https://dataverse.ird.fr/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.23708/U6WTLEhttps://dataverse.ird.fr/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.23708/U6WTLE
This dataset is a high spatial resolution Land Use / Land Cover (LULC) map of the region of Tori-Bossito, southern Benin. Its spatial resolution is 10 m. It was produced in 2011 and made available for a wide range of uses. It has been produced to study the environmental determinants of the presence and abundance of three malaria mosquito species. It contains 14 land cover classes: Freshwater, Herbswamp, Aquatic grassland, Coco tree, Eucalyptus tree, Thicket, Palm tree, Savanna, Teak tree, Pineapple, Degraded riparian forest, Degraded surfaces, Rain-fed agriculture, Forest. The method used to generate the map involved a supervised object-based image classification using mono- and multi-spectral SPOT 5 satellite products from 2010, ground-truth data (> 200 plots) acquired by fieldwork (2010-2011), and nearest-neighbor classifier. The classification accuracy was 98%. In addition to the LULC georeferenced raster data, we propose the following files in this release: the raster attribute table, as an .ods (libreoffice) file, including names and definitions of the land cover classes in both English and French ; a QGIS layer style file (.qml) for visualizing the raster in QGIS ; two color map files (.clr) (English and French) to be used in a variety of GIS software. the map as a .png miniature image ; representative pictures of the land cover classes ; The methodology used to generate the data was detailed in french in Moiroux 2012 (https://theses.hal.science/tel-00812118, p. 79-85 ) and in English, in a more concise way in Moiroux et al. 2013 ( https://doi.org/10.1186/1756-3305-6-71).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Queensland's spatial cadastre datasets are changing! From a planned date of 1 July 2025 the current Digital Cadastral Database (DCDB) will be migrated to an entirely new operating environment, and there will be some changes to the data provided. Visit our Spatial Applications Support page (https://spatial-qld-support.atlassian.net/wiki/spaces/QSUITE/pages/1067515932/Cadastre+and+Address+Modernisation+CAM) for more information.The Digital Cadastre is the spatial representation of every current parcel of land in Queensland, and its legal Lot on Plan description and relevant attributes. It provides the map base for systems dealing with land-related information. The Digital Cadastre is considered to be the point of truth for the graphical representation of property boundaries. It is not the point of truth for the legal property boundary or related attribute information, this will always be the plan of survey or the related titling information and administrative data sets. This data is updated weekly on Sunday.Data dictionary https://www.publications.qld.gov.au/dataset/queensland-digital-cadastral-database-supporting-documents/resource/b59bb1a1-3818-4754-8dc4-3669f0ec3f8b Spatial cadastre accuracy map https://www.publications.qld.gov.au/dataset/queensland-digital-cadastral-database-supporting-documents/resource/d6f029ad-b3a4-428b-bcf1-2f7c7326132b
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https://dataverse.ird.fr/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.23708/U6WTLEhttps://dataverse.ird.fr/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.23708/U6WTLE
This dataset is a high spatial resolution Land Use / Land Cover (LULC) map of the region of Tori-Bossito, southern Benin. Its spatial resolution is 10 m. It was produced in 2011 and made available for a wide range of uses. It has been produced to study the environmental determinants of the presence and abundance of three malaria mosquito species. It contains 14 land cover classes: Freshwater, Herbswamp, Aquatic grassland, Coco tree, Eucalyptus tree, Thicket, Palm tree, Savanna, Teak tree, Pineapple, Degraded riparian forest, Degraded surfaces, Rain-fed agriculture, Forest. The method used to generate the map involved a supervised object-based image classification using mono- and multi-spectral SPOT 5 satellite products from 2010, ground-truth data (> 200 plots) acquired by fieldwork (2010-2011), and nearest-neighbor classifier. The classification accuracy was 98%. In addition to the LULC georeferenced raster data, we propose the following files in this release: the raster attribute table, as an .ods (libreoffice) file, including names and definitions of the land cover classes in both English and French ; a QGIS layer style file (.qml) for visualizing the raster in QGIS ; two color map files (.clr) (English and French) to be used in a variety of GIS software. the map as a .png miniature image ; representative pictures of the land cover classes ; The methodology used to generate the data was detailed in french in Moiroux 2012 (https://theses.hal.science/tel-00812118, p. 79-85 ) and in English, in a more concise way in Moiroux et al. 2013 ( https://doi.org/10.1186/1756-3305-6-71).