scholarly journals Landslides triggered by the 12 January 2010 Mw 7.0 Port-au-Prince, Haiti, earthquake: visual interpretation, inventory compiling and spatial distribution statistical analysis

2014 ◽  
Vol 2 (2) ◽  
pp. 1259-1331 ◽  
Author(s):  
C. Xu ◽  
J. B. H. Shyu ◽  
X.-W. Xu
Keyword(s):  
Spatial Distribution ◽  
Slope Angle ◽  
Slope Aspect ◽  
Haiti Earthquake ◽  
Number Density ◽  
Landslide Area ◽  
Seismic Landslides ◽  
Seismic Landslide ◽  
Area Percentage ◽  
Landslide Area Percentage

Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and their erosion thicknesses with topographic factors, seismic parameters, and their distance from roads. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolutions satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various landslide controlling parameters. These controlling parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons of any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to update on the basis of the abundant and more complete co-seismic landslide inventories recently available.

scholarly journals Landslides triggered by the 12 January 2010 Port-au-Prince, Haiti, <i>M</i><sub>w</sub> = 7.0 earthquake: visual interpretation, inventory compiling, and spatial distribution statistical analysis

2014 ◽  
Vol 14 (7) ◽  
pp. 1789-1818 ◽  
Author(s):  
C. Xu ◽  
J. B. H. Shyu ◽  
X. Xu
Keyword(s):  
Spatial Distribution ◽  
Slope Angle ◽  
Slope Aspect ◽  
Haiti Earthquake ◽  
Number Density ◽  
Landslide Area ◽  
Seismic Landslides ◽  
Seismic Landslide ◽  
Area Percentage ◽  
Landslide Area Percentage

Abstract. The 12 January 2010 Port-au-Prince, Haiti, earthquake (Mw= 7.0) triggered tens of thousands of landslides. The purpose of this study is to investigate the correlations of the occurrence of landslides and the thicknesses of their erosion with topographic, geologic, and seismic parameters. A total of 30 828 landslides triggered by the earthquake covered a total area of 15.736 km2, distributed in an area more than 3000 km2, and the volume of landslide accumulation materials is estimated to be about 29 700 000 m3. These landslides are of various types, mostly belonging to shallow disrupted landslides and rock falls, but also include coherent deep-seated landslides and rock slides. These landslides were delineated using pre- and post-earthquake high-resolution satellite images. Spatial distribution maps and contour maps of landslide number density, landslide area percentage, and landslide erosion thickness were constructed in order to analyze the spatial distribution patterns of co-seismic landslides. Statistics of size distribution and morphometric parameters of co-seismic landslides were carried out and were compared with other earthquake events in the world. Four proxies of co-seismic landslide abundance, including landslides centroid number density (LCND), landslide top number density (LTND), landslide area percentage (LAP), and landslide erosion thickness (LET) were used to correlate co-seismic landslides with various environmental parameters. These parameters include elevation, slope angle, slope aspect, slope curvature, topographic position, distance from drainages, lithology, distance from the epicenter, distance from the Enriquillo–Plantain Garden fault, distance along the fault, and peak ground acceleration (PGA). A comparison of these impact parameters on co-seismic landslides shows that slope angle is the strongest impact parameter on co-seismic landslide occurrence. Our co-seismic landslide inventory is much more detailed than other inventories in several previous publications. Therefore, we carried out comparisons of inventories of landslides triggered by the Haiti earthquake with other published results and proposed possible reasons for any differences. We suggest that the empirical functions between earthquake magnitude and co-seismic landslides need to be updated on the basis of the abundant and more complete co-seismic landslide inventories recently available.

Analysis of factors affecting the Spatial Distribution of co-seismic landslides triggered by the 2011 (Mw 6.9) Sikkim earthquake

2021 ◽  
Author(s):  
Saurav Kumar ◽  
Sengupta Aniruddha
Keyword(s):  
Slope Failure ◽  
Slope Angle ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Past Study ◽  
Topographic Wetness Index ◽  
Landslide Occurrence ◽  
Seismic Landslides ◽  
Sikkim Earthquake ◽  
Seismic Landslide

&lt;p&gt;The Himalayan region is known as an earthquake-triggered landslides prone area. It is characterized by high seismicity, large relative relief, steep slopes, and dense precipitation. These seismically triggered landslides are likely to affect substantial societal impacts, including loss of life, damage to houses, public buildings, various lifeline structures like highways, railways tracks, etc. Further, they obstruct post-earthquake emergency response efforts. A past study by Martha et al. 2014 reported that an earthquake of Mw 6.9 in 2011 triggered 1196 landslides in Sikkim which is a part of the eastern Himalayas. The slope failure events are controlled by several factors, which can be grouped into four main classes: seismology, topography, lithology, and hydrology. Each class contains several sub-factors. Having in-depth knowledge of these factors and their influence on the density of landslide events in the affected area due to the 2011 Sikkim earthquake is essential to realize the level of threat of co-seismic landslide due to future earthquakes. Eight landslide controlling factors is considered in this analysis including peak ground acceleration (PGA), slope, aspect, elevation, curvature, lithology, distance from rivers, and topographic wetness index (TWI). Further, the frequency ratio model using the GIS framework is applied to evaluate the contribution of each landslide controlling factor to landslide occurrence. Scatter plots between the number of landslides per km&lt;sup&gt;2&lt;/sup&gt; (LN) and percentage of landslide area (LA) and causative factors indicate that distance from the river, slope angle, and PGA are the dominant factors that control the landslides. The results of the above analysis showed that the majority of co-seismic landslides occurred at slope &gt;30&amp;#176;, preferably in East, Southeast, and South directions and near river within a distance of 1500 m. The detailed study of interactions among these factors can improve the understanding of the mechanisms of co-seismic landslide occurrence in Sikkim and will be useful for producing a co-seismic landslide susceptibility map of the area.&lt;/p&gt;

