Canopy height and biomass distribution across the forests of Iberian Peninsula
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.
Authors
- François Ritter (ORCID: https://orcid.org/0000-0001-6123-2145)
- Miguel Angel Zavala (ORCID: https://orcid.org/0000-0003-1456-0132)
- Martin Schwartz (ORCID: https://orcid.org/0000-0003-4038-9068)
- Ibrahim Fayad
- Julen Astigarraga (ORCID: https://orcid.org/0000-0001-9520-3713)
- Mariano Garcı́a (ORCID: https://orcid.org/0000-0001-6260-5791)
- Julián Tijerín‐Triviño (ORCID: https://orcid.org/0000-0001-9576-4780)
- Verónica Cruz‐Alonso (ORCID: https://orcid.org/0000-0002-0642-036X)
- Yang Su (ORCID: https://orcid.org/0000-0002-4717-9971)
- Nikola Bešič (ORCID: https://orcid.org/0000-0002-2597-4042)
- Alexandre d’Aspremont (ORCID: https://orcid.org/0000-0003-3851-216X)
- Siyu Liu (ORCID: https://orcid.org/0000-0003-1997-1037)
- Philippe Ciais (ORCID: https://orcid.org/0000-0001-8560-4943)
- Xianglin Zhang
- Songchao Chen (ORCID: https://orcid.org/0000-0003-1245-0482)
Institutions
- Universidad Complutense de Madrid (ES)
- Centre National de la Recherche Scientifique (FR)
- Zhejiang University of Science and Technology (CN)
- Copenhagen Business School (DK)
- Universidad de Alcalá (ES)
- Université de Versailles Saint-Quentin-en-Yvelines (FR)
- AgroParisTech (FR)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Saclay (FR)
- École Normale Supérieure - PSL (FR)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- École Normale Supérieure (BI)
- Centre National de la Propriété Forestière (FR)
- Laboratoire des Sciences du Climat et de l'Environnement (FR)
- Zhejiang University (CN)
- Université de Lorraine (FR)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1038/s41597-025-05021-9
- Citations
- 9
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 7.19
Funders
- National Aeronautics and Space Administration
- European Space Agency
- Agence Nationale de la Recherche
- Ministerio de Ciencia e Innovación
- Ministerio para la Transición Ecológica y el Reto Demográfico
- Agencia Estatal de Investigación