Synergistic Approach of Remote Sensing and GIS Techniques for Flash-Flood Monitoring and Damage Assessment in Thessaly Plain Area, Greece

Psomiadis, Emmanouil; Soulis, Konstantinos X.; Zoka, Melpomeni; Dercas, Nicholas

doi:10.3390/w11030448

Cited by 47 publications

(39 citation statements)

References 61 publications

(93 reference statements)

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“…The present study allowed to map and estimate the flooded areas. The results confirm the works of several authors including Cazals et al [3] and Psomiadis et al [5]. Indeed, these authors also used Sentinel 1 radar data for mapping and spatiotemporal monitoring of floods.…”

Section: Flood Mappingsupporting

confidence: 89%

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Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Kouassı

N'go

Anoh

et al. 2020

AJGR

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Floods result from the overflow of water which submerges the surrounding land. They are frequent on the coast of Côte d'Ivoire during the rainy season and have more or less serious consequences on the populations, property and the environment. The study site is the San Pedro river basin. It is a coastal catchment area characterized by an average annual rainfall of up to 2000 mm and subject to recurrent flooding. The objective of this study is to assess the risk of flooding during the great rainy season of 2017. The study aims to study flood hazard, assess vulnerability and map flood risk areas. The methodological approach is based on the use of C-band (5.6 cm) radar remote sensing Kouassi et al.; AJGR, 3(2): 1-8, 2020; Article no.AJGR.55959 2 data acquired by the Sentinel-1 sensor at 12-day intervals. These data are in GRD (Ground Range Detected) level 1 format and were used to calculate the radar backscatter coefficient. The results obtained allowed to map the extent of the flooded areas and showed that more than 6,000 ha of land is flooded for more than 3 days. Sentinel-1 has enormous potential to identify flooding risky areas and to continuously monitor them. Original Research Article

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Section: Flood Mappingsupporting

confidence: 89%

“…Several authors Cazals et al [3], Romanescu et al [4], Psomiadis et al [5], Devranche et al [6] use remote sensing data to map the extent of floods. The study of flooding is not difficult with optical remote sensing data because the period of the study (June and July) is particularly cloudy.…”

Section: Introductionmentioning

confidence: 99%

Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Kouassı

N'go

Anoh

et al. 2020

AJGR

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“…The present study proposes the computation of both indices which were highlighted in numerous studies aiming at determining the areas prone to these natural hazards. The outputs of the analysis make this study relevant, as other studies [32,35,36,38,40,47,72] propose the computation of only one index for creating hazard maps. The optimal split of the training and testing set used in the present paper was determined through successive testing using different split ratios-80-20 and 90-10.…”

Section: Discussionmentioning

confidence: 99%

“…According to the National Administration of "Romanian Waters" and the National Meteorological Administration, the South East European Flash Flood Guidance System (SEEFFG), European Flood Awareness System (EFAS), and the Romanian Flash Flood Guidance System (ROFFG) are used to forecast flood and flash-floods for 8851 small river catchments. Previous researchers, who have used these indices to determine the areas prone to this type of natural risk phenomena, have shown the importance of knowing how to implement these indices to help local authorities to manage their interventions and minimize economic and human losses [35][36][37][38][39][40][41][42].…”

Section: General Characteristics Of the Study Areamentioning

confidence: 99%

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Popa

Peptenatu

Drăghici

et al. 2019

Water

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The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

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“…Reference source not found.]. Although these inland surface water bodies only hold about 0.013% of Earth's total water, i.e., about 178,000 km 3 [2][3][4], they are important compartments in the global terrestrial water cycle and mapping inundation areas of inland surface water bodies is of great significance for flood prediction and prevention [5][6][7][8], flood risk and damage assessments [9][10][11][12][13][14][15][16][17], estimation of water storage in rivers, lakes and reservoirs [18][19][20], calculation of evaporation from wetlands and lakes/reservoirs [21], retrieval of lake water level and river stage [22][23][24][25], reservoir operation and management [26], and assessment of ecological functions and health in wetlands and marshes [27,28]. In addition to the above mentioned practical applications, surveying inland surface water body can provide critical measurements/observations for improving our understanding of the water cycle and inundation dynamics at multiple spatial and temporal scales [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

Pan¹,

Xi²,

Wang³

2019

Preprint

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To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEIns, and AWEIs) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEIns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEIns and AWEIs are better than NDWI, MNDWI and MNDWI2. AWEIns works better than AWEIs under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEIns.

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Synergistic Approach of Remote Sensing and GIS Techniques for Flash-Flood Monitoring and Damage Assessment in Thessaly Plain Area, Greece

Cited by 47 publications

References 61 publications

Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Contribution of Sentinel 1 Radar Data to Flood Mapping in the San-Pédro River Basin (South-west Côte d’Ivoire)

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

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