Quantifying snow drift on Arctic structures: A case study at Summit, Greenland, using UAV-based structure-from-motion photogrammetry

Hawley, R. L.; Millstein, Joanna

doi:10.1016/j.coldregions.2018.10.007

Cited by 8 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the study period, snowfall was manually documented when visual snowfall was observed or physical snowfall was collected (Appendix Table A1; snow collection setup described in Steen-Larsen et al, 2014). We use this simple, manual documentation of snowfall as well as the ERA5 snowfall product from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2017;Hersbach et al, 2020) to obtain information about the time of snowfall events. ERA5 is increasingly used and provides reliable near-surface variables over the Greenland Ice Sheet (Delhasse et al, 2020).…”

Section: Additional Snow Height and Snowfall Datamentioning

confidence: 99%

See 1 more Smart Citation

Local-scale deposition of surface snow on the Greenland ice sheet

et al. 2021

View full text Add to dashboard Cite

Abstract. Ice cores from polar ice sheets and glaciers are an important climate archive. Snow layers, consecutively deposited and buried, contain climatic information from the time of their formation. However, particularly low-accumulation areas are characterised by temporally intermittent precipitation, which can be further redistributed after initial deposition, depending on the local surface features at different spatial scales. Therefore, the accumulation conditions at an ice core site influence the quantity and quality of the recorded climate signal in proxy records. This study aims to characterise the local accumulation patterns and the evolution of the snow height to describe the contribution of the snow (re-)deposition to the overall noise level in climate records from ice cores. To this end, we applied a structure-from-motion photogrammetry approach to generate near-daily elevation models of the surface snow for a 195 m2 area in the vicinity of the deep drilling site of the East Greenland Ice-core Project in northeast Greenland. Based on the snow height information we derive snow height changes on a day-to-day basis throughout our observation period from May to August 2018 and find an average snow height increase of ∼ 11 cm. The spatial and temporal data set also allows an investigation of snow deposition versus depositional modifications. We observe irregular snow deposition and erosion causing uneven snow accumulation patterns, a removal of more than 60 % of the deposited snow, and a negative relationship between the initial snow height and the amount of accumulated snow. Furthermore, the surface roughness decreased by approximately a factor of 2 throughout the spring and summer season at our study site. Finally, our study shows that structure from motion is a relatively simple method to demonstrate the potential influences of depositional processes on proxy signals in snow and ice.

show abstract

Section: Additional Snow Height and Snowfall Datamentioning

confidence: 99%

“…Baltsavias et al, 2001), drones (e.g. Hawley and Millstein, 2019), or lidar operations (e.g. Deems et al, 2013).…”

Section: Sfm Photogrammetry As An Efficient Surface Snow Monitoring Toolmentioning

confidence: 99%

Local-scale deposition of surface snow on the Greenland ice sheet

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It does not require a permanent power supply, which can be a limiting factor for laser scanners and snow height sensors. No specific training for users is needed as is required for airborne studies with aircraft (e.g., Baltsavias et al, 2001), drones (e.g., Hawley and Millstein, 2019) or LiDAR operations (e.g., Deems et al, 2013). Even though our approach is limited by light availability and visual contrasts, which is also reported in many studies (e.g., Nolan et al, 2015;Harder et al, 2016;Cimoli et al, 2017), it has the advantage of being very easy to operate and that it can be used at other study sites without great effort.…”

Section: Surface Roughnessmentioning

confidence: 99%

Comment on tc-2021-36

Libois¹

2021

View full text Add to dashboard Cite

Ice cores from polar ice sheets and glaciers are an important climate archive. Snow layers, consecutively deposited and buried, contain climatic information of the time of their formation. However, particularly low-accumulation areas are characterised by temporally intermittent precipitation, which can be further re-distributed after initial deposition. Therefore, the local conditions of accumulation at an ice core site influence the quantity and quality of the recorded climate signal in proxy records. Local surface features at different spatial scales further affect the signal imprint. This study therefore aims to characterise the local accumulation patterns and the evolution of the snow height to describe the contribution of snow (re-)deposition to noise in climate records from ice cores. By using a photogrammetry Structure-from-Motion approach, we generated near-daily elevation models of the snow surface for a 195 m 2 area in the vicinity of the deep drilling site of the East Greenland Ice Core Project in northeast Greenland. Based on the snow height information we derived snow height changes on a day-to-day basis throughout our observation period from May to August 2018. Specifically, the average snow height increased by ∼11 cm. The spatial and temporal data set allowed an investigation of snow deposition versus depositional modifications. We observed irregular snow deposition, erosion, and the re-distribution of snow, which caused uneven snow accumulation patterns, a removal of more than 60 % of the deposited snow, and a negative relationship between the initial snow height and the amount of accumulated snow. Furthermore, the surface roughness decreased from 4 to 2 cm throughout the spring and summer season at our study site. Finally, our study further shows that our method has several advantages over previous approaches, making it possible to demonstrate the importance of accumulation intermittency, and the potential influences of depositional processes on proxy signals in snow and ice.

show abstract

“…[10] used UAV-SfM and Light Detection and Ranging (LiDAR) data to determine and monitor topographic changes. [11] used UAV-SfM to create a DEM. The resulting DEM has been used for calculation of drift volume in quantifying snow drift on Arctic structures.…”

Section: Introductionmentioning

confidence: 99%

Performance of UAV-Based Digital Orthophoto Generation for Emergency Response Applications

Akpınar¹

2021

TEM Journal

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) have been used for accurate orthophoto generation based on advanced Global Navigation Satellite System (GNSS) techniques. In recent years, the UAV systems have become an effective tool for fast monitoring of damages caused by disasters such as the earthquake hazards. The conventional orthophoto generation based on ground control points takes too much time during emergency situations. In the study, different methodologies for the processing of the acquired GNSS Positioning data for direct georeferencing of UAVs were investigated in terms of various orbit products. Evaluating the fitness for emergency response applications, the ground control points (GCPs) also used for validation of the generated orthophoto without using GCPs and based on Precise Point Positioning (PPP) approach. In this study, Ultra-Rapid, Rapid and Final PPP methods based on GNSS observations were used for direct geo-referencing. Thirteen GCPs were located at the study area for the validation of the orthophoto accuracy generated by direct geo-referencing.

show abstract

Quantifying snow drift on Arctic structures: A case study at Summit, Greenland, using UAV-based structure-from-motion photogrammetry

Cited by 8 publications

References 27 publications

Local-scale deposition of surface snow on the Greenland ice sheet

Local-scale deposition of surface snow on the Greenland ice sheet

Comment on tc-2021-36

Performance of UAV-Based Digital Orthophoto Generation for Emergency Response Applications

Contact Info

Product

Resources

About