To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be ...delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.
Estimating the magnitude and the intensity of rapid landslides like debris flows is fundamental to evaluate quantitatively the hazard in a specific location. Dynamic run-out models are able to ...characterize the distribution of the material, its intensity and define the zones where the exposed elements will experience an impact. These models can provide valuable inputs for vulnerability and risk calculations. However, most dynamic run-out models assume a constant volume during the motion of the flow, ignoring the important role of material entrained along its path. Consequently, they neglect that the increase of volume can enhance or reduce the mobility of the flow and can significantly influence the size of the potential impact area.
Limited work has been done to quantify the entrainment process and only a few have proposed physical explanations for it. One of the reasons is that material entrainment is a complex process and an adequate understanding of the phenomenon is needed to facilitate the development of appropriate dynamic models. A proper erosion mechanism needs to be established in the analyses of debris flows that will improve the results of dynamic modeling and consequently the quantitative evaluation of risk.
The objective of this paper is to present and evaluate the performance of a 1D debris flow model with a material entrainment concept based on limit equilibrium considerations and the generation of excess pore water pressure through undrained loading of the in-situ bed material. The debris flow propagation model is based on a one dimensional continuum mechanics approach using a depth-integrated approximation based on the shallow water assumption (Saint–Venant equations). The flow is treated as a laminar one-phase material, in which behavior is controlled by a visco-plastic Coulomb–Bingham rheology. The model parameters are evaluated and the model performance is tested on a debris flow event that occurred in 2003 in the Faucon torrent (Southern French Alps).
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.
An integrated, modeling method for shallow landslides, debris flows and catchment hydrology is developed and presented in this paper. Existing two-phase debris flow equations and an adaptation on the ...infinite slope method are coupled with a full hydrological catchment model. We test the approach on the 4 km2 Scaletta catchment, North-Eastern Sicily, where the 1-10-2009 convective storm caused debris flooding after 395 shallow landslides. Validation is done based on the landslide inventory and photographic evidence from the days after the event. Results show that the model can recreate the impact of both shallow landslides, debris flow runout, and debris floods with acceptable accuracy (91 percent inventory overlap with a 0.22 Cohens Kappa). General patterns in slope failure and runout are well-predicted, leading to a fully physically based prediction of rainfall induced debris flood behavior in the downstream areas, such as the creation of a debris fan at the coastal outlet.
•We develop a new open source multi-hazard model.•Our model simulates catchment hydrology, landslides, debris flows and flash flooding.•We apply a novel method for fast estimations of landslide failure volumes.•By including hydrology and debris flow runout, we simulate multi-hazard behavior.•We apply the model to a study case and found high accuracy in hazard behavior.
Mapping complex landslides under forested terrain requires an appropriate quality of digital terrain models (DTMs), which preserve small diagnostic features for landslide classification such as ...primary and secondary scarps, cracks, and displacement structures (flow-type and rigid-type). Optical satellite imagery, aerial photographs and synthetic aperture radar images are less effective to create reliable DTMs under tree coverage. Here, we utilized a very high density airborne laser scanning (ALS) data, with a point density of 140 points m
−
2
for generating a high quality DTM for mapping landslides in forested terrain in the Barcelonnette region, the Southern French Alps. We quantitatively evaluated the preservation of morphological features and qualitatively assessed the visualization of ALS-derived DTMs. We presented a filter parameterization method suitable for landslide mapping and compared it with two default filters from the hierarchical robust interpolation (HRI) and one default filter from the progressive TIN densification (PTD) method. The results indicate that the vertical accuracy of the DTM derived from the landslide filter is about 0.04
m less accurate than that from the PTD filter. However, the landslide filter yields a better quality of the image for the recognition of small diagnostic features as depicted by expert image interpreters. Several DTM visualization techniques were compared for visual interpretation. The openness map visualized in a stereoscopic model reveals more morphologically relevant features for landslide mapping than the other filter products. We also analyzed the minimal point density in ALS data for landslide mapping and found that a point density of more than 6 points m
−
2
is considered suitable for a detailed analysis of morphological features. This study illustrates the suitability of high density ALS data with an appropriate parameterization for the bare-earth extraction used for landslide identification and characterization in forested terrain.
The quantification of risk has gained importance in many disciplines, including landslide studies. The literature on landslide risk assessment illustrates the developments which have taken place in ...the last decade and that quantitative risk assessment is feasible for geotechnical engineering on a site investigation scale and the evaluation of linear features (e.g., pipelines, roads). However, the generation of quantitative risk zonation maps for regulatory and development planning by local authorities still seems a step too far, especially at medium scales (1:10,000-1:50,000). This paper reviews the problem of attempting to quantify landslide risk over larger areas, discussing a number of difficulties related to the generation of landslide inventory maps including information on date, type and volume of the landslide, the determination of its spatial and temporal probability, the modelling of runout and the assessment of landslide vulnerability. An overview of recent developments in the different approaches to landslide hazard and risk zonation at medium scales is given. The paper concludes with a number of new advances and challenges for the future, such as the use of very detailed topographic data, the generation of event-based landslide inventory maps, the use of these maps in spatial-temporal probabilistic modelling and the use of land use and climatic change scenarios in deterministic modelling.
PDF
