Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

Lee, Saro

doi:10.1080/01431160412331331012

Cited by 630 publications

(326 citation statements)

References 31 publications

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“…Bivariate and multivariate approaches are the most common statistical techniques, though probabilistic (e.g. Lee 2005;Goetz et al 2011Goetz et al , 2015Youssef et al 2016;Castro Camilo et al 2017) and Machine-Learning approaches (e.g. Lee et al 2004;Ermini et al 2005;Marjanovic et al 2011;Pham et al 2017) are also used in this type of analyses.…”

Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

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In the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.

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Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

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“…Regression is extensively used to model various phenomena such as land use and land cover [4,11], NDVI [12,13], urban heat island [4], and landslide susceptibility [14], but spatial autocorrelation has been barely accommodated in modeling remotely-sensed data. Remotely-sensed data has a strong positive spatial autocorrelation in most cases: even one with a fragmented (e.g., land use) pattern with a coarse resolution (e.g., 250 m of MODIS).…”

Section: Discussionmentioning

confidence: 99%

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

Griffith

Chun

2016

Remote Sensing

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Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes.

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“…This method can also be useful in the identification and extraction of cliff information for other site-specific needs. 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.…”

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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Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

Cited by 630 publications

References 31 publications

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

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

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