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Advancement of Remote Sensing in Landslide Susceptibility Assessment

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 13414

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Special Issue Editors

Dr. Zhijie Zhang

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Guest Editor

Department of Geography, University of Connecticut, U-4148, Storrs, CT, USA
Interests: potential landslide detection; landslide susceptibility mapping; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Yaning Yi

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Guest Editor

National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: landslide detection; landslide susceptibility mapping; deep learning; remote sensing applications

Special Issue Information

Dear Colleagues,

Landslides are one of the most widespread natural disasters that pose severe threats to many areas of the world. Landslide susceptibility assessment has proven helpful in designing landslide mitigation strategies for reducing disaster risk and societal and economic losses. Moreover, landslide susceptibility maps are essential for land use planning, hazard prevention, and risk management. In the last decade, remote sensing technology has achieved rapid development, and more and more high spatial and temporal resolution remote sensing images are available to the public for free. It brings an opportunity for the development of landslide mapping and susceptibility assessment techniques, and it becomes possible to obtain higher spatial and temporal resolution landslide susceptibility results.

This Special Issue is dedicated to sharing the latest advances in remote sensing technology for landslide susceptibility assessment. Contributions focused on improving landslide susceptibility assessment methods (e.g., using machine learning or deep learning approaches) are also welcome, but a detailed description of the innovation of the proposed method is needed. We especially encourage the use of freely available remote sensing data and open-source processing software, as it helps us to conduct analysis anywhere in the world. The data and code are recommended to be uploaded as supplementary material. Review papers will also be considered.

Potential topics for this Special Issue may include, but are not limited to, the following:

Rapid landslide susceptibility mapping using remote sensing data/approaches;
Update of existing landslide susceptibility maps using remote sensing data/approaches;
Dynamic analysis of landslide susceptibility using remote sensing data/approaches;
Application of remote sensing in physical- and statistical-based landslide susceptibility models;
Application of remote sensing in association with artificial intelligence technology in landslide susceptibility assessment.

Dr. Zhijie Zhang
Dr. Yaning Yi
Guest Editors

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Keywords

landslide susceptibility
slope instability
susceptibility modeling
earth observation
machine learning
artificial intelligence

Published Papers (6 papers)

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Research

19 pages, 5391 KiB

Open AccessArticle

TerraSAR-X and GNSS Data for Deformation Detection and Mechanism Analysis of a Deep Excavation Channel Section of the China South–North Water-Diversion Project

by Qingfeng Hu, Yingchao Kou, Jinping Liu, Wenkai Liu, Jiuyuan Yang, Shiming Li, Peipei He, Xianlin Liu, Kaifeng Ma, Yifan Li, Peng Wang, Weiqiang Lu and Hongxin Hai

Remote Sens. 2023, 15(15), 3777; https://doi.org/10.3390/rs15153777 - 29 Jul 2023

Viewed by 936

Abstract

Due to expansive soils and high slopes, the deep excavated channel section of the China South–North Water-Diversion Middle-Route Project has a certain risk of landslide disaster. Therefore, examining the deformation law and mechanism of the channel slope in the middle-route section of the project is an extreme necessity for safe operation. However, the outdated monitoring method limits research on the surface deformation law and mechanism of the entire deep excavation channel section. For these reasons, we introduced a novel approach that combines SBAS-InSAR and GNSS, enabling the surface domain monitoring of the study area at a regional scale as well as real-time monitoring of specific target regions. By using SBAS-InSAR technology and leveraging 11-view high-resolution TerraSAR-X data, we revealed the spatiotemporal evolution law of surface deformations in the channel slopes within the study area. The results demonstrate that the predominant deformation in the study area was uplifted, with limited evidence of subsidence deformation. Moreover, there is a distinct region of significant uplift deformation, with the highest annual uplift rate reaching 19 mm/y. Incorporating GNSS and soil-moisture-monitoring timeseries data, we conducted a study on the correlation between soil moisture and the three-dimensional deformation of the ground surface, revealing a positive correlation between the soil moisture content and vertical displacement of the channel slope. Furthermore, combining field investigations on surface uplift deformation characteristics, we identified that the main cause of surface deformation in the study area was attributed to the expansion of the soil due to water absorption in expansive soils. The research results not only revealed the spatiotemporal evolution law and mechanism of the channel slope deformation in the studied section of the deep excavation channel but also provide successful guidance for the prevention and control of channel slope-deformation disasters in the study area. Furthermore, they offer effective technical means for the safe monitoring of the entire South–North Water-Diversion Middle-Route Project and similar long-distance water-conveyance canal projects. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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21 pages, 7406 KiB

