journal article·2025-01-22Open access

Integrating Ground and UAV Mapping for GIS-Based Application of the Flash Flood Impact Severity Scale (FFISS) for the 2009 and 2020 Evia (Greece) Flash Floods

Nafsika Ioanna Spyrou, Michalis Diakakis, Spyridon Mavroulis, Georgios Deligiannakis, Emmanuel Andreadakis, Christos Filis, Evelina Kotsi, Zacharias Antoniadis, Maria Melaki, Marilia Gogou, Katerina-Navsika Katsetsiadou, Eirini-Spyridoula Stanota, Emmanuel Skourtsos, Emmanuel Vassilakis, Efthymios Lekkas

Applied Sciences· Volume 15· Issue 3

Abstract

Flash floods have been the cause of some of the most devastating events worldwide. The wide diversity of the effects, as well as the variety in the severity of the impacts, lead to major obstacles in obtaining a realistic understanding of the damages caused by a flood event, thus hampering at the same time our ability to predict future impacts. In assessing flood impacts and their severity, most existing methods use a qualitative characterization (e.g., major, catastrophic, etc.) or view the impacts from a single viewpoint or discipline (e.g., economic losses). In this study, we apply the Flash Flood Impact Severity Scale (FFISS) to assess, map, and classify the impacts of two flash floods from the Lilas River in Greece in 2009 and 2020. This application aims to discuss the different severity levels in terms of how one flood can affect the impacts of the next event. The methodology encompasses comprehensive field research, including the collection of ground-based and aerial observations utilizing UAV technology to document the impacts. These observations are subsequently georeferenced, followed by application of the Flash Flood Impact Severity Scale (FFISS) and generation of detailed maps to assess and evaluate the severity of the impacts associated with the two flood events. The results show that despite the higher water stage of the second flood, the impacts in previously hit areas indicate lower severity values, attributed to the gradual adaptation of the community and its infrastructure, as well as significant local widening of the river channel. On the contrary, high severity remains an issue in newly flooded areas during the second event. Overall, the application of the FFISS can show the spatial patterns of severity impacts, providing insights into the nature of floods locally but also indicating a potential reduction in the overall risk in the post-flood period.
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Research context

OpenAlex topics: Flood Risk Assessment and Management

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Affiliations

Department of Dynamic, Tectonic, Applied Geology, Faculty of Geology and Geoenvironment, School of Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece

Department of Geography and Climatology, Faculty of Geology and Geoenvironment, School of Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece

Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 10447 Athens, Greece

References (63)

