Uncertainty assessment of a permanent long-range terrestrial laser scanning system for the quantification of snow dynamics on Hintereisferner (Austria)

Abstract

A permanently installed terrestrial laser scanner (TLS) helps to investigate surface changes at high spatio-temporal resolution. Previous studies show that the annual and seasonal glacier volume, and subsequently the mass balance, can be measured by TLSs. This study systematically identifies and quantifies uncertainties and their sources of the permanent long-range TLS system at Hintereisferner glacier (Ötztal Alps, Austria) in order to assess its potential and limitations for detecting glaciologically relevant small-scale surface elevation changes, such as snowfall and redistribution events. Five uncertainty sources are analyzed: the registration method, the influence of the instrument and hardware limitations of the TLS, the effect of atmospheric conditions on the laser beam, the scanning geometry, and the uncertainty caused by rasterization. The instrument and hardware limitations cause the largest uncertainty to the TLS data, followed by the scanning geometry and influence of varying atmospheric conditions on the laser beam. The magnitude of each uncertainty source depends on the distance (range) between the TLS and the target surface, showing a strong decrease of the obtained spatial resolution and a concurrent increase in uncertainty with increasing distance. An automated registration method results in an uncertainty of ±0.50 m at grids of 100 by 100 m. After post-processing, a 0.1-m vertical accuracy can be obtained allowing the detection of surface changes of respective magnitudes and especially making it possible to quantify snow dynamics at Hintereisferner.

Brief communication: The Glacier Loss Day as indicator for extreme glacier melt in 2022

“…The data from the TLS is acquired as point clouds and registered manually in the RiSCAN Pro software (RIEGL, 2019). After registration and rasterization, one-by-one metre DEMs with an accuracy of ±10 cm are acquired (Voordendag et al, 2023). This accuracy is within previously reported errors of airborne laser scanning acquisitions (Klug et al, 2018) and enables the calculation of volume change rates and, ultimately, the GLD.…”

“…The authors do, however, not explicitly indicate a GLD. A second way is to measure daily volume changes over an entire glacier, as was done on Hintereisferner (HEF) in the three consecutive mass balance years 2019/20, 2020/21 and 2021/22 from daily terrestrial laser scanning (TLS) acquisitions (Voordendag et al, 2021(Voordendag et al, , 2023. By assuming that the mass and volume of a glacier change analogously in a first 25 approximation, we derive mass balance variations from respective volume change records, identify the GLD, and compare the extremely negative mass balance of 2021/22 with the two precedent "normally" negative mass balance years for which near daily TLS data are available.…”

Seasonal Changes of Mélange Thickness Coincide With Greenland Calving Dynamics

“…Generally, five main sources of uncertainty exist when using terrestrial laser scanner (TLS) data. These sources being registration, atmospheric conditions, scanning geometry, instrument and hardware limitations, rasterization, and surface reflectance properties [61]. From our 2019-2020 registration scan we can conclude an average vertical accuracy of ± 0.10 m for each scan.…”

“…From our 2019-2020 registration scan we can conclude an average vertical accuracy of ± 0.10 m for each scan. Though this accuracy does vary with distance from the scanner [61].…”

A review on how Big Data can help to monitor the environment and to mitigate risks due to climate change

“…It also offers continuous and comprehensive monitoring capabilities unlike traditional surveying methods, which are often periodic and point-based. Permanent laser scanning stations allow studying geomorphological changes of, e.g., dunes or glaciers [25] or [26] for a correlation analysis with InSAR (and GBInSAR). A significant hurdle associated with these Big Data lies in effectively handling multi-temporal point clouds to extract information and identify patterns of change.…”