Delineating and defining the boundaries of an active landslide in the rainforest of Puerto Rico using a combination of airborne and terrestrial LIDAR data

Light detection and ranging (LIDAR) is a remote sensing technique that uses light, often using pulses from a laser to measure the distance to a target. Both terrestrial- and airborne-based LIDAR techniques have been frequently used to map landslides. Airborne LIDAR has the advantage of identifying large scarps of landslides covered by tree canopies and is widely applied in identifying historical and current active landslides hidden in forested areas. However, because landslides naturally have relatively small vertical surface deformation in the foot area, it is practically difficult to identify the margins of landslide foot area with the limited spatial resolution (few decimeters) of airborne LIDAR. Alternatively, ground-based LIDAR can achieve resolution of several centimeters and also has the advantages of being portable, repeatable, and less costly. Thus, ground-based LIDAR can be used to identify small deformations in landslide foot areas by differencing repeated terrestrial laser scanning surveys. This study demonstrates a method of identifying the superficial boundaries as well as the bottom boundary (sliding plane) of an active landslide in National Rainforest Park, Puerto Rico, USA, using the combination of ground-based and airborne LIDAR data. The method of combining terrestrial and airborne LIDAR data can be used to study landslides in other regions. This study also indicates that intensity and density of laser point clouds are remarkably useful in identifying superficial boundaries of landslides.     

An InSAR and depth-integrated coupled model for potential landslide hazard assessment

Assessing the hazard of potential landslides is crucial for developing mitigation strategies for landslide disasters. However, accurate assessment of landslide hazard is limited by the lack of landslide inventory maps and difficulty in determining landslide run-out distance. To address these issues, this study developed a novel method combining the InSAR technique with a depth-integrated model. Within this new framework, potential landslides are identified through InSAR and their potential impact areas are subsequently estimated using the depth-integrated model. To evaluate its capability, the proposed method was applied to a landslide event that occurred on November 3, 2018 in Baige village, Tibet, China. The simulated results show that the area with a probability of more than 50% to be affected by landslides matched the real trimlines of the landslide and that the accuracy of the proposed method reached 85.65%. Furthermore, the main deposit characteristics, such as the location of maximum deposit thickness and the main deposit area, could be captured by the proposed method. Potential landslides in the Baige region were also identified and evaluated. The results indicate that in the event of landslides, the collapsed mass has a high probability to block the Jinsha River. It is therefore necessary to implement field monitoring and prepare hazard mitigation strategies in advance. This study provides new insights for regional-scale landslide hazard management and further contributes to the implementation of landslide risk assessment and reduction activities.

     

Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees

As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.     

Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography

Large deep-seated landslides can be reactivated during intense events, and they can evolve into destructive failures. They are generally difficult to recognize in the field, especially when they develop in densely forested areas. A detailed and constantly updated inventory map of such phenomena, and the recognition of their topographic signatures is absolutely a key tool for landslide risk mitigation.The aim of this work is to test in forested areas, the performance of the new automatic and objective methodology developed by Tarolli et al. (2012) for geomorphic features extraction (landslide crowns) from high resolution topography (LiDAR derived Digital Terrain Models – DTMs). The methodology is based on the detection of landslides through the use of thresholds obtained by the statistical analysis of variability of landform curvature. The study was conducted in a high-risk area located in the central-south Taiwan, where an accurate field survey on landsliding processes and a high-quality set of airborne laser scanner elevation data are available. The area has been chosen because some of the deep-seated landslides are located near human infrastructures and their reactivation is highly dangerous. Thanks to LiDAR’s capability to detect the bare ground elevation data in forested areas, it was possible to recognize in detail landslide features also in remote regions difficult to access. The results, if compared with the previous work of Tarolli et al. (2012), mainly focused on shallow landslides, and in a not forested area, indicate that for deep-seated landslides, where the crowns are more evident, and they are present at large scale, the tested methodology performs better (higher quality index). The method can be used to interactively assist the interpreter/user on the task of deep-seated landslide hazard mapping, and risk assessment planning of such regions.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data

In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy), and the Barcelonnette Basin (France), and from each inventory, a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with the extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1,340 landslides. Then a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil, and land cover type was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. A quantitative validation was only possible for Norway, Spain, and two regions in Italy. The first results are promising and suggest that, with regard to preparedness for and response to landslide disasters, the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available.     

基于降雨响应的黄土丘陵区滑坡危险性预测研究——以宝鸡市麟游县为例

极端降雨易造成群发滑坡灾害,难以作为单体预测.为预测评估黄土丘陵区不同降雨强度诱发滑坡灾害危险性,论文在区域滑坡灾害特征研究的基础上,分析降雨强度特征及滑坡分布特征.以岭南滑坡为代表分析降雨诱发黄土-丘陵区滑坡的形成机制,介绍了无限斜坡模型原理、参数选取,利用GIS空间建模与分析功能,定量完成了无降雨、25 mm、50...     