The Evaluation of Lithology Effects on Wenchuan Seismic Landslides

2012 ◽  
Vol 166-169 ◽  
pp. 2483-2489
Author(s):  
Meng Han ◽  
Xiao Yi Fan
Keyword(s):  
Combined Effects ◽  
Contribution Rate ◽  
Landslide Area ◽  
Height Difference ◽  
Seismic Landslides ◽  
Landslide Volume ◽  
Gravelly Soil ◽  
Property Losses ◽  
Seismic Landslide ◽  
Landslide Development

The landslides caused serious casualties and property losses in Wenchuan earthquake, China. According to the survey data of the landslides, the landslide lithology, contribution rate of lithology, height difference and volume of the landslides were studied. The results indicated that the 13 lithologies had the larger relationship with seismic landslide development. In these lithologies, The Phyllite (Ph) had the greatest contribution rate on the landslide number, landslide area and landslide volume. The Stone soil (Ss) had the largest volume of landslide on unit landslide area. Metamorphic sandstone (Ms), Gravelly soil (Gs), Sandstone (Sa), Phyllite (Ph), Limestone (Li) had the greater combined effects on the landslide development.

scholarly journals Influence of Slope Gradient and Aspect on Soil Organic Carbon Content in the Region of Niš, Serbia

2021 ◽  
Vol 13 (15) ◽  
pp. 8332
Author(s):  
Snežana Jakšić ◽  
Jordana Ninkov ◽  
Stanko Milić ◽  
Jovica Vasin ◽  
Milorad Živanov ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Slope Aspect ◽  
Available Phosphorus ◽  
Organic Carbon Content ◽  
Slope Gradient ◽  
Phosphorus And Potassium ◽  
Uncultivated Land

Topography-induced microclimate differences determine the local spatial variation of soil characteristics as topographic factors may play the most essential role in changing the climatic pattern. The aim of this study was to investigate the spatial distribution of soil organic carbon (SOC) with respect to the slope gradient and aspect, and to quantify their influence on SOC within different land use/cover classes. The study area is the Region of Niš in Serbia, which is characterized by complex topography with large variability in the spatial distribution of SOC. Soil samples at 0–30 cm and 30–60 cm were collected from different slope gradients and aspects in each of the three land use/cover classes. The results showed that the slope aspect significantly influenced the spatial distribution of SOC in the forest and vineyard soils, where N- and NW-facing soils had the highest level of organic carbon in the topsoil. There were no similar patterns in the uncultivated land. No significant differences were found in the subsoil. Organic carbon content was higher in the topsoil, regardless of the slope of the terrain. The mean SOC content in forest land decreased with increasing slope, but the difference was not statistically significant. In vineyards and uncultivated land, the SOC content was not predominantly determined by the slope gradient. No significant variations across slope gradients were found for all observed soil properties, except for available phosphorus and potassium. A positive correlation was observed between SOC and total nitrogen, clay, silt, and available phosphorus and potassium, while a negative correlation with coarse sand was detected. The slope aspect in relation to different land use/cover classes could provide an important reference for land management strategies in light of sustainable development.

scholarly journals How Can Geomorphology Inform Ecological Restoration? A Synthesis of Geophysical and Biological Assessment to Determine Restoration Priorities

2021 ◽  
Author(s):  
◽  
Leicester Cooper
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Reference Site ◽  
Slope Angle ◽  
Slope Aspect ◽  
Total N ◽  
Olsen P ◽  
Restoration Site ◽  
Soil Variables ◽  
Indigenous Forest

