Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview

The aim of this paper is to discuss a number of issues related to the use of spatial information for landslide susceptibility, hazard, and vulnerability assessment. The paper centers around the types of spatial data needed for each of these components, and the methods for obtaining them. A number of concepts are illustrated using an extensive spatial data set for the city of Tegucigalpa in Honduras. The paper intends to supplement the information given in the “Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning” by the Joint ISSMGE, ISRM and IAEG Technical Committee on Landslides and Engineered Slopes (JTC-1). The last few decades have shown a very fast development in the application of digital tools such as Geographic Information Systems, Digital Image Processing, Digital Photogrammetry and Global Positioning Systems. Landslide inventory databases are becoming available to more countries and several are now also available through the internet. A comprehensive landslide inventory is a must in order to be able to quantify both landslide hazard and risk. With respect to the environmental factors used in landslide hazard assessment, there is a tendency to utilize those data layers that are easily obtainable from Digital Elevation Models and satellite imagery, whereas less emphasis is on those data layers that require detailed field investigations. A review is given of the trends in collecting spatial information on environmental factors with a focus on Digital Elevation Models, geology and soils, geomorphology, land use and elements at risk.     

Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment

The objective of this paper is to evaluate the importance of geomorphological expert knowledge in the generation of landslide susceptibility maps, using GIS supported indirect bivariate statistical analysis. For a test area in the Alpago region in Italy a dataset was generated at scale 1:5,000. Detailed geomorphological maps were generated, with legends at different levels of complexity. Other factor maps, that were considered relevant for the assessment of landslide susceptibility, were also collected, such as lithology, structural geology, surficial materials, slope classes, land use, distance from streams, roads and houses. The weights of evidence method was used to generate statistically derived weights for all classes of the factor maps. On the basis of these weights, the most relevant maps were selected for the combination into landslide susceptibility maps. Six different combinations of factor maps were evaluated, with varying geomorphological input. Success rates were used to classify the weight maps into three qualitative landslide susceptibility classes. The resulting six maps were compared with a direct susceptibility map, which was made by direct assignment of susceptibility classes in the field. The analysis indicated that the use of detailed geomorphological information in the bivariate statistical analysis raised the overall accuracy of the final susceptibility map considerably. However, even with the use of a detailed geomorphological factor map, the difference with the separately prepared direct susceptibility map is still significant, due to the generalisations that are inherent to the bivariate statistical analysis technique.     

Remote sensing techniques for landslide studies and hazard zonation in Europe

An inventory is presented of researches concerning the use of remote sensing for landslide studies and hazard zonation as mainly carried out in the countries belonging to the European Community. An overview is given of the applicability of remote sensing in the following phases of landslide studies:

1. (1) Detection and classification of landslides. Special emphasis is given to the types of imagery required at different scales of analysis.

2. (2) Monitoring the activity of existing landslides using G.P.S., photogrammetrical techniques and radar interferometry.

3. (3) Analysis and prediction in space and time of slope failures. The different factors required in a landslide hazard study are evaluated, and the optimum remote sensing imagery for obtaining each of these factors is indicated.

Examples are given of research work carried out in these three phases from EC countries. Finally an evaluation is given of the aspects of uncertainty associated with the use of remote sensing data, and conclusions are given as to the incorporation of remote sensing techniques within the overall framework of techniques.     

Empirical prediction of coseismic landslide dam formation

In this study we develop an empirical method to estimate the volume threshold for predicting coseismic landslide dam formation using landscape parameters obtained from digital elevation models (DEMs). We hypothesize that the potential runout and volume of landslides, together with river features, determine the likelihood of the formation of a landslide dam. To develop this method, a database was created by randomly selecting 140 damming and 200 non‐damming landslides from 501 landslide dams and > 60 000 landslides induced by the M_w 7.9 2008 Wenchuan earthquake in China. We used this database to parameterize empirical runout models by stepwise multivariate regression. We find that factors controlling landslide runout are landslide initiation volume, landslide type, internal relief (H) and the H/L ratio (between H and landslide horizontal distance to river, L). In order to obtain a first volume threshold for a landslide to reach a river, the runout regression equations were converted into inverse volume equations by taking the runout to be the distance to river. A second volume threshold above which a landslide is predicted to block a river was determined by the correlation between river width and landslide volume of the known damming landslides. The larger of these two thresholds was taken as the final damming threshold. This method was applied to several landslide types over a fine geographic grid of assumed initiation points in a selected catchment. The overall prediction accuracy was 97.4% and 86.0% for non‐damming and damming landslides, respectively. The model was further tested by predicting the damming landslides over the whole region, with promising results. We conclude that our method is robust and reliable for the Wenchuan event. In combination with pre‐event landslide susceptibility and frequency–size assessments, it can be used to predict likely damming locations of future coseismic landslides, thereby helping to plan emergency response. Copyright © 2014 John Wiley & Sons, Ltd.     

