DInSAR analysis of ALOS PALSAR images for the assessment of very slow landslides: the Tena Valley case study

In this work we analyse the performance of advanced land observing satellite (ALOS) phased array type L-band syntetic aperture radar (PALSAR) images for mapping and monitoring of very slow landslides using conventional differential interferometry in the Tena Valley (Central Pyrenees, Spain). These results are compared with those retrieved in previous works where multi-band advanced differential interferometric synthetic aperture radar (DInSAR) analysis was performed for the same area using PSI techniques. The study area is largely underlain by slates (ca. 80 %) where large deep-seated very slow earth flows are dominant. The results reveal that DInSAR analysis is able to measure displacements of landslides with a greater spatial coverage than PSI analysis, but for a lower amount of them (nine against 51). Overall, the combination of the DInSAR and multi-band PSI analysis permitted to map and monitor 68 % of the landslides in Tena Valley. From this amount, 63 landslides are considered as active. The main advantage of DInSAR with respect to PSI analysis is the capability to detect faster movements (up to 145 cm?year^?1) derived from the 46 days interferograms. That is the case of Sextas and La Selva landslides where an acceleration of the moving mass was measured after intense rainfall periods producing major damages to linear infrastructures. The combination of measured displacement from ALOS interferograms, with the observed damages on the A-136 road, was useful to assess the potential damage that could cause these slow movements. In general, it is demonstrated that even though PSI analysis provides a better performance in terms of landslide mapping, L-band DInSAR analysis provides an added value for landslide hazard assessment through radar remote sensing. For this reason it is necessary to encourage the launch of new satellite missions similar to ALOS PALSAR that could operate with shorter revisiting time periods.     

How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): the PSI-based matrix approach

We provide a step-by-step analysis and discussion of the ‘PSI-based matrix approach’, a methodology employing ground deformation velocities derived through Persistent Scatterer Interferometry (PSI) for the assessment of the state of activity and intensity of extremely to very slow landslides. Two matrices based on PSI data are designed respectively for landslides already mapped in preexisting inventories and for newly identified phenomena. Conversely, a unique intensity scale is proposed indiscriminately for both. Major influencing factors of the approach are brought to light by the application in the 14 km² area of Verbicaro, in Northern Calabria (Italy). These include lack of PSI data within the landslide boundaries, temporal coverage of the available estimates, and need of field checks as well as the operative procedures to set the activity and intensity thresholds. For the area of Verbicaro, we exploit 1992–2011 PSI data from ERS1/2 and RADARSAT1/2 satellites, projecting them along the maximum slope directions. An activity threshold of ±5 mm/year is determined by applying the average projection factor of local slopes to the PSI data precision. The intensity threshold between extremely and very slow phenomena (16 mm/year) is reduced by ~20 % to account for temporal and spatial averages being applied to attribute representative velocities to each landslide. The methodology allows assessing the state of activity and the intensity for 13 of the 24 landslides premapped in the 2007 inventory and for two newly identified phenomena. Current limitations due to characteristics and spatial coverage of PSI data are critically tackled within the discussion, jointly with respective implications.     

A comparison of logistic regression-based models of susceptibility to landslides in western Colorado,USA

The Paonia-McClure Pass area of Colorado has been recognized as a region highly susceptible to mass movement. Because of the dynamic nature of this landscape, accurate methods are needed to predict susceptibility to movement of these slopes. The area was evaluated by coupling a geographic information system (GIS) with logistic regression methods to assess susceptibility to landslides. We mapped 735 shallow landslides in the area. Seventeen factors, as predictor variables of landslides, were mapped from aerial photographs, available public data archives, ETM + satellite data, published literature, and frequent field surveys. A logistic regression model was run using landslides as the dependent factor and landslide-causing factors as independent factors (covariates). Landslide data were sampled from the landslide masses, landslide scarps, center of mass of the landslides, and center of scarp of the landslides, and an equal amount of data were collected from areas void of discernible mass movement. Models of susceptibility to landslides for each sampling technique were developed first. Second, landslides were classified as debris flows, debris slides, rock slides, and soil slides and then models of susceptibility to landslides were created for each type of landslide. The prediction accuracies of each model were compared using the Receiver Operating Characteristic (ROC) curve technique. The model, using samples from landslide scarps, has the highest prediction accuracy (85 %), and the model, using samples from landslide mass centers, has the lowest prediction accuracy (83 %) among the models developed from the four techniques of data sampling. Likewise, the model developed for debris slides has the highest prediction accuracy (92 %), and the model developed for soil slides has the lowest prediction accuracy (83 %) among the four types of landslides. Furthermore, prediction from a model developed by combining the four models of the four types of landslides (86 %) is better than the prediction from a model developed by using all landslides together (85 %).     

