Empirical-based seismically induced slope displacements in a geographic information system environment: a case study

Landslides are one of the most damaging and threatening hazards associated with seismically induced slope movements. Estimations of support conditions for slope displacements are important for taking preventive measures to avoid landslide events in future. California's Division of Mines and Geology (DMG) procedure is utilised in the present paper for estimating the slope failure mechanism under seismic conditions. In this study, the DMG procedure has been explained and has also been incorporated in a Geographic Information System (GIS) using Arc-GIS software from Environmental Systems Research Institute. Further, it is utilised for establishing a seismically induced slope displacement map for the Skien municipality area of Telemark County in Norway. The motivation for selecting this site was the availability of geotechnical parameters for the site. Three different displacement maps have been produced for earthquake scenarios of magnitude 5, 6 and 7, respectively. The maximum displacement of 133 cm is estimated for earthquake scenario of magnitude 7. It is noticed that the sensitive areas for slope failure remain the same under different earthquake scenarios. A displacement tool based on the DMG procedure has been created in the Arc-tool box in Arc-GIS software. This tool minimises the efforts for inserting formulas for making raster displacement maps. By using the displacement tool one can generate final products like displacement maps automatically at high accuracy and in quick time. The prepared slope displacement maps of study area are used for landslide susceptibility zonation (LSZ) map preparation. The LSZ maps are useful for landslide hazard assessment and further can be utilised by planners, civil engineers and local administrators for town planning and policy-making.     

Landslide susceptibility mapping of Tevankarai Ar sub-watershed,Kodaikkanal taluk,India, using weighted similar choice fuzzy model

This paper deals with the landslide susceptibility zonation of Tevankarai Ar sub-watershed using weighted similar choice fuzzy method in a GIS environment. There has been a rapid increase in landslide occurrences in the Kodaikkanal town and area surrounding the town specially in the settlements around the town and road links leading to and from the town. This necessitates a detailed study of slope instability problems in this area. It is observed that these incidences occur frequently during the monsoon and summer showers. Rainfall is identified as the prime triggering factor. Eleven physical factors that cause instability are identified as causative factors from the field investigations and landslide occurrences. Land use pattern, slope gradient, curvature and aspect, weathering index which are evaluated from the weathering ratios of different chemical constituents of the three major lithological variations, soil type, hydraulic conductivity of soil and soil thickness, geomorphology, drainage, and lineament have been utilized to prepare the spatial variation. A weighted similar choice fuzzy model which ranks a set of alternatives by identifying the similarity between the outcome of alternatives and outcome of ideal alternatives is used to rank the causative factors. Each causative factor is classified into sub-categories and rated based on their effect on stimulating the landslide event using qualitative judgment derived from field studies and landslide history. The prepared thematic maps of causative factors are integrated, utilizing the GIS software Arcmap. The outcome has projected the low, moderate, high, and very high landslide susceptibility zones. The high-hazard and very high-hazard areas fall in the northwestern part characterized by croplands and agricultural plantations, while the moderate hazard zones are seen in prominent settlements and low-hazard zones are observed in the sparse settlements and zones of less agricultural activity. The model is verified using the relative landslide density (R) index, and the susceptibility map is found to be consistent with the mapped landslide incidences. The results from this study illustrate that the use of weighted similar choice fuzzy method is suitable for landslide susceptibility mapping on regional scale in growing hill towns as Kodaikkanal town.     

GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey)

Devrek town with increasing population is located in a hillslope area where some landslides exist. Therefore, landslide susceptibility map of the area is required. The purpose of this study was to generate a landslide susceptibility map using a bivariate statistical index and evaluate and compare the results of the statistical analysis conducted with three different approaches in seed cell concept resulting in different data sets in Geographical Information Systems (GIS) based landslide susceptibility mapping applied to the Devrek region. The data sets are created from the seed cells of (a) crowns and flanks, (b) only crowns, and (c) only flanks of the landslides by using ten different causative parameters of the study area. To increase the data dependency of the analysis, all parameter maps are classified into equal frequency classes based directly on the percentile divisions of each corresponding seed cell data set. The resultant maps of the landslide susceptibility analysis indicate that all data sets produce fairly acceptable results. In each data set analysis, elevation, lithology, slope, aspect, and drainage density parameters are found to be the most contributing factors in landslide occurrences. The results of the three data sets are compared using Seed Cell Area Indexes (SCAI). This comparison shows that the crown data set produces the most accurate and successful landslide susceptibility map of the study area.     

