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This dataset shows the global distribution of\nseamounts and knolls identified using global bathymetric data at 30 arc-sec\nresolution. A total of 33,452 seamounts and 138,412 knolls were identified,\nrepresenting the largest global set of identified seamounts and knolls to date.\nSeamount habitat was found to constitute approximately 4.7% of the ocean floor,\nwhilst knolls covered 16.3%. The research leading to these results received\nfunding from the European Community\u2019s Seventh Framework Programme, and from the\nInternational Union for Conservation of Nature (IUCN)<\/span><\/div>", "copyrightText": "Yesson C, Clark MR, Taylor M, Rogers AD (2011). The global distribution of seamounts based on 30-second bathymetry data. Deep Sea Research Part I: Oceanographic Research Papers 58: 442-453. DOI: http://dx.doi.org/10.1016/j.dsr.2011.02.004. 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This dataset shows the global distribution of seamounts and knolls identified using global bathymetric data at 30 arc-sec resolution. A total of 33,452 seamounts and 138,412 knolls were identified, representing the largest global set of identified seamounts and knolls to date. Seamount habitat was found to constitute approximately 4.7% of the ocean floor, whilst knolls covered 16.3%. The research leading to these results received funding from the European Community\u2019s Seventh Framework Programme, and from the International Union for Conservation of<\/div>
Nature (IUCN).<\/div>", "copyrightText": "Yesson C, Clark MR, Taylor M, Rogers AD (2011). The global distribution of\nseamounts based on 30-second bathymetry data. Deep Sea Research Part I:\nOceanographic Research Papers 58: 442-453. DOI:\nhttp://dx.doi.org/10.1016/j.dsr.2011.02.004. 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Il s\u2019agit de la base de données des agglomérations de\nl\u2019Afrique de l\u2019Ouest, tirée de la grande base d\u2019Africapolis portant sur\nl\u2019ensemble du territoire africain. C\u2019est une base de données standardisée et\ngéospatiale indispensable sur la dynamique de l'urbanisation en Afrique, dans\nle but de rendre les données urbaines en Afrique comparables d'un pays à\nl'autre et dans le temps. Cette base de données sur les agglomérations de\nl\u2019Afrique de l\u2019Ouest concerne les petites villes et villes intermédiaires entre\n10 000 et 300 000 habitants. Cependant, la base d\u2019Africapolis n\u2019intègre\npas certaines villes et villages de moins de 10 000 habitants, qui peuvent\nnéanmoins constituer des zones de bâti non négligeables pour caractériser\nl\u2019organisation et les dynamiques d\u2019extension urbaine du littoral de l\u2019Afrique\nde l\u2019ouest. Sur l\u2019ensemble des 11 pays de la MOLOA, 271 agglomérations ont été\najoutées dans le buffer de 25 km par rapport au trait de côte, qui a été élargi\npour le Sénégal, la Gambie et la Guinée Bissau le long des fleuves Gambie,\nGambie et Rio Cacheu. Ces agglomérations correspondent à des villages\nprésentant une densité de bâti qui le rend identifiable en parcourant les\nimages à la volée. L\u2019identification sur les images à très haute résolution\ns\u2019est concentrée sur les villages se trouvant sur la côte, dans les zones de\nmangrove, sur les bras des principaux fleuves, ou encore les villages\nsatellites des agglomérations. On a aussi cherché à identifier tous les noyaux carrefours\ndes principales voies de communication, en croisant les images à haute\nrésolution et la couche vectorisée et hiérarchisée du réseau routier de la\nrégion. <\/font><\/span><\/p>

<\/p>

Limites ou contraintes : <\/span><\/b>Pas d\u2019informations<\/span><\/font><\/p>\n\n

Projections cartographiques : <\/span><\/b>WGS\n1984<\/span><\/font><\/p>\n\n

Liens en rapport avec les données : <\/font><\/span><\/b><\/p>\n\n

Éclairer les politiques pour\nl'avenir urbain de l'Afrique : <\/span>https://www.africapolis.org/home<\/span><\/a><\/span> <\/span><\/font><\/p>\n\n

Attributs<\/span><\/b><\/font><\/p>\n\n

ID<\/span><\/b> : Identifiant unique des agglomérations<\/span><\/font><\/p>\n\n

NOM<\/span><\/b> : Nom de l\u2019agglomération<\/span><\/font><\/p>\n\n

PAYS<\/span><\/b> : Nom du pays dans lequel se trouve l\u2019agglomération<\/span><\/font><\/p>\n\n

