Multi-hazard risk assessment using GIS in urban areas: a case study for the city of Turrialba, Costa Rica

In the framework of the UNESCO sponsored project on “Capacity Building for Natural Disaster Reduction” a case study was carried out on multi-hazard risk assessment of the city of Turrialba, located in the central part of Costa Rica. The city with a population of 33,000 people is located in an area, which is regularly affected by flooding, landslides and earthquakes. In order to assist the local emergency commission and the municipality, a pilot study was carried out in the development of a GIS –based system for risk assessment and management. The work was made using an orthophoto as basis, on which all buildings, land parcels and roads, within the city and its direct surroundings were digitized, resulting in a digital parcel map, for which a number of hazard and vulnerability attributes were collected in the field. Based on historical information a GIS database was generated, which was used to generate flood depth maps for different return periods. For determining the seismic hazard a modified version of the Radius approach was used and the landslide hazard was determined based on the historical landslide inventory and a number of factor maps, using a statistical approach. The cadastral database of the city was used, in combination with the various hazard maps for different return periods to generate vulnerability maps for the city. In order to determine cost of the elements at risk, differentiation was made between the costs of the constructions and the costs of the contents of the buildings. The cost maps were combined with the vulnerability maps and the hazard maps per hazard type for the different return periods, in order to obtain graphs of probability versus potential damage. The resulting database can be a tool for local authorities to determine the effect of certain mitigation measures, for which a cost-benefit analysis can be carried out. The database also serves as an important tool in the disaster preparedness phase of disaster management at the municipal level

Low-cost UAV surveys of hurricane damage in Dominica: automated processing with co-registration of pre-hurricane imagery for change analysis

In 2017, hurricane Maria caused unprecedented damage and fatalities on the Caribbean island of Dominica. In order to ‘build back better’ and to learn from the processes causing the damage, it is important to quickly document, evaluate and map changes, both in Dominica and in other high-risk countries. This paper presents an innovative and relatively low-cost and rapid workflow for accurately quantifying geomorphological changes in the aftermath of a natural disaster. We used unmanned aerial vehicle (UAV) surveys to collect aerial imagery from 44 hurricane-affected key sites on Dominica. We processed the imagery using structure from motion (SfM) as well as a purpose-built Python script for automated processing, enabling rapid data turnaround. We also compared the data to an earlier UAV survey undertaken shortly before hurricane Maria and established ways to co-register the imagery, in order to provide accurate change detection data sets. Consequently, our approach has had to differ considerably from the previous studies that have assessed the accuracy of UAV-derived data in relatively undisturbed settings. This study therefore provides an original contribution to UAV-based research, outlining a robust aerial methodology that is potentially of great value to post-disaster damage surveys and geomorphological change analysis. Our findings can be used (1) to utilise UAV in post-disaster change assessments; (2) to establish ground control points that enable before-and-after change analysis; and (3) to provide baseline data reference points in areas that might undergo future change. We recommend that countries which are at high risk from natural disasters develop capacity for low-cost UAV surveys, building teams that can create pre-disaster baseline surveys, respond within a few hours of a local disaster event and provide aerial photography of use for the damage assessments carried out by local and incoming disaster response teams

Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions

Assessment of landslide susceptibility for civil protection purposes by means of GIS and statistical analysis: lessons from the Province of Modena, Italy

This paper is focused on the analysis of landslide susceptibility for civil protection purposes. A methodology was developed and applied to support measures aiming at landslide risk mitigation. It is based on GIS and the Weight of Evidence (WofE) method, which was preferred among several other statistical approaches because it is suitable for large areas, easy to interpret and simple to program. The latter feature is important for implementing a GIS tool aimed to facilitate Civil Protection in the updating of susceptibility maps. An application of the methodology was performed in a mountainous and hilly area of the Northern Apennines (Italy) located in the Province of Modena where landslides are a critical issue in terms of civil protection due to the recurrent damages to buildings, roads and infrastructures. According to the Region Emilia-Romagna Landslide Inventory Map (RER LIM), shallow slides and earth flows are by far the most widespread mass movement types. Hence, the susceptibility assessment concerned these two types of movements. The choice of the training set, based on active landslides, took into account possible limitations of the input data. The predisposing factors were lithology, slope, curvature, Slope Position Index, aspect, land use, distance from roads. The validation was conducted through the PRC and SRC curves, and direct checking (comparison with past occurrences, multi-temporal orthophotos and field surveys). The resulting models predicted the location of landslides in an acceptable manner. One map for each type of landslides was produced and afterwards they were combined in a single document to improve their intelligibility in a Civil Protection framework

