RT Journal Article
T1 Canopy height and biomass distribution across the forests of Iberian Peninsula
A1 Su, Yang
A1 Schwartz, Martin
A1 Fayad, Ibrahim
A1 García, Mariano
A1 Zavala, Miguel A.
A1 Tijerín-Triviño, Julián
A1 Astigarraga, Julen
A1 Cruz Alonso, Verónica
A1 Liu, Siyu
A1 Zhang, Xianglin
A1 Chen, Songchao
A1 Ritter, François
A1 Besic, Nikola
A1 d’Aspremont, Alexandre
A1 Ciais, Philippe
AB Accurate mapping of vegetation canopy height and biomass distribution is essential for effective forest monitoring, climate change mitigation, and sustainable forestry. Here we present high-resolution remote sensing-based canopy height (10 m resolution) and above ground biomass (AGB, 50 m resolution) maps for the forests of the Iberian Peninsula from 2017 to 2021, using a deep learning framework that integrates Sentinel-1, Sentinel-2, and LiDAR data. Two UNET models were developed: one trained on Airborne Laser Scanning (ALS) data (MAE: 1.22 m), while another using Global Ecosystem Dynamics Investigation (GEDI) footprints (MAE: 3.24 m). External validation with 6,308 Spanish National Forest Inventory (NFI) plots (2017–2019) confirmed canopy height reliability, showing MAEs of 2–3 m in tree-covered areas. AGB estimates were obtained through Random Forest models that linked UNET derived height predictions to NFI AGB data, achieves an MAE of ~29 Mg/ha. The creation of high-resolution maps of canopy height and biomass across various forest landscapes in the Iberian Peninsula provides a valuable new tool for environmental researchers, policy makers, and forest management professionals, offering detailed insights that can inform conservation strategies, carbon sequestration efforts, and sustainable forest management practices.
PB Nature Research
YR 2025
FD 2025-04
LK https://hdl.handle.net/20.500.14352/120649
UL https://hdl.handle.net/20.500.14352/120649
LA eng
NO Su, Y., Schwartz, M., Fayad, I. et al. Canopy height and biomass distribution across the forests of Iberian Peninsula. Sci Data 12, 678 (2025). https://doi.org/10.1038/s41597-025-05021-9
NO Acknowledgements:MAZ, JTT, JA and VCA acknowledge support from the Spanish Ministry of Science and Innovation (grant LARGE, Nº PID2021-123675OB-C41, Agencia Estatal de Investigación). MG acknowledges support from the Spanish Ministry of Science and Innovation (grant REMOTE, Nº PID2021-123675OB-C42). VCA was supported by the Ministry of Universities, Spain, and Next Generation-EU, with “Maria Zambrano” fellowship. PC acknowledges support from the European Space Agency Climate Space RECCAP2-CS project (ESA ESRIN/4000144908) and the CALIPSO project funded by the generosity of Schmidt Science. YS, PC, MS, IF and AD are supported by the French German project AI4FOREST (ANR-22-FAI1-0002-01) funded by ANR and DLR.
NO European Commission (EU)
NO Ministerio de Ciencia e Innovación (España)
NO Agencia Estatal de Investigación (España)
NO Ministerio de Universidades (España)
NO European Space Agency
NO Agence Nationale de la Recherche (France)
NO Deutsches Zentrum für Luft- und Raumfahrt (Deutschland)
NO Schmidt Science
DS Docta Complutense
RD 16 jul 2025