NIV_TOPO<\/span><\/b> : C\u2019est le niveau d\u2019hiérarchisation des agglomérations : ville principale,\nville secondaire, village et campement.<\/span><\/font><\/p><\/span>", "copyrightText": "CSE", "editFieldsInfo": null, "syncCanReturnChanges": false, "relationships": [], "isDataVersioned": false, "isDataArchived": false, "isDataBranchVersioned": false, "isDataReplicaTracked": false, "isCoGoEnabled": false, "supportsRollbackOnFailureParameter": true, "supportsStatistics": true, "supportsAdvancedQueries": true, "supportsValidateSQL": true, "supportsCoordinatesQuantization": true, "supportsCalculate": true, "supportsASyncCalculate": true, "advancedQueryCapabilities": { "supportsPagination": true, "supportsTrueCurve": true, "supportsQueryWithDistance": true, "supportsReturningQueryExtent": true, "supportsStatistics": true, "supportsPercentileStatistics": true, "supportsHavingClause": true, "supportsOrderBy": true, "supportsDistinct": true, "supportsCountDistinct": true, 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"standardMaxRecordCountNoGeometry": 16000, "maxRecordCountFactor": 1, "supportedQueryFormats": "JSON", "maxRecordCount": 2000, "allowGeometryUpdates": true, "capabilities": "Query", "enableZDefaults": true, "zDefault": 0, "allowUpdateWithoutMValues": true, "hasGeometryProperties": true, "advancedEditingCapabilities": { "supportedSqlFormatsInCalculate": [ "standard" ] }, "supportsApplyEditsWithGlobalIds": false, "serviceItemId": "4fbb2af154c74d2e8b1b24bd8bc69458", "hasMetadata": true, "sqlParserVersion": "PG_10.8.1" }, { "currentVersion": 10.81, "cimVersion": "2.6.0", "id": 3, "name": "Site_nidification_tortue_marine", "type": "Feature Layer", "parentLayer": null, "defaultVisibility": true, "minScale": 0, "maxScale": 0, "canScaleSymbols": false, "geometryType": "esriGeometryPolyline", "description": "

This dataset shows the known locations of sea\nturtle nesting sites, for all seven species: hawksbill turtle (Eretmochelys\nimbricata), Kemp's ridley turtle (Lepidochelys kempii), leatherback turtle\n(Dermochelys coriacea), green turtle (Chelonia mydas), loggerhead turtle (Caretta\ncaretta), olive ridley turtle (Lepidochelys olivacea), and flatback turtle\n(Natator depressus).<\/span><\/p>", "copyrightText": "UNEP-WCMC (1999). Global distribution of sea turtle nesting sites (version 1.1). Cambridge (UK): UNEP World Conservation Monitoring Centre. 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<\/p>

La version initiale du Schéma\nDirecteur du Littoral d\u2019Afrique de l\u2019Ouest (SDLAO) avait été publiée et validé\nen 2011. La rapidité du développement de l\u2019occupation des littoraux qui avait\nété constatée en 2011 impliquait d\u2019une part la mise en place d\u2019un mécanisme\nrégional d\u2019observation, et d\u2019autre part la planification d\u2019une actualisation de\nce schéma directeur. Un an après la validation du SDLAO, la Mission\nd\u2019Observation du Littoral Ouest Africain (MOLOA) a été établie, comprenant une\ncoordination régionale et des antennes nationales au niveau de chacun des onze\npays partie prenant de l\u2019initiative.<\/font><\/span><\/p>\n\n

L\u2019ensemble du travail de suivi qui a été mis en œuvre\ndans le cadre de ce dispositif est livré ici au travers de deux documents : une\nactualisation du schéma directeur général, et une actualisation du schéma\ndirecteur détaillé accompagnée d\u2019une cartographie actualisée du SDLAO à\nl\u2019échelle du 1/500 000ème. Dans le cadre de cette actualisation, les priorités\nqui avaient été initialement fixées pour l\u2019intervention en matière de défense\ndes côtes et de réduction des risques côtiers et en matière de suivi et\nd\u2019observation ont été largement amendées en fonction des évolutions constatées.<\/font><\/span><\/p>\n\n