Proposal of a method to define areas of landslide hazard and application to an area of the Dolomites, Italy

A numerical–cartographical method has been developed to create landslide hazard maps. This method allows the assigning of a rating to the various parameters which contribute to landslides. The parameters considered are: (1) erodibility and degradability of the rocks and Quaternary deposits; (2) permeability of the ground to identify areas prone to hydraulic overpressure; (3) the geometric ratio between discontinuities and slope, and thickness of Quaternary deposits; (4) angle of the slopes; and (5) land use. A thematic map is constructed for each factor considered which defines different areas through ratings, after which all the thematic maps are overlaid and the ratings added up (or multiplied). The map which is thus obtained is reclassified in order to create the final map of landslide hazard. This method, which has already been tested in various areas, has produced excellent results in this case too, allowing a map to be constructed which corresponds to the actual instability problems.     

Use of targets to track 3D displacements in highly vegetated areas affected by landslides

Monitoring landslides with terrestrial laser scanning (TLS) is currently a well-known technique. One problem often encountered is the vegetation that produces shadow areas on the scans. Indeed, the points behind a given obstacle are hidden and thus occluded on the point cloud. Thereby, locations monitored with terrestrial laser scanner are mostly rock instabilities and few vegetated landslides, being difficult or even impossible to survey vegetated slopes using this method with its classical non-full wave form. The Peney landslide (Geneva, Switzerland) is partially vegetated by bushes and trees, and in order to monitor its displacements during the drawdown of the Verbois reservoir located at its base, an alternative solution has been found. We combined LiDAR technique with 14 targets made of polystyrene placed at different locations inside and outside the landslide area. The obtained displacements were compared with classical measurement methods (total station and extensometer), showing good resemblance of results, indicating that the use of targets in highly vegetated areas could be an efficient alternative for mass movements monitoring.     

滑坡在地形图上的表现特征和识别——以六盘水煤田为例

六盘水煤田盘县、普安、水城地区龙潭组含煤地层的上部及下部均为石灰岩等硬质岩层,易产生滑坡。区内滑坡的分布与含煤地层的分布基本一致。以区内典型滑坡为例,说明了滑坡各要素在地形图上的表现特征,总结出其识别标志,为野外滑坡的识别和滑坡体范围的圈定提供了辅助手段。     

海底滑坡链式灾害研究进展

我国海洋能源开发已步入深远海域,面临的深海地质灾害问题也日益凸显,尤以海底滑坡最为典型,一旦发生将会形成链式灾害,严重危害水下基础设施的安全。本文聚焦“滑坡形成→运动演化→冲击设施”这一链式灾害过程,首先梳理了不同触发因素作用下海底滑坡的形成机制,进而论述了海底滑坡的运动过程及不同演化阶段的判识标准,分析了滑坡运动演化过程中环境水与土体的耦合作用机理,提出了适用于中小尺度运动演化过程的流固耦合分析方法,并探讨了当前海底滑坡运动演化过程试验模拟技术和原位监测手段的适用范畴与技术瓶颈;进一步地,针对滑坡冲击海底管缆等水下基础设施问题,评析了海底滑坡冲击效应的量化评估方法和研究手段。最后,指出当前海底滑坡链式灾害研究存在的不足和未来的发展方向,以期为海底滑坡地质灾害链的模拟、预测和预警等提供重要参考。     

Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)

Preparation of landslide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, many procedures have been used to produce such maps. In this study, a new attempt is tried to produce landslide susceptibility map of a part of West Black Sea Region of Turkey. To obtain the fuzzy relations for producing the susceptibility map, a landslide inventory database is compiled by both field surveys and airphoto studies. A total of 266 landslides are identified in the study area, and dominant mode of failure is rotational slide while the other mode of failures are soil flow and shallow translational slide. The landslide inventory and the parameter maps are analyzed together using a computer program (FULLSA) developed in this study. The computer program utilizes the fuzzy relations and produces the landslide susceptibility map automatically. According to this map, 9.6% of the study area is classified as very high susceptibility, 10.3% as high susceptibility, 8.9% as moderate susceptibility, 27.5% as low susceptibility and 43.8% as very low susceptibility or nonsusceptible areas. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. For this purpose, strength of the relation (r_ij) and the root mean square error (RMSE) values are calculated as 0.867 and 0.284, respectively. These values show that the produced landslide susceptibility map in the present study has a sufficient reliability. It is believed that the approach employed in this study mainly prevents the subjectivity sourced from the parameter selection and provides a support to improve the landslide susceptibility mapping studies.     

