Understanding and Predicting the Spatial and Temporal Variability of Snow Processes Under Different Vegetation Covers Combining Laser Observations and Point Measurements SPENSER -> Snow Processes vEgetatioN laSer obsERvation

全面了解植被和森林结构对积雪和消融过程的影响尤为重要，因为森林复盖是变化最快的土地复盖类型之一。此外，气候条件的变化还将导致林线以上森林和灌木面积的扩大，从而改变森林树冠的性质。最近的研究证实，积雪和森林植被下的能量平衡项存在着高度的时空变异性，这取决于不同的树冠条件、树种和气候条件。拟议的研究将侧重于研究不同树种和不同森林结构对积雪动态的影响。结合多点连续测量(SnoMoS)和基于无人机(无人机)的频繁双波长激光扫描观测的创新观测方法将用于观测两个不同气候区域(高山和中海拔山脉)森林和灌木树冠下的积雪演变。这一独特的数据集将用于改进现有的雪模型，测试新的植被算法，并从大规模数据集中得出稳健的植被结构指数，以用于大型模型。因此，拟议的研究将有助于填补这一领域仍然存在的关于森林对积雪的影响的知识的一个重要空白，特别是对于温带气候区域。

A comprehensive knowledge about vegetation and forest structures influences on snow accumulation and ablation processes is especially important since forest covers are one of the most rapidly changing land cover types. Furthermore, changing climatic conditions will also result in expanding forest and shrub areas above timber line and hence changing the properties of forest canopies. Recent studies confirmed the high spatial and temporal variability present in the snow cover and the energy balance terms under forest vegetation dependent on various canopy conditions, tree species and climate conditions. The proposed study will focus on examining the effects of different tree species and different forest structures on the snow cover dynamics. An innovative observation approach that combines numerous continuous point measurements (SnoMoS) with frequent UAV (drone) based dual wavelength laser scanning observations will be used to observe the snow cover evolution under forest and shrub canopies in two different climate regions (alpine and mid elevation mountains). The unique dataset will be used to improve existing snow models and to test new vegetation algorithms and to derive robust vegetation structure indices from large-scale datasets to be used in large-scale models. The proposed study will therefore help to fill an important gap of knowledge about forest effects on snow covers that still exists in this field especially for the temperate climate regions.