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Exploring possibilities of including detailed ALS derived biomass information into physically-based slope stability models at regional scale

机译:探索将详细的ALS衍生生物量信息包含到区域规模的基于物理的边坡稳定性模型中的可能性

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The causes of landslides are manifold and highly influenced by multiple interacting natural and anthropogenic factors. In particular human induced land cover changes, such as deforestation and afforestation are known to strongly influence slope stability. Thus, we investigate the understanding of differences between forested and non-forested conditions of an area is crucial in order to develop sustainable preventive countermeasures. One possibility to evaluate the influence of biomass changes on landslide activity is to apply physically based slope stability models where the dynamic influence of spatially and temporally variable vegetation areas on soil strength and hydrology is explicitly included. Some of these models also require detailed information on biomass related parameters (e.g. wood and crown volume, weight, Leaf Area Index) as well as surface and subsurface conditions. Newly developed algorithms allow deriving biomass parameters from highly resolved multi-temporal 3D Airborne Laser Scanning (ALS). This allows an improved parameterization of hydro-mechanical slope stability models since it accounts for the spatiotemporal variability in vegetation conditions. The BioSLIDE project aims to combine vegetation related parameters derived from ALS data with physically based slope stability modelling to allow a better understanding of geomorphic interdependencies at regional scale. The objective of this paper is to evaluate possibilities and potential limitations of an inclusion of ALS-derived biomass information within dynamic physically based hydro-mechanical slope stability modelling. Hereto both synthetic and real case study data will be used. This interdisciplinary approach is expected to improve spatio-temporal scenarios of anthropogenic effects and environmental changes on landslide activity.
机译:滑坡的原因是多种多样的,并且受到多种相互作用的自然和人为因素的强烈影响。特别是人为引起的土地覆盖变化,例如毁林和绿化,对坡度的稳定性有很大影响。因此,我们调查了解一个地区的森林状况和非森林状况之间的差异对于开发可持续的预防对策至关重要。评估生物量变化对滑坡活动影响的一种可能性是应用基于物理的边坡稳定性模型,其中明确包括时空变化的植被面积对土壤强度和水文学的动态影响。其中一些模型还需要有关生物量相关参数(例如木材和树冠体积,重量,叶面积指数)以及表面和地下条件的详细信息。最新开发的算法允许从高度解析的多时间3D机载激光扫描(ALS)导出生物质参数。这可以改善水力机械边坡稳定性模型的参数,因为它考虑了植被条件下的时空变化。 BioSLIDE项目旨在将ALS数据中与植被相关的参数与基于物理的边坡稳定性建模相结合,以更好地了解区域尺度上的地貌相互依存关系。本文的目的是评估在基于动态物理的水力机械边坡稳定性建模中包含ALS衍生的生物量信息的可能性和潜在的局限性。迄今为止,将使用综合和实际案例研究数据。这种跨学科方法有望改善人为影响和环境变化对滑坡活动的时空变化。

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