Using an airborne laser scanner for the identification of shallow landslides and susceptibility assessment in an area of ignimbrite overlain by permeable pyroclastics

An airborne laser scanner can identify shallow landslides even when they are only several meters in diameter and are hidden by vegetation, if the vegetation is coniferous or deciduous trees in a season with fewer leaves. We used an airborne laser scanner to survey an area of the 1998 Fukushima disaster, during which more than 1,000 shallow landslides occurred on slopes of vapor-phase crystallized ignimbrite overlain by permeable pyroclastics. We identified landslides that have occurred at the 1998 event and also previous landslides that were hidden by vegetation. The landslide density of slopes steeper than 20° was 117 landslides/km² before the 1998 disaster. This event increased the density by 233 landslides/km² indicating that this area is highly susceptible to shallow landsliding.     

The performance evaluation of information value,density area,LNRF, and frequency ratio methods for landslide zonation at Miandarband area,Kermanshah Province,Iran

Landslide is one of the natural disasters which causes a lot of annual damage directly or indirectly in the world. Many planned areas, especially in hilly regions, are prone to different types of landslides; therefore, landslide susceptibility maps become an urgent issue, so that landslide damages and impact can be minimized. The best method for studying landslides, which has long been of interest to researchers, is hazard zonation. In this method, due to the affecting factors in landslide occurrence, study areas are classified into areas with low to very high risk. Different methods have been developed for this purpose. In this paper, the four bivariate statistical methods namely information value, density area, LNRF, and frequency ratio are used to investigate the hazard zonation of landslide in Miandarband located north of the Kermanshah Province. The density ratio (D _r) and Qs values for information value, density area, frequency ratio, and LNRF methods used in this study were calculated to be 2.245312, 0.98146; 2.857816, 1.071185; 2.858085, 0.783945; and 2.418375, 1.070928, respectively. The results indicate that although there are minor differences, the frequency ratio method compared to the density area method that was used for the study of landslide zonation presents better results.     

基于OpenFOAM与PFC耦合方法的水下滑坡数值模拟研究

水下滑坡是常见的地质灾害之一,为解决离散元PFC难以模拟水下流体环境的问题,提出采用计算流体力学OpenFOAM与离散元PFC耦合的计算方法。针对流固耦合中的尺度相似性问题,提出了适用于该耦合方法的相似性方法并验证了其可行性。通过典型案例分析了基于该耦合方法的水下滑坡动力学特性及堆积形态,并与单向耦合水下滑坡和陆上滑坡结果进行了对比。结果表明该耦合方法能够较好地模拟水下滑坡运动规律,主要表现为滑坡体前端厚度较大并呈椭圆面;水下滑坡运动过程和堆积形态与陆上滑坡差异较大,OpenFOAM-PFC双向耦合与单向耦合方法相比具有优越性。     

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 %).     

The spatial correlation between earthquakes and landslides in Hokkaido (Japan), a GIS-based analysis of the past and the future

Although earthquakes are thought to be one of the factors responsible for the occurrence of landslides in Hokkaido, there exist no enough records which can allow correlating many of the old slope failures in the island with earthquakes. In the absence of these records, an attempt was done in this study to use the abundance, frequency, magnitude, depth, and distribution of historical earthquakes to deduce that many of the slope failures in the region were triggered by strong and continuous seismicity. The determination of the zones of influences of selected earthquakes using an existing empirical function has also supported this conclusion. Moreover, the use of a 10% probability of exceedance of earthquake intensity in 50 years, and the geological and slope maps has allowed preparing a landslide hazard map which explains the role of future earthquakes in the formation of slope failures. The result indicates a high probability of occurrences of landslides in the hilly regions of the southeastern part of Hokkaido due to expected strong seismicity and earthquake intensities in these areas. On the other hand, the low level of intensity in the north has given rise to low probability of landslide hazard. There are also places in the center of the island and high intensity regions in the east where the probability of landslide hazard was influenced by the contribution of the geological and slope maps.     

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.     

Evolution of landslide hotspots in Taiwan

Among the disasters facing Taiwan, earthquakes and typhoons incur the greatest monetary losses, and landslide disasters inflict the greatest damage in mountainous areas. The nationwide landslide susceptibility map gives an indication of where landslides are likely to occur in the future; however, there is no objective index indicating the location of landslide hotspots. In this study, we used statistical analysis to locate landslide hotspots in catchments in Taiwan. Global and local spatial autocorrelation analysis revealed the existence of landslide clusters between 2003 and 2012 and identified a concentration of landslide hotspots in the eastern part of Central Taiwan. The extreme rainfall brought by typhoon Morakot also led to the formation of new landslide hotspots in Southern Taiwan. This study provides a valuable reference explaining changes in landslide hotspots and identifying areas of high hotspot concentration to facilitate the formulation of strategies to deal with landslide risk.     

Application and verification of a fractal approach to landslide susceptibility mapping

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable.