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A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data

Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning...

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Detalles Bibliográficos
Autores principales: Pedzisai, Ezra, Mutanga, Onisimo, Odindi, John, Bangira, Tsitsi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988494/
https://www.ncbi.nlm.nih.gov/pubmed/36895372
http://dx.doi.org/10.1016/j.heliyon.2023.e13332
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author Pedzisai, Ezra
Mutanga, Onisimo
Odindi, John
Bangira, Tsitsi
author_facet Pedzisai, Ezra
Mutanga, Onisimo
Odindi, John
Bangira, Tsitsi
author_sort Pedzisai, Ezra
collection PubMed
description Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning, efficient response during evacuation, search, rescue and recovery. Furthermore, accurate FEM is crucial for policy formulation, planning and management, rehabilitation, and promoting community resilience for sustainable occupation and use of floodplains. Recently, remote sensing has become valuable in flood studies. However, whereas free passive remote sensing images have been common input into predictive models, damage assessment and FEM, their utility is constrained by clouds during flooding events. Conversely, microwave-based data is unconstrained by clouds, hence is important for FEM. Hence, to increase the reliability and accuracy of FEM using Sentinel-1 radar data, we propose a three-step process that builds an ensemble of scenarios pyramid (ESP) based on change detection and thresholding technique. We deployed the ESP technique and tested it on a use-case based on two, five and 10 images. The use-case calculated three co-polarized Vertical-Vertical (VV) and three cross-polarized Vertical-Horizontal (VH) normalized difference flood index scenarios to form six binary classified FEMs at the base. We ensembled the base scenarios to three dual-polarized centre FEMs, and likewise the centre scenarios to a final pinnacle flood extent map. The base, centre and pinnacle scenarios were validated using six binary classification performance metrics. The results show that the ESP increased the base-to-pinnacle minimum classification performance metrics with overall accuracy, Cohen's Kappa, intersect over union, recall, F1-score, and Matthews Correlation coefficient of 93.204%, 0.864, 0.865, 0.870, 0.927, and 0.871 respectively. The study also established that the VV channels were superior in FEM than VH at the ESP base. Overall, this study demonstrates the efficacy of the ESP for operational flood disaster management.
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spelling pubmed-99884942023-03-08 A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data Pedzisai, Ezra Mutanga, Onisimo Odindi, John Bangira, Tsitsi Heliyon Research Article Flood disasters destroy infrastructure, disrupt ecosystem processes, adversely affect social and economic activities and cause human fatalities. As such, flood extent mapping (FEM) is critical to mitigate these impacts. Specifically, FEM is essential to mitigate adverse impacts through early warning, efficient response during evacuation, search, rescue and recovery. Furthermore, accurate FEM is crucial for policy formulation, planning and management, rehabilitation, and promoting community resilience for sustainable occupation and use of floodplains. Recently, remote sensing has become valuable in flood studies. However, whereas free passive remote sensing images have been common input into predictive models, damage assessment and FEM, their utility is constrained by clouds during flooding events. Conversely, microwave-based data is unconstrained by clouds, hence is important for FEM. Hence, to increase the reliability and accuracy of FEM using Sentinel-1 radar data, we propose a three-step process that builds an ensemble of scenarios pyramid (ESP) based on change detection and thresholding technique. We deployed the ESP technique and tested it on a use-case based on two, five and 10 images. The use-case calculated three co-polarized Vertical-Vertical (VV) and three cross-polarized Vertical-Horizontal (VH) normalized difference flood index scenarios to form six binary classified FEMs at the base. We ensembled the base scenarios to three dual-polarized centre FEMs, and likewise the centre scenarios to a final pinnacle flood extent map. The base, centre and pinnacle scenarios were validated using six binary classification performance metrics. The results show that the ESP increased the base-to-pinnacle minimum classification performance metrics with overall accuracy, Cohen's Kappa, intersect over union, recall, F1-score, and Matthews Correlation coefficient of 93.204%, 0.864, 0.865, 0.870, 0.927, and 0.871 respectively. The study also established that the VV channels were superior in FEM than VH at the ESP base. Overall, this study demonstrates the efficacy of the ESP for operational flood disaster management. Elsevier 2023-02-18 /pmc/articles/PMC9988494/ /pubmed/36895372 http://dx.doi.org/10.1016/j.heliyon.2023.e13332 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Pedzisai, Ezra
Mutanga, Onisimo
Odindi, John
Bangira, Tsitsi
A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data
title A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data
title_full A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data
title_fullStr A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data
title_full_unstemmed A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data
title_short A novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using Sentinel-1 data
title_sort novel change detection and threshold-based ensemble of scenarios pyramid for flood extent mapping using sentinel-1 data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9988494/
https://www.ncbi.nlm.nih.gov/pubmed/36895372
http://dx.doi.org/10.1016/j.heliyon.2023.e13332
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