Landslide inventory map for the Upper Muddy Creek Area, Sanpete and Sevier Counties, Utah

2015 ◽  
Keyword(s):  
Landslide Inventory ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Muddy Creek

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 Landslide inventory map of the upper Sinni River valley, Southern Italy

2014 ◽  
Vol 11 (3) ◽  
pp. 444-453 ◽  
Author(s):  
Michele Santangelo ◽  
Dario Gioia ◽  
Mauro Cardinali ◽  
Fausto Guzzetti ◽  
Marcello Schiattarella
Keyword(s):  
Southern Italy ◽  
River Valley ◽  
Landslide Inventory ◽  
Landslide Inventory Map ◽  
Inventory Map

scholarly journals Loess Landslide Inventory Map Based on GF-1 Satellite Imagery

2017 ◽  
Vol 9 (4) ◽  
pp. 314 ◽  
Author(s):  
Wenyi Sun ◽  
Yuansheng Tian ◽  
Xingmin Mu ◽  
Jun Zhai ◽  
Peng Gao ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Landslide Inventory ◽  
Loess Landslide ◽  
Landslide Inventory Map ◽  
Inventory Map

scholarly journals Landslide inventory map for the Briga and the Giampilieri catchments, NE Sicily, Italy

2012 ◽  
Vol 8 (2) ◽  
pp. 176-180 ◽  
Author(s):  
F. Ardizzone ◽  
G. Basile ◽  
M. Cardinali ◽  
N. Casagli ◽  
S. Del Conte ◽  
...  
Keyword(s):  
Landslide Inventory ◽  
Ne Sicily ◽  
Landslide Inventory Map ◽  
Inventory Map

scholarly journals Characterization of Susceptible Landslide Zones by an Accumulated Index

2020 ◽  
Author(s):  
Rocío N. Ramos-Bernal ◽  
René Vázquez-Jiménez ◽  
Sulpicio Sánchez Tizapa ◽  
Roberto Arroyo Matus
Keyword(s):  
Spatial Model ◽  
Central Zone ◽  
Landslide Inventory ◽  
Individual Contribution ◽  
Conditioning Factors ◽  
Jackknife Test ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
The Individual

In order to characterize the landslide susceptibility in the central zone of Guerrero State in Mexico, a spatial model has been designed and implemented, which automatically generates cartography. Conditioning factors as geomorphological, geological, and anthropic variables were considered, and as a detonating factor, the effect of the accumulated rain. The use of an inventory map of landslides that occurred in the past (IL) was also necessary, which was produced by an unsupervised detection method. Before the design of the model, an analysis of the contribution of each factor, related to the landslide inventory map, was performed by the Jackknife test. The designed model consists of a susceptibility index (SI) calculated pixel by pixel by the accumulation of the individual contribution of each factor, and the final index allows the susceptibility cartography to slide in the study area. The evaluation of the obtained map was performed by applying an analysis of the frequency ratio (FR) graphic, and an analysis of the receiver operating characteristic (ROC) curve was developed. Studies like this can help different safeguarding institutions, locating the areas where there is a greater vulnerability according to the considered factors, and integrating disaster attention management or prevention plans.

scholarly journals UPDATING LANDSLIDE INVENTORY MAPS USING HIGH RESOLUTION DIGITAL ORTHOPHOTOS AND DIGITAL SURFACE AND ELEVATION MODELING: THE CASE STUDY OF BRUSQUE CITY, SANTA CATARINA STATE, BRAZIL

2021 ◽  
Vol V-3-2021 ◽  
pp. 251-255
Author(s):  
L. S. Osako
Keyword(s):  
Landslide Inventory ◽  
Unconsolidated Sediments ◽  
Santa Catarina ◽  
Digital Elevation ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Gis Environment ◽  
City State ◽  
Shaded Relief

Abstract. This study reports the updating of the landslide inventory map of Brusque city, State of Santa Catarina, Southern Brazil. Twenty-six digital orthophotos acquired in 2010 with a ground resolution of 0.4 meters were analyzed together with shaded relief images obtained by Digital Surface and Digital Elevation modelling with spatial resolution of 1 meter. These remote sensing products were treated, analyzed and visualized in a Geographic Information System – GIS environment. The landslide inventory included a total of 500 landslides, corresponding to a mean density of 1.76 landslides per km2. The total area of landslide occurrences is 0.81 km2, which corresponds to 0.29% of the study area. 0.22 km2 of the total area landslides occur inside the urban perimeter and 0.59 km2 outside Brusque. The geological context and the occurrence of landslides were analyzed together: 277 landslides affect altered metamorphic rocks, 179 landslides granite, and 44 landslides unconsolidated sediments. The updated landslide inventory map showed that 80% of mapped landslides occur in areas of high and moderate susceptibility.

scholarly journals ASSIMILATING GEOSPATIAL METAMODEL AND INVENTORY MAPPING FOR NON-STRUCTURAL MITIGATION OF LANDSLIDE

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 217-228
Author(s):  
N. S. Nasir ◽  
M. F. Abdul Khanan ◽  
S. H. Othman ◽  
M. Z. Abdul Rahman ◽  
K. A. Razak ◽  
...  
Keyword(s):  
Disaster Management ◽  
Aerial Photograph ◽  
Image Interpretation ◽  
Digital Terrain Model ◽  
Shallow Landslide ◽  
Airborne Lidar ◽  
Landslide Inventory ◽  
Landslide Inventory Map ◽  
Inventory Map ◽  
Using Data

<p><strong>Abstract.</strong> In Malaysia, issues related to disaster management are always given attention in society and by the responsible parties. However, in general, citizen do not think of the consequential impact of disaster due to less of knowledge regarding the early phase in disaster management. Therefore, citizen in those areas will be more vulnerable to landslide as the citizen face difficulties in identifying specific areas with the tendency of landslides. This paper presents a geospatial metamodel approach for non-structural mitigation of landslide using data from airborne LiDAR and aerial photograph. Disaster management metamodel with geospatial element combines activity for managing disaster along with geospatial database that makes it handy for appreciating the metamodel. On the other hand, the digital terrain model (DTM) from LiDAR and aerial photograph is required to produce landslide inventory mapping. The case study area is located in Kundasang, Sabah, where landslides occur frequently. In order to get better visual in identifying landslides in the study area, three types of data are required to carry out image interpretation. The three types of data are hillshade, topographic openness and colour composite. The result of the landslide inventory map shows that there are five types of landslide, which is debris flow, debris fall, mud flow, deep-seated landslide and shallow landslide. Finally, the result of landslide inventory map will be integrated into the developed metamodel for presentation to the users. This landslide inventory map is used as a non-structural mitigation step in one of disaster management phases that is suitable to prepare and use in mitigating the landslide hazard impact.</p>

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.

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