scholarly journals An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping

2020 ◽  
Vol 9 (10) ◽  
pp. 561
Author(s):  
Omid Ghorbanzadeh ◽  
Khalil Didehban ◽  
Hamid Rasouli ◽  
Khalil Valizadeh Kamran ◽  
Bakhtiar Feizizadeh ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Expert Knowledge ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Data Driven ◽  
Susceptibility Maps ◽  
Normalised Difference Vegetation Index ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Sentinel 2

In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

scholarly journals The importance of input data on landslide susceptibility mapping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Krzysztof Gaidzik ◽  
María Teresa Ramírez-Herrera
Keyword(s):  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Input Data ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Topographic Data ◽  
Susceptibility Maps ◽  
Landslide Mass ◽  
The Impact

AbstractLandslide detection and susceptibility mapping are crucial in risk management and urban planning. Constant advance in digital elevation models accuracy and availability, the prospect of automatic landslide detection, together with variable processing techniques, stress the need to assess the effect of differences in input data on the landslide susceptibility maps accuracy. The main goal of this study is to evaluate the influence of variations in input data on landslide susceptibility mapping using a logistic regression approach. We produced 32 models that differ in (1) type of landslide inventory (manual or automatic), (2) spatial resolution of the topographic input data, (3) number of landslide-causing factors, and (4) sampling technique. We showed that models based on automatic landslide inventory present comparable overall prediction accuracy as those produced using manually detected features. We also demonstrated that finer resolution of topographic data leads to more accurate and precise susceptibility models. The impact of the number of landslide-causing factors used for calculations appears to be important for lower resolution data. On the other hand, even the lower number of causative agents results in highly accurate susceptibility maps for the high-resolution topographic data. Our results also suggest that sampling from landslide masses is generally more befitting than sampling from the landslide mass center. We conclude that most of the produced landslide susceptibility models, even though variable, present reasonable overall prediction accuracy, suggesting that the most congruous input data and techniques need to be chosen depending on the data quality and purpose of the study.

scholarly journals Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks

2005 ◽  
Vol 5 (6) ◽  
pp. 979-992 ◽  
Author(s):  
M. Ercanoglu
Keyword(s):  
Black Sea ◽  
Landslide Susceptibility ◽  
Landslide Inventory ◽  
The West ◽  
Data Set ◽  
Black Sea Region ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
West Black Sea ◽  
West Black Sea Region

Abstract. Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN) method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index) were used. To obtain maps of these parameters, Digital Elevation Model (DEM) and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi). Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (rij) value. Much of the areal extents of the landslides (87.2%) were classified as susceptible to landsliding, and rij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.

Influence of DEM spatial resolution on the susceptibility mapping with SHALSTAB in the Rio Garcia hydrographic basin, municipality of Blumenau/SC / Influência de la resolución espacial del MDE en la cartografía de susceptibilidad con SHALSTAB en la cuenca hidrográfica del Río García, municipio de Blumenau/SC

2021 ◽  
Vol 5 (3) ◽  
pp. 1475-1491
Author(s):  
Gisele Marilha Pereira Reginatto ◽  
Regiane Mara Sbroglia ◽  
Camilo Andrade Carreño ◽  
Bianca Rodrigues Schvartz ◽  
Pâmela Betiatto ◽  
...  
Keyword(s):  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Hydrographic Basin ◽  
Susceptibility Maps ◽  
Digital Elevation ◽  
Stability Model ◽  
Elevation Model ◽  
Del Rio

In translational landslide susceptibility analysis with SHALSTAB (Shallow Landsliding Stability Model), the resolution of the digital elevation model (DSM) is determinant for defining the type of mapping generated (preliminary or not). In this study, in order to verify the influence of the SDM scale on the SHALSTAB stability classes, susceptibility maps were prepared at two scales: 1:50,000 and 1:10,000. The study area was the Garcia River watershed, belonging to the municipality of Blumenau, Santa Catarina, affected by landslides in the 2008 catastrophe, which enabled the validation of the simulations with the scars mapped in the field. Thus, the influence of scale on the distribution of the model's stability classes and on its performance was verified. SHALSTAB performed better at the 1:10,000 scale, predicting 70% of the instabilities in a percentage of unstable area approximately three times smaller than at the 1,50,000 scale.

