# Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. LSP Modeling Process

- (1)
- Landslide inventory and related environmental factors in the study area were obtained to build the spatial datasets for LSP modelling, and the FR method is adopted to calculate their correlation;
- (2)
- Neighborhood characteristics were performed on spatial datasets (the LAI was considered, and neighborhood analysis was performed). The LAI is an improvement compared to FR. The neighborhood analysis used the obtained neighborhood environmental factors as the extended environmental factors; other topographic and hydrologic environmental factors were extracted based on elevation. Then, the FRs of each environmental factor were calculated under the original spatial datasets, as well as the spatial datasets considering the neighborhood characteristics;
- (3)
- The FRs of environmental factors under 10 combination conditions were taken as input variables. The 10 combination conditions were described as the following models, namely the slope and grid-based machine learning model, the slope–neighborhood factors, slope–landslide aggregation, and the slope–neighborhood datasets-based machine learning model;
- (4)
- Uncertainty analysis was carried out for the LSP results under various combination conditions, including the accuracy evaluation, statistical differences, and the distribution patterns of the LSIs.

#### 2.2. Mapping Unit

#### 2.3. Neighborhood Characteristics of Spatial Datasets

#### 2.3.1. Landslide Aggregation Index

_{ij}is the frequency ratio in class i interval of the j environmental factor, ${l}_{ij}$ is the number of landslides occurring in class i interval of the j environmental factor, ${s}_{ij}$ is the total number of landslides in the study area, $S$ is the total area of the study area, and $L$ is the area in class i interval of the j environmental factor. However, it is not appropriate to quantify the importance of each interval of environmental factor for landslide occurrence by only using the FR, which ignores the spatial aggregation of landslides. Due to the complexity of their formation environment, landslides often show a high degree of heterogeneity in space. As a result, the spatial distribution of landslides is usually not completely chaotic but aggregated at different scales [14]. The expression of the FR does not consider the spatial aggregation of landslides in this case.

_{a}represents the adjusted frequency ratio; in this way, the influence of anomalous high values of frequency ratio for classes with clustered landslides can be reduced.

#### 2.3.2. Neighborhood Analysis

#### 2.4. Acquisition of Environmental Factors Based on Remote Sensing and GIS

#### 2.4.1. Acquisition of Topographic Factors

#### 2.4.2. Acquisition of Geological and Hydrological Environment Factors

#### 2.4.3. Acquisition of Surface Cover Factors

#### 2.5. Machine Learning Models

#### 2.5.1. SVM

_{i}, y

_{i}) is supposed, where i = 1, 2$\dots $, and n, x

_{i}is an input vector containing 11 landslide environment factors. The cases y

_{i}=1, −1 are two corresponding output classes which represent the landslide and non-landslide. Here, n is the number of training datasets, and the goal of SVM is to find an n-dimensional hyperplane to distinguish the maximum difference between the two classes. Mathematically, it can be represented as follows:

#### 2.5.2. RF Model

_{1}, D

_{1}, …, D

_{k}from the total training datasets D using bootstrap sampling and pre-built k classification trees; (2) randomly select m from n indicators at each node of the classification tree and then choose the optimal segmentation indicator to segment; (3) repeat step (2) and traverse the pre-built k classification trees, which forms the random forest; (4) use the k trees in the random forest to judge the new data and finally vote to confirm the category.

#### 2.6. Uncertainty Evaluation Indexes

#### 2.6.1. ROC

#### 2.6.2. Distribution Patterns of LSIs

#### 2.6.3. Statistical Differences

## 3. Materials

#### 3.1. Description of Chongyi County

^{2}. The terrain of Chongyi County is mostly hilly and mountainous, with an altitude of 900~2600 m. The terrain of the county tilts from southwest to northeast. The landform types are mainly low and medium altitude mountains (≥500 m), high hills (300~500 m), and valley terraces (≤300 m), accounting for 47.67%, 45.06% and 7.27% of the total area, respectively. The geological units of the area are complex, and mainly include the magmatic rock, carbonate rock, clastic rock, and metamorphic rock. Chongyi County is located at low to mid-latitudes and belongs to the central subtropical monsoonal humid zone with abundant rainfall. The average rainfall from 1980 to 2021 reaches 1629.6 mm, and the average annual temperature is about 17.8 °C. The land cover types mainly include forests, agricultural land, buildings, etc.

