{"title":"激光雷达对山林风极端事件中林分结构和地形的洞察:维亚案例研究","authors":"","doi":"10.1016/j.agrformet.2024.110267","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LiDAR insights on stand structure and topography in mountain forest wind extreme events: The Vaia case study\",\"authors\":\"\",\"doi\":\"10.1016/j.agrformet.2024.110267\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50839,\"journal\":{\"name\":\"Agricultural and Forest Meteorology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Agricultural and Forest Meteorology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168192324003800\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural and Forest Meteorology","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168192324003800","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
LiDAR insights on stand structure and topography in mountain forest wind extreme events: The Vaia case study
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.
期刊介绍:
Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published.
Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.