Concept for a novel airborne LiDAR system combining high-resolution snow height mapping with co-registered spatial information on the water content of the snowpack

Rathmann, Lars; Geissler, Joschka; Reiterer, Alexander

doi:10.1117/12.2591815

Cited by 2 publications

(2 citation statements)

References 75 publications

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“…To measure snow distribution spatially continuously (e.g., for model validation), airborne Light Detection and Ranging (LiDAR) surveys have become the state-of-the-art technology in the field of snow hydrology, as LiDAR can create more robust snow depth maps (HS-maps) compared to other systems and is suitable to measure sub-canopy snow depth (Harder et al, 2020). As commercially available LiDAR sensors are becoming light-weight and affordable, an increasing number of studies have been published using LiDAR-systems mounted on multi-rotor Unmanned Aerial Vehicles (UAV) instead of airplanes (Harder et al, 2020;Jacobs et al, 2021;Koutantou et al, 2022;Rathmann et al, 2021). Compared to LiDAR data acquired using airplanes, UAVs allow reduced revisiting time between surveys (Koutantou et al, 2022), increase point densities and help analyze snow processes at very high spatial resolutions (Russell et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

Geissler

Rathmann

Weiler

2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

Geissler

Rathmann

Weiler

2023

Preprint

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“…Many previous studies proved their capability of measuring sub‐canopy snow distribution at very high‐resolution (Currier et al., 2019; Harder et al., 2020). As LiDAR sensors are becoming light‐weight and affordable (King et al., 2023), recent studies use LiDAR‐sensors mounted on multi‐rotor Uncrewed Aerial Vehicles (UAV) instead of airplanes (Harder et al., 2020; Jacobs et al., 2021; Koutantou et al., 2022; Rathmann et al., 2021). Compared to LiDAR data acquired using airplanes, UAVs are more affordable to purchase and operate, can increase point densities and therefore help to analyze snow processes at very high spatial resolutions (Koutantou et al., 2022; Russell et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest

Geissler,

Rathmann,

Weiler

2023

Water Resources Research

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Snow interacts with its environment in many ways and thus has a highly heterogeneous spatial and temporal variability. Therefore, modeling snow variability is difficult, especially in forested environments. To increase the understanding of the spatio‐temporal variability of snow and to validate snow models, reliable observation data at similar spatial and temporal scales is needed. For these purposes, airborne LiDAR surveys or time series derived from ground‐based snow sensors are commonly used. However, these are limited either to one point in space or in time. A new, extensive dataset of daily snow variability in a sub‐alpine forest in the Alptal valley, Switzerland is presented. 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, four distinct spatial clusters of similar snow depth dynamics are determined. By combining these spatial clusters with the observed snow depth time series, daily high‐resolution maps of snow depth and snow water equivalent (SWE) are derived. These products are the first to our knowledge that provide spatio‐temporally continuous snow depth and SWE based almost exclusively on field data. The presented workflow is transferrable to different regions, climates and scales. Moreover, the results allow future field campaigns to find representative sensor locations and target their LiDAR surveys to derive similar continuous products with less involved effort.

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Concept for a novel airborne LiDAR system combining high-resolution snow height mapping with co-registered spatial information on the water content of the snowpack

Cited by 2 publications

References 75 publications

Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

Spatio-temporal Snow Variability in a Sub-Alpine Forest predicted by Machine Learning and UAV-based LiDAR Snow Depth Maps

Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest

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