#### 3.2. Landslide Inventory Information

#### 3.3. Landslide Environmental Factors

#### 3.3.1. Topographic Factors

#### 3.3.2. Hydrological and Geological Factors

#### 3.3.3. Surface Cover Factors

## 4. LSP Results

#### 4.1. Spatial Datasets Preparation

#### 4.2. LSP by SVM Model

#### 4.3. LSP by RF Model

## 5. Discussion

#### 5.1. Comparative Analysis of AUC Accuracy

#### 5.2. Distribution Patterns of LSIs

_{(slope-based RF)}> mean

_{(slope–landslide aggregation-based RF)}> mean

_{(slope-neighborhood factors-based RF)}> mean

_{(slope-neighborhood datasets-based RF)}. The LSIs of slope–neighborhood datasets-based RF are mostly distributed in the very low and low susceptibility intervals, ranging from 0 to 0.2, and the high and very high susceptibility intervals are less distributed. These findings indicate that the LSIs predicted by the slope–neighborhood datasets-based RF are generally small. Combined with the AUC values, it can be seen that the slope-neighborhood datasets-based RF model has a strong ability to predict the susceptibility of landslides. In addition, the dispersion degree of LSIs is opposite to the mean value, as follows: SD

_{(slope–neighborhood datasets-based RF)}> SD

_{(slope–neighborhood factors-based RF)}> SD

_{(slope–landslide aggregation-based RF)}> SD

_{(slope-based RF)}. This shows that the LSIs predicted by the slope–neighborhood datasets-based RF have a better discrimination, and the model can better reflect the difference of LSIs in different hydrological slope units with a lower uncertainty. This fact also indicates that the slope–neighborhood datasets-based RF can reflect more landslide inventory information with fewer high LSIs, and that it has a better effect on the regional LSP.

#### 5.3. Analysis of Statistical Differences in LSP Results

#### 5.4. Influence of Evaluation Units on LSP Results

#### 5.5. Comprehensive Discussion of LSP Results

#### 5.6. For Further Study

- (1)
- High-resolution RS images, satellite-borne interferometric radar measurements, lidar, and airborne laser altimeters are expected to be used to improve the identification and monitoring of landslides, and more advanced remote sensing interpretation technology will be combined with a local geological field survey to improve the landslide inventory information in the study area [62,63]. Manual mapping will be introduced on the basis of GIS spatial analysis to improve the professional level of interpreters. Then, a comprehensive verification of the obtained landslide inventory and the actual situation of landslide occurrence will be carried out to further improve the accuracy of landslide samples [64].
- (2)
- The nonlinear correlation analysis between landslides and environmental factors is an important link between the occurrence of landslides and the environmental factors of landslides, and the coupling values can be directly used as the input variables of the LSP modelling [65]. The frequency ratio, information value, and weight of evidence are commonly used connection methods [20]. Each connection method has its own data processing principle, and different connection methods can be used for the LSP modelling in further studies to avoid the uncertainty caused by environmental factor connection methods.
- (3)
- The hydrologic slope units can effectively reflect the physical relationship between landslides and basic topographic elements and, as such, have received extensive attention in terms of LSP modelling. Although the hydrological method has been used to extract slope units, an effective and automatic extraction of slope units is difficult and urgent. To overcome this problem, an innovative multi-scale segmentation (MSS) method [30,47] is proposed to extract slope units.
- (4)
- Shortcomings still exist in the LSP modelling with conventional machine learning models, such as the insufficient landslide samples and low accuracy of non-landslide samples selected randomly and subjectively. The existing studies show that a combination with more advanced semi-supervised machine learning models and deep learning can improve the prediction ability of landslide susceptibility [66].
- (5)
- In previous studies, neighborhood analysis is seldom considered in the LSP modelling. Ten types of statistical analysis can be carried out based on neighborhood, including majority, maximum, mean, standard deviation, etc. [40]. In this study, the standard deviation is taken within the range of 3 × 3 rectangles. Different values, such as the mean and extreme value, can be taken into account for the LSP modelling in different shape ranges in the next study.

## 6. Conclusion

- (1)
- The models based on hydrological slope units that consider the neighborhood characteristics of spatial datasets have a higher prediction accuracy and a lower uncertainty than the models that consider a certain neighborhood characteristic alone or not at all. It can be seen that, compared with directly performing the LSP modelling on original datasets, considering the neighborhood characteristics of spatial datasets can predict more accurate and reliable susceptibility results, and the predicted LSIs are more consistent with the actual landslide probability distribution.
- (2)
- For the LSP modelling, the uncertainty patterns of the LSP results predicted by the RF and SVM models are consistent. However, compared with SVM models, RF models have a higher prediction accuracy under various combination conditions, with smaller mean values and larger standard deviations. Moreover, the slope–neighborhood datasets-based RF model reflects a more accurate distribution pattern of landslide susceptibility than other models.

