Preprints
https://doi.org/10.5194/essd-2021-77
https://doi.org/10.5194/essd-2021-77

  03 May 2021

03 May 2021

Review status: this preprint is currently under review for the journal ESSD.

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

Simon Besnard1,2, Sujan Koirala1, Maurizio Santoro5, Ulrich Weber1, Jacob Nelson1, Jonas Gütter1,4, Bruno Herault6,7, Justin Kassi8, Anny N'Guessan8, Christopher Neigh9, Benjamin Poulter9, Tao Zhang10,11, and Nuno Carvalhais1,3 Simon Besnard et al.
  • 1Max Planck Institute for Biogeochemistry, Germany
  • 2Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, The Netherlands
  • 3Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Portugal
  • 4DLR, Institute of Data Science Data Management and Analysis, Germany
  • 5Gamma Remote Sensing, Switzerland
  • 6INP-HB, Institut National Polytechnique Félix Houphouët-Boigny, Côte d'Ivoire
  • 7Cirad, University of Montpellier, UR Forests & Societies, France
  • 8Université Félix Houphouët-Boigny, UFR Biosciences, Laboratoire de Botanique, Côte d'Ivoire
  • 9NASA Goddard Space Flight Center, Biospheric Sciences Lab., Greenbelt, MD, USA
  • 10University of Florida, Department of Biology, United States
  • 11University of Minnesota, Department of Forest Resources, United States

Abstract. Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, 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. In this study, we provide 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, evaluation against the plot level forest age measurements 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 ages (NSE = 0.6 and RMSE = 50 years). Yet, there are systematic biases with overestimation in young and underestimation in old forest stands, respectively. Globally, we find a large variability of forest age with the old-growth forests in the tropical regions of Amazon and Congo, and young forests in China and intermediate stands in Europe. On the other hand, we find that the regions with high rates of deforestation or forest degradation (e.g., the arc of deforestation in the Amazon) are largely composed of younger stands. Assessment of forest age in the climate-space shows that the old-forests are either in cold and dry regions or in warm and wet regions, while young-intermediate forests span a large climatic gradient. Finally, a comparison between the presented forest age estimates with a series of regional products reveals differences rooted in different approaches as well as in 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 in support of a better understanding of the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021). For anonymous access during review, please refer to the data availability section below. 

Simon Besnard et al.

Status: open (until 28 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Simon Besnard et al.

Data sets

The MPI-BGC global forest age dataset Besnard, S., Koirala, S., Santoro, M., Weber, U., Nelson, J., Gütter, J, Herault, B., Kassi, J., N'Guessan, A., Neigh, C., Poulter, B., Zhang, T., and Carvarhais, N. https://doi.org/10.17871/ForestAgeBGI.2021

Simon Besnard et al.

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Short summary
Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, 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. In this paper, we introduced a new global distribution of forest age inferred from forest inventory, remote sensing and climate data in support of a better understanding of the global dynamics in the forest water and carbon cycles.