Search

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020). [ABSTRACT FROM AUTHOR]

A prototype of a low-cost GNSS (Global Navigation Satellite System) monitoring system was installed on a deep-seated landslide in north-western Slovenia to test its performance under field conditions. The system consists of newly developed GNSS stations based on low-cost, dual-frequency receivers and open-source GNSS processing software. It automatically receives GNSS data and transmits them over the Internet. The system processes the data server-side and makes them available to the end user via a web portal. The detected surface displacements were evaluated through a comparison with the network of classic geodetic measurements. The results of a nine-month monitoring period using seven GNSS stations provided a detailed insight into the spatial and temporal pattern of deep-seated landslide surface movements. The displacement data were correlated with precipitation measurements at the site to reveal how different parts of the landslide react to rainfall. These data form the basis for the further development of an early-warning system which will help to manage the risk the landslide poses to the local population and infrastructure. [ABSTRACT FROM AUTHOR]

• 48 million people exposed to landslide hazard in Europe. • 3907 landslides (2015–2017), causing 39 deaths and 155 injuries. • 9 poor urban landscape practices provoked by weak laws and/or bad governance. • 50% of the participating countries have no legal guidelines on integrating landslides into urban planning. • The need for a common regulatory framework in Europe. An enquiry-based and participatory analysis approach is carried out to identify the strengths and weaknesses in the heterogeneous legislations across Europe that regulate the integration of landslide hazard into urban planning. Twenty one national and eight regional Geological Surveys (GSs) have participated in the analysis. The GSs report almost 4000 recent (2015–2017) damaging landslides events resulting in 39 fatalities and 155 injuries, destruction to housing, infrastructure and properties. In addition, 11 countries report 18 MORLE events over the past 10 years responsible for 150 fatalities and severe economic impacts. Results also reveal almost 48 million people living in areas with high and very high degrees of landslide-susceptibility (around 1 million km2 according to ELSUS v2). This work shows that almost half the participating countries (10 countries) have no legal guidance in the National Land Bill to stipulate consideration of landslides in urban planning practices, and mapping tools are often not adapted to a standard required to inform sustainable development. Furthermore, there is a wide range of laws and a large heterogeneity of mapping methods, scales and procedures. A relevant deficiency detected in many countries is the lack of landslide maps at a detailed resolution for urban planning. Additionally, some case studies of suboptimal urban development practices in areas of known instability have been discussed; they are found to be related to weak rule of law and/or absence of good governance. This paper shows inconsistencies across Europe in the handling of landslides and proposes a series of key actions to improve this situation, highlighting the need for a common regulatory framework to deal with this geohazard appropriately. [ABSTRACT FROM AUTHOR]

Slovenian national landslide prediction system, MASPREM, has started in September 2013 (Jemec Auflič et al., 2016). The MASPREM system forecasts landslide probability twice a day for 24-hours ahead. Forecasting landslides is based on the landslide susceptibility map, statistically defined rainfall threshold values and rainfall forecast models. Geological settings of the area, diverse geomorphology, tectonics and climate regimes increase the probability of landslide occurrences. It is estimated that more than 10,000 landslides were triggered in the past 30 years. Since the spatial distribution of the rainfall stations is very sparse (1 rainfall gauge per 460 km2, while for example in Tuscany it's 1 rain gauge per 70 km2) the MASPREM system is based on rainfall threshold values which were determined using a non-parametric statistical method chi-square (χ2) for each lithological unit. Maximum daily rainfall above 60-70 mm proved to be critical for landslide occurrence, especially in more loose soils and in less resistant rocks (e.g., Quaternary, Tertiary, Triassic, and Permo-Carbonian rocks). In accordance to different soil characteristics in the regions and available rainfall forecast models, ALADIN and nowcasting model INA, the five different MASPREM models have been developed. Therefore, MASPREM calculates the five different landslide scenarios parallelly. The validation of the five models was based on the computation of the statistical indicators from 2 x 2 contingency matrices. This elementary matrix shows the correlation between landslide events and warnings, both of them expressed in four classes: (TP) true positives (correct predictions), (TN) true negatives, (FP) false positive (false predictions), (FN) false negative (missed predictions). For the five year validation period of MASPREM models (2013 -2018) the results showed that the Probability Of Detection (POD), also known as Hit Rate, which is the proportion of the events that were predicted correctly, is 53% for the models with applied two-day antecedent rainfall, while for the model without antecedent rainfall it is only 11%. The Probability Of False Detection (POFD), also known as False Alarm Rate, which is the proportion of correct predictions when the event did not occur, is around 10% for models with antecedent rainfall and 2% for the model without antecedent rainfall. The Probability Of False Alarm (POFA), also known as False Alarm Ratio, which is the ratio between the number of false alarms and the total number of correct forecasts, ranges between 35% to 53%. The higher values are for the models with antecedent rainfall. Overall MASPREM performance indicates that the system is able to capture the crucial factors in determining the general landslide location, even if it cannot predict a specific area. Additional calibration of input parameters and the landslide inventory, as well as improved spatially distributed rainfall forecast data can further enhance the model's prediction.Jemec Auflič, M, Šinigoj, J, Krivic, M, Podboj, M, Peternel,T, Komac, M. Landslide prediction system for rainfall induced landslides in Slovenia (Masprem) Geologija, 2016, 59/2, 259-271, doi: 10.5474/geologija.2016.016. [ABSTRACT FROM AUTHOR]