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A multi-source remote sensing dataset for large-scale forest monitoring

DAmico_etal_2026_DataInBrief_A_multisource.pdf
DAmico_etal_2026_DataInBrief_A_multisource.pdf - Publisher's version - 1.4 MB
How to cite: Giovanni D’Amico, Davide Botticelli, Giacomo Marcelli, Walter Mattioli, Gherardo Chirici, Elia Vangi, Costanza Borghi, Piermaria Corona, Johannes Schumacher, Johannes Breidenbach, Yang Su, Lauri Mehtätalo, Saverio Francini, A multi-source remote sensing dataset for large-scale forest monitoring, Data in Brief, Volume 67, 2026, 112945, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2026.112945

Tiivistelmä

This data article presents a multi-source dataset of satellite-based auxiliary data designed for forest modelling and monitoring. The dataset integrates annual medoid composites derived from Sentinel-1, Sentinel-2, and Landsat imagery, together with spectral indices, Landsat-based 3I3D change metrics, forest mask and forest type layers, and terrain variables derived from the Copernicus GLO-30 DEM, offering comprehensive information on forest cover, spectral behavior, and change metrics. It provides harmonized predictors across seven European countries, ensuring consistency, scalability, and ease of use for researchers developing or validating models to understand forest dynamics and estimate forest-related variables such as biomass or canopy recovery. A curated subset of the dataset is distributed via Zenodo, along with direct public access links to the complete multi-terabyte archive. The data support applications in forest biodiversity conservation, carbon monitoring, biomass modelling, and climate-change impact assessment.

ISBN

OKM-julkaisutyyppi

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisusarja

Data in brief

Volyymi

67

Numero

Sivut

Sivut

13 p.

ISSN

2352-3409