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Soft grounds are often the result of weathering. Weathering is the chemical and physical change in time of ground under influence of atmosphere, hydrosphere, cryosphere, biosphere, and nuclear radiation (temperature, rain, circulating groundwater, vegetation, etc.). Erosion is the removal of material on or below the Earth surface due to flowing (ground ) water, ice, and wind. Quantities of weathered material do not need to be large to change the geotechnical properties of a groundmass, for example, weathering of discontinuity walls that reduce the shear strength. Weathering is the reason for many constructions and other engineering applications in which ground is used, to become a disaster. The processes involved in weathering are described briefly as well as the role of erosion in weathering and weathering depth. The dependence of weathering on lithology, implications for engineering applications, the methodology for describing and classification of weathering, and options for determining the susceptibility to weathering for forecasting future weathering are introduced. Hard layer or crust forming as result of weathering and tests for determining the state of weathering and susceptibility to weathering conclude the chapter.

This section discusses the analysis of multi-hazards in a mountainous environment at a medium scale (1:25,000) using Geographic Information Systems. Although the term ‘multi-hazards’ has been used extensively in literature there are still very limited approaches to analyze the effects of more than one hazard in the same area, especially related to their interaction. The section starts with an overview of the problem of multi-hazard risk assessment, and indicates the various types of multi-hazard interactions, such as coupled events, concatenated events, and events changing the predisposing factors for other ones. An illustration is given of multi-hazards in a mountainous environment, and their interrelationships, showing triggering factors (earthquakes, meteorological extremes), contributing factors, and various multi-hazard relationships. The second part of the section gives an example of a medium scale multi-hazard risk assessment for the Barcelonnette Basin (French Alps), taking into account the hazards for landslides, debris flows, rockfalls, snow avalanches and floods. Input data requirements are discussed, as well as the limitations in relation to the use of this data for initiation modeling at a catchment scale. Simple run-out modeling is used based on the energy-line approach. Problems related to the estimation of temporal and spatial probability are presented and discussed, and methods are shown for estimating the exposure, vulnerability and risk, using risk curves that expressed the range of expected losses for different return periods. The last part presents a software tool (Multi-Risk) developed for the analysis of multi-hazard risk at a medium scale.

Light Detection and Ranging (LiDAR) and its wide range of derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides, only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. So far, it has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study focuses on the application of OOA using LiDAR derivatives such as slope gradient, curvature, and difference in elevation (2m resolution). More specifically, the focus is on the possible use for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide inventory mapping. The test areas are the Flemish Ardennes (Belgium) and Vorarlberg (Austria). In a first phase, a relatively qualitative procedure based on expert-knowledge and basic statistical analysis was developed for a test area in the Flemish Ardennes. The procedure was then applied without further modification to a validation area in the same region. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides, because approximately 70% of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bed rock outcrops like Vorarlberg, on the other hand, it is more difficult to create a transferable model.

Landslide dams are a common type of river disturbance in tectonically active mountain belts with narrow and steep valleys. Here we present an unprecedented inventory of 828 landslide dams triggered by the 2008 Wenchuan earthquake, China. Of the 828 landslide dams, 501 completely dammed the rivers, while the others only caused partial damming. The spatial distribution of landslide dams was similar to that of the total landslide distribution, with landslide dams being most abundant in the steep watersheds of the hanging wall of the Yingxiu-Beichuan Thrust Fault, and in the northeastern part of the strike-slip fault near Qingchuan. We analyzed the relation between landslide dam distribution and a series of seismic, topographic, geological, and hydrological factors.

Soil slopes from six types of generic soils (three sands and three clays) were modeled systematically as step-like slopes with the stress-deformation plane-strain finite element method (FEM). The models are assessed at different slope heights (5–20 m for the clay slopes and 5–40 m for the sand slopes), and monochromatic seismic loads with different frequencies (1, 2, 4, 6 Hz). At yield, the peak accelerations and slope displacements computed with the FEM are compared to the predictions from two displacement-based methods: Californian (Blake TF, Hollingsworth RA, Stewart JP (2002) Recommended procedures for implementation of DMG special publication 117: Guidelines for analyzing and mitigating landslide hazards in California. Southern California Earthquake Center, University of Southern California, California, 110p), and USGS Jibson (Eng Geol 91:209–18, 2007). For the slopes in clay, the results from the Californian method are in agreement with the FEM results for 4 and 6 Hz, while better matches between the FEM and the USGS method are obtained for 2, 4, and 6 Hz. The FEM results for the slopes in sand were compared to the Californian displacement-method only because this approach is calibrated with different types of materials and failure types while the USGS cannot be compared to these results as it is restricted to translational slip surfaces and the sands show more circular slip surfaces. The Californian predictions from the sand slopes are comparable, to the FEM results but with higher scatter. The FEM outputs provide further valuable insight among the relations from the different variables.

