Affiliations 

  • 1 Q-ForestLab, Department of Environment, Ghent University, Ghent, Belgium
  • 2 ISOFYS - Isotope Bioscience Laboratory, Department of Green Chemistry and Technology, Ghent University, Ghent, Belgium
  • 3 GFZ German Research Centre for Geosciences, Potsdam, Germany
  • 4 Sylvera Ltd, London, UK
  • 5 Laboratoire Evolution and Biological Diversity (EDB), CNRS/IRD/UPS, Toulouse, France
  • 6 Geociencias, Federal University of Para, Belem, State of Para, Brazil
  • 7 UCL Department of Geography, London, UK
  • 8 School of Biological Sciences, University of Bristol, Bristol, UK
  • 9 Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Wageningen, Gelderland, the Netherlands
  • 10 Centre for Tropical Environmental and Sustainability Science and College of Science and Engineering, James Cook University, Cairns, Australia
  • 11 Finnish Meteorological Institute, FMI, Helsinki, Finland
  • 12 School of Geosciences, University of Edinburgh, Edinburgh, UK
  • 13 Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
  • 14 School of Informatics, Computing, and Cyber Systems, Northern Arizona University Flagstaff, Flagstaff, Arizona, USA
  • 15 Department of Geography, University College London, London, UK
Glob Chang Biol, 2024 Aug;30(8):e17473.
PMID: 39155688 DOI: 10.1111/gcb.17473

Abstract

Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non-destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory-based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ-400(i) TLS-based D and H data, allowing accuracy assessment of local forest inventory-based H:D allometries. Additionally, TLS-based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory-based H:D allometries underestimated H compared with TLS-based allometries. For 30-metre-tall trees, these underestimations varied from -1.6 m (-5.3%) to -7.5 m (-25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as -31.7 m (-41.3%). We propose a TLS-based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of -4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.