The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea

Lee, Saro; Choi, Jawon; Woo, Ik

doi:10.1007/bf02910278

Cited by 97 publications

(51 citation statements)

References 17 publications

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“…Many studies using remote sensing technology have explored the effects of spatial resolution (Irons et al, 1985;Turner et al, 1989;Benson and MacKenzie, 1995;Atkinson and Curran, 1995;Pax-Lenney and Woodcock, 1996;Qi and Wu, 1996;Schoorl et al, 2000;Chen et al, 2004;Lee et al, 2004;Claessens et al, 2005;Razak et al, 2011;Vander Jagt et al, 2013;Wang et al, 2013) to analyze landscape pattern (Turner et al, 1989;Qi and Wu, 1996) and landslide-susceptibility mapping (Lee et al, 2004;Claessens et al, 2005;Razak et al, 2011;Wang et al, 2013). Although, there have been studies focused on the effects of spatial resolution for landslide susceptibility mapping, to our best knowledge, none have focused on small failures.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

Mora

Lenzano

Toth

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, WG I/2 KEY WORDS: LiDAR, landslide, feature, extraction, spatial, resolution, DEM ABSTRACT:Spatial resolution plays an important role in remote sensing technology as it defines the smallest scale at which surface features may be extracted, identified, and mapped. Remote sensing technology has become a vital component in recent developments for landslide susceptibility mapping. The spatial resolution is essential, especially when landslides are small and the dimensions of slope failures vary. If the spatial resolution is relevant to the surface features found in the landslide morphology, it will help improve the extraction, identification and mapping of landslide surface features. Although, the spatial resolution is a well-known issue, few studies have demonstrated the potential effects it may have on small landslide susceptibility mapping. For these reasons, an evaluation to assess the impact of spatial resolution was performed using data acquired along a transportation corridor in Zanesville, Ohio. Using a landslide susceptibility mapping algorithm, landslide surface features were extracted and identified on a cell-by-cell basis from Digital Elevation Models (DEM) generated at 50, 100, 200 and 400 cm spatial resolution. The performance of the landslide surface feature extraction algorithm was then evaluated using an inventory map and a confusion matrix to assess the effects of spatial resolution. In addition to assessing the performance of the algorithm, we statistically analyzed the surface features and their relevant patterns. The results from this evaluation reveal patterns caused by the varying spatial resolution. From this study we can conclude that the spatial resolution has an effect on the accuracy and surface features extracted for small landslide susceptibility mapping, as the performance is dependent on the scale of the landslide morphology.

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Section: Introductionmentioning

confidence: 99%

Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

Mora

Lenzano

Toth

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, the logical method is to reveal the landslide-responsible pixels. Lee et al [37] suggested that when the scale of the map was 1:5000-1:50,000, the 5 m, 10 m, and 30 m pixel sizes yield similar accuracy. In this study, a pixel size of 30 m × 30 m was adopted, and landslides larger than one cell size were used for the analyses.…”

Section: Conditional Factorsmentioning

confidence: 99%

Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

Deng

Tan

2017

IJGI

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Abstract:In this paper, we propose a methodology for validating landslide susceptibility results in the Pinggu district (Beijing, China). A landslide inventory including 169 landslides was prepared, and eight factors correlated to landslides (lithology, tectonic faults, topographic elevation, slope gradient, aspect, slope curvature, land use, and road network) were processed, integrating two techniques, namely the frequency ratio (FR) and the certainty factor (CF), in a geographic information system (GIS) environment. The area under the curve (success rate curve and prediction curve) analysis was used to evaluate model compatibility and predictability. Validation results indicated that the values of the area under the curve for the FR model and the CF model were 0.769 and 0.768, respectively. Considering spatial correlation, an alternative complementary method for validating landslide susceptibility maps was introduced. The spatially approximate maps could be discriminated from their matrices which carry structural information, and the structural similarity index (SSI) was then proposed to quantify the similarity. As a specific example, the SSI value of the FR (74.15%) scored higher than that of the CF model (69.36%), demonstrating its promise in validating different landslide susceptibility maps. These results show that the FR model outperforms the CF model in producing a landslide susceptibility map in the study area.

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“…Lee et al (2004b) undertook a comparative study of landslide susceptibility assessments at different spatial resolutions: relationship between the landslide distribution and the predictive factors generated by DEM (constructed from a 1:5.000 scale topographic map) and vector data were evaluated with spatial resolutions of 5, 10, 30, 100 and 200 m. The authors determined that a 30 m pixel size was the maximum advised to obtain suitable predictive capacity. For this study, it was decided to use the best precision level of the available data (20 m) as the basic mapping unit.…”

Section: Landslide Predictive Factorsmentioning

confidence: 99%

Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study

Cimini¹,

Portoghesi²,

Madonna³

et al. 2016

iForest

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(3-4) , Piermaria Corona (5) Forests are increasingly valued for services beyond timber and non-timber products including land protection with respect to events such as landslides, soil erosion, floods and avalanches. The most important properties of a protective forest are its ecological and mechanical stability. Planning and implementing multifunctional forest management in protective forests is challenging because of the trade-offs and synergies among the many functions of the forest. In this study, a multifactor empirical method is presented for assessing the protective role of forests on a stand scale with respect to landslide occurrence. Multifactor methodologies typically estimate landslide susceptibility exploiting the relationship between past landslide patterns and site characteristics. Two statistical approaches were here applied to assess the probability of landslide occurrence: the weight-of-evidence technique and the logistic regression technique. Statistical analysis was performed on the basis of landslide detachment zone only. The question of how to estimate protective forest function was answered through the comparison of models established with different sets of predicting factors. This study ultimately aims to provide a decision-support tool focused on mapping the potential role of forests in landslideprone areas. A case study from the Italian Alps was considered. The density of landslide detachment outside forest areas proves to be more than twice than that within forest areas.

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The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea

Cited by 97 publications

References 17 publications

Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping

Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study

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