Mapping of slow landslides on the Palos Verdes Peninsula using the California landslide inventory and persistent scatterer interferometry

Extremely slow landslides, those with a displacement rate <16 mm/year, may be imperceptible without proper instrumentation. These landslides can cause infrastructure damage on a long-term timescale. The objective is to identify these landslides through the combination of information from the California landslide inventory (CLI) and ground displacement rates using results from persistent scatterer interferometry (PSI), an interferometric synthetic aperture radar (InSAR) stacking technique, across the Palos Verdes Peninsula in California. A total of 34 ENVISAT radar images (acquired between 2005 and 2010) and 40 COSMO-SkyMed radar images (acquired between 2012 and 2014) were processed. An InSAR landslide inventory (ILI) is created using four criteria: minimum PS count, average measured ground velocity, slope angle, and slope aspect. The ILI is divided into four categories: long-term slides (LTSs), potentially active slides (PASs), relatively stable slopes (RSSs), and unmapped extremely slow slides (UESSs). These categories are based on whether landslides were previously mapped on that slope (in the CLI), if persistent scatterers (PSs) are present, and whether PSs are unstable or stable. The final inventory includes 263 mapped landslides across the peninsula, of them 67 landslides were identified as UESS. Although UESS exhibit low velocity and are relatively small (average area of 8865 m² per slide), their presence in a highly populated area such as the Palos Verdes Peninsula could lead to destruction of infrastructure and property over the long term.     

Application of Persistent Scatterer Interferometry (PSI) in monitoring slope movements in Nainital,Uttarakhand Lesser Himalaya,India

Orogenic movements and sub-tropical climate have rendered the slopes of the Himalayan region intensely deformed and weathered. As a result, the incidences of slope failure are quite common all along the Himalayan region. The Lesser Himalayan terrane is particularly vulnerable to mass-movements owing to geological fragility, and many parts of it are bearing a high-risk of associated disaster owing to the high population density. An important step towards mitigation of such disasters is the monitoring of slope movement. Towards this, the Persistent Scatterer Interferometry (PSI) technique can be applied. In the present study, the PSI technique is employed in Lesser Himalayan town of Nainital in Uttarakhand state of India to decipher and monitor slope movements. A total of 15 multi-date ENVISAT ASAR satellite images, acquired during August 2008 to August 2010 period, were subjected to PSI, which revealed a continuous creep movement along the hillslopes located towards the eastern side of the Nainital lake. The higher reaches of the hill seem to be experiencing accelerated creep of \({\sim }21\) mm/year, which decreases downslope to \({\sim }5\) mm/year. Based on spatial pattern of varying PSI Mean LOS Velocity (MLV) values, high (H), moderate (M), low (L) and very low (S) creeping zones have been delineated in the hillslopes. Given the long history of mass movements and continuously increasing anthropogenic activities in Nainital, these results call for immediate measures to avert any future disaster in the town.     