Qualitative landslide susceptibility assessment by multicriteria analysis : a case study from San Antonio del Sur, Guantánamo, Cuba

Geomorphological information can be combined with decision-support tools to assess landslide hazard and risk. A heuristic model was applied to a rural municipality in eastern Cuba. The study is based on a terrain mapping units (TMU) map, generated at 1:50,000 scale by interpretation of aerial photos, satellite images and field data. Information describing 603 terrain units was collected in a database. Landslide areas were mapped in detail to classify the different failure types and parts. Three major landslide regions are recognized in the study area: coastal hills with rockfalls, shallow debris flows and old rotational rockslides denudational slopes in limestone, with very large deep-seated rockslides related to tectonic activity and the Sierra de Caujerí scarp, with large rockslides. The Caujerí scarp presents the highest hazard, with recent landslides and various signs of active processes. The different landforms and the causative factors for landslides were analyzed and used to develop the heuristic model. The model is based on weights assigned by expert judgment and organized in a number of components such as slope angle, internal relief, slope shape, geological formation, active faults, distance to drainage, distance to springs, geomorphological subunits and existing landslide zones. From these variables a hierarchical heuristic model was applied in which three levels of weights were designed for classes, variables, and criteria. The model combines all weights into a single hazard value for each pixel of the landslide hazard map. The hazard map was then divided by two scales, one with three classes for disaster managers and one with 10 detailed hazard classes for technical staff. The range of weight values and the number of existing landslides is registered for each class. The resulting increasing landslide density with higher hazard classes indicates that the output map is reliable. The landslide hazard map was used in combination with existing information on buildings and infrastructure to prepare a qualitative risk map. The complete lack of historical landslide information and geotechnical data precludes the development of quantitative deterministic or probabilistic models

Airborne laser scanning of forested landslides characterization: terrain model quality and visualization

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 0.04m 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

Comparing two methods to estimate lateral force acting on stabilizing piles for a landslide in the Three Gorges Reservoir, China

Stabilizing piles are used worldwide to stabilize unstable slopes. However to date, there is little coverage in the scientific literature on the effects of the pore water pressure and the shear strength of the slip zone on the lateral force acting on the piles. This paper presents and compares two methods to estimate the lateral force acting on the stabilizing piles. These are the limit equilibrium method (LEM), and the finite element shear strength reduction method (FE-SRM). For both methods, the COMSOL software was used to model the infiltration of reservoir water and rainfall into the sliding mass. The distribution of lateral force was then simulated by the normal stress distribution on the piles with FE-SRM in PLAXIS 8.2 software. The effect of shear strength of the slip zone on the lateral force and its distribution were also studied. The lower Ercengyan landslide in the Three Gorges Reservoir was used as study area, with four different hydrological scenarios based on the reservoir water level fluctuations and rainfall amounts. The results show that the lateral force with the LEM method is slightly greater than with the FE-SRM method. The lateral force reaches a peak when the level of the reservoir rises to 175 m combined with high rainfall amount, and decreases linearly as the effective shear strength of the slip zone increases. The distribution of the lateral force is parabolic, and varies with the shear strength parameters of the slip zone. In conclusion, the LEM and FE-SRM methods can be used to calculate the lateral force acting on the stabilizing piles, and estimate the lateral force distribution. This technique effectively analyzes the behavior of stabilizing piles for landslides. It can be applied in the routine practical design of stabilizing piles in the Three Gorges Reservoir as well as other similar areas

Analysis of the distribution of landslides and landslide dams induced by the Wenchuan Earthquake

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