Reunion island - 2017, Land cover map (Pleiades)
As part of THEIA (the French Data and Services center for continental surfaces) CIRAD's TETIS research unit is developing an automated mapping method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing. <br> The methodology uses jointly a Very High Spatial Resolution image (Spot6/7 or Pleiades) and one or more time series of High Spatial Resolution optical images such as Sentinel-2 and Landsat-8 for a classification combining segmentation and object classification (use of the Random Forest algorithm) driven by a learning database constituted from in situ collection and photo-interpretation. <br> The land use maps are produced as part of the GABIR project (Gestion Agricole des Biomasses à l'échelle de l'Ile de la Réunion) and are all distributed on CIRAD's spatial data catalogue in Réunion: <a href="http://aware.cirad.fr">http://aware.cirad.fr/</a> <br> This Dataverse entry concerns the maps produced, for the year 2017, using a mosaic of Pleiades images to calculate segmentation (extraction of homogeneous objects from the image). We use a field database with a nested nomenclature with 3 levels of accuracy allowing us to produce a classification by level. The most detailed level distinguishing crop types has an overall accuracy of 86% and a Kappa index of 0.85. Level 2, distinguishing crop groups, has an overall accuracy of 92% and a Kappa index of 0.90. Level 1, distinguishing major land use groups, has an overall accuracy of 97% and a Kappa index of 0.94. A detailed sheet presenting the validation method and results is available for download.
| Main Authors: | , |
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| Format: | Dataset biblioteca |
| Language: | French |
| Published: |
CIRAD Dataverse
2019
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| Subjects: | Agricultural Sciences, Computer and Information Science, Earth and Environmental Sciences, Social Sciences, télédétection, remote sensing, environnement, environment, base de données spatiale, spatial database, Landsat-8, Sentinel-2, Pleiades, landsat, forêt primaire, primary forests, |
| Online Access: | https://doi.org/10.18167/DVN1/RTAEHK |
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| Summary: | As part of THEIA (the French Data and Services center for continental surfaces) CIRAD's TETIS research unit is developing an automated mapping method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing.
<br>
The methodology uses jointly a Very High Spatial Resolution image (Spot6/7 or Pleiades) and one or more time series of High Spatial Resolution optical images such as Sentinel-2 and Landsat-8 for a classification combining segmentation and object classification (use of the Random Forest algorithm) driven by a learning database constituted from in situ collection and photo-interpretation.
<br>
The land use maps are produced as part of the GABIR project (Gestion Agricole des Biomasses à l'échelle de l'Ile de la Réunion) and are all distributed on CIRAD's spatial data catalogue in Réunion: <a href="http://aware.cirad.fr">http://aware.cirad.fr/</a>
<br>
This Dataverse entry concerns the maps produced, for the year 2017, using a mosaic of Pleiades images to calculate segmentation (extraction of homogeneous objects from the image). We use a field database with a nested nomenclature with 3 levels of accuracy allowing us to produce a classification by level. The most detailed level distinguishing crop types has an overall accuracy of 86% and a Kappa index of 0.85. Level 2, distinguishing crop groups, has an overall accuracy of 92% and a Kappa index of 0.90. Level 1, distinguishing major land use groups, has an overall accuracy of 97% and a Kappa index of 0.94. A detailed sheet presenting the validation method and results is available for download. |
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