Le présent SDLAO est une version actualisée de celle de\n2016, qui décrit les évolutions observées depuis 2011 et les niveaux de\ncriticité. Cette mise à jour est réalisée en rapport avec les antennes\nnationales de la MOLOA (11 états côtiers de l\u2019Afrique de l\u2019Ouest, depuis la\nMauritanie jusqu\u2019au Bénin, plus Sao Tomé et Principe) et le comité scientifique\nrégional.<\/font><\/span><\/p>\n\n

Ce Schéma Directeur Détaillé vise à\ncaractériser un territoire et mettre en exergue les problématiques majeures de\ncelui-ci en prenant en compte notamment l\u2019impact du changement climatique sur\nle littoral. À ce stade, le pays identifie la nécessité de mettre en place des\nactions, mais ne les définit pas et ne les situe pas dans le temps. Ce travail\nde définition et de priorisation est un travail interne à chaque territoire qui\npourrait être facilité par l\u2019adhésion des territoires à l\u2019Observatoire Régional\ndu Littoral Ouest Africain (ORLOA). À ce jour, il revient à chaque territoire,\navec l\u2019appui de ses structures dédiées et de ses experts de définir celle-ci à\nl\u2019issue de l\u2019analyse des éléments présentés dans le rapport à l\u2019échelle d\u2019un\nterritoire ou d\u2019une problématique et au regard des ressources matérielles,\nhumaines et économiques disponibles.<\/font><\/span><\/p>\n\n

Limites : <\/span><\/b>No informations<\/span><\/font><\/p>\n\n

Projection cartographique : <\/span><\/b>WGS 1984<\/span><\/font><\/p>\n\n

Liens en rapport avec la couche d\u2019information\ngéographique<\/font><\/span><\/b><\/p>\n\n

>https://www.iucn.org/fr/regions/afrique-centrale-et-occidentale/notre-travail/les-programme-thematiques-regionaux-dans-la-zone-du-paco/projets-acheves/developpement-du-mecanisme-dobservation-du-littoral-ouest-africain<\/span><\/a><\/span><\/font><\/p>\n\n

>https://www.wacaprogram.org/sites/waca/files/knowdoc/Littoraux%20d%27Afrique%20de%20l%27Ouest%20document%20general%20Version%20Francaise.pdf<\/span><\/a><\/span><\/font><\/p>\n\n

Date de création de la couche :<\/span><\/b>  <\/span>2010<\/span><\/font><\/p>\n\n

Date de mise à jour de la couche :\n<\/span><\/b> <\/span>02 décembre 2020<\/span><\/font><\/p>

<\/p>

Attributs<\/span><\/b><\/font><\/p>\n\n

FID_1<\/span><\/b> : <\/span>C\u2019est l\u2019identifiant unique d\u2019un secteur donné. <\/span><\/font><\/p>\n\n

ZONE<\/span><\/b> : <\/span>C\u2019est le code de la zone à laquelle\nappartient un secteur donné. Il est construit selon le code du pays (TCP ISO\n3166), suivi d\u2019un incrément numérique égal au nombre de zone dans un pays.<\/span><\/font><\/p>\n\n

PAYS<\/span><\/b> : Nom du pays auquel une zone ou un secteur appartient.<\/span><\/font><\/p>\n\n

REF<\/span><\/b> : <\/span>C\u2019est le code d\u2019un secteur donné.\nIl est construit en se basant du code de la zone plus un incrément alphabétique\négal au nombre de secteur présent dans une zone.<\/span><\/font><\/p>\n\n

SEGMENTS<\/span><\/b> : C\u2019est la délimitation des secteurs suivant des repères\ndifférents : les villes, les villages, les aires naturelles, etc.<\/span><\/font><\/p>\n\n

NOM ZONE<\/span><\/b> : <\/span>Le nom de la zone.<\/span><\/font><\/p>\n\n

N<\/span><\/b>° : C\u2019est le numéro du secteur. Il est créé partant du nord vers\nle sud. <\/span><\/font><\/p>\n\n

Type<\/span><\/b> : La cartographie porte sur l\u2019échelle la plus fine et qui\nconcerne les secteurs. <\/span><\/font><\/p>\n\n

Dénominat<\/span><\/b> : Nom associé à un secteur donné. <\/span><\/font><\/p>\n\n