时序InSAR技术三峡库区藕塘滑坡稳定性监测与状态更新

坡体表面形变是表征坡体稳定性的重要信息,因此,非常有必要对滑坡多发区域进行时序常规变形监测.近年来,星载合成孔径雷达数据由于其覆盖范围大、形变监测精度高的特点,被越来越多的用于山区滑坡识别与探测.首先介绍了联合分布式目标与点目标的时序InSAR方法,并将该方法应用于分析覆盖三峡藕塘滑坡的2007年至2011年的19景ALOS PALSAR数据和2015年至2018年的47景Sentinel-1数据,提取了数据覆盖时间段内的藕塘地区的变形速率.发现相比于2007年至2011年,2015年至2018年新增三处不稳定斜坡.进一步对滑坡的时序变形分析表明,降雨和水位变化是坡体稳定性最大的两个影响因素.实验证明时序InSAR方法可以作为常规形变手段来识别与监测三峡库区等地区潜在的滑坡,为防灾减灾提供支持与依据.      

Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh

Remote sensing is the most practical method available to managers of flood-prone areas for quantifying and mapping flood impacts. This study explored large inundation areas in the Maghna River Basin, around the northeastern Bangladesh, as determined from passive sensor LANDSAT data and the cloud-penetrating capabilities of the active sensors of the remote imaging microwave RADARSAT. This study also used passive sensor LANDSAT wet and dry images for the year 2000. Spatial resolution was 30 m by 30 m for comparisons of the inundation area with RADARSAT images. RADARSAT images with spatial resolution of 50 m by 50 m were used for frequency analysis of floods from 2000 to 2004. Time series images for 2004 were also used. RADARSAT remote sensing data, GIS data, and ground data were used for the purpose of flood monitoring, mapping and assessing. A supervised classification technique was used for this processing. They were processed for creating a maximum water extent map and for estimating inundation areas. The results of this study indicated that the maximum extent of the inundation area as estimated using RADARSAT satellite imaging was about 29, 900.72 km² in 2004, which corresponded well with the heavy rainfall around northeast region, as seen at the Bhairab Bazar station and with the highest water level of the Ganges–Brahmaputra–Meghna (GBM) Rivers. A composite of 5 years of RADARSAT inundation maps from 2000 to 2004, GIS data, and damage data, was used to create unique flood hazard maps. Using the damage data for 2004 and the GIS data, a set of damage maps was also created. These maps are expected to be useful for future planning and flood disaster management. Thus, it has been demonstrated that RADARSAT imaging data acquired over the Bangladesh have the ability to precisely assess and clarify inundation areas allowing for successful flood monitoring, mapping and disaster management.     

Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake

On 14 April 2010 at 07:49 (Beijing time), a catastrophic earthquake with Ms 7.1 struck Yushu County, Qinghai Province, China. A total of 2,036 landslides were interpreted from aerial photographs and satellite images, verified by selected field checking. These landslides cover about a total area of 1.194 km². The characteristics and failure mechanisms of these landslides are presented in this paper. The spatial distribution of the landslides is evidently strongly controlled by the locations of the main co-seismic surface fault ruptures. The landslides commonly occurred close together. Most of the landslides are small; there were only 275 individual landslide (13.5 % of the total number) surface areas larger than 1,000 m². The landslides are of various types. They are mainly shallow, disrupted landslides, but also include rock falls, deep-seated landslides, liquefaction-induced landslides, and compound landslides. Four types of factors are identified as contributing to failure along with the strong ground shaking: natural excavation of the toes of slopes, which mean erosion of the base of the slope, surface water infiltration into slopes, co-seismic fault slipping at landslide sites, and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by the co-seismic ground shaking. To analyze the spatial distribution of the landslides, the landslide area percentage (LAP) and landslide number density (LND) were compared with peak ground acceleration (PGA), distance from co-seismic main surface fault ruptures, elevation, slope gradient, slope aspect, and lithology. The results show landslide occurrence is strongly controlled by proximity to the main surface fault ruptures, with most landslides occurring within 2.5 km of such ruptures. There is no evident correlation between landslide occurrences and PGA. Both LAP and LND have strongly positive correlations with slope gradient, and additionally, sites at elevations between 3,800 and 4,000 m are relatively susceptible to landslide occurrence; as are slopes with northeast, east, and southeast slope aspects. Q₄ al-pl, N, and T₃ kn ¹ have more concentrated landslide activity than others. This paper provides a detailed inventory map of landslides triggered by the 2010 Yushu earthquake for future seismic landslide hazard analysis and also provides a study case of characteristics, failure mechanisms, and spatial distribution of landslides triggered by slipping-fault generated earthquake on a plateau.     