The recent census in India revealed that India is now housing 17% of the world’s population, and India is on the way to become the most populated country. Landslides are an increasing concern in India due to the rapid population expansion in hilly and mountainous terrain. Landslides affect vast areas within India, in particular in the Himalayan chain in the North and Eastern part of the country and the Western Ghats in the Southwest. The Geological Survey of India (GSI) has been designated as the nodal agency for landslides by the Indian government, and they are responsible for landslide inventory, susceptibility and hazard assessment. Until recently their landslide susceptibility assessment was based on a heuristic approach using fixed weights or ranking of geofactors, based on guidelines of the Bureau of Indian Standards (BIS). However, this method is disputed as it doesn’t provide accurate results. This paper gives an overview of recent research on how the existing methods for landslide inventory, susceptibility and hazard assessment in India could be improved, and how these could be used in (semi)quantitative risk assessment. Due to the unavailability of airphotos in large parts of India, satellite remote sensing data has become the standard data input for landslide inventory mapping. The National Remote Sensing Center (NRSC) has developed an approach using semi-automatic image analysis algorithms that combine spectral, shape, texture, morphometric and contextual information derived from high resolution satellite data and DTMs for the preparation of new as well as historical landslide inventories. Also the use of existing information in the form of maintenance records, and other information to generate event-based landslide inventories is presented. Event-based landslide inventories are used to estimate the relation between temporal probability, landslide density and landslide size distribution. Landslide susceptibility methods can be subdivided in heuristic, statistical and deterministic methods. Examples are given on the use of these methods for different scales of analysis. For medium scales a method is presented to analyze the spatial association between landslides and causal factors, including those related to structural geology, to select the most appropriate spatial factors for different landslide types, and combine them using the multivariate methods. For transportation corridors a method is presented for quantitative hazard and risk assessment based on a landslide database. Deterministic methods using several dynamic slope-hydrology and slope stability models have been applied to evaluate the relation between land use changes and slope stability in a steep watershed. The paper ends with an overview how the susceptibility maps can be combined with the landslide databases to convert them into hazard maps which are subsequently used in (semi) quantitative risk assessment at different scales of analysis, and how the results can be used in risk reduction planning.

To mitigate the consequences of increasingly frequent disasters across the globe, better real-time collaborative post-disaster management tools are needed. The International Charter ‘Space and Major Disasters’, in conjunction with intermediary agencies, provides for space resources to be available to support disaster response. It is widely seen as a successful example of international humanitarian assistance following disasters. However, the Charter is also facing challenges with respect to lack of collaboration and validation, with the information flow being largely mono-directional. It is, therefore, fundamental to move away from static map data provision to a more dynamic, distributed and collaborative environment. Geo Web Services can bring together vast stores of data from heterogeneous sources, along with geospatial services that can interact in a loosely coupled environment and be used to create more suitable information for different stakeholders. The aim of this chapter is to evaluate the relevance and importance of Geo Web Services in the disaster management domain and present a suitable Geo Web Service architecture for a collaborative post-disaster damage mapping system. We focus particularly on satellite image-based post-disaster support situations, and present our ideas for a prototype based on this architecture with possibilities for User Generated Content.

A devastating earthquake occurred on 27 May 2006 in the Yogyakarta area in Indonesia. Response activities began immediately, and included extensive ground-based mapping by Indonesian entities, as well as an activation of the International Charter “Space and Major Disasters”, which led to the rapid production of image based damage maps and other assistance. The aim of this paper is to assess the Geoinformation that became available and was used in the aftermath of the disaster. It shows that some of the map products, largely because of lack of field data and communication with forces in the disaster area, were not as effective as they could have been. It further provides a preliminary quality assessment of those damage maps, using data from a house-by-house damage assessment. Disaster response and data processing are still ongoing, and further analysis will be required to determine how the use of Geoinformatics, and the utility of international assistance based on Charter products in particular, can be improved.

Natural and man-made disasters lead to challenging situations for the affected communities, where comprehensive and reliable information on the nature, extent, and the consequences of an event are required. Providing timely information, however, is particularly difficult following sudden disasters, such as those caused by earthquakes or industrial accidents. In those situations only partial, inaccurate or conflicting ground-based information is typically available, creating a well-recognized potential for satellite remote sensing to fill the gap. Despite continuous technical improvements, however, currently operational, non-classified, space-based sensors may not be able to provide timely data. In addition, even high spatial resolution satellites (< 1m) are limited in their capacity to reveal true 3D structural damage at a level of detail necessary for appropriate disaster response in urban areas.