Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification

Sun, Xiaohui; Chen, Jianping; Han, Xudong; Bao, Yiding; Zhou, Xin; Peng, Wei

doi:10.1007/s10064-020-01849-0

Cited by 43 publications

(16 citation statements)

References 37 publications

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“…A frequently used method to delineate slope-units is intersecting the basin polygons derived from the original DEM and the inverse DEM [23,[47][48][49][50]. Slope-units can also be delineated based on curvature [51][52][53]. There is also software that can automatically delineate slope-units [45,54].…”

Section: Methodsmentioning

confidence: 99%

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Lan

2020

IJERPH

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Landslide spatial probability and size are two essential components of landslide susceptibility. However, in existing slope-unit-based landslide susceptibility assessment methods, landslide size has not been explicitly considered. This paper developed a novel slope-unit based approach for landslide susceptibility assessment that explicitly incorporates landslide size. This novel approach integrates the predicted occurrence probability (spatial probability) of landslides and predicted size (area) of potential landslides for a slope-unit to obtain a landslide susceptibility value for that slope-unit. The results of a case study showed that, from a quantitative point of view, integrating spatial probability and size in slope-unit-based landslide susceptibility assessment can bring remarkable increases of AUC (Area under the ROC curve) values. For slope-unit-based scenarios using the logistic regression method and the neural network method, the average increase of AUC brought by incorporating landslide size is up to 0.0627 and 0.0606, respectively. Slope-unit-based landslide susceptibility models incorporating landslide size had utilized the spatial extent information of historical landslides, which was dropped in models not incorporating landslide size, and therefore can make potential improvements. Nevertheless, additional case studies are still needed to further evaluate the applicability of the proposed approach.

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Section: Methodsmentioning

confidence: 99%

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Lan

2020

IJERPH

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“…8(e), resulting in better recognition results. From Table Ⅷ, it can be seen that the kappa coefficients of FCN-8s and SegNet in the Jiuzhaigou area were both below 0.8, e.g., they did not meet the consistency requirement [71]. In contrast, U-Net, ResU-Net and FC-DenseNet all obtained a better recognition accuracy.…”

Section: Accuracy Assessment and Comparisonmentioning

confidence: 95%

“…Its value usually falls between 0 and 1, and can be divided into 5 groups to indicate different levels of consistency (0.0~0.2: slight, 0.2~0.4: fair, 0.4~0.6: moderate, 0.6~0.8: substantial, 0.8~1.0: almost perfect). Therefore, when its value is greater than 0.8, the consistency can be considered to be good [71,72]. The formula is as follows:…”

Section: B Evaluation Metrics and Comparison Modelsmentioning

confidence: 99%

“…To quantitatively analyze the performance of the models, we calculated six evaluation factors based on the confusion matrix. As seen in Table Ⅶ, the kappa coefficients of both FCN-8s and SegNet are below 0.8, which means they do not meet the consistency requirement [71]. In practical applications, they can be used at most for landslide location but not for landslide boundary outlining [72].…”

Section: Accuracy Assessment and Comparisonmentioning

confidence: 99%

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Recognition and Mapping of Landslide Using a Fully Convolutional DenseNet and Influencing Factors

Xiao

Chen

Niu

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The recognition and mapping of landslide (RML) is an important task in hazard and risk research and can provide a scientific basis for the prevention and control of landslide disasters. However, traditional RML methods are inefficient, costly, and not intuitive. With the rapid development of computer vision, methods based on convolutional neural networks have attracted great attention due to their numerous advantages. However, problems such as insufficient feature extraction, excessive parameters, and slow model testing have restricted the development of this technology. This research proposes a new RML framework based on a new semantic segmentation network termed the fully convolutional DenseNet (FC-DenseNet). In this network, the features extracted from each layer are repeatedly used in a dense connection, and the parameters are controlled by a bottle-neck structure. Meanwhile, the structure of the encoder-decoder solves the problem of the slowness of model testing. Finally, the landslide influencing factors are added, which enriches the training data. To verify the effectiveness of the proposed method, we focused on several deep networks for comparison and analysis. The results show that FC-DenseNet can better recognize the boundary and interior of landslides, and there are fewer missing and excessive recognition results. The kappa value of the new method is 94.72%in Site 1, which is 6% and 4% higher than that of U-Net and ResU-Net, respectively, and 94.56% in Site 2, which is 6% and 3% higher than that of U-Net and ResU-Net, respectively, indicating that FC-DenseNet has great potential in RML applications.

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“…The average curvature is the average of profile curvature and plan curvature [35]. Thus, its maximum and minimum can indicate the ridge line, valley line, tableland margin and wide valley margin [36]. In this way, the study area can be divided into slope units by using the curvature method.…”

Section: The Mapping Unitmentioning

confidence: 99%

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model

Chen

2020

Symmetry

Self Cite

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Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.

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Landslide susceptibility mapping along the upper Jinsha River, south-western China: a comparison of hydrological and curvature watershed methods for slope unit classification

Cited by 43 publications

References 37 publications

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

Recognition and Mapping of Landslide Using a Fully Convolutional DenseNet and Influencing Factors

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model

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