Show source-provided references
1. Review article “assessment of economic flood damage”10.5194/nhess-10-1697-2010 ↗
2. Gruntfest, E., and Handmer, J. (2001). Impacts of Flash Floods. Coping with Flash Floods, Springer.10.1007/978-94-010-0918-8 ↗
3. An integrated approach of ground and aerial observations in flash flood disaster investigations. The case of the 2017 Mandra flash flood in Greece10.1016/j.ijdrr.2018.10.015 ↗
4. Characterisation of selected extreme flash floods in Europe and implications for flood risk management10.1016/j.jhydrol.2010.07.017 ↗
5. A unified flash flood database across the United States10.1175/BAMS-D-12-00198.1 ↗
6. Stonefield, R., and Jackson, J. (2024, December 15). An Abbreviated Flash Flood Climatology (1994–2007) for the WFO Blacksburg, Virginia County Warning Area, Available online: https://repository.library.noaa.gov/view/noaa/6598/noaa_6598_DS1.pdf.
7. Comparative hazard analysis of processes leading to remarkable flash floods (France, 1930–1999)10.1016/j.jhydrol.2016.05.032 ↗
8. A quantitative methodology for the assessment of the regional economic vulnerability to flash floods10.1016/j.jhydrol.2018.08.029 ↗
9. Dobrovičová, S., Dobrovič, R., and Dobrovič, J. (2015). The Economic Impact of Floods and their Importance in Different Regions of the World with Emphasis on Europe. Procedia Econ. Financ., 34.10.1016/S2212-5671(15)01681-0 ↗
10. Floods and their effects on agricultural productivity
11. An agricultural flash flood loss estimation methodology: The case study of the Koiliaris basin (Greece), February 2003 flood10.1007/s11069-015-1882-8 ↗
12. Using GIS to map impacts upon agriculture from extreme floods in Vietnam10.1016/j.apgeog.2013.03.014 ↗
13. Flash floods, hydro-geomorphic response and risk management10.1016/j.jhydrol.2016.08.005 ↗
14. The impact of extreme rainfall and flash floods on the flood risk management process and geomorphological changes in small Carpathian catchments: A case study of the Kasiniczanka river (Outer Carpathians, Poland)10.1007/s11069-017-2858-7 ↗
15. The development of a flash flood severity index10.1016/j.jhydrol.2016.04.005 ↗
16. Boudou, Μ. (2016). Approche Multidisciplinaire pour la Caractérisation d’inondations Remarquables: Enseignements tirés de neuf Évènements en France (1910–2010), Université Paul-Valéry Montpellier III. Environnement et Société.
17. Supplementing flash flood reports with impact classifications10.1016/j.jhydrol.2012.09.036 ↗
18. Proposal of a flash flood impact severity scale for the classification and mapping of flash flood impacts10.1016/j.jhydrol.2020.125452 ↗
19. (2024, December 12). National Statistical Service of Greece (NSSG). Available online: https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.41/2000/files/Greece/Presentation%20of%20the%20census%202001.pdf.
20. Barsaki, V. (2012). Flood hazard Assessment by using GIS in Lilas River (Central Evia). [Master Thesis, University of Athens].
21. (2024, December 12). Meteo, n.d. Available online: https://www.meteo.gr/gmap.cfm.
22. Lekkas, E., Spyrou, N.I., Kotsi, E., Filis, C., Diakakis, M., Cartalis, C., Vassilakis, E., Mavrakou, T., Sartabakos, P., and Gogou, G. (2024, December 15). August 9, 2020 Evia (Central Greece) Flood. Preliminary Report: National and Kapodistrian University of Athens 2020. Available online: https://edcm.edu.gr/images/docs/newsletters/Newsletter_19_2020_Evia_flood.pdf.10.3390/app15031100 ↗
23. Geological and hydrometeorological hazards and related disasters amid COVID-19 pandemic in Greece: Post-disaster trends and factors affecting the COVID-19 evolution in affected areas10.1016/j.ssci.2021.105236 ↗
24. Spyrou, N.I., Stanota, E.S., Diakakis, M., Andreadakis, E., Lekkas, E., and Vassilakis, E. (2020, January 4–8). Estimation of Flow Velocity During Flash Floods with the synergy of Unmanned Aerial Systems (UAS) data and Ground Observations: The Case of 2017 Mandra Flash Flood, Greece. Proceedings of the EGU 2020 International Conference, Vienna, Austria.10.5194/egusphere-egu2020-17845 ↗
25. Andreadakis, E., Dakakis, M., Vassilakis, E., Deligiannakis, G., Antoniadis, A., Andriopoulos, P., Spyrou, N.-I., and Nikolopoulos, E. (2020). Unmanned aerial systems-aided post-flood peak discharge estimation in ephemeral streams. J. Remote Sens., 12.10.3390/rs12244183 ↗
26. Lekkas, E., Lozios, S., Vassilakis, E., Skourtsos, E., Kranis, H., Soukis, K., Diakakis, M., Antoniou, V., Mavroulis, S., and Filis, C. (2021). Design of Immediate Risk Reduction Actions in the Post-Disaster Phase of the Flood Events of August 9, 2020. Evia. Applied Research Program, National and Kapodistrian University of Athens.
27. Bormann, P. (2012). Intensity and Intensity Scales. New Manual of Seismological Observatory Practice 2, IASPEI, GFZ German Research Centre for Geosciences.
28. The challenge of forecasting impacts of flash floods: Test of a simplified hydraulic approach and validation based on insurance claim data10.5194/hess-21-5911-2017 ↗
29. Kotsi, E., Vassilakis, E., Diakakis, M., Mavroulis, S., Konsolaki, A., Filis, C., Lozios, S., and Lekkas, E. (2023). Using UAS-Aided Photogrammetry to Monitor and Quantify the Geomorphic Effects of Extreme Weather Events in Tectonically Active Mass Waste-Prone Areas: The Case of Medicane Ianos. Appl. Sci., 13.10.3390/app13020812 ↗