GIS-based statistical analysis of the spatial distribution of earthquake-induced landslides in the island of Lefkada, Ionian Islands, Greece

This is the first landslide inventory map in the island of Lefkada integrating satellite imagery and reports from field surveys. In particular, satellite imagery acquired before and after the 2003 earthquake were collected and interpreted with the results of the field survey that took place 1 week after this strong (Mw?=?6.3) event. The developed inventory map indicates that the density of landslides decreases from west to east. Furthermore, the spatial distribution of landslides was statistically analyzed in relation to the geology and topography for investigating their influence to landsliding. This was accomplished by overlaying these causal factors as thematic layers with landslide distribution data. Afterwards, weight values of each factor were calculated using the landslide index method and a landslide susceptibility map was developed. The susceptibility map indicates that the highest susceptibility class accounts for 38 % of the total landslide activity, while the three highest classes that cover the 10 % of the surface area, accounting for almost the 85 % of the active landslides. Our model was validated by applying the approaches of success and prediction rate to the dataset of landslides that was previously divided into two groups based on temporal criteria, estimation and validation group. The outcome of the validation dataset was that the highest susceptibility class concentrates 18 % of the total landslide activity. However, taking into account the frequency of landslides within the three highest susceptibility classes, more than 85 %, the model is characterized as reliable for a regional assessment of earthquake-induced landslides hazard.     

西藏樟木口岸震后滑坡灾害变形InSAR监测分析

位于中国和尼泊尔边境的西藏樟木口岸是国家一类陆路通商口岸,也是西藏最大的边贸中心口岸。2015年尼泊尔大地震之后,西藏樟木口岸因多次发生滑坡灾害,而导致口岸关闭。为了调查樟木口岸区域滑坡灾害的分布和变形情况及更好的服务于区域减灾防灾,利用InSAR技术对覆盖该区域的Sentinel-1A和ALOS-2两种卫星影像数据进行了处理,并通过分析视线向年均形变速率图,圈定了17处疑似滑坡,并对其中的5处典型滑坡进行时间序列形变特征分析,监测识别出的滑坡基本沿318国道所在一侧的波曲河左岸分布。InSAR调查结果表明受地震影响樟木地区的滑坡多分布在沿波曲河左岸的陡峭山体上,中尼公路迪斯岗至友谊桥段的古滑坡出现了局部复活的现象,同时樟木镇居民所在的城区也发育有扎美拉山危岩体崩塌滑坡灾害。      

The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas

In the Grande da Pipa river basin, north of Lisbon, 64 % of the total number of landslides inventoried is totally or partially included in a lithological unit composed by marl, clay, and sandstone intercalation complex that is present in 58 % of the study area. The Persistent Scatterer synthetic aperture radar interferometry technique is applied to a data set of TerraSAR-X SAR images, from April of 2010 to March of 2011, firstly to the Laje-Salema test site and further exported to the Grande da Pipa river basin. This work’s specific objectives are the following: (i) to assess the potential of the Persistent Scatterer displacement maps to the identification of new landslides/unstable areas and in the redefinition of landslide limits, (ii) to update the landslide state of activity, and (iii) to evaluate the capacity of the Persistent Scatterer deformation maps in assessing landslide susceptibility at the regional scale. Based on this approach, it was possible to increment the number of landslides and to redefine the landslide limits in the test site in 3.8 %. For 39 landslides, it was possible to update the landslide state of activity, in particular from dormant to reactivated or dormant-reactivated (23 landslides) or from stabilized to reactivated (5 landslides). Landslide susceptibility map based in Persistent Scatterer deformation rates, independently validated with a deep rotational slide map, obtained the best value of area under the curve (0.668).     

Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.     

