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Remote Sensing
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Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques
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Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 28 July 2024 | Viewed by 8972

Special Issue Editors

Dr. Giulia Tessari

E-Mail Website
Guest Editor
Sarmap SA, 6987 Caslano, Switzerland
Interests: InSAR; landslides; infrastructure monitoring; ground deformations
Special Issues, Collections and Topics in MDPI journals
Dr. Benedetta Dini

E-Mail Website
Guest Editor
ISTerre, Université Grenoble Alpes, 38610 Gières, France
Interests: landslides; remote sensing; InSAR; in situ monitoring; monitoring with LoRa wireless networks; landslide susceptibility
Special Issues, Collections and Topics in MDPI journals
Dr. Chiara Crippa

E-Mail Website
Guest Editor
Institute for Earth Observation, Eurac Research, Bolzano, Italy
Interests: InSAR; remote sensing; geomorphology; slope instabilities; landslides; geohazards
Dr. Anna Barra

E-Mail Website
Guest Editor
Remote Sensing Department, Division of Geomatics, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Av. Gauss, 7, E-08860 Castelldefels, Barcelona, Spain
Interests: remote sensing; SAR data; SAR interferometry; geohazard monitoring; landslide mapping and monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Elisa Destro

E-Mail Website
Guest Editor
Sarmap SA, 6987 Caslano, Switzerland
Interests: hydrological and slope stability models; geomorphological mapping

Special Issue Information

Dear Colleagues,

Landslides represent a major natural hazard worldwide, affecting developed and developing countries in different ways, causing economical losses and casualties that are often underestimated. Rapid and accurate detection of ongoing instabilities or of landslides that have reached failure is extremely important for monitoring and early warning, hazard assessment, susceptibility zonation as well as supporting recovery operations.

In the last two decades, the generation and update of landslide inventories, as well as the monitoring of ongoing surface deformations related to landslides has been able to take advantage of the use of ever-growing remote sensing techniques, ranging from optical to multispectral data, to SAR and Lidar data, acquired from ground-based platforms, drones, airplane or satellites.

This Special Issue welcomes all publications highlighting the benefit of remote sensing data for landslide detection, monitoring, mapping, as well as the generation of landslide susceptibility zonation or hazard assessment, the identification of triggering factors and the modelling of the deformation mechanisms.

Research and review papers are encouraged to cover a wide range of remote sensing applications, combined with traditional ground measurement and/or monitoring techniques, for landslide mapping and monitoring purposes, as well as to demonstrate the use of remote sensing to further the understanding of landslide kinematics, controls and evolution. Both methodological and real applications are welcome, possibly coupling different modelling approaches, parameter correlations, statistical models, and artificial intelligence, including the following topics:

  • Innovative processing of remote sensing data for land deformation detection
  • Improvement of landslide mapping and monitoring
  • Integration of large geospatial data for landslide disaster management
  • Integration of data with different temporal and spatial resolutions
  • Real cases of landslides monitoring and mapping through remotely sensed data

Dr. Giulia Tessari
Dr. Benedetta Dini
Dr. Chiara Crippa
Dr. Anna Barra
Dr. Elisa Destro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing techniques
  • landslide mapping
  • landslide monitoring
  • natural hazards
  • earth observation

Published Papers (7 papers)

