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Abstract:Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called "hyperclouds", i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements.

Abstract. The 17 June 2017 rock avalanche in the Karrat Fjord, West Greenland, caused a tsunami that flooded the nearby village of Nuugaatsiaq and killed four people. The disaster was entirely unexpected since no previous records of large rock slope failures were known in the region, and it highlighted the need for better knowledge of potentially hazardous rock slopes in remote Arctic regions. The aim of the paper is to explore our ability to detect and locate unstable rock slopes in remote Arctic regions with difficult access. We test this by examining the case of the 17 June 2017 Karrat rock avalanche. The workflow we apply is based on a multidisciplinary analysis of freely available data comprising seismological records, Sentinel-1 spaceborne synthetic-aperture radar (SAR) data, and Landsat and Sentinel-2 optical satellite imagery, ground-truthed with limited fieldwork. Using this workflow enables us to reconstruct a timeline of rock slope failures on the coastal slope here collectively termed the Karrat Landslide Complex. Our analyses show that at least three recent rock avalanches occurred in the Karrat Landslide Complex: Karrat 2009, Karrat 2016, and Karrat 2017. The latter is the source of the abovementioned tsunami, whereas the first two are described here in detail for the first time. All three are interpreted as having initiated as dip-slope failures. In addition to the recent rock avalanches, older rock avalanche deposits are observed, demonstrating older (Holocene) periods of activity. Furthermore, three larger unstable rock slopes that may pose a future hazard are described. A number of non-tectonic seismic events confined to the area are interpreted as recording rock slope failures. The structural setting of the Karrat Landslide Complex, namely dip slope, is probably the main conditioning factor for the past and present activity, and, based on the temporal distribution of events in the area, we speculate that the possible trigger for rock slope failures is permafrost degradation caused by climate warming. The results of the present work highlight the benefits of a multidisciplinary approach, based on freely available data, to studying unstable rock slopes in remote Arctic areas under difficult logistical field conditions and demonstrate the importance of identifying minor precursor events to identify areas of future hazard.

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