A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster

A model building strategy is tested to assess the susceptibility for extreme climatic events driven shallow landslides. In fact, extreme climatic inputs such as storms typically are very local phenomena in the Mediterranean areas, so that with the exception of recently stricken areas, the landslide inventories which are required to train any stochastic model are actually unavailable. A solution is here proposed, consisting in training a susceptibility model in a source catchment, which was implemented by applying the binary logistic regression technique, and exporting its predicting function (selected predictors regressed coefficients) in a target catchment to predict its landslide distribution. To test the method, we exploit the disaster that occurred in the Messina area (southern Italy) on 1 October 2009 where, following a 250-mm/8-h storm, approximately two thousand debris flow/debris avalanches landslides in an area of 21 km² triggered, killing 37 people and injuring more than 100, and causing 0.5 M € worth of structural damage. The debris flows and debris avalanches phenomena involved the thin weathered mantle of the Varisican low to high-grade metamorphic rocks that outcrop in the eastern slopes of the Peloritani Mounts. Two 10-km²-wide stream catchments, which are located inside the storm core area, were exploited: susceptibility models trained in the Briga catchment were tested when exported to predict the landslides distribution in the Giampilieri catchment. The prediction performance (based on goodness of fit, prediction skill, accuracy and precision assessment) of the exported model was then compared with that of a model prepared in the Giampilieri catchment exploiting its landslide inventory. The results demonstrate that the landslide scenario observed in the Giampilieri catchment can be predicted with the same high performance without knowing its landslide distribution: we obtained, in fact, a very poor decrease in predictive performance when comparing the exported model to the native random partition-based model.     

Statistical analysis of landslides caused by the Mw 6.9 Yushu,China, earthquake of April 14, 2010

The April 14, 2010 Yushu, China, earthquake (Mw 6.9) triggered a great number of landslides. At least 2,036 co-seismic landslides, with a total coverage area of 1.194 km², were delineated by visual interpretation of aerial photographs and satellite images taken following the earthquake, and verified by field inspection. Based on the mapping results, a statistical analysis of the spatial distribution of these landslides is performed using the landslide area percentage (LAP), defined as the percentage of the area affected by the landslides, and landslide number density (LND), defined as the number of landslides per square kilometer. The purpose is to clarify how the landslides correlate the control factors, which are the elevation, slope angle, slope aspect, slope position, distance from drainages, lithology, distance from the surface rupture, and peak ground acceleration (PGA). The results show that both LAP and LND have strongly positive correlations with slope angle and negative correlations with distance from the surface rupture and distance from drainages. The highest LAP and LPD values are in places of elevations from 3,800 to 4,000 m. The slopes producing landslides are mostly facing toward NE, E, and SE. The geological units of Q₄ al-pl, N, and T₃ kn ¹ have the highest concentrations of co-seismic landslides. No apparent correlations are present between LAP and LND values and PGA. On both sides of the surface rupture, the landslide distributions are almost similar except a few exceptions, likely associated with the nature of the strike-slip seismogenic fault for this event. The bivariate statistical analysis shows that, in descending order, the earthquake-triggered landslide impact factors are distance from surface rupture > slope angle > distance from drainages > lithology > PGA. Besides, as the detailed co-seismic landslides inventories related to strike-slip earthquakes are still few compared with that of thrusting-fault earthquakes, this case study would shed new light on the subject. For instance, the landslide spatial distribution on both sides of the strike-slip seismogenic fault is rather different from that of thrusting-fault earthquakes. It reminds us to take different strategies of measures for prevention and mitigation of landslides induced by earthquakes with different mechanisms.     