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 5.**Environmental factors: (

**a**) elevation; (

**b**) slope; (

**c**) aspect; (

**d**) profile curvature; (

**e**) topographic relief; (

**f**) lithology; (

**g**) annual rainfall; (

**h**) NDVI; (

**i**) NDBI. (Road density, plane curvature and river density are not present).

**Figure 6.**Slope units extracted by the hydrological method. (

**a**) Hydrological-Case1; (

**b**) Hydrological-Case2.

**Figure 7.**LSP under different combination conditions based on the SVM model. (

**a**) Slope-based SVM; (

**b**) Slope–landslide aggregation-based SVM; (

**c**) Slope–neighborhood factors-based SVM; (

**d**) Slope–neighborhood datasets-based SVM.

**Figure 8.**LSP under different combination conditions based on the RF model. (

**a**) Slope-based RF; (

**b**) Slope–landslide aggregation-based RF; (

**c**) Slope–neighborhood factors-based RF; (

**d**) Slope–neighborhood datasets-based RF.

**Figure 10.**Distribution patterns of LSIs based on the RF model. (

**a**) Grid-based RF; (

**b**) Slope-based RF; (

**c**) Slope–landslide aggregation-based RF; (

**d**) Slope–neighborhood factors-based RF; (

**e**) Slope–neighborhood datasets-based RF.

**Figure 11.**Landslide susceptibility map results using the grid units. (

**a**) Grid-based SVM; (

**b**) Grid-based RF.

Conditional Factors | Values | FR | LAI | FR_{a} |
---|---|---|---|---|

Elevation (m) | 143~329 | 2.559 | 0.072 | 0.183 |

329~452 | 0.920 | 0.027 | 0.025 | |

452~582 | 0.470 | 0.015 | 0.007 | |

582~724 | 0.538 | 0.017 | 0.009 | |

724~866 | 0.484 | 0.014 | 0.007 | |

866~1015 | 0.308 | 0.010 | 0.003 | |

1015~1218 | 0.101 | 0.003 | 0.000 | |

>1218 | 0.000 | 0.000 | 0.000 | |

Slope (°) | 0~4 | 1.324 | 0.042 | 0.055 |

4~8 | 2.249 | 0.069 | 0.155 | |

8~13 | 1.352 | 0.038 | 0.052 | |

13~17 | 0.678 | 0.021 | 0.014 | |

17~22 | 0.837 | 0.026 | 0.022 | |

22~26 | 0.562 | 0.018 | 0.010 | |

26~31 | 0.642 | 0.020 | 0.013 | |

>31 | 0.000 | 0.000 | 0.000 | |

Lithology | Magmatic rocks | 0.577 | 0.016 | 0.009 |

Metamorphic rocks | 1.135 | 0.032 | 0.037 | |

Clastic rocks | 1.038 | 0.033 | 0.034 | |

Carbonate rocks | 4.332 | 0.136 | 0.591 | |

Water | 0.349 | 0.011 | 0.004 | |

NDVI | 0.02~0.13 | 0.000 | 0.000 | 0.000 |

0.13~0.21 | 1.765 | 0.056 | 0.098 | |

0.21~0.26 | 3.012 | 0.092 | 0.276 | |

0.26~0.30 | 1.852 | 0.055 | 0.102 | |

0.30~0.32 | 0.930 | 0.029 | 0.027 | |

0.32~0.34 | 0.794 | 0.024 | 0.019 | |

0.34~0.37 | 0.782 | 0.025 | 0.019 | |

0.37~0.44 | 0.335 | 0.011 | 0.004 | |

Gully density | 0~0.23 | 0.177 | 0.006 | 0.001 |

0.23~0.42 | 0.448 | 0.011 | 0.005 | |

0.42~0.59 | 0.973 | 0.028 | 0.028 | |

0.59~0.74 | 1.174 | 0.032 | 0.038 | |

0.74~0.89 | 0.722 | 0.022 | 0.016 | |

0.89~1.04 | 1.398 | 0.041 | 0.057 | |

1.04~1.22 | 1.399 | 0.043 | 0.060 | |

1.22~1.60 | 2.004 | 0.060 | 0.120 | |

Road density | 0~0.24 | 0.222 | 0.005 | 0.001 |

0.24~0.52 | 0.681 | 0.019 | 0.013 | |

