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Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco

2022Artigo científico Acesso aberto
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dc.contributor.authorKhouz, Abdellah
dc.contributor.authorTrindade, Jorge
dc.contributor.authorOliveira, Sérgio
dc.contributor.authorEl Bchari, Fatima
dc.contributor.authorBougadir, Blaid
dc.contributor.authorGarcia, Ricardo
dc.contributor.authorJadoud, Mourad
dc.date.accessioned2023-01-04T12:29:19Z
dc.date.available2023-01-04T12:29:19Z
dc.date.issued2022
dc.description.abstractIn recent decades, multiple researchers have produced landslide susceptibility maps using different techniques and models, including the information value method, which is a statistical model that is widely applied to various coastal environments. This study aimed to evaluate susceptibility to landslides in the Essaouira coastal area using bivariate statistical methods. In this study, 588 distinct landslides were identified, inventoried, and mapped. Landslides are performed by means of observation and interpretation of different data sources, namely high-resolution satellite images, aerial photographs, topographic maps, and extensive field surveys. The rocky coastal system of Essaouira is located in the middle of the Atlantic coast of Morocco. The study area was split into 1534 cliff terrain units that were 50 m in width. For training and validation purposes, the landslide inventory was divided into two independent groups: 70 % for training and 30 % for validation. Twenty-two layers of landslide conditioning factors were prepared – namely, elevation, slope angle, slope aspect, plan curvature, profile curvature, cliff height, topographic wetness index, topographic position index, slope over area ratio, solar radiation, presence of faulting, lithological units, toe lithology, presence and type of cliff toe protection, layer tilt, rainfall, streams, land-use patterns, normalised difference vegetation index, lithological material grain size, and presence of springs. The statistical relationship between the conditioning factors and the different landslide types was calculated using the bivariate information value method in a pixel-based model and in the elementary terrain units-based model. Coastal landside susceptibility maps were validated using landslide training group partitions. The receiver operating characteristic curve and area under the curve were used to assess the accuracy and prediction capacity of the different coastal landslide susceptibility models. Two methodologies, considering a pixel-based approach and using coastal terrain units, were adopted to evaluate coastal landslide susceptibility. The results allowed for the classification of 38 % of the rocky coast subsystem as having high susceptibility to landslides, which were mostly located in the southern part of the Essaouira coastal area. These susceptibility maps will be useful for future planned development activities as well as for environmental protection.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationKhouz, A., Trindade, J., Oliveira, S. C., El Bchari, F., Bougadir, B., Garcia, R. A. C., & Jadoud, M. (2022). Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco. Natural Hazards and Earth System Sciences, 22(11), 3793–3814. https://doi.org/10.5194/nhess-22-3793-2022pt_PT
dc.identifier.doi10.5194/nhess-22-3793-2022pt_PT
dc.identifier.issn1561-8633
dc.identifier.issn1684-9981
dc.identifier.urihttp://hdl.handle.net/10451/55619
dc.language.isoengpt_PT
dc.publisherEuropean Geosciences Unionpt_PT
dc.relationSOE3/P4/E0868pt_PT
dc.relationLandslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change
dc.relation.publisherversionttps://nhess.copernicus.org/articles/22/3793/2022/pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectLandslide susceptibilitypt_PT
dc.subjectRocky coast subsystem of Essaouirapt_PT
dc.subjectMoroccopt_PT
dc.titleLandslide susceptibility assessment in the rocky coast subsystem of Essaouira, Moroccopt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberEXPL/GES-AMB/1246/2021
oaire.awardNumberPTDC/GES-AMB/30052/2017
oaire.awardTitleLandslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/EXPL%2FGES-AMB%2F1246%2F2021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FGES-AMB%2F30052%2F2017/PT
oaire.citation.endPage3814pt_PT
oaire.citation.issue11pt_PT
oaire.citation.startPage3793pt_PT
oaire.citation.titleNatural Hazards and Earth System Sciencespt_PT
oaire.citation.volume22pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
person.familyNameTrindade
person.familyNameOliveira
person.familyNameGarcia
person.givenNameJorge
person.givenNameSérgio
person.givenNameR.A.C.
person.identifier.ciencia-id1411-A43D-9266
person.identifier.ciencia-id1B10-8CE2-1F13
person.identifier.ciencia-idBC12-77B6-7801
person.identifier.orcid0000-0001-5610-5942
person.identifier.orcid0000-0003-0883-8564
person.identifier.orcid0000-0002-1036-6271
person.identifier.ridM-9060-2013
person.identifier.ridM-8412-2016
person.identifier.ridJ-8719-2013
person.identifier.scopus-author-id7003458343
person.identifier.scopus-author-id24779631800
person.identifier.scopus-author-id55454646500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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