Open AccessArticle

Research on the Uncertainty of Landslide Susceptibility Prediction Using Various Data-Driven Models and Attribute Interval Division

by Yin Xing, Yang Chen, Saipeng Huang, Wei Xie, Peng Wang and Yunfei Xiang

Remote Sens. 2023, 15(8), 2149; https://doi.org/10.3390/rs15082149 - 19 Apr 2023

Cited by 3 | Viewed by 1436

Abstract

Two significant uncertainties that are crucial for landslide susceptibility prediction modeling are attribute interval numbers (AIN) division of continuous landslide impact factors in frequency ratio analysis and various susceptibility prediction models. Five continuous landslide impact factor interval attribute classifications (4, 8, 12, 16, 20) and three data-driven models (deep belief networks (DBN), random forest (RF), and neural network (back propagation (BP)) were used for a total of fifteen different scenarios of landslide susceptibility prediction studies in order to investigate the effects of these two factors on modeling and perform a landslide susceptibility index uncertainty analysis (including precision evaluation and statistical law). The findings indicate that: (1) The results demonstrate that for the same model, as the interval attribute value rises from 4 to 8 and finally to 20, the forecast accuracy of landslide susceptibility initially increases gradually, then progressively grows until stable. (2) The DBN model, followed by the RF and BP models, provides the highest prediction accuracy for the same interval attribute value. (3) AIN = 20 and DBN models have the highest prediction accuracy under 15 combined conditions, while AIN = 4 and BP models have the lowest. The accuracy and efficiency of landslide susceptibility modeling are higher when the AIN = 8 and DBN models are combined. (4) The landslide susceptibility index uncertainty predicted by the deeper learning model and the bigger interval attribute value is comparatively low, which is more in line with the real landslide probability distribution features. The conditions that the environmental component attribute interval is divided into eight parts and DBN models are used allow for the efficient and accurate construction of the landslide susceptibility prediction model. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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20 pages, 16482 KiB

Open AccessArticle

Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models

by Haiwei Yu, Wenjie Pei, Jingyi Zhang and Guangsheng Chen

Remote Sens. 2023, 15(7), 1886; https://doi.org/10.3390/rs15071886 - 31 Mar 2023

Cited by 10 | Viewed by 1890

Abstract

Landslides can cause severe damage to both the environment and society, and many statistical, index-based, and inventory-based methods have been developed to assess landslide susceptibility; however, it is still challenging to choose the most effective method and properly identify major driving factors for specific regions. Here, we applied four machine learning algorithms, adaptive boosting (AdaBoost), gradient-boosting decision tree (GBDT), multilayer perceptron (MLP), and random forest (RF), to predict the landslide susceptibility at 30 m spatial scale based on thirteen landslide conditioning factors (LCFs) in a landslide-vulnerable region. Based on inventory landslide points, the classification results were evaluated, and indicated that the performance of the RF (F1-score: 0.85, AUC: 0.92), AdaBoost (F1-score: 0.83, AUC: 0.91), and GBDT (F1-score: 0.83, AUC: 0.88) methods were significantly better than the MLP (F1-score: 0.76, AUC: 0.79) method. The results further indicated that the areas with high and very high landslide risk (susceptibility greater than 0.5) accounted for about 40% of the study region. All four models matched well and predicted similar spatial distribution patterns in landslide susceptibility, with the very high risk areas mostly distributed in the western and southeastern regions. Daoshi, Qingliangfeng, Jinnan, and Linglong towns have the highest landslide risk, with mean susceptibility levels greater than 0.5. The leading contributing factors to landslide susceptibility were slightly different for the four models; however, population density, distance to road, and relief amplitude were generally among the top leading factors for most towns. Our study provided significant information on the highly landslide-prone areas and the major contributing factors for decision-makers and policy planners, and suggested that different areas should take unique precautions to mitigate or avoid severe damage from landslide events. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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30 pages, 4434 KiB

