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 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.

Natural grasslands across mainland France: A dataset including a 10 m raster and ground reference points.

The data provided here include the first 10 m raster of natural grasslands across mainland France and related ground reference points. The latter consist of 1770 field observations that describe natural and artificial grasslands from respectively a compilation of hundreds of field-based vegetation maps and the European Union Land Parcel Identification System (LPIS). Based on analysis of aerial images, ground reference points were manually extracted from grassland polygons of the field-based vegetation maps and the LPIS within herbaceous areas larger than 30 × 30 m.

National wetland mapping using remote-sensing-derived environmental variables, archive field data, and artificial intelligence.

While wetland ecosystem services are widely recognized, the lack of fine-scale national inventories prevents successful implementation of conservation policies. Wetlands are difficult to map due to their complex fine-grained spatial pattern and fuzzy boundaries. However, the increasing amount of open high-spatial-resolution remote sensing data and accurately georeferenced field data archives, as well as progress in artificial intelligence (AI), provide opportunities for fine-scale national wetland mapping.