High resolution global industrial and smallholder oil palm map for 2019
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PANGAEA
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| Autores principales: | , , , , , |
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| Formato: | Dataset Open Access |
| Lenguaje: | en |
| Publicado: |
PANGAEA
2020
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| Acceso en línea: | |
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| _version_ | 1867169140057309184 |
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| author | Descals, Adrià Wich, Serge Meijaard, Erik Gaveau, David Peedell, Stephen Szantoi, Zoltan |
| author_facet | Descals, Adrià Wich, Serge Meijaard, Erik Gaveau, David Peedell, Stephen Szantoi, Zoltan |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | The data set contains 622 images of 100x100 km and covers the areas where oil palm plantations were detected at the global scale. The classification of oil palm plantations was firstly applied over a larger area where oil palm can potentially grow. The file 'grid.shp' contains the grid that covers the potential distribution of oil palm. The current data set, however, only contains the images where the presence of oil palm was confirmed. The file 'grid_withOP.shp' shows the 100x100 grid squares with presence of oil palm plantations. The classification images (in geotiff format) are the output of a convolutional neural network that takes Sentinel-1 and Sentinel-2 half-year composites as input data. The images have a spatial resolution of 10 meters and show three classes: 1) Industrial mature oil palm plantations, 2) Smallholder mature oil palm plantations, and 3) other land uses that are not mature oil palm. The file 'Validation_points_GlobalOilPalmLayer_2019.shp' includes the 13,252 points that were used to validate the product. Each point includes the attribute 'Class', which is the class assigned by visual interpretation, and the attribute 'predClass, which reflects the predicted class. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_918603 |
| institution | PANGAEA |
| language | en |
| publishDate | 2020 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | High resolution global industrial and smallholder oil palm map for 2019 Descals, Adrià Wich, Serge Meijaard, Erik Gaveau, David Peedell, Stephen Szantoi, Zoltan deep learning; global coverage; industrial plantation; Oil palm; smallholder The data set contains 622 images of 100x100 km and covers the areas where oil palm plantations were detected at the global scale. The classification of oil palm plantations was firstly applied over a larger area where oil palm can potentially grow. The file 'grid.shp' contains the grid that covers the potential distribution of oil palm. The current data set, however, only contains the images where the presence of oil palm was confirmed. The file 'grid_withOP.shp' shows the 100x100 grid squares with presence of oil palm plantations. The classification images (in geotiff format) are the output of a convolutional neural network that takes Sentinel-1 and Sentinel-2 half-year composites as input data. The images have a spatial resolution of 10 meters and show three classes: 1) Industrial mature oil palm plantations, 2) Smallholder mature oil palm plantations, and 3) other land uses that are not mature oil palm. The file 'Validation_points_GlobalOilPalmLayer_2019.shp' includes the 13,252 points that were used to validate the product. Each point includes the attribute 'Class', which is the class assigned by visual interpretation, and the attribute 'predClass, which reflects the predicted class. |
| title | High resolution global industrial and smallholder oil palm map for 2019 |
| topic | deep learning; global coverage; industrial plantation; Oil palm; smallholder |
| url | https://doi.org/10.1594/PANGAEA.918603 |