Mapping global forest age from forest inventories, biomass and climate data

Besnard Simon, Koirala Sujan, Santoro Maurizio, Weber Ulrich, Nelson Jacob, Gütter Jonas, Herault Bruno, Kassi Justin, N'Guessan Anny, Neigh Christopher, Poulter Benjamin, Zhang Tao, Carvalhais Nuno. 2021. Mapping global forest age from forest inventories, biomass and climate data. Earth System Science Data, 13 : 4881-4896.

Url - jeu de données - Entrepôt autre : https://doi.org/10.17871/ForestAgeBGI.2021

Quartile : Outlier, Sujet : GEOSCIENCES, MULTIDISCIPLINARY / Quartile : Outlier, Sujet : METEOROLOGY & ATMOSPHERIC SCIENCES

Résumé : Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles.

Mots-clés Agrovoc : inventaire forestier, biomasse, données climatiques, cartographie des fonctions de la forêt, forêt, séquestration du carbone, cycle du carbone, distribution géographique

Classification Agris : K01 - Foresterie - Considérations générales
P01 - Conservation de la nature et ressources foncières
K70 - Dégâts causés aux forêts et leur protection

Champ stratégique Cirad : CTS 1 (2019-) - Biodiversité

Agences de financement européennes : European Commission

Auteurs et affiliations

• Besnard Simon, Max Planck Institut für Biogeochemie (DEU) - auteur correspondant
• Koirala Sujan, Max Planck Institut für Biogeochemie (DEU)
• Santoro Maurizio, Gamma Remote Sensing (CHE)
• Weber Ulrich, Max Planck Institut für Biogeochemie (DEU)
• Nelson Jacob, Max Planck Institut für Biogeochemie (DEU)
• Gütter Jonas, Max Planck Institut für Biogeochemie (DEU)
• Herault Bruno, CIRAD-ES-UPR Forêts et sociétés (CIV) ORCID: 0000-0002-6950-7286
• Kassi Justin, UFHB (CIV)
• N'Guessan Anny, UFHB (CIV)
• Neigh Christopher, NASA (USA)
• Poulter Benjamin, NASA (USA)
• Zhang Tao, University of Florida (USA)
• Carvalhais Nuno, Max Planck Institut für Biogeochemie (DEU) - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/600028/)

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