30. Collaborative data acquisition and processing for post disaster management and surveillance related tasks using UAV-based IoT cloud10.1504/IJAHUC.2020.108579 ↗
31. Flood Extent Mapping: An Integrated Method Using Deep Learning and Region Growing Using UAV Optical Data10.1109/JSTARS.2021.3051873 ↗
32. Munawar, H.S., Hammad, A.W.A., Waller, S.T., Thaheem, M.J., and Shrestha, A. (2021). An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review. Sustainability, 13.10.3390/su13147925 ↗
33. Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques10.1016/j.ijdrr.2020.101642 ↗
34. Unmanned aerial systems for photogrammetry and remote sensing: A review10.1016/j.isprsjprs.2014.02.013 ↗
35. Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications10.1016/j.geomorph.2012.08.021 ↗
36. Mavroulis, S., Vassilakis, E., Diakakis, M., Konsolaki, A., Kaviris, G., Kotsi, E., Kapetanidis, V., Sakkas, V., Alexopoulos, J.D., and Lekkas, E. (2022). The Use of Innovative Techniques for Management of High-Risk Coastal Areas, Mitigation of Earthquake-Triggered Landslide Risk and Responsible Coastal Development. Appl. Sci., 12.10.3390/app12042193 ↗
37. Zuo, Z., Liu, C., Han, Q.L., and Song, J. (2022). Unmanned Aerial Vehicles: Control Methods and Future Challenges. IEEE/CAA J. Autom. Sin., 9.10.1109/JAS.2022.105410 ↗
38. Hussain, Y., Schlögel, R., Innocenti, A., Hamza, O., Iannucci, R., Martino, S., and Havenith, H.B. (2022). Review on the Geophysical and UAV-Based Methods Applied to Landslides. Remote Sens., 14.10.3390/rs14184564 ↗
39. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management10.1117/1.3216822 ↗
40. UAV for 3D mapping applications: A review10.1007/s12518-013-0120-x ↗
41. Iqbal, U., Riaz, M.Z.B., Zhao, J., Barthelemy, J., and Perez, P. (2023). Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review. Drones, 7.10.3390/drones7010032 ↗
42. Applications of drone in disaster management: A scoping review10.1016/j.scijus.2021.11.002 ↗
43. Denizli, A., Alencar, M.S., Nguyen, T.A., and Motaung, D.E. (2022). Chapter 9—Unmanned aerial vehicles (UAVs) for disaster management. Micro and Nano Technologies, Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention, Elsevier.
44. Lightweight unmanned aerial vehicles will revolutionize spatial ecology10.1890/120150 ↗
45. Accuracy Assessment of Low-Cost Unmanned Aerial Vehicle (UAV) Photogrammetry10.1016/j.aej.2021.04.011 ↗
46. Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends
47. Science, technology and the future of small autonomous drones10.1038/nature14542 ↗
48. Andrews, L.S. (2024, December 12). Attack of the Drones: Ethical, Legal and Strategic Implications of UAV Use. MIT Center for International Studies, n.d. Available online: https://cis.mit.edu/publications/audits/attack-drones-ethical-legal-and-strategic-implications-uav-use.
49. Leveraging Drones for Effective Disaster Management: A Comprehensive Analysis of the 2024 Noto Peninsula Earthquake Case in Japan10.1016/j.pdisas.2024.100348 ↗
50. Jackman, A., and Hooper, L. (2023). Drone Incidents and Misuse: Legal Considerations, University of Reading. Available online: https://research.reading.ac.uk/drone-geographies/wp-content/uploads/sites/271/2023/12/Drone-incidents_Jackman-Hooper.pdf.
51. Federal Aviation Administration (FAA) (2024, December 12). Unmanned Aircraft Systems (UAS). n.d, Available online: https://www.faa.gov/uas/.
52. European Union Aviation Safety Agency (EASA) (2024, December 12). Unmanned Aircraft Systems (UAS). n.d. Available online: https://www.easa.europa.eu/domains/civil-drones-rpas.
53. (2024, December 12). ICAO, Unmanned Aircraft Systems (UAS) Cir 328 AN/190, n.d. Available online: https://www.icao.int/meetings/uas/documents/circular%20328_en.pdf.
54. Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue10.1109/TNET.2022.3226458 ↗
55. MahmoudZadeh, S., Yazdani, A., Kalantari, Y., Ciftler, B., Aidarus, F., and Al Kadri, M.O. (2024). Holistic Review of UAV-Centric Situational Awareness: Applications, Limitations, and Algorithmic Challenges. Robotics, 13.10.3390/robotics13080117 ↗
56. Gao, M., Hugenholtz, C.H., Fox, T.A., Kucharczyk, M., Barchyn, T.E., and Nesbit, P.R. (2021). Weather constraints on global drone flyability. Sci. Rep., 11.10.1038/s41598-021-91325-w ↗
57. (2024, December 12). Climavision, Navigating the Skies: How Weather Impacts UAV Operations. Blog Article, n.d. Available online: https://climavision.com/blog/navigating-the-skies-how-weather-impacts-uav-operations/.
58. Emerging UAV technology for disaster detection, mitigation, response, and preparedness10.1002/rob.22075 ↗
59. Mohsan, S.A.H., Khan, M.A., Noor, F., Ullah, I., and Alsharif, M.H. (2022). Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review. Drones, 6.10.3390/drones6060147 ↗
60. Ouaissa, M., Khan, I.U., Ouaissa, M., Boulouard, Z., and Hussain Shah, S.B. (2022). Investigation on Challenges of Big Data Analytics in UAV Surveillance. Computational Intelligence for Unmanned Aerial Vehicles Communication Networks, Springer. Studies in Computational Intelligence.10.1007/978-3-030-97113-7 ↗
61. A survey of mobile cloud computing: Architecture applications and approaches10.1002/wcm.1203 ↗
62. Stability of Cloud-Based UAV Systems Supporting Big Data Acquisition and Processing10.1109/TCC.2017.2696529 ↗
63. Sofia, G., and Nikolopoulos, E.I. (2022). Floods and rivers: A circular causality perspective. Sci. Rep., 10.10.1038/s41598-020-61533-x ↗