Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

Abstract

Abstract. Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability in snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellite (Pléiades), airplane (Ultracam Eagle M3), unmanned aerial system (eBee+ RTK with SenseFly S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D) platforms, were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while unmanned aerial system (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing as well as by using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements, the root mean square error (RMSE) values and the normalized median absolute deviation (NMAD) values were 0.52 and 0.47 m, respectively, for the satellite snow depth map, 0.17 and 0.17 m for the airplane snow depth map, and 0.16 and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with four manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 and 0.38 m for the satellite snow depth map, 0.12 and 0.11 m for the airplane snow depth map, and 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded an RMSE value of 0.92 m and an NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.

Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

“…The snow depth accuracy was assessed by comparing the lidar snow depth measurements to the magnaprobe measurements. Here, accuracy is the measure of the agreement of the lidar snow depth measurements relative to the in situ measurements (Eberhard et al, 2021;Maune and Nayegandhi, 2018). Error statistics were calculated, and the results were summarized by forest and field locations.…”

Fractional snow-covered area: scale-independent peak of winter parameterization

“…Observed σ HS from the TLS, as well as from the Pléiades data in France, was considerably larger than parameterized σ HS , but mean slope angles alone can also not explain this behavior (between 6 and 23 • for the TLS data and between 13 and 50 • for the Pléiades data). While we were not able to clearly relate some of the poorer regionwise performances to uncertainties related to the platform, other studies entirely focused on performing extensive intercomparisons between platforms for largescale snow depth mapping in alpine terrain (e.g., Bühler et al, 2015Bühler et al, , 2016Eberhard et al, 2021;Deschamps-Berger et al, 2020).…”

“…The second platform was an unmanned aerial system (UAS) recording optical imagery with real-time kinematic (RTK) positioning of the image acquisition points of the snow cover with a standard camera over two different smaller regions near Davos in the eastern Swiss Alps (Bühler et al, 2016;Eberhard et al, 2021). These images were photogrammetrically processed into a digital surface model (DSM).…”

“…By subtracting the snow-free DSM from the winter flight, the HS values were obtained (Bühler et al, 2017). An UASderived snow depth data set was used from 7 April 2018 ("uav-CH 9 ") from Schürlialp, together with a UAS-acquired summer DEM (Eberhard et al, 2021). The Schürlialp data set covers about 3.2 km 2 which we used at 30 cm resolution.…”

“…New measurement methods such as terrestrial laser scanning (TLS), airborne laser scanning (ALS) and airborne digital photogrammetry (ADP) nowadays provide a wealth of spatial snow data at fine-scale horizontal resolutions. Since recently, digital photogrammetry can also be applied to high-resolution optical satellite imagery (Marti et al, 2016;Deschamps-Berger et al, 2020;Eberhard et al, 2021;Shaw et al, 2020). Snow depth data at these high resolutions now enable statistical analyses of spatial snow depth patterns for various purposes (e.g., Melvold and Skaugen, 2013;Grünewald et al, 2013;Kirchner et al, 2014;Grünewald et al, 2014;Revuelto et al, 2014;Helbig et al, 2015;Voegeli et al, 2016;López-Moreno et al, 2017;Helbig and van Herwijnen, 2017;Skaugen and Melvold, 2019).…”