Influence of seismic acceleration on landslide susceptibility maps: a case study from NE Turkey (the Kelkit Valley)

Particularly in the last decade, landslide susceptibility and hazard maps have been used for urban planning and site selection of infrastructures. Most of the procedures for preparing of landslide susceptibility maps need high-quality landslide inventory map. Although the rainfall and seismic activities are accepted as triggering factor for landslides, designation of the triggering factor for each landslide in the inventory is almost impossible when well-documented records are unavailable. Therefore, during preparation of landslide susceptibility map, whole landslide records in the inventory map are used together without classifying based on the triggering factors. Although seismic activity is accepted as a triggering factor, possible effect of the use of seismic activity on production of landslide susceptibility map was investigated in this study, and the subject is open to discussion. For this purpose, a series of stability analyses based on circular failure and infinite slope model were performed considering different pseudostatic conditions. The results of analyses show that gentle slopes have higher susceptibility to failure than steeper ones, even if their stability conditions (susceptibilities) are similar for static condition. The seismic forces acting on failure surfaces may not be sufficiently taken into consideration in the conventionally prepared landslide susceptibility maps. Employing the general decreasing trend in stability condition based on slope face angle and the seismic acceleration, a new procedure was introduced for preparing of the landslide susceptibility map for a scenario earthquake. The prediction performance of occurring landslides increased after the procedure was applied to the conventionally prepared landslide susceptibility map. According to the threshold independent spatial performance analyses of the proposed methodology and the produced landslide susceptibility maps, the area under ROC curve values were calculated as 0.801, 0.933, and 0.947 for the maps prepared by considering conventional method and scenario earthquakes having M _w values of 5.5 and 7.5, respectively.     

Geological features and landslide mechanisms of an unstable coastal slope (Petacciato, Italy)

On the basis of geological and geomorphological surveys, landslide phenomena are analysed on a slope along a stretch of the Adriatic coast, near Petacciato (Molise, Italy).

Locally, a blue clay sequence of Pleistocene outcrops, stratified with silty-sandy layers; bedding dips 3–8°NE and the slope has a similar attitude. This sequence evolves upwards to sands and conglomerates, with thickness of up to 40 m, on which the built-up area is located.

Several episodes of landslide reactivation occurred in the past century, involving the zone between the built-up area and the sea, along a coastal slope of over 2000 m long and 200 m high. Important roads and railway lines have been heavily damaged as well as the town itself.

The typology of the movement is a rotational–translational slide; the displacement reaches tens of centimetres at each reactivation episode along the entire coastal slope, extending well beyond the shore line.

Detailed studies already exists, but different mechanism failures have been proposed to explain the landslide phenomena. In this study, a new failure mechanism is proposed, (sudden spreading of Terzaghi, K., Peck, R.B., 1948. Soil Mechanics in Engineering Practice. Wiley and Sons New York) and analytical approaches have been adopted to evaluate the slope stability, based on the geotechnical and monitoring data and the geometrical and geological features of the slope.     