HABITAT<\/span><\/b> : Il s\u2019agit des principales formes d\u2019habitats naturels ou\nartificiels notés dans un secteur donné. Ils concernent surtout les espaces\nurbains, rurbains, rural, campement, petit village, mangrove zones humides et les\nzones désertiques.<\/span><\/font><\/p>\n\n

ENJEUX<\/span><\/b> : Ce sont les différentes formes d\u2019enjeux notés dans un secteur.<\/span><\/font><\/p>\n\n

ALEAS<\/span><\/b> : Ce sont les différentes formes d\u2019aléas rencontrés dans un\nsecteur donné.<\/span><\/font><\/p>\n\n

Criti_2011<\/span><\/b> : <\/span>Il s\u2019agit du\nniveau de criticité en 2011, c\u2019est-à-dire l\u2019impact des risques littoraux\n(érosion côtière, inondations, les submersions marines, etc.) sur les personnes\net les biens. La criticité peut être aussi plus élevée pour le compartiment\n« environnement » et avoir un impact notable sur la biodiversité, les\nservices écosystémiques, etc. l\u2019échelle de criticité adoptée ici comporte 04\nniveaux : faible, moyenne, élevée et très élevée. <\/span><\/font><\/p>\n\n

Criti_2016<\/span><\/b> : <\/span>Il s\u2019agit du\nniveau de criticité en 2016. <\/span><\/font><\/p>\n\n

Criti_2020<\/span><\/b> : <\/span>Il s\u2019agit du\nniveau de criticité en 2020.<\/span><\/font><\/p>\n\n

Evolu_Criti<\/span><\/b> : <\/span>C\u2019est l\u2019évolution\ndu niveau de criticité entre 2016 et 2020. Il s\u2019agit de voir si le niveau a\nconnu une baisse, hausse ou une stabilité.<\/span><\/font><\/p>\n\n

Suivi_2011<\/span><\/b> : <\/span>Il s\u2019agit de la\nqualification de la nécessité d\u2019une surveillance spatio-temporelle du secteur\naxée sur les enjeux retenus. L\u2019échelle de suivi comporte 04 niveaux : pas\nde recommandation, veille, suivi régulier et léger et suivi régulier et\nintensif. <\/span><\/font><\/p>\n\n

Suivi_2016<\/span><\/b> : <\/span>Idem suivi 2011<\/span><\/font><\/p>\n\n

Suivi_2020<\/span><\/b> : <\/span>Idem suivi 2011<\/span><\/font><\/p>\n\n

Evolu_Suiv<\/span><\/b> : <\/span>C\u2019est\nl\u2019évolution du niveau de suivi entre 2016 et 2020. Il s\u2019agit de voir si le\nniveau a connu une baisse, hausse ou une stabilité.<\/span><\/font><\/p>\n\n

AIRE_PROTG<\/span><\/b> : <\/span>Il s\u2019agit de la\nprésence d\u2019une aire protégée dans le secteur.<\/span><\/font><\/p>\n\n

PROBLEMAT<\/span><\/b> : <\/span>C\u2019est la\nproblématique centrale rencontrée dans un secteur donné.<\/span><\/font><\/p>\n\n

