Articles | Volume 23, issue 7
https://doi.org/10.5194/bg-23-2365-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-2365-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Apr 2026
Research article |
| 10 Apr 2026
| 10 Apr 2026
Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France
Henri Cuny
CORRESPONDING AUTHOR
IGN, Service de l'Information Forestière, Champigneulles, 54250, France
Jean-Daniel Bontemps
Université de Lorraine, Géodata Paris, IGN, LIF, 54000, Nancy, France
Université Gustave Eiffel, Géodata Paris, IGN, Laboratoire d'inventaire forestier, 54000, Nancy, France
Nikola Besic
Université de Lorraine, Géodata Paris, IGN, LIF, 54000, Nancy, France
Université Gustave Eiffel, Géodata Paris, IGN, Laboratoire d'inventaire forestier, 54000, Nancy, France
Antoine Colin
IGN, Service de l'Information Forestière, Champigneulles, 54250, France
Lionel Hertzog
Université de Lorraine, Géodata Paris, IGN, LIF, 54000, Nancy, France
Université Gustave Eiffel, Géodata Paris, IGN, Laboratoire d'inventaire forestier, 54000, Nancy, France
Amaël Le Squin
IGN, Service de l'Information Forestière, Champigneulles, 54250, France
William Marchand
IGN, Service de l'Information Forestière, Nogent-sur-Vernisson, 45290, France
Cédric Vega
Université de Lorraine, Géodata Paris, IGN, LIF, 54000, Nancy, France
Université Gustave Eiffel, Géodata Paris, IGN, Laboratoire d'inventaire forestier, 54000, Nancy, France
Jean-Michel Leban
INRAE, BEF, Champenoux, 54280, France
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-378, https://doi.org/10.5194/essd-2025-378, 2025
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We created the first long-term map of tree height in Italy, showing yearly changes from 2004 to 2024 at a 30-meter resolution. Using satellite images and laser data from both aircraft and space, we applied deep learning and statistical fusion to produce accurate estimates. This map helps reveal where forests have been disturbed and how they recover over time, offering a valuable tool to support forest protection and climate policy.
Gabriel Destouet, Nikola Besic, Emilie Joetzjer, and Matthias Cuntz
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Over the past two decades, global flux tower networks have provided valuable insights into ecosystem functioning. However, the standard eddy-covariance method used for processing flux data has limitations, leading to data loss and limited resolution due to fixed time steps. This paper introduces a new method using wavelet analysis to increase temporal resolution and improve data retention. Applied at the Hesse forest flux tower in France, this approach provides high-resolution flux estimates, enhancing the accuracy of flux measurements.
Nikola Besic, Nicolas Picard, Cédric Vega, Jean-Daniel Bontemps, Lionel Hertzog, Jean-Pierre Renaud, Fajwel Fogel, Martin Schwartz, Agnès Pellissier-Tanon, Gabriel Destouet, Frédéric Mortier, Milena Planells-Rodriguez, and Philippe Ciais
Geosci. Model Dev., 18, 337–359, https://doi.org/10.5194/gmd-18-337-2025, https://doi.org/10.5194/gmd-18-337-2025, 2025
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The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive analysis of these remote-sensing-based products.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
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As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
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Short summary
We analysed wood density variation in mainland France using 110,000 records from 156 tree species. Models calibrated for 44 species revealed species identity as the dominant driver, followed by tree size and temperature. While neglecting wood density variation had little impact on forest biomass estimates nationally, it caused significant bias at finer scales, highlighting the need to account for wood density variation in forest biomass and carbon assessments.
We analysed wood density variation in mainland France using 110,000 records from 156 tree...
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