Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite Data
Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data...
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MDPI AG
2025-05-01
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| author | Triantafyllos Falaras Anna Dosiou Stamatina Tounta Michalis Diakakis Efthymios Lekkas Issaak Parcharidis |
| author_facet | Triantafyllos Falaras Anna Dosiou Stamatina Tounta Michalis Diakakis Efthymios Lekkas Issaak Parcharidis |
| author_sort | Triantafyllos Falaras |
| collection | DOAJ |
| description | Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. |
| format | Article |
| id | doaj-art-88113e8b465b4df094b7631d48bbcd8c |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-88113e8b465b4df094b7631d48bbcd8c2025-08-20T01:56:45ZengMDPI AGRemote Sensing2072-42922025-05-011710175010.3390/rs17101750Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite DataTriantafyllos Falaras0Anna Dosiou1Stamatina Tounta2Michalis Diakakis3Efthymios Lekkas4Issaak Parcharidis5Department of Geography, Harokopio University of Athens, Eleftheriou Venizelou 70, 17676 Kallithea, GreeceDepartment of Geography, Harokopio University of Athens, Eleftheriou Venizelou 70, 17676 Kallithea, GreeceDepartment of Geography, Harokopio University of Athens, Eleftheriou Venizelou 70, 17676 Kallithea, GreeceFaculty of Geology and Geoenvironment, School of Sciences, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15784 Athens, GreeceFaculty of Geology and Geoenvironment, School of Sciences, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15784 Athens, GreeceDepartment of Geography, Harokopio University of Athens, Eleftheriou Venizelou 70, 17676 Kallithea, GreeceFloods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning.https://www.mdpi.com/2072-4292/17/10/1750flood mappingflood evolutionStorm DanielGoogle Earth Enginemulti-sensor satellite imagerygeospatial impact analysis |
| spellingShingle | Triantafyllos Falaras Anna Dosiou Stamatina Tounta Michalis Diakakis Efthymios Lekkas Issaak Parcharidis Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite Data Remote Sensing flood mapping flood evolution Storm Daniel Google Earth Engine multi-sensor satellite imagery geospatial impact analysis |
| title | Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite Data |
| title_full | Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite Data |
| title_fullStr | Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite Data |
| title_full_unstemmed | Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite Data |
| title_short | Mapping of Flood Impacts Caused by the September 2023 Storm Daniel in Thessaly’s Plain (Greece) with the Use of Remote Sensing Satellite Data |
| title_sort | mapping of flood impacts caused by the september 2023 storm daniel in thessaly s plain greece with the use of remote sensing satellite data |
| topic | flood mapping flood evolution Storm Daniel Google Earth Engine multi-sensor satellite imagery geospatial impact analysis |
| url | https://www.mdpi.com/2072-4292/17/10/1750 |
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