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true, "zDefault": 0, "allowUpdateWithoutMValues": true, "hasGeometryProperties": true, "advancedEditingCapabilities": { "supportedSqlFormatsInCalculate": [ "standard" ] }, "supportsApplyEditsWithGlobalIds": false, "geometryProperties": { "shapeLengthFieldName": "SHAPE__Length", "units": "esriMeters" }, "serviceItemId": "4fbb2af154c74d2e8b1b24bd8bc69458", "hasMetadata": true, "sqlParserVersion": "PG_10.8.1" }, { "currentVersion": 10.81, "cimVersion": "2.6.0", "id": 6, "name": "Canyons", "type": "Feature Layer", "parentLayer": null, "defaultVisibility": true, "minScale": 0, "maxScale": 0, "canScaleSymbols": false, "geometryType": "esriGeometryPolyline", "description": "The aim of this study is to assess the global occurrence of large submarine canyons to provide context and guidance for discussions regarding canyon occurrence, distribution, geological and oceanographic significance and conservation. Based on an analysis of the ETOPO1 data set, this study has compiled the first inventory of 5849 separate large submarine canyons in the world ocean. Active continental margins contain 15% more canyons (2586, equal to 44.2% of all canyons) than passive margins (2244, equal to 38.4%) and the canyons are steeper, shorter, more dendritic and more closely spaced on active than on passive continental margins. This study confirms observations of earlier workers that a relationship exists between canyon slope and canyon spacing (increased canyon slope correlates with closer canyon spacing).<\/span>", "copyrightText": "Harris and Whiteway 2011. Global distribution of large submarine canyons: Geomorphic differences\nbetween active and passive continental margins. 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Il s\u2019agit des contours\nindiquant la profondeur des fonds marins au format vectoriel. Cette couche\nd\u2019information sur la profondeur des fonds marins est extraite de l\u2019ensemble de\nla base de données bathymétrique maillée de GEPCO (la grille GEPCO_2020). Elle\nconstitue un modèle de terrain global pour l\u2019océan et la terre à des\nintervalles de 15 secondes d\u2019arc.<\/span><\/p>", "copyrightText": "CSE", "editFieldsInfo": null, "syncCanReturnChanges": false, "relationships": [], "isDataVersioned": false, "isDataArchived": false, "isDataBranchVersioned": false, "isDataReplicaTracked": false, "isCoGoEnabled": false, "supportsRollbackOnFailureParameter": true, "supportsStatistics": true, "supportsAdvancedQueries": true, "supportsValidateSQL": true, "supportsCoordinatesQuantization": true, "supportsCalculate": true, "supportsASyncCalculate": true, "advancedQueryCapabilities": { "supportsPagination": true, "supportsTrueCurve": true, "supportsQueryWithDistance": true, "supportsReturningQueryExtent": true, "supportsStatistics": true, "supportsPercentileStatistics": true, "supportsHavingClause": true, "supportsOrderBy": true, "supportsDistinct": true, "supportsCountDistinct": true, "supportsQueryWithResultType": true, 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This is a portion of the data used to calculate 2008 and 2013 cumulative human impacts in: Halpern et al. 2015. Spatial and temporal changes in cumulative human impacts on the world's ocean. Seven data packages are available for this project: (1) supplementary data (habitat data and other files); (2) raw stressor data (2008 and 2013); (3) stressor data rescaled by one time period (2008 and 2013, scaled from 0-1); (4) stressor data rescaled by two time periods (2008 and 2013, scaled from 0-1); (5) pressure and cumulative impacts data (2013, all pressures); (6) pressure and cumulative impacts data (2008 and 2013, subset of pressures updated for both time periods); (7) change in pressures and cumulative impact (2008 to 2013). All raster files are .tif format and coordinate reference system is mollweide wgs84. 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2015. Spatial and temporal changes in cumulative human impacts on the world's ocean. Seven data packages are available for this project: (1) supplementary data (habitat data and other files); (2) raw stressor data (2008 and 2013); (3) stressor data rescaled by one time period (2008 and 2013, scaled from 0-1); (4) stressor data rescaled by two time periods (2008 and 2013, scaled from 0-1); (5) pressure and cumulative impacts data (2013, all pressures); (6) pressure and cumulative impacts data (2008 and 2013, subset of pressures updated for both time periods); (7) change in pressures and cumulative impact (2008 to 2013). All raster files are .tif format and coordinate reference system is mollweide wgs84. 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This\ndataset shows the known locations of sea turtle feeding sites, for five of the\nseven species: hawksbill turtle (Eretmochelys imbricata), leatherback turtle\n(Dermochelys coriacea), green turtle (Chelonia mydas), loggerhead turtle\n(Caretta caretta), and olive ridley turtle (Lepidochelys olivacea).<\/span><\/p>\n\n

