No abstract

Landslide risk assessment for large areas at a country level requires a different approach and data than what is standard practice at large scales. The main goal of this research was to design a methodology for a nationwide landslide risk assessment for Georgia taking into account the limitations in data availability and detail, which do not allow the use of physically based models or statistical methods. Given these limitations, we decided to generate a qualitative landslide risk index using spatial multicriteria evaluation (SMCE). An attempt was made to compile a national landslide inventory, using old and partly destroyed archives from the Soviet period, combined with information from annual field surveys. A web-based interface for the reporting of landslide events was developed to improve the updating of the inventory in future. Relevant factor maps were prepared for the entire country, partly based on remote sensing data. As the available landslide inventory was not sufficient to use statistical methods, the factor maps were weighted using the expert-based SMCE method, and the resulting susceptibility map was validated using the available landslide inventory. The inventory was also used to make an estimation of the spatial probability of landslide occurrence within the various susceptibility classes. The resulting map was used in combination with element-at-risk maps to calculate exposure maps and to make a tentative assessment of the expected landslide losses in a 50-year time period.

Global warming is causing glaciers in the Caucasus Mountains and around the world to lose mass at an accelerated pace. As a result of this rapid retreat, significant parts of the glacierized surface area can be covered with debris deposits, often making them indistinguishable from the surrounding land surface by optical remote-sensing systems. Here, we present the DebCovG-carto toolbox to delineate debris-covered and debris-free glacier surfaces from non-glacierized regions. The algorithm uses synthetic aperture radar-derived coherence images and the normalized difference snow index applied to optical satellite data. Validating the remotely-sensed boundaries of Ushba and Chalaati glaciers using field GPS data demonstrates that the use of pairs of Sentinel-1 images (2019) from identical ascending and descending orbits can substantially improve debris-covered glacier surface detection. The DebCovG-carto toolbox leverages multiple orbits to automate the mapping of debris-covered glacier surfaces. This new automatic method offers the possibility of quickly correcting glacier mapping errors caused by the presence of debris and makes automatic mapping of glacierized surfaces considerably faster than the use of other subjective methods.