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Article Dans Une Revue (Data Paper) Data in Brief Année : 2023

A 5 m dataset of digital terrain model derivatives across mainland France

Résumé

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.
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halshs-04158229 , version 1 (26-07-2023)

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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, 2023, 49, pp.109369. ⟨10.1016/j.dib.2023.109369⟩. ⟨halshs-04158229⟩
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