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

Léa Panhelleux; Sébastien Rapinel; Blandine Lemercier; Guillaume Gayet; Laurence Hubert-Moy

doi:10.1016/j.dib.2023.109369

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

Data Brief. 2023 Jul 4:49:109369. doi: 10.1016/j.dib.2023.109369. eCollection 2023 Aug.

Authors

Léa Panhelleux¹, Sébastien Rapinel¹, Blandine Lemercier², Guillaume Gayet³, Laurence Hubert-Moy¹

Affiliations

¹ LETG UMR 6554, University of Rennes - CNRS, place du recteur Henri Le Moal, Rennes, 35000, France.
² SAS UMR 1069, Institut Agro Rennes-Angers - INRAE, 65 rue de Saint-Brieuc, Rennes, 35000, France.
³ PatriNat OFB-MNHN-CNRS-IRD, 57 rue Cuvier, Paris, 75231, France.

Abstract

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.

Keywords: Big data; DTM; Environmental modeling; Hydrogeomorphology; Soil moisture; Wetland.