<p>The central concern that this study addresses is how an understanding of geomorphological processes and forms may inform ecological restoration; particularly practical restoration prioritisation. The setting is that of a hill country gully system covered in grazing pasture which historically would have been cloaked in indigenous forest. The study examines theory in conjunction with an application using a case study centred on Whareroa Farm (the restoration site) and Paraparaumu Scenic Reserve (the reference site) on the southern Kapiti Coast, north of Wellington. The impact that the change of land use has had on the soil and geomorphic condition of Whareroa and the influence the changes may have on the sites restoration is investigated. The thesis demonstrates a method of choosing reference sites to be used as templates for rehabilitating the restoration site. Geographical Information Systems and national databases are used and supplemented with site inspection. The reference site chosen, Paraparaumu Scenic Reserve, proved to be a good template for the restoration site particularly given that it is located in the midst of a heavily modified area. On-site inspection considering dendritic pattern and floristic composition confirms the database analysis results. Soil variables (bulk density, porosity, soil texture, pH, Olsen P, Anaerobic Mineralisable N, Total N (AMN), Total C and C:N ratio) are investigated and statistical comparisons made between the sites to quantify changes due to land-use change, i.e. deforestation and subsequent pastoral grazing. Factors investigated that may explain the variation in the soil variables were site (land use), hillslope location, slope aspect, and slope angle. Permutation tests were conducted to investigate the relationships between the independent factors and the SQI (dependent soil variables). Land use and slope angle were most frequent significant explanatory factors of variation, followed by hillslope location whilst slope aspect only influenced soil texture. A number of soil variables at Whareroa were found to be outside the expected range of values for an indigenous forest soil including AMN, Total N, Olsen P, and pH ...</p>

scholarly journals Landslides susceptibility modelling using Multivariate Logistic Regression Model in the Sahla Watershed in Northern Morocco

2021 ◽  
Vol 33 ◽  
Author(s):  
Mohammed El-Fengour ◽  
Hanifa El Motaki ◽  
Aissa El Bouzidi
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Google Earth ◽  
Slope Aspect ◽  
Multivariate Logistic Regression Model ◽  
Susceptibility Map ◽  
Potential Hazard ◽  
Topographic Wetness Index ◽  
Landslide Occurrence

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

scholarly journals 3D ESTIMATION OF SLOW GROUND MOTION USING INSAR AND THE SLOPE ASPECT ASSUMPTION, A CASE STUDY: THE PUNCAK PASS LANDSLIDE, INDONESIA

2019 ◽  
Vol IV-2/W5 ◽  
pp. 623-630
Author(s):  
N. H. Isya ◽  
W. Niemeier ◽  
M. Gerke
Keyword(s):  
Continuous Monitoring ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Ground Movement ◽  
Slope Aspect ◽  
Main Body ◽  
Landslide Area ◽  
Phase Filter ◽  
Active Landslide

<p><strong>Abstract.</strong> The Indonesian Centre of Volcanology and Geological Hazard Mitigation classified the Ciloto district as one of the most landslide prone areas in Indonesia. Some evidence of ground movement and the landslide failures occurred in recent years. Thus, continuous monitoring is necessary for supporting the precautions of an upcoming landslide. This study applies Small Baselines - Slowly Decorrelated Phase Filter (SDPF) for InSAR processing both for the ascending and the descending data. The primary objective is to generate horizontal and vertical components of InSAR results from two different tracks and slope aspect information in order to retrieve a projection to the northward direction. We used the available Sentinel-1 SAR data from 2014 until 2018. Combination of two orbits is approached by the surface and the nearest-neighbor gridding method. The 3D components were examined at the Puncak Pass, Ciloto, an active landslide area. For the case study area, it appeared that soil materials transferred slowly from the top of main body landslide to the accumulated zone near to the buildings owned by a local resort. The cumulative 3D displacements for three years were computed for the depleted zone: it moved &amp;minus;47, 23, &amp;minus;10 mm for dU, dE and dN, respectively. Meanwhile, the accumulated zone was considered having the up-lift motion to maximum 43, &amp;minus;13, 7 mm, respectively.</p>

Landslide susceptibility mapping using GIS and Remote Sensing: A case study of the Rangamati District, Bangladesh

2020 ◽  
Author(s):  
Afruja Begum ◽  
Md Shofiqul Islam ◽  
Md. Muyeed Hasan
Keyword(s):  
Remote Sensing ◽  
Water Pressure ◽  
Research Work ◽  
Slope Angle ◽  
Steep Slope ◽  
Landslide Susceptibility Mapping ◽  
Dominant Factor ◽  
Natural Phenomenon ◽  
Slope Aspect ◽  
Gis And Remote Sensing

Abstract The landslide is a natural phenomenon and one of the most commonplace disasters in the Rangamati Hill tract area which appeals for better forecasting and specify the landslide susceptible zonation. This research work examines the application of GIS and Remote Sensing techniques based on different parameters such as altitude, slope angle, slope aspect, rainfall, land-use land-cover (LULC), geology and stream distance by heuristic model to identify the landslide susceptible zones for the study area. Among the parameters, rainfall, steep slope, geology and LULC are the dominant factor that triggering the landslide. Clayey or silty soils of the study area during heavy and prolong rainfall behave a flow of debris due to water pressure within the soil, resulting landslides. Steep slope has greater influences for weather zones of the rock-masses for susceptible landslides. Result and field observation indicate that the population density and LULC has a vital effect on landslide within the study area. However, landslide susceptible zones were created based on the susceptibility map of the study area which shows that about 19.43% of the area are at low susceptible zone, 56.55% of the area are at medium susceptible zone, 19.19% of the area are in the high susceptible zone and 4.81% of the area is at the very high susceptible zone.

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

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