Reunion Island - 2018, learning spatial database
The spatial learning database for 2018 contains 5620 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. <br> The sources and techniques used to build the database by land use groups are described below: <ul> <li> <b> For agricultural areas</b>, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2017). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas <li> <b>For natural areas </b>, there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). <li> <b> For wet land areas </b>, the "marsh" and "water" classes were obtained by photo-interpretation of the 2018 Pleiades image. These classes are easily recognizable on this type of image. <li> <b> For urban areas </b> we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i.e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2018 Pleiades image. </li>
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| Format: | Observational data biblioteca |
| Language: | French |
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CIRAD Dataverse
2019
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| Subjects: | Agricultural Sciences, Computer and Information Science, Earth and Environmental Sciences, Base de données spatiale, remote sensing, spatial database, environment, Environnement, Télédétection, Forêt primaire, primary forests, |
| Online Access: | http://dx.doi.org/10.18167/DVN1/8QJABD |
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