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27 pages, 10021 KiB  
Article
Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan
by Nafees Ali, Jian Chen, Xiaodong Fu, Rashid Ali, Muhammad Afaq Hussain, Hamza Daud, Javid Hussain and Ali Altalbe
Remote Sens. 2024, 16(6), 988; https://doi.org/10.3390/rs16060988 - 12 Mar 2024
Viewed by 712
Abstract
Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to [...] Read more.
Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales. Full article
(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)
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20 pages, 29643 KiB  
Article
Multi-Scale Engineering Geological Zonation for Linear Projects in Mountainous Regions: A Case Study of National Highway 318 Chengdu-Shigatse Section
by Yongchao Li, Shengwen Qi, Bowen Zheng, Xianglong Yao, Songfeng Guo, Yu Zou, Xiao Lu, Fengjiao Tang, Xinyi Guo, Muhammad Faisal Waqar and Khan Zada
Remote Sens. 2023, 15(18), 4619; https://doi.org/10.3390/rs15184619 - 20 Sep 2023
Viewed by 801
Abstract
In response to the challenges of long crossing distances and difficult site selection for linear engineering projects in mountainous areas, this article proposes a multi-scale engineering geological zoning (EGZ) method. This method is based on the linear engineering construction stage and transitions from [...] Read more.
In response to the challenges of long crossing distances and difficult site selection for linear engineering projects in mountainous areas, this article proposes a multi-scale engineering geological zoning (EGZ) method. This method is based on the linear engineering construction stage and transitions from regional EGZ to EGZ of key sections (areas with poor or worst engineering geological conditions). This method not only ensures the effect of EGZ but also reduces the workload. When carrying out the EGZ of key sections, the assessment ideas of geological disaster hazards were taken into consideration. An improved method for calculating the time probability and magnitude probability of disaster occurrence is proposed. Taking the National Highway 318 Chengdu-Shigatse section as an example, EGZ was carried out. Its results revealed that the Nyingchi section was the key section with poor and worst engineering geological conditions. EGZ of the key section showed that the areas with poor and worst engineering geological conditions were mainly distributed in the curved sections on the northern side of the linear project. The proposed method in this article provides guidance for EGZ for linear engineering projects in mountainous areas. Full article
(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)
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17 pages, 41015 KiB  
Article
An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm
by Jianfeng Han, Xuefei Guo, Runcheng Jiao, Yun Nan, Honglei Yang, Xuan Ni, Danning Zhao, Shengyu Wang, Xiaoxue Ma, Chi Yan, Chi Ma and Jia Zhao
Remote Sens. 2023, 15(17), 4287; https://doi.org/10.3390/rs15174287 - 31 Aug 2023
Viewed by 1075
Abstract
InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and [...] Read more.
InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation. Full article
(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)
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28 pages, 15623 KiB  
Article
Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity
by Taorui Zeng, Zizheng Guo, Linfeng Wang, Bijing Jin, Fayou Wu and Rujun Guo
Remote Sens. 2023, 15(16), 4111; https://doi.org/10.3390/rs15164111 - 21 Aug 2023
Cited by 8 | Viewed by 1156 | Correction
Abstract
The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land [...] Read more.
The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land resource management. In this study, an analysis was conducted on the landslide caused by Typhoon Megi in 2016. A representative mountainous area along the eastern coast of China—characterized by urban development, deforestation, and severe road expansion—was used to analyze the spatial distribution of landslides. For this purpose, high-precision Planet optical remote sensing images were used to obtain the landslide inventory related to the Typhoon Megi event. The main innovative features are as follows: (i) the newly developed patch generating land-use simulation (PLUS) model simulated and analyzed the driving factors of land-use land-cover (LULC) from 2010 to 2060; (ii) the innovative stacking strategy combined three strong ensemble models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—to calculate the distribution of landslide susceptibility; and (iii) distance from road and LULC maps were used as short-term and long-term dynamic factors to examine the impact of human engineering activities on landslide susceptibility. The results show that the maximum expansion area of built-up land from 2010 to 2020 was 13.433 km2, mainly expanding forest land and cropland land, with areas of 8.28 km2 and 5.99 km2, respectively. The predicted LULC map for 2060 shows a growth of 45.88 km2 in the built-up land, mainly distributed around government residences in areas with relatively flat terrain and frequent socio-economic activities. The factor contribution shows that distance from road has a higher impact than LULC. The Stacking RF-XGB-LGBM model obtained the optimal AUC value of 0.915 in the landslide susceptibility analysis in 2016. Furthermore, future road network and urban expansion have intensified the probability of landslides occurring in urban areas in 2015. To our knowledge, this is the first application of the PLUS and Stacking RF-XGB-LGBM models in landslide susceptibility analysis in international literature. The research results can serve as a foundation for developing land management guidelines to reduce the risk of landslide failures. Full article
(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)
