Mapping Forest Age at High-Resolution Using Inventory Data
- 1Helmholtz Center Potsdam GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, 14473 Potsdam, Germany
- 2Gamma Remote Sensing, Bern, Switzerland
- 3Data Management and Analysis, Institute of Data Science, German Aerospace Center (DLR), Jena, Germany
- 4Institut National Polytechnique Félix Houphouët-Boigny (INP-HB), Côte d'Ivoire
- 5Research Unit Forests and Societies, CIRAD, University of Montpellier, Montpellier, France
- 6Laboratoire de Botanique, UFR Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire
- 7Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
- 8Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- 9Department of Biology, University of Florida, Gainesville, Florida, USA
- 10Department of Forest Resources, University of Minnesota, Minneapolis, Minnesota, USA
- 11Departamento de Ciências e Engenharia do Ambiente (DCEA), Faculdade de Ciências e Tecnologia (FCT), Universidade Nova de Lisboa, Lisbon, Portugal
The forest age can be defined as the time since the last stand-replacement event. Within the local context, it determines the forest successional stage, a fundamental variable for diagnosing the net carbon fluxes in terrestrial ecosystems. To accurately quantify the carbon sink-source strength in these ecosystems and inform adaptation-mitigation strategies, it is essential to be able to infer forest age at high resolution.
This study presents an updated version of the MPI-BGC forest age product, featuring global distributions of forest age for 2010, 2017, 2018, and 2020 at 100m spatial resolution. We employed two machine learning approaches, XGBoost and a multi-layer perceptron model, to create data-driven estimates of forest age based on over 40,000 forest inventory plots, biomass, remote-sensing, and climate data. One key innovation of our approach is the incorporation of Landsat-based disturbance history metrics as input variables. Our updated estimates show better precision in identifying old-growth forests and reduce overestimation biases in young forests and underestimation biases in old forests, but not completely. Additionally, we found substantial regional variations related to changes in covariate strength and improvement in the model. Also, we discussed the uncertainty layers, created using model ensembles, that materialize the quantification of methodological uncertainty in the forest age estimates.
An analysis of the global distribution of forest age reveals significant variations across the years studied. We also quantified the changes in forest age in regions with high deforestation or forest degradation rates, where younger stands are becoming more prevalent. We discuss the challenges and limitations of using regression-based mapping approaches, including the choice of machine learning algorithm, spatial cross-validation techniques, and the caveats of extrapolation, given data limitations. Our research highlights the complementary biomass-based approaches for determining forest age and underscores the importance of detailed global estimates at high spatial resolutions. Overall, this study advances our understanding of forest age, a key variable for understanding the carbon cycle in terrestrial ecosystems.
How to cite: Besnard, S., Santoro, M., Herold, M., Cartus, O., Gütter, J., Herault, B., Kassi, J., Koirala, S., N'Guessan, A., Neigh, C., Nelson, J., Poulter, B., Weber, U., Zhang, T., and Carvalhais, N.: Mapping Forest Age at High-Resolution Using Inventory Data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6473, https://doi.org/10.5194/egusphere-egu23-6473, 2023.