Influence of the sediment delivery ratio index on the analysis of silting and break risk in the Plasencia reservoir (Central System,Spain)

The random forest method was used to generate susceptibility maps for debris flows, rock slides, and active layer detachment slides in the Donjek River area within the Yukon Alaska Highway Corridor, based on an inventory of landslides compiled by the Geological Survey of Canada in collaboration with the Yukon Geological Survey. The aim of this study is to develop data-driven landslide susceptibility models which can provide information on risk assessment to existing and planned infrastructure. The factors contributing to slope failure used in the models include slope angle, slope aspect, plan and profile curvatures, bedrock geology, surficial geology, proximity to faults, permafrost distribution, vegetation distribution, wetness index, and proximity to drainage system. A total of 83 debris flow deposits, 181 active layer detachment slides, and 104 rock slides were compiled in the landslide inventory. The samples representing the landslide free zones were randomly selected. The ratio of landslide/landslide free zones was set to 1:1 and 1:2 to examine the results of different sample ratios on the classification. Two-thirds of the samples for each landslide type were used in the classification, and the remaining 1/3 were used to evaluate the results. In addition to the classification maps, probability maps were also created, which served as the susceptibility maps for debris flows, rock slides, and active layer detachment slides. Success and prediction rate curves created to evaluate the performance of the resulting models indicate a high performance of the random forest in landslide susceptibility modelling.     

Analysis of the Chiufengershan landslide triggered by the 1999 Chi-Chi earthquake in Taiwan

A disastrous earthquake rocked Taiwan on September 21, 1999, with magnitude M_L=7.3 and an epicenter near the small town of Chi-Chi in central Taiwan. The Chi-Chi earthquake triggered landslide on the dip slope at the Chiufengershan. In this study, a review of the topography and geology of this area was followed by field investigations. Laboratory testing was applied to understand the geomaterial composing the slope. Then, based on a series of limit equilibrium analyses, the failure mechanism of this landslide and the risk of the residual slope were studied.

According to the stability analyses, the pre-quake slope is quite stable, with factor of safety of 1.77 (dry) to 1.35 (full groundwater level); explaining why there is no written record of a landslide here for the past 100 years. In contrast, a back analysis shows that the Chi-Chi earthquake-induced dynamic loading is far more than the dip slope can sustain, due in part to the short distance to the epicenter. A Monte Carlo type probability analysis suggests that the residual slope is more dangerous than the pre-quake slope and needs more attention.     

杭州市滑坡地质灾害危险性区划与评价

基于证据权法构建滑坡地质灾害评价模型,进行杭州市滑坡地质灾害危险性区划研究。主要数据源包括1930－2009年杭州市域采集到的1 905个地质灾害个例以及杭州市地质图、土地利用数据及数字高程模型（DEM）等。利用Arcgis空间分析及信息提取功能,筛选强降水、地层岩性、坡度、坡向、坡高、河网与道路缓冲等证据因子,并运用证据权法客观确定各因子权重, 最后通过Arc-WofE扩展模块对多种优选因子的叠加,计算任意格网单元的滑坡发生概率,实现对潜在滑坡点位的空间预测。经分离样本法验证,区划准确率为88.3%,分析结果与现有滑坡的分布情况比较吻合。据此表明证据权法在多指标评价及其权重确定等方面具有普适性,值得在滑坡地质灾害危险性区划等方面推广应用。     

光学遥感用于贵州发耳镇尖山营滑坡监测研究

文章选取贵州省发耳镇尖山营采矿型滑坡为研究对象,利用多期光学遥感数据,基于亚像元相关性匹配技术,实现该滑坡长时序定量化二维形变监测。利用多期存档Google earth影像及无人机影像,探测出该地区1#滑坡在2013年已开始发生明显的地表形变,且随着时间的推移,地表形变愈发严重;再基于Sentinel-2遥感数据求解出1#滑坡自2016年7月31日至2020年3月22日期间的二维形变时间序列,结果表明:该滑坡最大水平形变方向与斜坡方向一致,且在东西向和南北向上均表现为持续增大的趋势,最大累积形变量分别达到了44 m和-58 m。      