Evaluating the impact of classification algorithms and spatial resolution on the accuracy of land cover mapping in a mountain environment in Pakistan

Satellite images of various spatial resolutions and different image classification techniques have been utilized for land cover (LC) mapping at local and regional scale studies. Mapping capabilities and achievable accuracies of LC classification in a mountain environment are, however, influenced by the spatial resolution of the utilized images and applied classification techniques. Hence, developing and characterizing regionally optimized methods are essential for the planning and monitoring of natural resources. In this study, the potential of four non-parametric image classification techniques, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and neural network (NN) on the accuracy of LC classification was evaluated in the Hindu Kush mountains ranges of northern Pakistan. Moreover, we have assessed the impact of the spatial resolution of the utilized satellite imagery, i.e., SPOT-5 with 2.5 m and Landsat-8 with 30 m on the accuracy of the derived LC classification. For the classification of LC based on SPOT-5, we have achieved the highest overall classification accuracy (OCA) = 89% with kappa coefficient (KC = 0.86) using SVM followed by k-NN, RF, and NN. However, for LC classification derived from Landsat-8 imagery, we achieved the highest OCA = 71% with KC = 0.59 using RF and SVM followed by k-NN and NN. The higher accuracy derived from SPOT-5 versus Landsat-8 indicated that the results of LC classification based on SPOT-5 are more accurate and reliable than Landsat-8. The findings of the present study will be useful for the classification and mapping task of LC in a mountain environment using SPOT-5 and Landsat-8 at local and regional scale studies.     

Volume,gravitational potential energy reduction,and regional centroid position change in the wake of landslides triggered by the 14 April 2010 Yushu earthquake of China

In recent years, earthquake-triggered landslides have attracted much attention in the scientific community as a main form of seismic ground response. However, little work has been performed concerning the volume and gravitational potential energy reduction of earthquake-triggered landslides and their severe effect on landscape change. This paper presents a quantitative study on the volume, gravitational potential energy reduction, and change in landscape related to landslides triggered by the 14 April 2010 Yushu earthquake. At least 2,036 landslides were triggered by the earthquake. A total landslide scar area of 1.194 km² was delineated from the visual interpretation of aerial photographs and satellite images and was supported by selected field checking. In this paper, we focus on possible answers to the following five questions: (1) What is the total volume of the 2,036 landslides triggered by the earthquake, and what is the average landslide erosion thickness in the earthquake-stricken area? (2) What are the elevations of all landslide materials in relation to pre- and post-landsliding? (3) How much was the gravitational potential energy reduced due to the sliding of these landslide materials? (4) What is the average elevation change caused by these landslides in the study area? (5) What is the vertical change of the regional centroid position above sea level, as induced by these landslides? It is concluded that the total volume of the 2,036 landslides is 2.9399?×?10⁶ m³. The landslide erosion thickness throughout the study area is 2.02 mm. The materials of these landslides moved from an elevation of 4,145.243 to 4,104.697 m, resulting in a decreased distance of 40.546 m. The gravitational potential energy reduction related to the landslides triggered by the earthquake was 2.9213?×?10¹² J. The average regional elevation of the study area is 4,427.160 m, a value consistent with the assumption that the accumulated materials were remained in situ. This value changes from 4,427.160 to 4,427.158 m with all landslide materials moved out of the study area, resulting in a reduction in elevation of 2 mm. Based on the assumption that all landslide materials moved out of the study area, the elevations of the centroid of the study area’s crust changed from 2,222.45967 to 2,222.45867 m, which means the centroid value decreased by 1 mm. This value is 0.001 mm when assuming that the materials were remained in situ, which is almost negligible, compared with the situation of “all landslide materials moved out of the study area.”     

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.     

Assessing shallow landslide hazards using the TRIGRS and SHALSTAB models,Serra do Mar,Brazil

The hillslopes of the Serra do Mar, a system of escarpments and mountains that extend more than 1500 km along the southern and southeastern Brazilian coast, are regularly affected by heavy rainfall that generates widespread mass movements, causing large numbers of casualties and economic losses. This paper evaluates the efficiency of susceptibility mapping for shallow translational landslides in one basin in the Serra do Mar, using the physically based landslide susceptibility models SHALSTAB and TRIGRS. Two groups of scenarios were simulated using different geotechnical and hydrological soil parameters, and for each group of scenarios (A and B), three subgroups were created using soil thickness values of 1, 2, and 3 m. Simulation results were compared to the locations of 356 landslide scars from the 1985 event. The susceptibility maps for scenarios A1, A2, and A3 were similar between the models regarding the spatial distribution of susceptibility classes. Changes in soil cohesion and specific weight parameters caused changes in the area of predicted instability in the B scenarios. Both models were effective in predicting areas susceptible to shallow landslides through comparison of areas predicted to be unstable and locations of mapped landslides. Such models can be used to reduce costs or to define potentially unstable areas in regions like the Serra do Mar where field data are costly and difficult to obtain.     

