# Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Areas

^{2}and contains 42 catchments with some of the most important rivers of Peloponnese. One of these catchments is the (main) study area which was chosen for the analysis of more detailed scale. With an extent of 366 km

^{2}, this catchment is drained by Selinous River, the largest (with a length of 49 km) Peloponnesian river (Figure 1b).

## 3. Data

#### 3.1. Landslide Inventory

^{3}, respectively. Their size also ranges from 10 to 650 m for the length and from 20 to 580 m for the width.

#### 3.2. Geo-Environmental Factors

## 4. Methodology

_{i}(i = 1, 2,…, n) are the independent variables, b

_{0}is the constant of the model, and b

_{i}(i = 1, 2,…, n) are the coefficients. The coefficient represents a measure of the association between a certain causal factor and the landslide occurrence. For a positive association the coefficient is positive, whereas for a negative association it is negative. A coefficient of or very close to 0 indicates a factor not being influential in landslide occurrence. The LR model estimates the coefficients and statistics, based on the values of independent variables and the status of the dependent variable in a sample of data, using a maximum likelihood method [46]. Using the outcomes derived from the implementation of model on the selected sample, the probability of landslide occurrence can be calculated.

- (a)
- Data sampling for the dependent variable. An important issue in the LR modeling is the sample of data used to create the dependent variable. In this study, each of the two landslide inventories was split into two separate groups: A training dataset with 80% of landslide data for the implementation of the model, and a validation dataset with 20% of landslide data for the evaluation of LS outputs. Thus, for the regional scale analysis, among the 411 landslide location points, 329 points were randomly selected as the training dataset, and the remaining 82 points events made up the validation dataset. On the other hand, for the more detailed scale analysis, among the 76 landslide polygons, 61 polygons were randomly selected as the training dataset, and the remaining 15 polygons made up the validation dataset. These polygons were then converted into points (centroids of grid cells) by tiling the entire study area (Selinous catchment) into grid cells of size 20 m. It resulted to 5140 training landslide points and 446 validation landslide points. Furthermore, for each of the two analyses, an equal number of points from the landslide-not-occurrence part of the corresponding study area was randomly selected for both the training (giving totals of 658 and 10,280 respectively, points) and validation (giving totals of 164 and 892, respectively, points) datasets. The target value of 1 was assigned to the landslide points, while the target value of 0 to the non-landslide points.
- (b)
- Preparation of independent variables. As it was mentioned above, the LR model allows the integration of both continuous and categorical independent variables. However, combining data with different measuring scales can lead to problems in the interpretation of final results [47]. The common method for resolving this issue is to normalize them. Thus, the factor data needed to be categorized and normalized in order to generate an accurate model for both analyses. The GIS-based “Natural Breaks (Jenks)” categorization was preferred for the factors with continuous values (elevation, slope angle, distance to roads, stream density, and NDVI) in both analyses, except for profile curvature factor whose categorization was executed in a manually generalized way based on its presented values. In Natural Breaks, class breaks are identified that best group similar values and that maximize the differences between classes, according to the deviations about the median [48]. Moreover, by grouping the initial categories based on their common characteristics for the regional scale analysis and preserving the initial categories for the more detailed scale analysis, the categorized geology factor was created (Figure 2). The factor data were then normalized in the range 0.1–0.9 by coding and ranking their various categories based on the relative landslide density values.
- (c)
- Creation of input database. The totals of 658 and 10,280 respectively, training points were matched with the relative normalized category values of causal factors, through a GIS-based spatial analysis tool, to create a database for each of the two analyses.
- (d)
- Multicollinearity checking. It was required to check the correlation of independent variables. The calculation of tolerance (TOL) and variance inflation factor (VIF) indexes is the most known method for this purpose [49].
- (e)
- Implementation of LR model. The databases derived from step (c), with the seven normalized causal factors as independent variables, and the presence and absence of landslide (binary target value of 0 and 1) as dependent variable were imported into the LR algorithm within the SPSS 22.2 software package.
- (f)
- Production of final LS map. After assigning coefficients to all the independent variables, a GIS-based weighted overlay was applied using Equation (2). Consequently, by inserting the output into the Equation (1), the final LS map was created for each of the two analyses. These maps were categorized into five categories (“Very Low”, “Low”, “Moderate”, “High” and “Very High” susceptibility) based on the “Natural Breaks (Jenks)” method.
- (g)
- Validation of the models. Validation is an essential process to know the accuracy and prediction ability of the LS assessment models. A validation method, named as receiver operating characteristics (ROC) analysis, has been widely applied to evaluate the overall performance of these models [50,51]. In ROC analysis, the model’s sensitivity is shown as a function of the specificity. The sensitivity refers to the percentage of positively predicted cases among the positive observations, whereas specificity refers to the percentage of negatively predicted cases among the negative observations [52]. The relationship between sensitivity and specificity is graphically represented by the ROC curve. The ROC graph consists of two axes: y-axis represents the sensitivity and x-axis represents the difference 1–specificity. Thus, high sensitivity indicates a high number of correct predictions, and high specificity (low 1−specificity) indicates a low number of incorrect predictions [53]. Among the statistics derived from ROC analysis, the area under the curve (AUC) value also plays a significant role. With a range from 0.5 to 1.0, the higher this value is, the more optimal is the model. In this study, the ROC analysis was applied for both analyses using the relative validation datasets.

