A 5 m dataset of digital terrain model derivatives across mainland France
A dataset of three digital terrain model (DTM) derivatives was produced at 5 m spatial resolution across mainland France. This dataset includes (i) a topographic wetness index (TWI) that characterizes potential soil wetness as a function of the contributing area and local slope, (ii) a multi-scale t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923004869 |
| _version_ | 1827866478297219072 |
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| author | Léa Panhelleux Sébastien Rapinel Blandine Lemercier Guillaume Gayet Laurence Hubert-Moy |
| author_facet | Léa Panhelleux Sébastien Rapinel Blandine Lemercier Guillaume Gayet Laurence Hubert-Moy |
| author_sort | Léa Panhelleux |
| collection | DOAJ |
| description | A dataset of three digital terrain model (DTM) derivatives was produced at 5 m spatial resolution across mainland France. This dataset includes (i) a topographic wetness index (TWI) that characterizes potential soil wetness as a function of the contributing area and local slope, (ii) a multi-scale topographic position color composite (MTPCC) that describes the position of a pixel relative to its neighborhood at three spatial scales, and (iii) a vertical distance to channel network index (VDCNI) that expresses the vertical height between the elevation of a pixel and the nearest channel. These three raster layers were derived from the French national airborne DTM at 5 m spatial resolution and the vector layer of the channel network of the national hydrological database. This unprecedented fine-scale dataset opens new insights for geomorphological analysis. It can be used for several purposes, such as environmental modeling, risk assessment, or water-resource management. |
| first_indexed | 2024-03-12T15:04:19Z |
| format | Article |
| id | doaj.art-5851cd8b47d34f61918ac805dfaa3f34 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-12T15:04:19Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-5851cd8b47d34f61918ac805dfaa3f342023-08-13T04:54:07ZengElsevierData in Brief2352-34092023-08-0149109369A 5 m dataset of digital terrain model derivatives across mainland FranceLéa Panhelleux0Sébastien Rapinel1Blandine Lemercier2Guillaume Gayet3Laurence Hubert-Moy4LETG UMR 6554, University of Rennes - CNRS, place du recteur Henri Le Moal, Rennes, 35000, FranceLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri Le Moal, Rennes, 35000, FranceSAS UMR 1069, Institut Agro Rennes-Angers - INRAE, 65 rue de Saint-Brieuc, Rennes, 35000, FrancePatriNat OFB-MNHN-CNRS-IRD, 57 rue Cuvier, Paris, 75231, FranceLETG UMR 6554, University of Rennes - CNRS, place du recteur Henri Le Moal, Rennes, 35000, France; Corresponding author.A dataset of three digital terrain model (DTM) derivatives was produced at 5 m spatial resolution across mainland France. This dataset includes (i) a topographic wetness index (TWI) that characterizes potential soil wetness as a function of the contributing area and local slope, (ii) a multi-scale topographic position color composite (MTPCC) that describes the position of a pixel relative to its neighborhood at three spatial scales, and (iii) a vertical distance to channel network index (VDCNI) that expresses the vertical height between the elevation of a pixel and the nearest channel. These three raster layers were derived from the French national airborne DTM at 5 m spatial resolution and the vector layer of the channel network of the national hydrological database. This unprecedented fine-scale dataset opens new insights for geomorphological analysis. It can be used for several purposes, such as environmental modeling, risk assessment, or water-resource management.http://www.sciencedirect.com/science/article/pii/S2352340923004869DTMWetlandEnvironmental modelingHydrogeomorphologySoil moistureBig data |
| spellingShingle | Léa Panhelleux Sébastien Rapinel Blandine Lemercier Guillaume Gayet Laurence Hubert-Moy A 5 m dataset of digital terrain model derivatives across mainland France Data in Brief DTM Wetland Environmental modeling Hydrogeomorphology Soil moisture Big data |
| title | A 5 m dataset of digital terrain model derivatives across mainland France |
| title_full | A 5 m dataset of digital terrain model derivatives across mainland France |
| title_fullStr | A 5 m dataset of digital terrain model derivatives across mainland France |
| title_full_unstemmed | A 5 m dataset of digital terrain model derivatives across mainland France |
| title_short | A 5 m dataset of digital terrain model derivatives across mainland France |
| title_sort | 5 m dataset of digital terrain model derivatives across mainland france |
| topic | DTM Wetland Environmental modeling Hydrogeomorphology Soil moisture Big data |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923004869 |
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