With climate change intensifying, forests globally are becoming more susceptible to extreme weather events, such as windstorms, which account for a significant share of Europe’s economic losses. The Vaia windstorm of late autumn 2018, striking Italy’s North-East alpine ecosystem, highlighted this vulnerability, toppling over 8.5 million cubic meters of timber and sparking debates on forest management’s role in mitigating such disasters. This study aims to evaluate the impact of structural and topographical characteristics on the damage caused by Vaia, using Airborne Light Detection And Ranging (LiDAR) data collected before the storm, in four heavily affected forest areas in the Italian Alps (Carezza in the Province of Bolzano-Bozen, Predazzo, Manghen, and Primiero in the Province of Trento). We analyzed structural metrics like forest height heterogeneity (HH), forest mean height, and density, alongside topographical features such as aspect, slope, and altitude, to discern their influence on the storm’s severity. Our results revealed that the most significant difference between affected and unaffected areas is forest mean height that was found higher in areas hit by the storm. Forest density played a lesser but important role, with denser areas experiencing more severe damage, though this was only significant in certain areas. Contrary to common assumptions, our analysis revealed that forest height heterogeneity (HH) did not have a significant effect on damage levels. The findings, consistent with previous research, revealed a significant association between specific aspects, particularly the South-East orientation, which aligned with the predominant wind direction during the Vaia storm, and an increased likelihood of damage. Both structural and topographical factors interact in complex ways to influence the outcome of such extreme events. The study emphasizes the dominant impact of the Vaia windstorm, noting that while managing forest height and density may help, the diverse topography complicates these efforts. Our study explicitly tested the effectiveness of using Airborne LiDAR data to explore forest structural and topographical factors that influenced Vaia storm damage. The achieved results demonstrate that LiDAR serves as a useful tool to field data, offering valuable insights for broader applications in this domain.

中文翻译：

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LiDAR 对山地森林风极端事件中林分结构和地形的洞察：Vaia 案例研究


随着气候变化的加剧，全球森林越来越容易受到暴风雨等极端天气事件的影响，这些事件在欧洲的经济损失中占很大一部分。2018 年深秋的 Vaia 风暴袭击了意大利东北部的高山生态系统，凸显了这种脆弱性，推倒了超过 850 万立方米的木材，并引发了关于森林管理在减轻此类灾害中的作用的辩论。本研究旨在使用风暴前收集的机载光探测和测距 （LiDAR） 数据，在意大利阿尔卑斯山的四个受灾严重的森林地区（博尔扎诺-博岑省的 Carezza、Predazzo、Manghen 和 Primiero 特伦托省）评估结构和地形特征对 Vaia 造成的破坏的影响。我们分析了森林高度异质性 （HH）、森林平均高度和密度等结构指标，以及坡向、坡度和海拔等地形特征，以辨别它们对风暴严重程度的影响。我们的结果表明，受影响地区和未受影响地区之间最显着的差异是森林平均高度，在受风暴袭击的地区发现的森林平均高度更高。森林密度起的作用较小但很重要，密度较大的地区受到的破坏更严重，尽管这仅在某些地区显着。与通常的假设相反，我们的分析表明，森林高度异质性 （HH） 对损害水平没有显着影响。这些发现与之前的研究一致，揭示了特定方面之间的显着关联，特别是东南方向，它与 Vaia 风暴期间的主要风向一致，并且损坏的可能性增加。 结构和地形因素都以复杂的方式相互作用，以影响此类极端事件的结果。该研究强调了 Vaia 风暴的主要影响，并指出虽然管理森林高度和密度可能会有所帮助，但多样化的地形使这些工作复杂化。我们的研究明确测试了使用机载 LiDAR 数据探索影响 Vaia 风暴破坏的森林结构和地形因素的有效性。取得的结果表明，LiDAR 是现场数据的有用工具，为该领域的更广泛应用提供了有价值的见解。