Seismic-event-based methodology to obtain earthquake-induced translational landslide regional hazard maps

A seismic-event-based methodology to generate earthquake-induced translational landslide maps using Newmark method is proposed. The steps are: (1) to construct a GIS-based geotechnical database; (2) to identify those areas that are susceptible to the occurrence of translational landslides based on available geological information; (3) to compute a static safety factor; (4) to compute the critical acceleration that defines the threshold acceleration required to cause a displacement; (5) to characterize the seismic hazard as a set of stochastic events, collectively exhaustive and mutually exclusive, that fully describes the hazard spatial distribution and annual frequency of occurrence (in accordance with the earthquake location, depth and magnitude) with the appropriate ground-motion prediction equations; (6) to compute the Newmark displacement; and finally, (7) to carry out a probabilistic translational landslide hazard analysis to estimate an exceedance rate of a given displacement. This methodology is applied to Mexico, and maps for return periods of 150 and 500 years are presented. Results shown in maps are estimations of where translational landslides may occur and should be useful to carry out local studies to elaborate recommendations of site specific hazard reduction plans as well as to calculate insurance rates. In addition, these results are useful to identify civil protection actions, risk management at regional and local level, and land use planning, as well as for promoting more detailed vulnerability and risk studies at different scales.     

Coastal lateral spreading in the world heritage site of the Tramuntana Range (Majorca,Spain). The use of PSInSAR monitoring to identify vulnerability

The Bàlitx area is located on the steep coastal side of the Tramuntana Range (Majorca), a mountainous region which was declared a World Heritage Site by UNESCO in 2011 in the cultural landscape category. The Bàlitx site was occupied by farming areas with dry stone constructions and water storing systems of both Roman and Islamic origin. The coastal landscape is characterised by a large fault escarpment of up to 260 m in height. Lateral spreading processes are favoured by local stratigraphy and tectonics in an energetic coastal dynamics scenario. Block spreading morphologies are identified along the escarpment, with large, rocky blocks of volumes up to 60?×?10³ m³ moving very slowly until their collapse. Consequently, a thick and highly karstified breccia deposit is accumulated at the base of the scarp. The lowest, oldest breccia outcrop has been dated (Th/U), and an age of 82.5?±?5.6 kyr was obtained, reflecting the time span this process has been active. Additionally, numerous geomorphological slope features are identified in the area: landslides, rockfalls, and, more specifically, long and deep cracks in the hanging wall block of the fault, which also reveal active lateral spreading processes. Coastal dynamics have been investigated by interpreting offshore geophysical studies, bathymetry data and borehole information to determine the role of wave energy in the stability of the slope. Additionally, 14 SAR images from the ALOS PALSAR satellite have been exploited for the present work, covering a period spanning from 2007 to 2010, an anomalous rainy period in the region. Images were processed using the Persistent Scattered Interferometry (PSI) technique. PSInSAR results reveal that the rate of movement for the Bàlitx lateral spreading is extremely low (??5.2 mm/year on average), but major activity has been detected in the NE sector, where velocity rates can reach values of up to ??16 mm/year Coastal dynamics in the area can explain this, as a small island generates wave refraction and reflection determining more intense erosive processes in the NE part, which lead to a greater destabilising effect on the slopes. A simple vulnerability approach has been developed to take the elements of cultural heritage into account. Vulnerability increases from SW to NE, in accordance with landslide activity. The Bàlitx case study could provide a testimony to the effects of mass movements and coastal dynamics in an exceptional example of Mediterranean agricultural landscape.