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arXiv:2611.05102 · v1

Characterizing the vertical continuity of fuel strata based on TLS point clouds Gaussian decomposition and overlap metrics

首次提交 2026-11-03 23:56
版本更新 2026-11-03 23:56
雷达观测 2026-11-04 23:39
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摘要

Vertical forest structure influences fire behavior by regulating understory-to-canopy fuel connectivity, yet operational fuel-type models represent this complexity through discrete classes that only indirectly capture vertical continuity. High-density point clouds offer an opportunity to characterize vertical fuel connectivity in a more explicit and continuous manner, but specific methodological approaches are still required. We applied Gaussian decomposition to vertical height profiles derived from terrestrial laser scanning (TLS) point clouds across 25 permanent forest plots to characterize geometric vertical fuel continuity. A multiscale analysis spanning from the plot scale to ~0.5 $m^2$ units was used to evaluate geometric, probabilistic, and overlap metrics for characterizing fuel strata. The method reproduced the vertical transition between shrub and tree strata, with median Pearson correlations ranging from 0.83 to 0.96 across analysis levels. Probability density overlap metrics and separability indicators, such as the Jeffries-Matusita distance, provided complementary descriptors of vertical continuity. Intermediate spatial scales (3-6 m cells) provided a useful compromise between spatial detail and the consistent representation of shrub-canopy interaction patterns. Finer scales increased variability due to point density reduction and profile irregularity, and low mean squared error values were found insufficient as sole performance indicators. Gaussian decomposition applied to TLS point clouds enables explicit quantification of vertical fuel continuity, a limitation of current discrete classification systems. Continuous vertical connectivity metrics derived from this approach offer a pathway for improving ladder fuel characterization and strengthening decision-making support in wildfire risk assessment.

作者备注:45 pages, 15 figures Author affiliations and ORCID: Jesús Torralba https://orcid.org/0000-0001-8644-8604 , Luis Á. Ruiz https://orcid.org/0000-0003-0073-7259 , Pablo Crespo-Peremarch https://orcid.org/0000-0003-2241-4493 , Marina Simó-Martí https://orcid.org/0000-0001-9605-9645 , Juan Pedro Carbonell-Rivera https://orcid.org/0000-0002-6724-6780

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v1 · 2026-11-03 23:56 · 最新观测版本

Characterizing the vertical continuity of fuel strata based on TLS point clouds Gaussian decomposition and overlap metrics

Vertical forest structure influences fire behavior by regulating understory-to-canopy fuel connectivity, yet operational fuel-type models represent this complexity through discrete classes that only indirectly capture vertical continuity. High-density point clouds offer an opportunity to characterize vertical fuel connectivity in a more explicit and continuous manner, but specific methodological approaches are still required. We applied Gaussian decomposition to vertical height profiles derived from terrestrial laser scanning (TLS) point clouds across 25 permanent forest plots to characterize geometric vertical fuel continuity. A multiscale analysis spanning from the plot scale to ~0.5 $m^2$ units was used to evaluate geometric, probabilistic, and overlap metrics for characterizing fuel strata. The method reproduced the vertical transition between shrub and tree strata, with median Pearson correlations ranging from 0.83 to 0.96 across analysis levels. Probability density overlap metrics and separability indicators, such as the Jeffries-Matusita distance, provided complementary descriptors of vertical continuity. Intermediate spatial scales (3-6 m cells) provided a useful compromise between spatial detail and the consistent representation of shrub-canopy interaction patterns. Finer scales increased variability due to point density reduction and profile irregularity, and low mean squared error values were found insufficient as sole performance indicators. Gaussian decomposition applied to TLS point clouds enables explicit quantification of vertical fuel continuity, a limitation of current discrete classification systems. Continuous vertical connectivity metrics derived from this approach offer a pathway for improving ladder fuel characterization and strengthening decision-making support in wildfire risk assessment.

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