This dataset is no longer being maintained and must be used with\ncaution.<\/span><\/p>", "copyrightText": "UNEP-WCMC (1999). Global distribution of sea turtle feeding sites (ver. 1.1). Cambridge (UK): UNEP World Conservation Monitoring Centre. 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Designing a representative network of high seas marine protected areas (MPAs) requires an acceptable scheme to classify the benthic (as well as the pelagic) bioregions of the oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 53,713 separate polygons comprising 11 different categories, that we have termed \u201cseascapes\u201d. A cross-check of the seascape classification was carried out by comparing the seascapes with existing maps of seafloor geomorphology and seabed sediment type and by GIS analysis of the number of separate polygons, polygon area and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Less than 20% of some seascapes occur in the high seas while other seascapes are largely confined to the high seas, indicating specific types of environment whose protection and conservation will require international cooperation. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by a simple GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hotspots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.<\/span><\/div>", "copyrightText": "Harris and Whiteway 2009. 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oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 53,713 separate polygons comprising 11 different categories, that we have termed \u201cseascapes\u201d. A cross-check of the seascape classification was carried out by comparing the seascapes with existing maps of seafloor geomorphology and seabed sediment type and by GIS analysis of the number of separate polygons, polygon area and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Less than 20% of some seascapes occur in the high seas while other seascapes are largely confined to the high seas, indicating specific types of environment whose protection and conservation will require international cooperation. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by a simple GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hotspots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.<\/span>", "copyrightText": "Harris and Whiteway 2009. 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[1988], Divins and Rabinowitz [1990], Hayes and LaBrecque [1991], and Divins [2003]; (ii) ocean drilling results, both from the Ocean Drilling Program (ODP) and the Deep Sea Drilling Project (DSDP); and (iii) seismic reflection profiles archived here as well as seismic data and isopach maps available as part of the IOC's International Geological-Geophysical Atlas of the Pacific Ocean [Udinstev, 2003].<\/span>", "copyrightText": "Divins, D.L., Total Sediment Thickness of the World's Oceans & Marginal Seas, NOAA National Geophysical Data Center, Boulder, CO, 2003.", "editFieldsInfo": null, "syncCanReturnChanges": false, "relationships": [], "isDataVersioned": false, "isDataArchived": false, "isDataBranchVersioned": false, "isDataReplicaTracked": false, "isCoGoEnabled": false, "supportsRollbackOnFailureParameter": true, "supportsStatistics": true, "supportsAdvancedQueries": true, "supportsValidateSQL": true, "supportsCoordinatesQuantization": true, "supportsCalculate": true, 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"supportsApplyEditsWithGlobalIds": false, "geometryProperties": { "shapeLengthFieldName": "SHAPE__Length", "shapeAreaFieldName": "SHAPE__Area", "units": "esriMeters" }, "serviceItemId": "4fbb2af154c74d2e8b1b24bd8bc69458", "hasMetadata": true, "sqlParserVersion": "PG_10.8.1" }, { "currentVersion": 10.81, "cimVersion": "2.6.0", "id": 22, "name": "Eaux_profondes", "type": "Feature Layer", "parentLayer": null, "defaultVisibility": true, "minScale": 0, "maxScale": 0, "canScaleSymbols": false, "geometryType": "esriGeometryPolygon", "description": "Designing a representative network of high seas marine protected areas (MPAs) requires an acceptable scheme to classify the benthic (as well as the pelagic) bioregions of the oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 53,713 separate polygons comprising 11 different categories, that we have termed \u201cseascapes\u201d. A cross-check of the seascape classification was carried out by comparing the seascapes with existing maps of seafloor geomorphology and seabed sediment type and by GIS analysis of the number of separate polygons, polygon area and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Less than 20% of some seascapes occur in the high seas while other seascapes are largely confined to the high seas, indicating specific types of environment whose protection and conservation will require international cooperation. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by a simple GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hotspots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.<\/span>", "copyrightText": "Harris and Whiteway 2009. 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Couche SIG contenant les différentes informations sur\nl\u2019extension urbaine autour du littoral couvrant les pays de l\u2019observatoire en\n2010, 2016 et 2020. Cette couche a été créée en 2010 en utilisant les données\nd\u2019Africapolis comme socle de départ. Les agglomérations de plus de 10 000\nhabitants sont aussi prises en compte dans cette armature urbaine. L\u2019échelle\ngéographique de cette couche est toute l\u2019Afrique de l\u2019ouest partant de la\nMauritanie jusqu\u2019au Bénin, plus Sao Tomé et Principe. Tandis que, la profondeur\nde l\u2019information est de 25 km de la côte à l\u2019intérieur des terres. <\/font><\/span><\/p>

<\/p>

Limites : <\/span><\/b>Pas\nd\u2019informations<\/span><\/font><\/p>\n\n

Projections cartographiques : <\/span><\/b>WGS84. <\/span><\/font><\/p>\n\n

Liens en rapport avec les données :\n<\/span><\/b>Pas d\u2019informations;<\/span><\/font><\/p>\n\n

Source\ndes données<\/span><\/b> : <\/span>Africapolis, Modifiées dans le cadre de la MOLOA.<\/span><\/font><\/p>\n\n

