Mean slope-angle frequency distribution and size frequency distribution of landslide masses in Higashikubiki area, Japan

Iwahashi, Junko; Watanabe, Shiaki; Furuya, Takahiko

doi:10.1016/s0169-555x(02)00222-2

Cited by 53 publications

(36 citation statements)

References 11 publications

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“…For example, with regard to a sample size of 100 and a subdividing ratio of 5, the regions of acceptance for γ estimated using the double Pareto distribution and the Inverse Gamma distribution are [−14.69, 13.75] and [−18.22, 15.24], respectively. Therefore, comparing the parameters of different subsets of a landslide dataset with an extreme small sample size, for instance less than 100 (Iwahashi et al 2003), is practically statistically meaningless.…”

Section: The Statistical Significance Of Comparing Parametersmentioning

confidence: 99%

“…The following strategies had been adopted to mitigate these complications: 1) using event-based rather than historical landslide datasets (Malamud et al 2004;Ghosh et al 2012); 2) using the same dataset prepared by the same author instead of datasets prepared by different authors (Iwahashi et al 2003;Guzzetti et al 2008;Chen 2009); and 3) using the maximum likelihood estimation (MLE) rather than linear regression to estimate both the power exponent and the rollover (Fiorucci et al 2011;Ghosh et al 2012). Nevertheless, even without these complications, limited sample size can also cast a shadow on the statistics of landslide size.…”

Section: Introductionmentioning

confidence: 99%

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How sample size can effect landslide size distribution

Lan

2016

Geoenviron Disasters

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Background: Landslide size distribution is widely found to obey a negative power law with a rollover in the smaller size, and has been exploited by many researchers to inspect landside physics or to assess landslide erosion and landslide hazard. Yet, sample size has effect on the statistics of landslide size even though we manage to avoid complications associated with landslide datasets and statistical treatments. Results: In this paper, a series of stochastic simulations were implemented to explicitly and systematically quantify the effect of sample size. The results show that, the errors of parameters estimated based on small sample size can be considerably large. For a sample size of 100, the relative error of the estimated landslide erosion rate that has a probability of 50 % can approach 100 %. In addition, small sample size also obscures the statistical significance of the variances in parameters between different subsets of the same dataset. Although inconsistency was found regarding how the power exponent varies with rainfall intensity, numerical results suggest that the variance observed in a dataset with a small sample size may be not statistically significant.

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Section: The Statistical Significance Of Comparing Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How sample size can effect landslide size distribution

Lan

2016

Geoenviron Disasters

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“…FS ≤ 1 represents an unstable slope and can be used for both rainstorm (e.g., Crosta, 1998;Iverson, 2000) and earthquake triggers (e.g., Newmark, 1965;Jibson, 2007). We test different FS formulations (Table 1) to 20 investigate the role of different conditions of the slope instability in the following states: dry infinite slope, submerged infinite slope, infinite slope with seepage parallel to slope and infinite slope with seepage and tree roots (Cruikshank, 2002;Lambe and Whitman, 1969;Turner and Schuster, 1996;Budhu, 2000;Abramson et al, 1995).…”

Section: Initiation Phase (Slope Failure)mentioning

confidence: 99%

“…We test different FS formulations (Table 1) to 20 investigate the role of different conditions of the slope instability in the following states: dry infinite slope, submerged infinite slope, infinite slope with seepage parallel to slope and infinite slope with seepage and tree roots (Cruikshank, 2002;Lambe and Whitman, 1969;Turner and Schuster, 1996;Budhu, 2000;Abramson et al, 1995). The following parameters are involved in the quantification of FS: C, cohesion of dry soil; C, cohesion of saturated soil; t, thickness of the slide; d, vertical distance of the sliding body; γ, unit weight of dry soil; θ, slope gradient; ϕ, angle of internal friction of dry soil; γ t, total unit 25 weight material; γ w, unit weight of water; ϕ, angle of internal friction of saturated soil, (Jibson, 2007). Landslides are initiated at locations that exceed D N = 15 cm (Jibson et al, 2000).…”

Section: Initiation Phase (Slope Failure)mentioning

confidence: 99%

Origin of the power-law exponent in the landslide frequency-size distribution

Jafarimanesh

Mignan

Danciu

2018

Preprint

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Landslide statistics is characterized by a power-law frequency-size distribution (FSD) with power exponent α centered on 2.2-2.4, independently of the landslide trigger. So far, the origin of the α-value, critical to probabilistic hazard assessment, remains hypothetical. We present a generic landslide cellular automaton (LSgCA) based on the rules of Self 10Organized Criticality and on the Factor of Safety (FS) concept. We show that it reproduces the power-law FSD for realistic parameter ranges (i.e. cohesion, soil unit weight, soil thickness, angle of friction, slope angle, pore water pressure) with LSgCA simulations yielding α = 2.17±0.49, which is in agreement with α = 2.21±0.53 obtained from an updated metaanalysis of the landslide literature. The parameter α remains stable despite changes in the landslide triggering process, with the trigger only influencing the spatial extent of the landslide initiation phase defined from an FS contour. Furthermore, 15 different FS formulations do not significantly alter the results. We find that α is constrained during the initiation phase of the landslide by the fractal properties of the topography, as we observed a positive correlation between fractal dimension and α while α did not change during the propagation phase of the LSgCA. Our results thus suggest that α can be estimated directly from the FS map for probabilistic landslide hazard assessment. However full modeling (including the propagation phase) would be required to combine the spatial distributions of landslide and exposure in probabilistic risk analysis. 20

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“…The variability in slope is used to detect and extract landslide scarps [96][97][98]. The slope is often initially employed to identify landslides [99], based on the assumption that the slope changes abruptly between two successive scarps and that scarps become more distinct from their surroundings as they evolve. The slope angle for each point in an image-based point cloud can be estimated using Equation (7) because the Eigenvector for each point is calculated based on the PCA analysis in the previous step.…”

Section: Eigenvalue Ratiosmentioning

confidence: 99%

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

et al. 2016

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Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ě λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes.

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Mean slope-angle frequency distribution and size frequency distribution of landslide masses in Higashikubiki area, Japan

Cited by 53 publications

References 11 publications

How sample size can effect landslide size distribution

How sample size can effect landslide size distribution

Origin of the power-law exponent in the landslide frequency-size distribution

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

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