How to cite :

LSCE. (2025): FORMS-T: Forest Multiple Source height, wood volume, and biomass time-series (2018-present) in France at 10 to 30 m resolution. Data Terra. (Collection)

doi:10.71961/0ts1-zz77
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Description

This collection contains France-wide forest attribute maps (2018 - present) derived from a deep learning framework that leverages data from Sentinel-1, Sentinel-2, and GEDI satellites.

The products are organized into 3 datasets:

- Forms-T Biomass: biomass map with a 30 m resolution. Units are expressed in megagrams per hectare (Mg.ha⁻¹)
- Forms-T Height: canopy height map with a 10 m resolution. Units are expressed in centimeters (10⁻² m)
- Forms-T Volume: standing wood volume map with a 30 m resolution (WVD files). Units are expressed in cubic meters per hectare (m³.ha⁻¹)

Recommended usage: products can also be visualized and accessed in Python without downloading, via the following STAC catalog: https://browser-theia.stac.teledetection.fr/. A Python code snippet for accessing and using the data via STAC is available here.

Products can also be visualized in QGIS by following the instructions provided in the CDS Theia Montpellier catalog or by directly adding XYZ tiles.
Products can also be downloaded as .tif files directly from this Zenodo repository.

For more details on the methodology and validation, please refer to the associated publication Schwartz & al., 2025[1]

License

The products are under the open source license Creative Commons License - Attribution Non Commercial 4.0 International (CC-BY-NC 4.0)

Access to products

The collection is disseminated using FAIR principles by the DATA TERRA Research Infrastructure (Solid Earth Data Hub - Continental Surfaces Data Hub – THEIA) through the diffusion service CDS-MTD (https://browser-theia.stac.teledetection.fr/) hosted at Maison de la Télédétection.

Further details

  1. Schwartz, M., Ciais, P., Sean. E., de Truchis, A., Vega, C., Besic, N., Fayad, I., Wigneron, J-P., Brood, S., Pelissier-Tanon, A., Pauls, J., Belouze, G., Xu, Y., (2025). Retrieving yearly forest growth from satellite data: A deep learning based approach, Remote Sensing of Environment, Volume 330, 114959 https://doi.org/10.1016/j.rse.2025.114959, HTML