Impact of Adverse Weather Conditions on Snow Depht Monitoring with Automated Terrestrial Laser Scanning

63

Artikel aus einem Buch

200205253

Automated terrestrial laser scanning (ATLS) provides the possibility of monitoring surface height changes in the centimetre-range at several kilometres distance. In recent years, a number of ATLS setups have been presented, which are applied to slope-scale snow depth mapping at high spatio-temporal resolution in mountainous terrain. These setups typically feature scanners set in a weather-proof enclosure, controlled by automated data acquisition
routines. In case the scanner is remotely accessible, the acquired data can be transmitted and fed into a processing
and visualisation workflow to generate snow depth maps. However, since most ATLS setups work with optical instruments operating in near-infrared wavelengths, continuous ATLS monitoring may be hampered by adverse weather conditions often prevalent in wintry (high-)alpine terrain. These include poor visibility due to snowfall, blowing snow or fog, low temperatures or high solar radiation. In this contribution we present results from a systematic analysis of the impact of adverse weather conditions on ATLS snow depth acquisitions. During the winter 2016/17, 1,057 scans were performed at a high-Alpine study site in the Tuxer Alps of Western Austria using a Riegl LPM-321 scanner, embedded in an ATLS workflow. The scans cover a 6-month period and were performed during poor weather conditions as indicated by the webcam observing the study site and an automated weather station (AWS) located 50 m from the ATLS setup. Each scan mapped the same section of an east-facing slope with an altitude ranging from 2,000 to 2,700 m a.s.l. along a vertical strip (36° high, 4° wide; approx. 250,000 point measurements). We chose this profile scan window to minimise the duration of data acquisition (8-10 min), while covering distances of 40 to 1,600 m from the scanner location. The scans were georeferenced and their quality analysed with regard to completeness (data vs. no data / air vs. surface point) and precision (repeatability
of measurements classified as ‘surface point’).
KEYWORDS: forest avalanche, remote sensing, vegetation height model, avalanche simulation, RAMMS