0.52~0.77 | 0.778 | 0.022 | 0.017 | |

0.77~1.02 | 0.790 | 0.023 | 0.019 | |

1.02~1.28 | 1.352 | 0.041 | 0.055 | |

1.28~1.57 | 1.332 | 0.040 | 0.053 | |

1.57~1.97 | 2.219 | 0.066 | 0.146 | |

1.97~2.75 | 2.634 | 0.078 | 0.205 |

Neighborhood Factors | Values | FR | LAI | FR_{a} |
---|---|---|---|---|

Standard deviation of elevation (m) | 0.000~3.355 | 1.727 | 0.0735 | 0.127 |

3.355~4.959 | 2.116 | 0.0274 | 0.058 | |

4.959~6.272 | 0.917 | 0.0127 | 0.012 | |

6.272~7.511 | 0.755 | 0.0169 | 0.013 | |

7.511~8.824 | 0.713 | 0.0122 | 0.009 | |

8.824~10.283 | 0.994 | 0.0117 | 0.012 | |

10.283~12.179 | 0.439 | 0.0032 | 0.001 | |

12.179~18.596 | 0.340 | 0.0000 | 0.000 | |

Standard deviation of slope (°) | 0.000~1.334 | 2.845 | 0.0417 | 0.119 |

1.334~1.990 | 2.643 | 0.1089 | 0.288 | |

1.990~2.387 | 1.406 | 0.0346 | 0.049 | |

2.387~2.738 | 0.831 | 0.0182 | 0.015 | |

2.738~3.066 | 0.788 | 0.0196 | 0.015 | |

3.066~3.417 | 0.345 | 0.0129 | 0.004 | |

3.417~3.909 | 0.743 | 0.0505 | 0.038 | |

3.909~5.968 | 0.916 | 0.0000 | 0.000 | |

Standard deviation of NDVI | 0.001~0.017 | 0.725 | 0.0213 | 0.015 |

0.017~0.021 | 0.750 | 0.1204 | 0.090 | |

0.021~0.024 | 0.764 | 0.0917 | 0.070 | |

0.024~0.027 | 0.844 | 0.0480 | 0.041 | |

0.027~0.030 | 0.745 | 0.0258 | 0.019 | |

0.030~0.036 | 2.356 | 0.0203 | 0.048 | |

0.036~0.045 | 4.256 | 0.0197 | 0.084 | |

0.045~0.069 | 3.177 | 0.0363 | 0.115 | |

Standard deviation of gully density | 0.000~0.005 | 2.104 | 0.0056 | 0.012 |

0.005~0.008 | 0.861 | 0.0132 | 0.011 | |

0.008~0.011 | 0.571 | 0.0278 | 0.016 | |

0.011~0.014 | 0.680 | 0.0324 | 0.022 | |

0.014~0.017 | 0.464 | 0.0244 | 0.011 | |

0.017~0.021 | 1.348 | 0.0379 | 0.051 | |

0.021~0.026 | 1.371 | 0.0453 | 0.062 | |

0.026~0.052 | 8.321 | 0.0601 | 0.500 | |

Standard deviation of road density | 0.000~0.004 | 0.955 | 0.0047 | 0.004 |

0.004~0.008 | 1.137 | 0.0180 | 0.020 | |

0.008~0.011 | 1.028 | 0.0230 | 0.024 | |

0.011~0.014 | 0.588 | 0.0220 | 0.013 | |

0.014~0.017 | 0.861 | 0.0426 | 0.037 | |

0.017~0.021 | 1.236 | 0.0395 | 0.049 | |

0.021~0.027 | 1.615 | 0.0619 | 0.100 | |

0.027~0.044 | 3.389 | 0.0829 | 0.281 |

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## Share and Cite

**MDPI and ACS Style**

Huang, F.; Tao, S.; Li, D.; Lian, Z.; Catani, F.; Huang, J.; Li, K.; Zhang, C.
Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies. *Remote Sens.* **2022**, *14*, 4436.
https://doi.org/10.3390/rs14184436

**AMA Style**

Huang F, Tao S, Li D, Lian Z, Catani F, Huang J, Li K, Zhang C.
Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies. *Remote Sensing*. 2022; 14(18):4436.
https://doi.org/10.3390/rs14184436

**Chicago/Turabian Style**

Huang, Faming, Siyu Tao, Deying Li, Zhipeng Lian, Filippo Catani, Jinsong Huang, Kailong Li, and Chuhong Zhang.
2022. "Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies" *Remote Sensing* 14, no. 18: 4436.
https://doi.org/10.3390/rs14184436