New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests

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.

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Publication Details

Journal
Global Change Biology
Published
2024-08-01
DOI
https://doi.org/10.1111/gcb.17473
Citations
17
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
3.50

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article

New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests

Marijn Bauters, Louise Terryn, Kim Calders, Eduardo Eiji Maeda et al.
17 citations
Global Change Biology
Remote Sensing and LiDAR Applications
3.50
article

New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests

Marijn Bauters, Louise Terryn, Kim Calders, Eduardo Eiji Maeda, Susan G. W. Laurance, Pascal Boeckx, Tom Verhelst, Sruthi M. Krishna Moorthy, Tommaso Jucker, Thomas Sibret, Barbara D’hont, Antônio C. L. da Costa, Mathias Disney, Patrick Meir, Jérôme Chave, Alvaro Lau, Andrew Burt, Matheus Henrique Nunes, Alexander Shenkin, Phil Wilkes, Benjamin Brede, Félicien Meunier, Hans Verbeeck
article en
17 citations

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.

Global Change BiologyVol. 30(8)
Finnish Meteorological Institute (FI), Centre National de la Recherche Scientifique (FR), Museu Paraense Emílio Goeldi (BR), University of Helsinki (FI), Université Toulouse III - Paul Sabatier (FR), Northern Arizona University (US), Ghent University (BE), University of Bristol (GB), University of Oxford (GB), Evolution et Diversité Biologique (FR), National Centre for Earth Observation (GB), GFZ Helmholtz Centre for Geosciences (DE), Institut de Recherche pour le Développement (FR), University College London (GB), Universidade Federal do Pará (BR), University of Maryland, College Park (US), James Cook University (AU), Wageningen University & Research (NL), University of Edinburgh (GB)
Sight Research UK, Agence Nationale de la Recherche, Belgian Federal Science Policy Office, Fonds Wetenschappelijk Onderzoek, Universiteit Gent, Natural Environment Research Council, Bijzonder Onderzoeksfonds UGent
Life in Land
Openalex Percentile: Top 7%
Remote Sensing and LiDAR Applications
3.50
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