First insights on the potential of Sentinel-1 for landslides detection

This paper illustrates the potential of Sentinel-1 for landslide detection, Accepted 23 March 2016 mapping and characterization with the aim of updating inventory maps and monitoring landslide activity. The study area is located in Molise, one of the smallest regions of Italy, where landslide processes are frequent. The results achieved by integrating Differential Synthetic Aperture Radar Interferometry (DInSAR) deformation maps and time series, and Geographical Information System (GIS) multilayer analysis (optical, geological, geomorphological, etc.) are shown. The adopted methodology is described followed by an analysis of future perspectives. Sixty-two landslides have been detected, thus allowing the updating of pre-existing landslide inventory maps. The results of our ongoing research show that Sentinel-1 might represent a significant improvement in terms of exploitation of SAR data for landslide mapping and monitoring due to both the shorter revisit time (up to 6 days in the close future) and the wavelength used, which determine an higher coherence compared to other SAR sensors

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model

Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

Mapping of Landslide Hazard Distribution in Alo Watershed Gorontalo Regency

Landslide occurrence can be influenced by physical factors and human activities. Thus, research related to the provision of information about landslide distribution in Alo watershed is needed as a basis in enhancing community preparedness in dealing with disasters. The method used in this study is the scoring method based on the Minister of Public Works Regulation No.22 / PRT / M / 2017 which is processed through a geographical information system through the overlay of all physical parameters. The result shows that the Alo watershed area is divided into three vulnerability categories. "Low" category covers 7171.8 ha, "medium" category covers 12008.7 ha, and "high" category covers 5039.5 ha out of 24.221 ha the total area of Alo watershed. Information provided in this research is expected to be able to help the local government in making policies in managing the Alo watershed area and enhancing the understanding of the local community in Alo watershed in dealing with disasters

Landslide mapping for susceptibility and hazard assessment: North York Moors, UK

The British Geological Survey (BGS) has developed a multi-stage methodology for landslide mapping by augmenting traditional mapping techniques with new geospatial technologies. This allows better characterisation and understanding of the country’s landslides: an essential requirement for landslide susceptibility modelling, risk assessment and resilient infrastructure planning. The BGS methodology has most recently been applied to the North York Moors National Park in northern England, UK: an area with steep slopes, landslide-prone lithologies and an exposed coastal section but few recorded landslide events. Over 550 landslides have now been identified and data on the characteristics and mechanisms of these have been used to inform hazard assessments and susceptibility modelling research including the National Landslide Database, the National Landslide Domains Map and the National Geohazard Assessment

Automated spatiotemporal landslide mapping over large areas using RapidEye time series data

In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale

Mapping a nation’s landslides: a novel multi-stage methodology

Through combining new technologies and traditional mapping techniques, the British Geological Survey (BGS) has developed a novel, multi-stage methodology for landslide mapping. 3-D aerial photograph interpretation, variable-perspective 3-D topographic visualisation and field mapping with digital data capture are being used to map the UK’s landslides. The resulting ESRI ArcGIS polygons are published on BGS 1:50,000 geological maps and as digital data products. Data collected during mapping are also uploaded directly into the National Landslides Database maintaining a systematic, nationally-uniform landslide inventory. Repeat monitoring of selected landslides using terrestrial LIDAR and dGPS allows the database to be frequently updated and the proactive Landslide Response Team means that new landslide events can be mapped within days, if not hours, of their occurrence. The long-term aim is to apply this methodology throughout the UK, providing a wealth of data for scientific research and hazard assessment. This methodology is also suitable for application in an international context

Spatiotemporal evolution, mineralogical composition, and transport mechanisms of long-runout landslides in Valles Marineris, Mars

Long-runout landslides with transport distances of >50 km are ubiquitous in Valles Marineris (VM), yet the transport mechanisms remain poorly understood. Four decades of studies reveal significant variation in landslide morphology and emplacement age, but how these variations are related to landslide transport mechanisms is not clear. In this study, we address this question by conducting systematic geological mapping and compositional analysis of VM long-runout landslides using high-resolution Mars Reconnaissance Orbiter imagery and spectral data. Our work shows that: (1) a two-zone morphological division (i.e., an inner zone characterized by rotated blocks and an outer zone expressed by a thin sheet with a nearly flat surface) characterizes all major VM landslides; (2) landslide mobility is broadly dependent on landslide mass; and (3) the maximum width of the outer zone and its transport distance are inversely related to the basal friction that was estimated from the surface slope angle of the outer zone. Our comprehensive Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) compositional analysis indicates that hydrated silicates are common in landslide outer zones and nearby trough-floor deposits. Furthermore, outer zones containing hydrated minerals are sometimes associated with longer runout and increased lateral spreading compared to those without detectable hydrated minerals. Finally, with one exception we find that hydrated minerals are absent in the inner zones of the investigated VM landslides. These results as whole suggest that hydrated minerals may have contributed to the magnitude of lateral spreading and long-distance forward transport of major VM landslides

Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations

Earthquake‐induced landslide scenarios for seismic microzonation. Application to the Accumoli area (Rieti, Italy)

Scenarios of earthquake-induced landslides are necessary for seismic microzonation (SM) studies since they must be integrated with the mapping of instability areas. The PARSIFAL (Probabilistic Approach to pRovide Scenarios of earthquake‐Induced slope FAiLures) approach provides extensive analyses, over tens to thousands of square kilometers, and is designed as a fully comprehensive methodology to output expected scenarios which depend on seismic input and saturation conditions. This allows to attribute a rating, in terms of severity level, to the landslide-prone slope areas in view of future engineering studies and designs. PARSIFAL takes into account first-time rock- and earth-slides as well as re-activations of existing landslides performing slope stability analyses of different failure mechanisms. The results consist of mapping earthquake-induced landslide scenarios in terms of exceedance probability of critical threshold values of co-seismic displacements (P[D≥Dc|a(t),ay]). PARSIFAL was applied in the framework of level 3 SM studies over the municipality area of Accumoli (Rieti, Italy), strongly struck by the 2016 seismic sequence of Central Apennines. The use of the PARSIFAL was tested for the first time to screen the Susceptibility Zones (ZSFR) from the Attention Zones (ZAFR) in the category of the unstable areas, according to the guidelines by Italian Civil Protection. The results obtained were in a GIS-based mapping representing the possibility for a landslide to be induced by an earthquake (with a return period of 475 years) in three different saturation scenarios (i.e. dry, average, full). Only 41% of the landslide-prone areas in the Municipality of Accumoli are existing events, while the remaining 59% is characterized by first-time earth- or rock-slides. In dry conditions, unstable conditions or P[D≥Dc|a(t),ay]>0 were for 54% of existing landslides, 17% of first-time rock-slides and 1% of first-time earth- slides. In full saturation conditions, the findings are much more severe since unstable conditions or P[D≥Dc|a(t),ay]>0 were found for 58% of the existing landslides and for more than 80% of first-time rock- and earth-slides. Moreover, comparison of the total area of the ZAFR versus ZSFR, resulted in PARSIFAL screening reducing of 22% of the mapped ZAFR