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Improving plant allometry by fusing forest models and remote sensing

Abstract : Allometry determines how tree shape and function scale with each other, related through size. Allometric relationships help scale processes from individual to global scale, and they constitute a core component of vegetation models. Allometric relationships have been expected to emerge from optimization theory, yet this does not suitably predict empirical data. Here we argue that the fusion of high-resolution data, such as those derived from airborne laser scanning, with individual-based forest modelling offers insight into how plant size contributes to large scale biogeochemical processes. We review the challenges in allometric scaling, how they can be tackled by advances in data-model fusion, and how individual-based models can serve as data integrators for dynamic global vegetation models.
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https://hal.umontpellier.fr/hal-02191073
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Soumis le : jeudi 22 juillet 2021 - 12:05:18
Dernière modification le : vendredi 5 août 2022 - 10:33:56
Archivage à long terme le : : samedi 23 octobre 2021 - 18:22:36

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Fabian Jörg Fischer, Isabelle Maréchaux, Jérôme Chave. Improving plant allometry by fusing forest models and remote sensing. New Phytologist, Wiley, 2019, 223 (3), pp.1159-1165. ⟨10.1111/nph.15810⟩. ⟨hal-02191073⟩

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