Date\nde mise à jour de la couche<\/span><\/b> :\nOctobre 2020.<\/span><\/font><\/p>\n\n

Source de données de mise à jour<\/span><\/b> : Google Earth 2019 / 2020.<\/span><\/font><\/p>\n\n

Producteur\nde la mise à jour : <\/span><\/b>Centre\nde Suivi Ecologique (CSE).<\/span><\/font><\/p>\n\n

Attributs<\/font><\/span><\/b><\/p>\n\n

ID_DH<\/span><\/b> :\nClé primaire de la table : c\u2019est l\u2019identifiant unique du type de littoral.<\/span><\/font><\/p>\n\n

ID_PAYS : <\/span><\/b>Clé\nsecondaire : c\u2019est l\u2019identifiant unique du pays auquel est rattaché un\ntype d\u2019enjeu<\/span><\/font><\/p>\n\n

PAYS : <\/span><\/b>Pays\nconcerné par la tache urbaine.<\/span><\/font><\/p>\n\n

ANNEE : <\/span><\/b>Etat\nde l\u2019urbain à une année t<\/i>1.<\/span><\/b><\/span><\/font><\/p>\n\n

TYPE_UR : <\/span><\/b>Types\nou formes d\u2019urbanisation <\/span>t<\/i>2.<\/span><\/b><\/span><\/font><\/p>\n\n

NOM : <\/span><\/b>Nom\nde la zone où l\u2019extension est notée.<\/span><\/font><\/p>\n\n

SURFACE : <\/span><\/b>Surface\nen hectare (Ha)<\/span><\/font><\/p>\n\n

1 <\/span><\/b>Année<\/span><\/b> :\n2010 \u2013 2015 \u2013 2020.<\/span><\/font><\/p>\n\n

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We mapped Low Elevation Coastal Zones at or below 10m in elevation and\nadjacent to the coastline for West Africa, from Senegal to Nigeria. This\nanalysis was conducted using MERIT DEM data, which was created by removing\nmultiple error types from SRTM3 v2.1 and AW3D-30m v1 to reduce vertical height\nbias (Yamakzai et al. 2018). Given this increased vertical accuracy, MERIT DEM\ncan map 10-meter LECZs with an 89% accuracy (Gesch 2018). <o:p></o:p><\/font><\/span><\/p>

To determine the 10-meter LECZ, we identified pixels that had a value less\nthan 10 and were adjacent to the coast or a coastal water body. We also masked\npermanent water bodies from the zone to better visually represent the\nsurrounding land areas most at risk.<\/font><\/span><\/p>

Limitations<\/font><\/span><\/b><\/p>

LIDAR derived Digital Elevation\nModels (DEMs), along with current, bathymetric and storm surge data, is widely\nacknowledged to be the most accurate way of modeling fine-scale SLR (Luger and\nGunduz 2015, Gesch 2018, Kulp and Strauss 2015). Although this is largely\nrecognized as the most accurate approach, LIDAR data is expensive to obtain,\noften unavailable in many parts of the world, and would require a large amount\nof processing power to analyze at the scale of the West African Coastline.\nRemotely sensed, globally available DEMs are also commonly used to map SLR\nvulnerability, although it has been shown that global DEMs are not suitable for\nmapping fine scale sea-level rise over relatively short time horizons with any\nacceptable amount of accuracy (Leon et al. 2014, Gesch 2018). Given these\naccuracy and data availability issues, we were not able to model seal level\nrise itself, but rather were able to identify 10-meter Low Elevation Coastal\nZones (LECZs) for the entire west coast of Africa (Senegal to Nigeria). We also\nhighlighted other key areas within the LECZ that are particularly vulnerable to\nthe impacts of sea level rise for information and planning purposes. <\/span><\/font><\/p>

Map projection : <\/span><\/b>It is currently Africa Albers Equal Area Conic\n(WGS84).<\/span><\/font><\/p>

Data links<\/font><\/span><\/b><\/p>

· <\/span><\/span>MERIT DEM<\/span> :<\/span><\/b> https://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/<\/a><\/span><\/font><\/p>

<!--· <\/span><\/span>https://www.wabicc.org/mdocs-posts/mapping-west-africas-low-elevation-coastal-zones/<\/span><\/a><\/span> <\/span><\/font><\/p>

·   <\/span><\/span>file:///C:/Users/ProDesk%20400/Downloads/Mapping%20West%20Africa&%23039%3Bs%20Low%20Elevation%20Coastal%20Zones.pdf<\/span><\/a><\/span> <\/span><\/font><\/p>