Rock slope instability assessment using spatially distributed structural orientation data in Darjeeling Himalaya (India)

We discuss a geographic information system (GIS)‐based methodology for rock slope instability assessment based on geometrical relationships between topographic slopes and structural discontinuities in rocks. The methodology involves (a) regionalization of point observations of orientations (azimuth and dip) of structural discontinuities in rocks in order to generate a digital structural model (DStM), (b) testing the kinematical possibility of specific modes of rock slope failures by integrating DStMs and digital elevation model (DEM)‐derived slope and aspect data and (c) computation of stability scenarios with respect to identified rock slope failure modes. We tested the methodology in an area of 90 km² in Darjeeling Himalaya (India) and in a small portion (9 km²) within this area with higher density of field structural orientation data. The results of the study show better classification of rock slope instability in the smaller area with respect to known occurrences of deep‐seated rockslides than with respect to shallow translational rockslides, implying that structural control is more important for deep‐seated rockslides than for shallow translational rockslides. Results of scenario‐based analysis show that, in rock slopes classified to be unstable, stress‐induced rock slope instability tends to increase with increasing level of water saturation. The study demonstrates the usefulness of spatially distributed data of orientations of structural discontinuities in rocks for medium‐ to small‐scale classification of rock slope instability in mountainous terrains. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantitative assessment of landslide hazard along transportation lines using historical records

In this paper, a quantitative landslide hazard model is presented for transportation lines, with an example for a road and railroad alignment, in parts of Nilgiri hills in southern India. The data required for the hazard assessment were obtained from historical records available for a 21-year period from 1987 to 2007. A total of 901 landslides from cut slopes along the railroad and road alignment were included in the inventory. The landslides were grouped into three magnitude classes based on the landslide type, volume, scar depth, and run-out distance. To calculate landslide hazard, we estimated the total number of individual landslides per kilometer of the (rail) road for different return periods, based on the relationship between past landslides (recorded in our database) and triggering events. These were multiplied by the probability that the landslides belong to a given magnitude class. This gives the hazard for a given return period expressed as the number of landslides of a given magnitude class per kilometer of (rail) road. The relationship between the total number of landslides and the return period was established using a Gumbel distribution model, and the probability of landslide magnitude was obtained from frequency–volume statistics. The results of the analysis indicate that the total number of landslides, from 1- to 50-year return period, varies from 56 to 197 along the railroad and from 14 to 82 along the road. In total, 18 hazard scenarios were generated using the three magnitude classes and six return periods (1, 3, 5, 15, 25, and 50 years). The hazard scenarios derived from the model form the basis for future direct and indirect landslide risk analysis along the transportation lines. The model was validated with landslides that occurred in the year 2009.     

Use of quantitative landslide hazard and risk information for local disaster risk reduction along a transportation corridor: a case study from Nilgiri district, India

The objective of analyzing hazard and risk in an area is to utilize the result in selecting appropriate landslide risk reduction strategies. However, this does not happen always, and most often results of the hazard and risk analysis remain at an academic level. The under or non-utilization of results in pre-disaster planning could be due to several reasons, including difficulties in understanding the scientific content/meaning of the models, and lack of information on the practical significance and utility of the models. In this study, an attempt is made to highlight the uses of hazard and risk information in different landslide risk reduction strategies along a transportation corridor in Nilgiri, India. At first, a quantitative analysis of landslide hazard and risk was made. The obtained information was then incorporated in risk reduction options such as land use zoning, engineering solutions, and emergency preparedness. For emergency preparedness, the perception of the local Nilgiri communities toward landslide risk was evaluated and simplified maps were generated for the benefit and understanding of end users. A rainfall threshold-based early warning system was presented, which could be used in risk awareness programs involving public participation. The use of quantitative risk information in the cost-benefit analysis for the planning of structural measures to protect the road and railway alignments was also highlighted, and examples were shown how the transport organizations could implement these measures. Finally, the study provided examples of the utility of hazard and risk information for spatial planning and zoning, indicating areas where the landslide hazard is too high for planning future developments.     

Landslide detection in the Himalayas using machine learning algorithms and U-Net

Landslides - Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover,...     

Accuracy assessment of InSAR derived input maps for landslide susceptibility analysis: a case study from the Swiss Alps

In recent years SAR interferometry has become a widely used technique for measuring altitude and displacement of the surface of the earth. Both these capabilities are highly relevant for landslide susceptibility studies. Although there are many problems that make the use of SAR interferometry less suitable for landslide inventory mapping, it’s use in landslide monitoring and in the generation of input maps for landslide susceptibility assessment looks very promising. The present work attempts to evaluate the usefulness and limitations of this technique based on a case study in the Swiss Alps. Input maps were generated from ERS repeat pass data using SAR interferometry. A land cover map has been generated by image classification of multi-temporal SAR intensity images. An InSAR DEM was generated and a number of maps were derived from it, such as slope-, aspect, altitude- and slope form classes. These maps were used to generate landslide and rockfall susceptibility maps, which give fairly well acceptable results. However, a comparison of the InSAR DEM with the conventional Swisstopo DEM, indicated significant errors in the absolute height and slope angles derived from InSAR, especially along the ridges and in the valleys. These errors are caused by low coherence mostly due to layover and shadow effects. Visual comparison of stereo images created from hillshading maps and corresponding DEMs demonstrate that a considerable amount of topographic details have been lost in the InSAR-derived DEM. It is concluded that InSAR derived input maps are not ideal for landslide susceptibility assessment, but could be used if more accurate data is lacking.     

Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation

Slope instability hazard assessment is based on the analysis of the terrain conditions at sites where slope failures occurred in the past. For the analysis of the causative factors the application of geographic information systems (GIS) is an essential tool in the data analysis and subsequent hazard assessment. Three scale levels of hazard mapping are defined. A direct experience-driven mapping at reconnaissance level, a statistical approach to determine the causative factors in a quantitative susceptibility mapping and a methodology at large-scale making using of deterministic models.