National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea

The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility of rock-cut slopes along expressways in Korea. A geographic information system (GIS) database was compiled based on data from topographical and geological maps, and rock-cut slope data, including the locations of past landslides. Seven factors (i.e., slope height, slope length, slope gradient, upper slope gradient, lithology, distance from nearest fault, and dip direction of slope) were extracted from the GIS database to assess the relationship between each factor and landslide events. Weight of evidence (WOE), analytic hierarchy process (AHP), and fuzzy logic methods, as well as hybrid methods, were used to establish the rating of classes for each factor, weightings for the factors, and to combine multiple factor layers into landslide-susceptibility maps. A comparison of the results obtained using several different methods, based on the area under curve technique, revealed that the WOE method showed the highest accuracy of 74%. The annual cost of traffic congestion resulting from slope failures was evaluated to identify those rock-cut slopes where detailed investigations and landslide warning systems are required.     

A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment

Landslides lead to a great threat to human life and property safety. The delineation of landslide-prone areas achieved by landslide susceptibility assessment plays an important role in landslide management strategy. Selecting an appropriate mapping unit is vital for landslide susceptibility assessment. This paper compares the slope unit and grid cell as mapping unit for landslide susceptibility assessment. Grid cells can be easily obtained and their matrix format is convenient for calculation. A slope unit is considered as the watershed defined by ridge lines and valley lines based on hydrological theory and slope units are more associated with the actual geological environment. Using 70% landslide events as the training data and the remaining landslide events for verification, landslide susceptibility maps based on slope units and grid cells were obtained respectively using a modified information value model. ROC curve was utilized to evaluate the landslide susceptibility maps by calculating the training accuracy and predictive accuracy. The training accuracies of the grid cell-based susceptibility assessment result and slope unit-based susceptibility assessment result were 80.9 and 83.2%, and the prediction accuracies were 80.3 and 82.6%, respectively. Therefore, landslide susceptibility mapping based on slope units performed better than grid cell-based method.     

Neural networks and landslide susceptibility: a case study of the urban area of Potenza

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network—ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology.     

Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.     

Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping

Generation of landslide susceptibility maps is important for engineering geologists and geomorphologists. The goal of this study is to generate a reliable susceptibility map based on digital elevation modeling and remote sensing data through clustering technique. This study focused on the landslide problems on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Two stages of analysis were used to extract the dominant attributes and thresholds: (1) calculate the entropy with regard to the measure of influenced variables to the occurrence of landslide and (2) use the clustering analysis K-means with particle swarm optimization (KPSO) to resolve the difficulties in creating landslide susceptibility maps. The knowledge scope with regard to core factors and thresholds are solved. The self-organization map (SOM) is used as a parallel study for comparison. The overall accuracy of the susceptibility map is 86 and 77 % for KPSO and SOM, respectively. Then, the susceptibility maps are drawn and verifications made. The generation of a susceptibility map is useful for decision makers and managers to handle the landslide risk area.     

Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat,Turkey)

Turkey confronts loss of life and large economic losses due to natural disasters caused by its morphologic structure, geographical placement, and climate characteristics. The Kuzulu (Koyulhisar) landslide, which caused loss of life and property on 17th March 2005, occurred in an area near the country’s most important active fault, the North Anatolian Fault Zone. To mitigate and prevent landslide damages, prediction of landslide susceptibility areas based on probabilistic methods has a great importance. The purpose of this study was to produce a landslide susceptibility map by the logistic regression and frequency ratio methodologies for a 733-km² area near the North Anatolian Fault Zone from the southeast of Niksar to Resadiye in Tokat province. Conditioning parameters, such as elevation, slope gradient, slope aspect, distance to streams, roads, and faults, drainage density, and fault density, were used in the analysis. Before susceptibility analysis, the landslides observed in the area were separated into two groups for use in analysis and verification, respectively. The susceptibility maps produced had five different susceptibility classes such as very low, low, moderate, high, and very high. To test the performance of the susceptibility maps, area under curve (AUC) approach was used. For the logistic regression method, the AUC value was 0.708; while for the frequency rate method, this value was 0.744. According to these AUC values, it could be concluded that the two landslide susceptibility maps obtained were successful.     

GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran,Iran)

The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Analysis of seismic hazard in landslide-prone regions: criteria and example for an area of Daunia (southern Italy)

In relation to the assessment of earthquake-induced landslide hazard, this paper discusses general principles and describes implementation criteria for seismic hazard estimates in landslide-prone regions. These criteria were worked out during the preparation of a hazard map belonging to the official Italian geological cartography and they are proposed as guidelines for future compilation of similar maps. In the presented case study, we used a procedure for the assessment of seismic hazard impact on slope stability adopting Arias intensity Ia as seismic shaking parameter and critical acceleration a _c as parameter representing slope strength to failures induced by seismic shaking. According to this procedure, after a preliminary comparison of estimated historical maximum values of Ia with values proposed in literature as landslide-triggering thresholds, a probabilistic approach, based on the Newmark’s model, is adopted: it allows to estimate the minimum critical acceleration a _c required for a slope to keep under a prefixed value, the probability of failures induced by seismic shakings expected in a given time interval. In this way, one can prepare seismic hazard maps where seismic shaking is expressed in an indirect way through a parameter (the critical acceleration) representing the “strength” that seismic shakings mobilise in slope materials (strength demand) with a prefixed exceedance probability. This approach was applied to an area of Daunia (Apulia—southern Italy) affected by frequent landslide phenomena. The obtained results indicate that shakings with a significant slope destabilisation potential can be expected particularly in the north-western part of the area, which is exposed to the seismic activity of Apennine tectonic structures.     

Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study

The crucial and difficult task in landslide susceptibility analysis is estimating the probability of occurrence of future landslides in a study area under a specific set of geomorphic and topographic conditions. This task is addressed with a data-driven probabilistic model using likelihood ratio or frequency ratio and is applied to assess the occurrence of landslides in the Tevankarai Ar sub-watershed, Kodaikkanal, South India. The landslides in the study area are triggered by heavy rainfall. Landslide-related factors—relief, slope, aspect, plan curvature, profile curvature, land use, soil, and topographic wetness index proximity to roads and proximity to lineaments—are considered for the study. A geospatial database of the related landslide factors is constructed using Arcmap in GIS environment. Landslide inventory of the area is produced by detailed field investigation and analysis of the topographical maps. The results are validated using temporal data of known landslide locations. The area under the curve shows that the accuracy of the model is 85.83%. In the reclassified final landslide susceptibility map, 14.48% of the area is critical in nature, falling under the very high hazard zone, and 67.86% of the total validation dataset landslides fall in this zone. This landslide susceptibility map is a vital tool for town planning, land use, and land cover planning and to reduce risks caused by landslides.     

Landslide hazard mapping along national highway-154A in Himachal Pradesh,India using information value and frequency ratio

For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area.     

The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data

Post-event Interferometric Synthetic Aperture Radar (InSAR) analysis on a stack of 45 C-band SAR images acquired by the ESA Sentinel-1 satellites from 9 October 2014 to 19 June 2017 allowed the identification of a clear precursory deformation signal for the Maoxian landslide (Mao County, Sichuan Province, China). The landslide occurred in the early morning of 24 June 2017 and killed more than 100 people in the village of Xinmo. Sentinel-1 images have been processed through an advanced multi-interferogram analysis capable of maximising the density of measurement points, generating ground deformation maps and displacement time series for an area of 460 km² straddling the Minjiang River and the Songping Gully. InSAR data clearly show the precursors of the slope failure in the source area of the Maoxian landslide, with a maximum displacement rate detected of 27 mm/year along the line of sight of the satellite. Deformation time series of measurement points identified within the main scarp of the landslide exhibit an acceleration starting from April 2017. A detailed time series analysis leads to the classification of different deformation behaviours. The Fukuzono method for forecasting the time of failure appear to be applicable to the displacement data exhibiting progressive acceleration. Results suggest that satellite radar data, systematically acquired over large areas with short revisiting time, could be used not only as a tool for mapping unstable areas, but also for landslide monitoring, at least for some typologies of sliding phenomena.