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Landslide is a common natural disaster occurring in Indonesia during the rainy season from November to February. Attempts have been made to develop an early warning system based on the rainfall derived from satellite observation. It is essential to verify the accuracy level of the rainfall threshold in predicting the occurrence of rainfall, causing landslides and non-landslides to model the lower limit that can be used as an early warning device of the landslides. In this analysis, modelling was carried out with an empirical (intensity—duration/ID) approach using 220 data of rainfall that triggered landslide with satellite-based TRMM in Indonesia territory. The intensity and duration of antecedent rainfall were utilized in rainfall threshold modelling. The rainfall threshold was validated with ROC analysis. This method used seven statistics indices and ROC curve to determine the accuracy rate of the rainfall threshold. The results showed empirical equation I = 7.83D−0.328 within the interval time 2–18 days. The results of the analysis of the ROC on the rainfall threshold indicate that the model has a good accuracy rate and can be used in an early warning system of landslide even though it still has a fairly high error rate.

© Springer International Publishing Switzerland 2015. The devastating 2008 Wenchuan earthquake with a magnitude of Mw 7.9 was the largest seismic event in China in more than 50 years. It triggered numerous landslides over a broad area, some of which dammed rivers, posing severe threats to downstream settlements. The coseismic landslides are classified into rock/debris avalanches, debris flows, rock/debris slides and rock falls. A number of representative examples of each type and the corresponding landslide dam features were studied. According to dam composition material and sedimen-tological features, landslide dams were categorized into three types: dams mainly composed of large boulders and blocks; dams composed of unconsolidated fine debris; and dams with partly intact rock strata at the base topped by large boulders and blocks or soil with rock fragments, showing two-layered or three-layered depositional structure. This classification is linked to the typology of damming landslides and considered to be a preliminary indicator of dam stability. In addition, dam stability also largely depends on valley morphometry as well as landslide runout distance and mechanism. .

Landslide mitigation measures are used to reduce the risk affecting mountain communities. The quantitative estimation of the change or reduction in risk, after implementing mitigation measures, requires modeling of past events and the forward prediction of possible future occurences. However, the forward-prediction of landslide hazard is subjected to uncertainties due to the lack of knowledge on some key aspects like the possible source volume that can be triggered and model parameters that determine the landslide runout. In this study, a back-analysis of a debris flow event was carried out using MassMov2D to create a set of parameter ranges for forward-predicting runouts with mitigation measures. We approached the issue of uncertainty by systematically sampling parameters from wide ranges and running hundreds of different runout scenarios. Simulations from back-analysis were compared with the forward-predicted models to determine changes in the spread and intensity of debris flows affecting elements at risk (e.g. houses and roads). This study is a first step towards a quantitative risk assessment (QRA) being carried out within the EC FP-7 funded CHANGES network (Grant Agreement No. 263953).

Landslides can result in enormous casualties and huge economic losses in mountainous regions. In recent years, interest in landslide risk assessment has grown substantially. Landslide risk assessment aims to determine the expected degree of loss due to landslides and the expected number of fatalities, people injured, damage to property and disruption of economic activity. This session addresses the landslide risk assessment, to establish criteria for ranking risk posed by different types of mass movements, to quantify the impact that mass movements have on population, structures and infrastructures, and to enhance strategies for avoiding or mitigating landslide risk.

Earthquakes may trigger a series of multiple cascading geohazard phenomena. For example, coseismic landslides may block rivers and form landslide dams, which occur frequently in tectonically active mountains with narrow and steep valleys. The catastrophic release of water masses from landslide-impounded lakes is capable to produce outburst floods and debris flows, causing loss of lives, housing and infrastructure. Quantifying the probability of these cascading phenomena following a triggering event has been a main research challenge. This study creates a conceptual event tree model for hazard assessment of earthquake-induced landslide dams, with the involvement of many discussions amongst specialists in different fields. Event tree (ET) is a graphical, hierarchical and tree-like representation of possible events, which has been successfully applied in the volcano hazard assessment, but the application in landslide research is rather limited. We attempt to elaborate the event tree model by applying it in estimating the hazard of landslide dams induced by the Wenchuan earthquake. The model starts from a scenario-earthquake on a known possible active faults; the model then progressively assesses the susceptibility to coseismic landslides and landslide dams, and, finally, provides an estimate of dam-break flood hazard. According to the literature and our best understanding of the seismic hazard, we suggested or proposed possible methods to estimate the probabilities at successive nodes, the cascading events.

India is now housing 17% of the world’s population. 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 agency 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, following guidelines of the Bureau of Indian Standards (BIS). However, this method is disputed as it doesn’t provide accurate results.

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.

Object-oriented analysis (OOA) has been demonstrated to produce more accurate results than pixel-based image processing. Studies carried out by previous researchers have shown how landslide inventories can be prepared from multispectral satellite images using OOA. However, panchromatic images are frequently the only data available after a landslide event. Furthermore, preparation of historical inventories relies on the analysis of satellite images and aerial photographs acquired over past few decades that are also mostly only available in black and white. In such cases the methodology developed using multispectral data cannot be used directly due to limited spectral information, in particular in near-infrared bands. In this paper we present a new methodology that addresses some of these issues. Using high resolution panchromatic images from Cartosat-1 (2.5 m) and IRS-1D (5.8 m), and a 10 m gridded DTM extracted from Cartosat-1, we developed a new approach which uses change detection techniques and a global contextual criteria in an object-based environment to detect and classify landslides into five different types. Continuous time series images from 1998 to 2006 were used to prepare annual landslide inventories in a highly rugged Himalayan terrain. The maximum and minimum detection percentages achieved for all landslides are 96.7% and 71.5%, respectively, with corresponding quality percentages of 88.1% and 55.3%, respectively. However, the lack of spectral information proved to be a hurdle resulting in a high branching factor that indicates that further work is required to eliminate false positives. Nevertheless, the method was able to create much needed historical landslide inventories, which are critical for landslide hazard and risk assessment studies.

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.

Landslide hazard and risk have increased over the last decades and pose a significant threat to modern society. Despite remarkable efforts of compiling and updating landslide maps at regional, national or global scales, the number of landslide events is often underestimated, especially in forested areas where the vegetation obscures the geomorphic features indicative of landsliding. The primary objective of this study was to investigate the suitability of an active remote sensing technique, airborne laser scanning (ALS), for mapping and classifying landslides in temperate and tropical forest environments. The methods were developed in two study areas: (1) the Bois Noir area in France (Southern French Alps), (2) the Cameron Highlands in Malaysia (tropical rainforest region). In conclusion, the emergence of ALS for investigating geomorphic processes and activities has improved our ability to map, monitor and model the topographic terrain signature and landslide-induced vegetation anomalies. This study explicitly proved that ALS can be a very important new data source and mapping tool to characterize landslides even in a complex environment. The increased prevalence of modern ALS system and advanced point cloud processing had led the ways to improve future landslide maps and subsequently reduce landslide risk. Given the complexity of the terrain, automating the inventorization will still be challenging in the tropics with extensive anthropogenic activity, and differentiating the vegetative reaction in response to different earth surface processes requires further research. Airborne remote sensing is a critical and supportive tool for better understanding of landslide geomorphology in a changing environment.