Data\nsource <\/font><\/span><\/b><\/p>

This data layer was developed using MERIT DEM\ndata, which is created <\/span>by removing multiple error types from <\/span>SRTM3\nv2.1 and AW3D-30m v1 to reduce vertical height bias. This dataset was produced\nby Yamakzai et al. 2018.<o:p></o:p><\/span><\/font><\/p>

Citation\n(s)<\/font><\/span><\/b><\/p>

Cori G., 2019.\nMapping weest Africa\u2019s low elevation costal zones. USAID, WA BiCC, Tetra Tech.<\/font><\/span><\/p>

Gesch,\nD., 2018. Best Practices for Elevation-Based Assessments of Sea-Level Rise and\nCoastal Flooding Exposure. Frontiers in Earth Science, 6.<\/font><\/span><\/p>

Gunduz,\nOrhan & Tulger Kara, Gülşah. (2015). \u2018Influence of DEM Resolution on\nGIS-Based Inundation Analysis\u2019. 9th World Congress of the European Water\nResources Association (EWRA<\/i>). İstanbul, Turkey. <\/font><\/span><\/p>

Kulp,\nS. and Strauss, B., 2015. \u2018The Effect Of DEM Quality On Sea Level Rise\nExposure Analysis\u2019. AGU Fall Meeting. 2015. <\/font><\/span><\/p>

Leon,\nJ., Heuvelink, G. and Phinn, S., 2014. Incorporating DEM Uncertainty in Coastal\nInundation Mapping. PLoS ONE, 9(9), p.e108727.<\/font><\/span><\/p>

Yamazaki D., D. Ikeshima, R. Tawatari,\nT. Yamaguchi, F. O'Loughlin, J.C. Neal, C.C. Sampson, S. Kanae & P.D.\nBates. A high accuracy map of global terrain elevations. Geophysical Research\nLetters, vol.44, pp.5844-5853, 2017 doi: 10.1002/2017GL072874.<\/span><\/font><\/p>

Geographic\ncoverage<\/span><\/b>Senegal to\nNigeria.<\/b><\/span><\/font><\/p>

Layer creation date<\/span><\/b> : 7/31/20.<\/span><\/font><\/p>

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<\/p>

Contacts : <\/span><\/b>Cori Grainger (<\/span>cori.grainger@tetratech.com<\/span><\/a><\/span>), Vaneska Litz (<\/span>vaneska.litz@tetratech.com<\/span><\/a><\/span>), Stephen Kelleher (<\/span>Stephen.Kelleher@wabicc.org<\/span><\/a><\/span>).<\/span><\/font><\/p>

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"standard" ] }, "supportsApplyEditsWithGlobalIds": false, "geometryProperties": { "shapeLengthFieldName": "SHAPE__Length", "shapeAreaFieldName": "SHAPE__Area", "units": "esriMeters" }, "serviceItemId": "4fbb2af154c74d2e8b1b24bd8bc69458", "hasMetadata": true, "sqlParserVersion": "PG_10.8.1" }, { "currentVersion": 10.81, "cimVersion": "2.6.0", "id": 26, "name": "Arc_insulaire", "type": "Feature Layer", "parentLayer": null, "defaultVisibility": true, "minScale": 0, "maxScale": 0, "canScaleSymbols": false, "geometryType": "esriGeometryPolygon", "description": "Designing a representative network of high seas marine protected areas (MPAs) requires an acceptable scheme to classify the benthic (as well as the pelagic) bioregions of the oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 53,713 separate polygons comprising 11 different categories, that we have termed \u201cseascapes\u201d. A cross-check of the seascape classification was carried out by comparing the seascapes with existing maps of seafloor geomorphology and seabed sediment type and by GIS analysis of the number of separate polygons, polygon area and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Less than 20% of some seascapes occur in the high seas while other seascapes are largely confined to the high seas, indicating specific types of environment whose protection and conservation will require international cooperation. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by a simple GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hotspots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.<\/span>", "copyrightText": "Harris and Whiteway 2009. 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La base de données sous régionale des aires protégées\nest extraite de celle mondiale (WDPA), qui est la seule base de données\nmondiale sur les aires protégées terrestres et marines disponibles <\/span>et\nconstitue la base de Protected Planet. 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