The impact of climate change on snow cover evolution is evident. Increasing amounts of winter precipitation as well as rising temperatures are causing the winter snow cover to change more and more rapidly within one season.To quantify the direct effects on hydrological cycles, spatially and temporally high-resolution information on snow height and the amount of water stored as snow (Snow Water Equivalent, SWE) is required on watershed scales. This paper presents the concept for a novel airborne light detection and ranging (LiDAR) system combining highresolution snow height mapping with co-registered spatial information on the water content of the snowpack. Based on the optical characteristics of snow, we outline a detailed plan for dual-wavelength LiDAR sensor working at wavelengths of 1030 nm and 515 nm. By comparing the intensities received in the two channels, snow cover parameters like the effective grain size can be inferred. By means of recent snow hydrological models, from these data and the topographic snow depth maps then high resolution SWE maps can be deduced. We supplement our outline with conceptual LiDAR snow depth and reflectance measurements using a commercially available system, pointing out the impact of view angle dependence of received intensity and general applicability for future airborne LiDAR surveys.

Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially when additional components such as vegetation add complexity. To increase our understanding of the spatio-temporal variability of snow and to validate snow models, we need reliable observation data at similar spatial and temporal scales. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are limited either to one point in space or in time. We present a new, extensive dataset of snow variability in a sub-alpine forest in the Alptal, Switzerland. The core dataset consists of a dense sensor network, repeated high-resolution LiDAR data acquired using a fixed-wing UAV, and manual snow depth and snow density measurements. Using machine learning algorithms, we determine four distinct spatial clusters of similar snow depth dynamics. These clusters are characterized and further used to derive daily snow depth and snow water equivalent (SWE) maps. The results underline the complex relation of topography and canopy cover towards snow accumulation and ablation. The derived products are the first to our knowledge that provide daily, high-resolution snow depth and SWE based almost exclusively on field data. They are therefore ideally suited for the validation of distributed snow models. Our approach can be applied to other project areas and improve our understanding of the spatio-temporal variability of snow in forested environments.

<p>Snow plays a crucial role in the hydrological cycle as it serves as an intermediate storage of winter precipitation and renews groundwater reserves. It is therefore of central importance for, among others, our drinking water supply and agriculture. Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal distribution. Therefore, modelling snow variability is difficult, especially when additional components such as forests add complexity. To increase our understanding of the spatiotemporal variability of snow as well as to validate snow models, we need reliable validation data. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are disadvantageously limited to one point either in space or in time. In this study, we profited from current advances in LiDAR and drone technology, as well as machine learning, to bridge this gap. We present a new dataset on snow variability in forests for the Alptal, a sub-alpine, forested valley in the pre-alps, Switzerland. The core dataset consists of a dense sensor network, repeated UAV-based LiDAR flights and manual snow height and density measurements. Using modern machine learning algorithms, we determine four clusters of similar spatiotemporal behaviour regarding their snowheight. These clusters are characterized and further used to derive daily snow depth and snow water equivalent maps. By using the latter, we obtain spatially continuous key hydrological variables. <span><span>The results</span> <span>suggest that snow occurs in clusters that reoccur in space. These clusters underline the </span><span>relation</span> <span>between</span> <span>canopy c</span><span>over and spatial </span><span>snow </span><span>accumulation patterns and (the much more complex) spatial ablation patterns</span><span>. </span></span>The presented dataset and derived products are the first to our knowledge that provide daily, high-resolution snow height and hydrologic variables based on field data. The results of this study can therefore improve our understanding of the spatiotemporal variability of snow in forested environments. Moreover, they are ideally suited for the validation of modern snow models.</p>

For both economic and research purposes, accurate information on forest composition, and the amount of dead wood is of paramount importance. It is not only important to know the biomass and the distribution of tree species but also to detect vermin and diseases to assess the health of a forest. Performing such inventory accurately with conventional methods of surveying (e.g., terrestrial laser scanners) is very labor and time-consuming as forests can be very dense, thus requiring many setups with a terrestrial laser scanner. We present an innovative approach for forest inventory combining UAV-borne light detection and ranging (LiDAR), multispectral aerial imagery, and terrestrial point cloud data measured with a handheld laser scanner. For such multi-sensor measurement campaigns, however, reliable extraction of forest parameters such as canopy height, the diameter at breast height (DBH), deadwood volumes, etc., strongly relies on the quality of sensor data fusion. Especially in dense forests, the GNSS signal, which is necessary for georeferencing the point clouds, can be very weak. However, terrestrial laser scans can capture much more information underneath the forest canopy which is partly obscured to the airborne data. To circumvent this mismatch, we propose an easily adaptable two-step workflow for fusing the directly georeferenced airborne LiDAR point clouds with the corresponding multi-spectral photogrammetric data and their unreferenced terrestrial counterparts. In the first step of the processing chain, individual terrestrial scans of the forest are coregistered using spherical laser targets located above exactly measured reference points. Secondly, these coarsely coregistered scans are then combined with the georeferenced airborne point clouds using control points. This is especially challenging as forests are very unstructured environments, and in addition, typical SLAM-features such as leaves, and branches tend to move. The result of this procedure are radiometrically enhanced 3D point clouds containing detailed information about the forest structure above and underneath its canopy. This provides a versatile basis for further processing such as tree species recognition, e.g., using convolutional neural networks. We demonstrate the applicability of the workflow on recent data sets measured in Landshut (Bavaria, Germany) under leaf-on and leaf-off condition.