Detecting and mapping flood inundation areas in Fogera-Dera Floodplain, Ethiopia during an extreme wet season using Sentinel-1 data

Melkamu, Tewabe; Bagyaraj, Murugesan; Adimaw, Melkamu; Ngusie, Ashenafi; Karuppannan, Shankar

doi:10.1016/j.pce.2022.103189

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…The hydrological dominates only one area of research, and all the processing applied to SAR data is simple. This is a trend that can be seen in Perrou et al (2018) and in Melkamu et al (2022) when describing pre-processing flowchart as a major part of the research and leaving the flood classification method as secondary information.…”

Section: Choice Of Sar Processing Methods By Different Areas Of Exper...mentioning

confidence: 96%

“…It is also very common that simulated flood maps produced in hydrodynamic models are validated through comparison to SAR images post flood classification. Both maps are submitted to statistical analysis at the final processing to evaluate the difference between the resulting areas (Melkamu et al, 2022).…”

Section: Histogram Thresholding Methodsmentioning

confidence: 99%

“…Among various SAR-based processing techniques for flood mapping are found visual interpretation, histogram thresholding, image segmentation, active contouring, rule-based classification, texture measures, data fusion approaches, change detection methods, neural network, fuzzy classifications, and others (Cao et al, 2019;Zhang et al, 2020;Kuntla and Manjusree, 2020;Melkamu et al, 2022). For Phan et al (2019) the flood detection methods can be divided into three categories: thresholding combined techniques, complex prediction models, and machine learning (ML) approaches.…”

Section: Rapid Bibliometric Analysismentioning

confidence: 99%

“…Sentinel-1 images were not available at the time, instead Envisat ASAR and ERS-2 SAR images were used for sensitivity analysis maps of the LISFLOOD-FP hydrodynamic model. Zhang et al (2020) indicate that the most common SAR-based techniques used to extract the flood extent are thresholding methods, image segmentation, statistical active contouring, rulebased classification and data-fusion procedures (Melkamu et al, 2022). Among these methods, the most successful methodology mentioned is the threshold-based delineation (Wan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods

Liz

Ribas

2022

Rev. Bras. Geog. Fis.

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Delineating the flood extent using SAR data has been an unexceptional activity since the launch of the first sensors. The methodologies applied to image classification vary in complexity and accuracy and, for Sentinel-1, the technical procedures must be adapted considering their polarization, band, temporal resolution, and other factors. Considering these aspects, this article aimed to realize a systematic literature review discussing the most frequent methodologies applied to flood mapping classification employing Sentinel-1 data; describe the preferred methods by areas of expertise; and, inform about their accuracy level. Results indicate that thresholding is the most common methodologies used to flood delineation whatsoever is the researcher area of work. Excellent accuracy levels (reaching 99%) can be obtained using thresholding as the major method, with refinement provided by additional procedures to correct limitations and misclassification. The Sentinel-1data is a good source of information to realize classification in flooded areas but has its limitations in small areas. The choice of methodology for processing SAR data is subjective and requires specific knowledge in the remote sensing field from who executes a hydrological study.

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Section: Choice Of Sar Processing Methods By Different Areas Of Exper...mentioning

confidence: 96%

Section: Histogram Thresholding Methodsmentioning

confidence: 99%

Section: Rapid Bibliometric Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods

Liz

Ribas

2022

Rev. Bras. Geog. Fis.

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“…Reflecting upon historical evaluations and model-based forecasts of rain-flood disaster risks, urban centers globally, particularly those endowed with extensive water networks, are actively devising targeted disaster prevention strategies. These strategies are focused on reducing the threats posed by rain-flood disasters to both socio-economic and ecological environments [25]. Shao has championed proactive adaptation and active mitigation approaches to combat increasingly severe urban waterlogging resulting from extreme weather phenomena, such as storm surges and heavy rainfalls.…”

Section: Introductionmentioning

confidence: 99%

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Jiang,

Xiong,

Yang

et al. 2023

Water

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Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.

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Identification of groundwater potential zones of Alappuzha (Kerala) in South India integrating AHP and GIS

Sekar,

Nath,

Roy

et al. 2024

Environ Dev Sustain

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Detecting and mapping flood inundation areas in Fogera-Dera Floodplain, Ethiopia during an extreme wet season using Sentinel-1 data

Cited by 12 publications

References 38 publications

Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods

Flood extent delineation using Sentinel-1 data as information source: systematic review of processing methods

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Identification of groundwater potential zones of Alappuzha (Kerala) in South India integrating AHP and GIS

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