在新西兰的塔斯曼,增加森林覆盖和限制砍伐可以大大减少滑坡的发生。

IF 1.5 4区 农林科学 Q2 FORESTRY
James W. Griffiths, C. Lukens, R. May
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引用次数: 3

摘要

背景:山体滑坡会对环境、社会和经济造成重大影响。在未来的气候情景下,引发山体滑坡事件的频率可能会增加。因此,土地管理者迫切需要可靠的高分辨率滑坡易感性模型,以便为有效的滑坡风险评估和管理提供信息。方法:在本研究中,利用网格化的降雨、地形、岩性和土地覆盖面,建立了一个高分辨率(10米× 10米)的滑坡空间模型,该模型是在前热带气旋吉塔给新西兰塔斯曼地区带来暴雨期间发生的。我们在同一数据集中分别对滑坡进行建模,作为侵蚀敏感性分类(ESC)数据层的函数,该数据层用于确定根据国家人工林环境标准(NES-PF)对林业活动实施的控制水平。模型使用增强回归树进行拟合。结果:我们的首选模型具有出色的预测能力(AUROC = 0.93),包括坡向、高程、中坡位置、土地覆盖、降雨量、坡度和描述性的7级地形指数。土地覆盖、海拔、降雨量、坡度和坡向是最强的滑坡预测因子,土地覆盖类别“几种原生植被”和砍伐殆尽的人工林预测滑坡的概率较高,而高大的原生森林和封闭的林冠人工林预测滑坡的概率较低。ESC对研究区域的滑坡预测较差(AUROC = 0.65)。结论:我们的研究表明,可以从滑坡分布、土地覆盖、地形和降雨数据中开发出准确、高分辨率的滑坡概率曲面。我们还表明,通过增加永久森林覆盖范围和限制在滑坡易发斜坡上砍伐人工林,可以大大减少塔斯曼地区的滑坡发生。支撑NES-PF的ESC框架不能很好地预测山体滑坡,因此,在管理新西兰塔斯曼的林业活动方面,它不是一个可靠的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Increased forest cover and limits on clear-felling could substantially reduce landslide occurrence in Tasman, New Zealand.
Background: Landslides can cause substantial environmental, social and economic impacts. Under future climate scenarios the frequency of landslide-triggering events is likely to increase. Land managers, therefore, urgently require reliable high-resolution landslide susceptibility models to inform effective landslide risk assessment and management. Methods: In this study, gridded rainfall, topography, lithology and land cover surfaces were used to develop a high-resolution (10 m x 10 m) spatial model of landslides that occurred in Tasman, New Zealand during a period when ex-tropical Cyclone Gita brought heavy rain to the region. We separately modelled landslides in the same dataset as a function of the erosion susceptibility classification (ESC) data layer used to determine the level of control applied to forestry activities under the National Environmental Standards for Plantation Forestry (NES-PF). Models were fit using boosted regression trees. Results: Our preferred model had excellent predictive power (AUROC = 0.93) and included the parameters: aspect, elevation, mid-slope position, land cover, rainfall, slope, and a descriptive seven-class topographical index. Land cover, elevation, rainfall, slope and aspect were the strongest predictors of landslides with the land cover classes ‘seral native vegetation’ and clear-felled plantation forest’ predicting higher probabilities of landslides and tall native forest and closed canopy plantation forest predicting lower probabilities of landslides. The ESC was a poor predictor of landslides in the study area (AUROC = 0.65). Conclusions: Our study shows that accurate, high-resolution landslide probability surfaces can be developed from landslide distribution, land cover, topographical and rainfall data. We also show that landslide occurrence in the Tasman region could be substantially reduced by increasing the extent of permanent forest cover and by limiting clear-fell harvest of plantation forests on landslide-prone slopes. The ESC framework that underpins the NES-PF was a poor predictor of landslides and, therefore, an unreliable basis for regulating forestry activities in the Tasman, New Zealand.
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来源期刊
CiteScore
2.20
自引率
13.30%
发文量
20
审稿时长
39 weeks
期刊介绍: The New Zealand Journal of Forestry Science is an international journal covering the breadth of forestry science. Planted forests are a particular focus but manuscripts on a wide range of forestry topics will also be considered. The journal''s scope covers forestry species, which are those capable of reaching at least five metres in height at maturity in the place they are located, but not grown or managed primarily for fruit or nut production.
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