Open AccessArticle

Bivariate Landslide Susceptibility Analysis: Clarification, Optimization, Open Software, and Preliminary Comparison

by Langping Li and Hengxing Lan

Remote Sens. 2023, 15(5), 1418; https://doi.org/10.3390/rs15051418 - 2 Mar 2023

Cited by 4 | Viewed by 1697

Abstract

Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight “conditional-probability-based” bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a “classification-free” modification, in which factor classifications are avoided and the result is dominated by a single parameter, “bin width”. Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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34 pages, 7094 KiB

Open AccessEditor’s ChoiceArticle

An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru

by Chandan Kumar, Gabriel Walton, Paul Santi and Carlos Luza

Remote Sens. 2023, 15(5), 1376; https://doi.org/10.3390/rs15051376 - 28 Feb 2023

Cited by 13 | Viewed by 2565

Abstract

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were prepared using remotely sensed and auxiliary datasets. The LIFs were evaluated using multi-collinearity statistics and their relative importance was measured to select the most discriminative LIFs using the ensemble feature selection method, which was developed using Chi-square, gain ratio, and relief-F methods. We evaluated the performance of ten different ML algorithms (linear discriminant analysis, mixture discriminant analysis, bagged cart, boosted logistic regression, k-nearest neighbors, artificial neural network, support vector machine, random forest, rotation forest, and C5.0) using different accuracy statistics (sensitivity, specificity, area under curve (AUC), and overall accuracy (OA)). We used suitable combinations of individual ML models to develop different ensemble ML models and evaluated their performance in LSM. We assessed the impact of LIFs on ML performance. Among all individual ML models, the k-nearest neighbors (sensitivity = 0.72, specificity = 0.82, AUC = 0.86, OA = 78%) and artificial neural network (sensitivity = 0.71, specificity = 0.85, AUC = 0.87, OA = 79%) algorithms showed the best performance using the top five LIFs, while random forest, rotation forest, and C5.0 (sensitivity = 0.76–0.81, specificity = 0.87, AUC = 0.90–0.93, OA = 82–84%) outperformed other models when developed using all twenty-four LIFs. Among ensemble models, the ensemble of k-nearest neighbors and rotation forest, k-nearest neighbors and artificial neural network, and artificial neural network and rotation forest outperformed other models (sensitivity = 0.72–0.73, specificity = 0.83–0.84, AUC = 0.86, OA = 79%) using the top five LIFs. The landslide susceptibility maps derived using these models indicate that ~2–3% and ~10–12% of the total study area fall within the “very high” and “high” susceptibility. The obtained susceptibility maps can be efficiently used to prioritize landslide mitigation activities. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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36 pages, 7418 KiB

Open AccessFeature PaperArticle

Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis

by Zhengli Yang, Chao Liu, Ruihua Nie, Wanchang Zhang, Leili Zhang, Zhijie Zhang, Weile Li, Gang Liu, Xiaoai Dai, Donghui Zhang, Min Zhang, Shuangxi Miao, Xiao Fu, Zhiming Ren and Heng Lu

Remote Sens. 2022, 14(16), 3879; https://doi.org/10.3390/rs14163879 - 10 Aug 2022

Cited by 9 | Viewed by 3275

Abstract

Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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