## 5. Results

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The landslide inventory map (

**a**) of systems of catchments for the regional scale analysis, including the location map (

**b**) of Selinous catchment for the more detailed scale analysis.

**Figure 2.**Causal factors: (

**a**) Elevation; (

**b**) slope angle; (

**c**) profile curvature; (

**d**) distance to roads; (

**e**) stream density; (

**f**) NDVI; (

**g**) geology.

**Figure 3.**The landslide susceptibility maps produced by the model (

**a**) for the regional scale analysis; (

**b**) for the more detailed scale analysis.

**Figure 4.**Diagrams with: (

**a**) The coverage area percentages; and (

**b**) the percentages of landslide points for landslide susceptibility categories (VL: Very Low, L: Low, M: Moderate, H: High, VH: Very High) derived from the two analyses focusing on the extent of Selinous catchment.

**Figure 5.**Receiver operating characteristics (ROC) curves of the LR model for the regional scale analysis (referring to both the entire system of catchments and Selinous catchment) and the more detailed scale analysis.

**Table 1.**Geographic Information Systems (GIS) Layers of the datasets representing the causal factors.

Type | Factors | Primary Data | Format | |
---|---|---|---|---|

Regional Scale Analysis (90 m Cell Size) | More Detailed Scale Analysis (20 m Cell Size) | |||

Topography | Elevation | SRTM dataset | Vector layers of 5 m contours and elevation points | Grid |

Slope angle | Derived from SRTM DEM | Derived from vector-based DEM | Grid | |

Profile curvature | Derived from SRTM DEM | Derived from vector-based DEM | Grid | |

Hydrology | Stream density | Vector layer of drainage network (General Use Map of Greece 1:250,000 by Hellenic Military Geographical Service) | Vector layer of drainage network (General Use Map of Greece 1:50,000 by Hellenic Military Geographical Service) | Grid |

Road proximity | Distance to roads | Vector layer of main road network (OpenStreetMap) | Vector layer of road network (OpenStreetMap) | Grid |

Geology | Geology | Generalized geological formations (Geological Map of Greece 1:50,000 by Institute of Geology and Mineral Exploration) | Detailed geological formations (Geological Map of Greece 1:50,000 by Institute of Geology and Mineral Exploration) | Vector (polygons) |

Vegetation | NDVI | Landsat-8 images (30 m spatial resolution, taken in February 2016) | Sentinel-2 images (10 m spatial resolution, taken in April 2017) | Grid |

Causal Factors | Regional Scale Analysis (90 m Cell Size) | More Detailed Scale Analysis (20 m Cell Size) | ||
---|---|---|---|---|

TOL | VIF | TOL | VIF | |

Elevation | 0.908 | 1.101 | 0.933 | 1.072 |

Slope angle | 0.891 | 1.122 | 0.748 | 1.337 |

Profile curvature | 0.969 | 1.032 | 0.998 | 1.002 |

Stream density | 0.930 | 1.076 | 0.936 | 1.068 |

Distance to roads | 0.914 | 1.094 | 0.675 | 1.482 |

Geology | 0.930 | 1.075 | 0.757 | 1.321 |

NDVI | 0.940 | 1.063 | 0.904 | 1.106 |

Causal Factors | Coefficients | |
---|---|---|

Regional Scale Analysis (90 m Cell Size) | More Detailed Scale Analysis (20 m Cell Size) | |

Elevation | 2.007 | 9.178 |

Slope angle | 5.664 | 4.474 |

Profile curvature | 2.505 | 3.056 |

Stream density | 4.027 | 4.796 |

Distance to roads | 6.227 | 2.258 |

Geology | 8.019 | 3.475 |

NDVI | 6.856 | 2.880 |

(Constant) | (−8.333) | (−7.309) |

**Table 4.**Coverage percentage (%)-based cross-comparisons for the landslide susceptibility (LS) categories between the Selinous catchment from the regional scale analysis and more detailed scale analysis.

Selinous Catchment from Regional Scale Analysis (90 m Cell Size) | More Detailed Scale Analysis (20 m Cell Size) | ||||
---|---|---|---|---|---|

VL | L | M | H | VH | |

VL | 7.5 | 4.4 | 3.0 | 1.0 | 0.2 |

L | 9.2 | 7.3 | 4.6 | 1.8 | 0.7 |

M | 7.1 | 5.9 | 3.8 | 1.7 | 1.0 |

H | 7.2 | 6.0 | 3.9 | 1.8 | 1.8 |

VH | 5.9 | 5.2 | 3.9 | 2.2 | 2.9 |

**Table 5.**ROC analysis results of the LR model for the regional scale analysis (referring both the entire system of catchments and Selinous catchment) and the more detailed scale analysis.

ROC Analysis Results | Regional Scale Analysis (90 m Cell Size) | Selinous Catchment from Regional Scale Analysis (90 m Cell Size) | More Detailed Scale Analysis (20 m Cell Size) |
---|---|---|---|

Number of cases | 164 | 31 | 892 |

Number correct | 115 | 24 | 679 |

Positive cases missed | 17 | 4 | 72 |

Negative cases missed | 32 | 3 | 141 |

Accuracy (%) | 70.1 | 69.6 | 76.1 |

Sensitivity (%) | 79.3 | 82.6 | 83.9 |

Specificity (%) | 61.0 | 62.5 | 68.4 |

AUC value | 0.77 | 0.74 | 0.84 |

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**MDPI and ACS Style**

Polykretis, C.; Faka, A.; Chalkias, C.
Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece. *Geosciences* **2018**, *8*, 261.
https://doi.org/10.3390/geosciences8070261

**AMA Style**

Polykretis C, Faka A, Chalkias C.
Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece. *Geosciences*. 2018; 8(7):261.
https://doi.org/10.3390/geosciences8070261

**Chicago/Turabian Style**

Polykretis, Christos, Antigoni Faka, and Christos Chalkias.
2018. "Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece" *Geosciences* 8, no. 7: 261.
https://doi.org/10.3390/geosciences8070261