Egypt. Bull Natl Res Centre 44(1):1–25

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Hassan AM (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ Earth Sci 62(3):611–623

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Sefry SA (2015) Remote sensing-based studies coupled with field data reveal urgent solutions to avert the risk of flash floods in the Wadi Qus (East of Jeddah) Kingdom of Saudi Arabia. Nat Hazards 75(2):1465–1488

    Article  Google Scholar 

  • Youssef AM, Sefry SA, Pradhan B, Alfadail EA (2016) Analysis on causes of flash flood in Jeddah city (Kingdom of Saudi Arabia) of 2009 and 2011 using multi-sensor remote sensing data and GIS. Geomat Nat Haz Risk 7(3):1018–1042

    Article  Google Scholar 

  • Zamot J, Afkareen M (2020) Geomorphological parameters by remote sensing and GIS techniques (A case study of flash flood in Mikhili Village, Al Jabal Al Akhdar, NE of Libya). In: The forth international conference for geospatial technologies – Libya GeoTec 4, Tripoli, Libya, 3–5 March 2020

    Google Scholar 

  • Zhang G, Feng G, Li X, Xie C, Pi X (2017) Flood effect on groundwater recharge on a typical silt loam soil. Water, 9(7):523

    Google Scholar 

  • Zurqani HA (2021) Introduction. In: Zurqani HA (ed) The soils of Libya, World soils book series. Springer, Cham. https://doi.org/10.1007/978-3-030-66368-1_1

    Chapter  Google Scholar 

  • Zurqani HA, Post CJ, Mikhailova EA, Schlautman MA, Sharp JL (2018) Geospatial analysis of land use change in the Savannah River basin using Google earth engine. Int J Appl Earth Obs Geoinf 69:175–185

    Google Scholar 

  • Zurqani HA, Post CJ, Mikhailova EA, Ozalas K Allen JS (2019) Geospatial analysis of flooding from hurricane Florence in the coastal South Carolina using Google Earth Engine. In: Graduate Research and Discovery Symposium (GRADS), p 230

    Google Scholar 

  • Zurqani HA, Allen JS, Post CJ, Pellett CA, Walker TC (2021) Mapping and quantifying agricultural irrigation in heterogeneous landscapes using Google earth engine. Remote Sens Appl Soc Environ 23:100590

    Google Scholar 

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Authors and Affiliations

  1. University of Arkansas Agricultural Experiment Station, Arkansas Forest Resources Center, College of Forestry, Agriculture and Natural Resources, University of Arkansas at Monticello, Monticello, AR, USA

    Hamdi A. Zurqani

  2. Department of Forest and Rangeland, Faculty of Natural Resources and Environmental Sciences, Omar Al-Mukhtar University, Al-Bayda, Libya

    Abdulsalam Al-Bukhari

  3. Department of Forest and Rangeland, Faculty of Natural Resources and Environmental Sciences, University of Omar Al-Mukhtar, Al-Bayda, Libya

    Alsaket O. Aldaikh

  4. Libyan National Meteorological Center, Tripoli, Libya

    Khalid I. Elfadli

  5. Department of Zoology, Faculty of Sciences, University of Omar Al-Mukhtar, Al-Bayda, Libya

    Ali A. Bataw

Authors
  1. Hamdi A. Zurqani
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  2. Abdulsalam Al-Bukhari
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  3. Alsaket O. Aldaikh
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  4. Khalid I. Elfadli
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  5. Ali A. Bataw
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Correspondence to Hamdi A. Zurqani .

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  1. University of Arkansas Agricultural Experiment Station, Arkansas Forest Resources Center, College of Forestry, Agriculture and Natural Resources, University of Arkansas at Monticello, Monticello, AR, USA

    Hamdi A. Zurqani

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Zurqani, H.A., Al-Bukhari, A., Aldaikh, A.O., Elfadli, K.I., Bataw, A.A. (2022). Geospatial Mapping and Analysis of the 2019 Flood Disaster Extent and Impact in the City of Ghat in Southwestern Libya Using Google Earth Engine and Deep Learning Technique. In: Zurqani, H.A. (eds) Environmental Applications of Remote Sensing and GIS in Libya. Springer, Cham. https://doi.org/10.1007/978-3-030-97810-5_10

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