Evaluating a new method of remote sensing for flood mapping in the urban and peri-urban areas: Applied to Addis Ababa and the Akaki catchment in Ethiopia

Bekele, Tilaye Worku; Haile, Alemseged Tamiru; Trigg, Mark A.; Walsh, Claire

doi:10.1016/j.nhres.2022.03.001

Cited by 24 publications

(5 citation statements)

References 26 publications

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“…The flood extent detected from the Sentinel-1 image can be useful for validating hydrodynamic flood models of the study area. Detailed characteristics and areas exposed and vulnerable to flooding by combining remote sensing satellite images and field surveys are very valuable for flood risk assessment (Risi et al, 2020;Bekele et al, 2022). This information can reduce costs incurred by cities due to free access to remote sensing images.…”

Section: Resultsmentioning

confidence: 99%

Mapping of Urban Flood Inundation Using 3d Digital Surface Model and Sentinel-1 Images

Sharif

Heidari²,

Hosseini³

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Flooding in urban areas poses serious risks to citizens, infrastructures, and transportation. Precise and real-time delineation of the inundated areas is crucial for a better understanding of the extent of damage and high-risk areas and people evacuation actions. It also increases citizens' awareness that living in areas with high flood risk. Yazd city is characterized by low rainfall (<70 mm/yr) and the desert climate is considered the study area of this research. This city encountered a flash flood event that was generated by severe rainfall with a depth of 75 mm in 3hr (i.e., the intensity of 25 mm/hr) on July 29, 2022. Many strategic infrastructures of this city especially the railway station were flooded, which caused heavy casualties and financial losses. This study aims to monitor the flood inundated areas of Yazd city due to this flood event using remote sensing. In this research, the Sentinel-1 polarimetric radar images and the 3D model of the Yazd city surf ace were used to delineate the flooded areas. The field information of the flooded areas and the available Sentinel-1 images during or near the occurrence time of maximum flood extension were adopted. The Convolutional Neural Network (CNN) model in combination with the 3D model of the studied area was used to identify the flooded pixels in the city of Yazd. The results showed that the adopted 3D model and CNN algorithm indicated a good ability to identify flooded areas with an accuracy of 88% and a kappa coefficient of 0.83.

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Section: Resultsmentioning

confidence: 99%

Mapping of Urban Flood Inundation Using 3d Digital Surface Model and Sentinel-1 Images

Sharif

Heidari²,

Hosseini³

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The resulting image was co-registered with the Range-Doppler Terrain Correction algorithm using the DEMs from the Shuttle Radar Topography Mission (SRTM) and a bilinear interpolation as the resampling method. The binary map with flooded and non-flooded areas was generated by applying an estimated threshold value from the bimodal histogram of the image ( Bekele et al., 2022 ; Jesudasan, Subbarayan & Devanantham, 2022 ). This method relies on the image having a bimodal histogram for proper separation, which means that the contrast in values between the flooded and non-flooded pixels should be as high as possible.…”

Section: Methodsmentioning

confidence: 99%

Combining multisensor images and social network data to assess the area flooded by a hurricane event

Hernández-Guzmán,

Ruiz-Luna

2024

PeerJ

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In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

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“…The DEM was obtained from the Ethiopian Geospatial Information Institute, and was generated from aerial photogrammetry using a ground sampling distance of 25 cm. The DEM has a reported vertical accuracy of 50 cm (Bekele et al, 2022). We removed off-terrain features including vegetation, buildings, bridges, and other anthropogenic objects from the DEM using the WhiteboxTools remove off-terrain objects tool (Lindsay, 2018).…”

Section: Flood Hazard Model Developmentmentioning

confidence: 99%

“…The frequency with which people and properties are exposed to flooding in Addis Ababa is also high; this is typical of cities in lower income countries that have little or no formal flood defences (Rentschler et al, 2022) and makes Addis Ababa a suitable test case for global datasets in lower income contexts. Addis Ababa experiences a warm and temperate climate with two rainy seasons: the primary one (Kiremt) falls from June to September; and a secondary one (Belg) occurs from mid-February to April (Bekele et al, 2022). The average annual rainfall is 1184mm; roughly equal to that in the Scottish city of Glasgow.…”

Section: Introductionmentioning

confidence: 99%

Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa

Carr,

Trigg,

Haile

et al. 2024

Front. Environ. Sci.

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Introduction: Cities located in lower income countries are global flood risk hotspots. Assessment and management of these risks forms a key part of global climate adaptation efforts. City scale flood risk assessments necessitate flood hazard information, which is challenging to obtain in these localities because of data quality/scarcity issues, and the complex multi-source nature of urban flood dynamics. A growing array of global datasets provide an attractive means of closing these data gaps, but their suitability for this context remains relatively unknown.Methods: Here, we test the use of relevant global terrain, rainfall, and flood hazard data products in a flood hazard and exposure assessment framework covering Addis Ababa, Ethiopia. To conduct the tests, we first developed a city scale rain-on-grid hydrodynamic flood model based on local data and used the model results to identify buildings exposed to flooding. We then observed how the results of this flood exposure assessment changed when each of the global datasets are used in turn to drive the hydrodynamic model in place of its local counterpart.Results and discussion: Results are evaluated in terms of both the total number of exposed buildings, and the spatial distribution of exposure across Addis Ababa. Our results show that of the datasets tested, the FABDEM global terrain and the PXR global rainfall data products provide the most promise for use at the city scale in lower income countries.

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Evaluating a new method of remote sensing for flood mapping in the urban and peri-urban areas: Applied to Addis Ababa and the Akaki catchment in Ethiopia

Cited by 24 publications

References 26 publications

Mapping of Urban Flood Inundation Using 3d Digital Surface Model and Sentinel-1 Images

Mapping of Urban Flood Inundation Using 3d Digital Surface Model and Sentinel-1 Images

Combining multisensor images and social network data to assess the area flooded by a hurricane event

Using global datasets to estimate flood exposure at the city scale: an evaluation in Addis Ababa

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