A Constantly Updated Flood Hazard Assessment Tool Using Satellite-Based High-Resolution Land Cover Dataset Within Google Earth Engine

Alexandra Gemitzi, Odysseas Kopsidas, Foteini Stefani, Aposotolos Polymeros and Vasilis Bellos ()
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Alexandra Gemitzi: Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Odysseas Kopsidas: Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Foteini Stefani: National School of Public Administration and Local Government, 17778 Athens, Greece
Aposotolos Polymeros: Ministry of Rural Development and Food, 10176 Athens, Greece
Vasilis Bellos: Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece

Land, 2024, vol. 13, issue 11, 1-18

Abstract: This work aims to develop a constantly updated flood hazard assessment tool that utilizes readily available datasets derived by remote sensing techniques. It is based on the recently released global land use/land cover (LULC) dataset Dynamic World, which is readily available, covering the period from 2015 until now, as an open data source within the Google Earth Engine (GEE) platform. The tool is updated constantly following the release rate of Sentinel-2 images, i.e., every 2 to 5 days depending on the location, and provides a near-real-time detection of flooded areas. Specifically, it identifies how many times each 10 m pixel is characterized as flooded for a selected time period. To investigate the fruitfulness of the proposed tool, we provide two different applications; the first one in the Thrace region, where the flood hazard map computed with the presented herein approach was compared against the flood hazard maps developed in the frames of the EU Directive 2007/60, and we found several inconsistencies between the two approaches. The second application focuses on the Thessaly region, aiming to assess the impacts of a specific, unprecedented storm event that affected the study area in September 2023. Moreover, a new economic metric is proposed, named maximum potential economic loss, to assess the socioeconomic implications of the flooding. The innovative character of the presented methodology consists of the use of remotely sensed-based datasets, becoming available at increasing rates, for developing an operational instrument that defines and updates the flood hazard zones in real-time as required.

Keywords: flood hazard; remote sensing; dynamic world; land cover; Google Earth Engine (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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