We present the Global Forest Attribute Dataset (GFAD), a comprehensive set of spatial layers and quantitative indicators derived from the “Global Forest Cover 2020” (GFC2020). GFAD characterizes the connectivity, morphology, and spatial arrangement of forest land cover at a global scale. Central to the development of this dataset is the introduction of pyguidos, a new open-source Python module designed to automate complex image processing for landscape analysis within a Python environment. By leveraging this automated processing chain, we generated global spatial forest attribute layers, which are color-coded with standard ramps. The layers allow for intuitive visualization as well as precise identification of spatial forest attributes. All indicators are normalized on a scale of [0–100] and aggregated by country, facilitating neutral reporting and direct cross-country comparisons of forest conditions. By providing a standardized set of layers and indicators, GFAD and the underlying pyguidos framework address key forest monitoring components that can be applied in various fields, such as conservation, management, and policymaking. The versatility of these tools makes them a robust reference for a wide range of applications, allowing end-users to adapt the analysis to their specific reporting priorities. The entire dataset is publicly available on Zenodo at: https://doi.org/10.5281/zenodo.18924625, and the metadata are available at: https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/7937a918-c80c-4879-b20e-24081c56ae0b.

CAUDULLO Giovanni;  VOGT Peter; 

2026-05-20

SPRINGER FRANCE

JRC143152

1297-966X (online),   

https://link.springer.com/article/10.1186/s13595-026-01332-y,    https://publications.jrc.ec.europa.eu/repository/handle/JRC143152,   

10.1186/s13595-026-01332-y (online),