Factors Contributing to Landslide Susceptibility of the Kope Formation, Cincinnati, Ohio

2021 ◽  
Author(s):  
Michael P. Glassmeyer ◽  
Abdul Shakoor
Keyword(s):  
Shear Strength ◽  
Landslide Susceptibility ◽  
Stream Water ◽  
Strength Parameters ◽  
Slake Durability Index ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Colluvial Soil ◽  
Kope Formation ◽  
Representative Samples

ABSTRACT The objective of this study was to evaluate the factors that contribute to the high frequency of landslides in the Kope Formation and the overlying colluvial soil present in the Cincinnati area, southwestern Ohio. The Kope Formation consists of approximately 80 percent shale inter-bedded with 20 percent limestone. The colluvium that forms from the weathering of the shale bedrock consists of a low-plasticity clay. Based on field observations, LiDAR data, and information gathered from city and county agencies, we created a landslide inventory map for the Cincinnati area, identifying 842 landslides. From the inventory map, we selected 10 landslides that included seven rotational and three translational slides for detailed investigations. Representative samples were collected from the landslide sites for determining natural water content, Atterberg limits, grain size distribution, shear strength parameters, and slake durability index. For the translational landslides, strength parameters were determined along the contact between the bedrock and the overlying colluvium. The results of the study indicate that multiple factors contribute to landslide susceptibility of the Kope Formation and the overlying colluvium, including low shear strength of the colluvial soil, development of porewater pressure within the slope, human activity such as loading the top or cutting the toe of a slope, low to very low durability of the bedrock that allows rapid disintegration of the bedrock and accumulation of colluvial soil, undercutting of the slope toe by stream water, and steepness of the slopes.

scholarly journals Characteristic analysis of large-scale loess landslides: a case study in Baoji City of Loess Plateau of Northwest China

2011 ◽  
Vol 11 (7) ◽  
pp. 1829-1837 ◽  
Author(s):  
H. B. Wang ◽  
B. Zhou ◽  
S. R. Wu ◽  
J. S. Shi ◽  
B. Li
Keyword(s):  
Loess Plateau ◽  
Large Scale ◽  
Northwest China ◽  
Landslide Inventory ◽  
Small Scale ◽  
Land Utilization ◽  
Characteristic Analysis ◽  
Landslide Inventory Map ◽  
Loess Landslides ◽  
Inventory Map

Abstract. Landslides are one of the most common geologic hazards in the Loess Plateau of northwest China, especially with some of the highest landslide densities found in Shaanxi and adjacent provinces. Prior to assessing the landslide hazard, a detailed landslide inventory map is fundamental. This study documents the landslides on the northwest Loess Plateau with high accuracy using high-resolution Quickbird imagery for landslide inventory mapping in the Changshou valley of Baoji city. By far the majority of landslides are in loess, representing small-scale planar sliding. Most of the large-scale landslides involve loess and bedrock, and the failure planes occurred either along the contacts between fluvial deposits and Neogene argillites, or partially within the bedrock. In the sliding zones of a large scale landslide, linear striations and fractures of the soils were clearly developed, clay minerals were oriented in the same direction and microorganism growths were present. From the analysis of microstructure of sliding soils, it is concluded that the Zhuyuan landslide can be reactivated if either new or recurring water seepage is caused in the sliding surface. It can be concluded that most landslides are attributed to the undercutting of the slope associated with gullying, and numerous ancillary factors including bedrock-loess interface, slope steepness, vegetation cover and land utilization.

scholarly journals Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

2020 ◽  
Vol 10 (18) ◽  
pp. 6335 ◽  
Author(s):  
Kamila Pawluszek-Filipiak ◽  
Natalia Oreńczak ◽  
Marta Pasternak
Keyword(s):  
Landslide Susceptibility ◽  
Research Question ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
High Susceptibility ◽  
Geological Structures ◽  
Area Index ◽  
Landslide Occurrence ◽  
Very High

To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

scholarly journals GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

2019 ◽  
Vol 11 (1) ◽  
pp. 708-726
Author(s):  
Zorgati Anis ◽  
Gallala Wissem ◽  
Vakhshoori Vali ◽  
Habib Smida ◽  
Gaied Mohamed Essghaier
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Satellite Image ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
North Western ◽  
Landslide Susceptibility Maps

AbstractThe Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.

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