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17 pages, 13297 KiB  
Article
Potential Rockfall Source Identification and Hazard Assessment in High Mountains (Maoyaba Basin) of the Tibetan Plateau
by Juanjuan Sun, Xueliang Wang, Songfeng Guo, Haiyang Liu, Yu Zou, Xianglong Yao, Xiaolin Huang and Shengwen Qi
Remote Sens. 2023, 15(13), 3273; https://doi.org/10.3390/rs15133273 - 26 Jun 2023
Viewed by 1093
Abstract
Potential rockfall source areas are widely distributed in the high mountain areas of the Tibetan Plateau, posing significant hazards to human lives, infrastructures, and lifeline facilities. In a combination of field investigation, high-precision aerial photogrammetry, and numerical simulation, we took the Maoyaba basin [...] Read more.
Potential rockfall source areas are widely distributed in the high mountain areas of the Tibetan Plateau, posing significant hazards to human lives, infrastructures, and lifeline facilities. In a combination of field investigation, high-precision aerial photogrammetry, and numerical simulation, we took the Maoyaba basin as an example to explore a rapid identification method for high-altitude rockfall sources. An automatic potential rockfall source identification (PRSI) procedure was introduced to simplify the process of rockfall source identification. The study revealed that rockfall sources are concentrated in areas with intense frost weathering. Our identification results were validated using rockfall inventory data detection from remote sensing images and field investigation. Of the rockfall source areas identified by the PRSI procedure, 80.85% overlapped with the remote sensing images result. The accuracy assessment using precision, recall, and F1 score was 0.91, 0.81, and 0.85, respectively, which validates the reliability and effectiveness of the PRSI procedure. Meanwhile, we compared the rockfall source distribution of five DEMs with different resolutions and four neighborhood areas. We discovered that, in addition to high-resolution DEMs (i.e., 1 m and 2 m), medium-resolution DEMs (i.e., 5 m, 12.5 m) also perform well in identifying rockfall sources. Finally, we conducted a hazard assessment based on Culmann’s two-dimensional slope stability model and rockfall hazard vector method. Appropriate protective measures should be taken at high-hazard sections to safeguard pedestrians, vehicles, and related infrastructure from rockfalls. Full article
(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)
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18 pages, 7672 KiB  
Article
Retrieving the Kinematic Process of Repeated-Mining-Induced Landslides by Fusing SAR/InSAR Displacement, Logistic Model, and Probability Integral Method
by Hengyi Chen, Chaoying Zhao, Roberto Tomás, Liquan Chen, Chengsheng Yang and Yuning Zhang
Remote Sens. 2023, 15(12), 3145; https://doi.org/10.3390/rs15123145 - 16 Jun 2023
Cited by 1 | Viewed by 1137
Abstract
The extraction of underground minerals in hilly regions is highly susceptible to landslides, which requires the application of InSAR techniques to monitor the surface displacement. However, repeated mining for multiple coal seams can cause a large displacement beyond the detectable gradient of the [...] Read more.
The extraction of underground minerals in hilly regions is highly susceptible to landslides, which requires the application of InSAR techniques to monitor the surface displacement. However, repeated mining for multiple coal seams can cause a large displacement beyond the detectable gradient of the traditional InSAR technique, making it difficult to explore the relationship between landslides and subsurface excavations in both temporal and spatial domains. In this study, the Tengqing landslide in Shuicheng, Guizhou, China, was chosen as the study area. Firstly, the large-gradient surface displacement in the line of sight was obtained by the fusion of SAR offset tracking and interferometric phase. Subsequently, a multi-segment logistic model was proposed to simulate the temporal effect induced by repeated mining activities. Next, a simplified probability integral method (SPIM) was utilized to invert the geometry of the mining tunnel and separate the displacement of the mining subsidence and landslide. Finally, the subsurface mining parameters and in situ investigation were carried out to assess the impact of mining and precipitation on the kinematic process of Tengqing landslides. Results showed that the repeated mining activities in Tengqing can not only cause land subsidence and rock avalanches at the top of the mountain, but also accelerate the landslide displacement. The technical approach presented in this study can provide new insights for monitoring and modeling the effects of repeated mining-induced landslides in mountainous areas. Full article
(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)
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14 pages, 8146 KiB  
Technical Note
Rapid Mapping of Slow-Moving Landslides Using an Automated SAR Processing Platform (HyP3) and Stacking-InSAR Method
by Yaning Yi, Xiwei Xu, Guangyu Xu and Huiran Gao
Remote Sens. 2023, 15(6), 1611; https://doi.org/10.3390/rs15061611 - 16 Mar 2023
Cited by 2 | Viewed by 2139
Abstract
The increasing number of landslide hazards worldwide has placed greater demands on the production and updating of landslide inventory maps. As an important data source for landslide detection, interferometric synthetic aperture radar (InSAR) data processing is time-consuming and also requires specialized knowledge, which [...] Read more.
The increasing number of landslide hazards worldwide has placed greater demands on the production and updating of landslide inventory maps. As an important data source for landslide detection, interferometric synthetic aperture radar (InSAR) data processing is time-consuming and also requires specialized knowledge, which severely hinders its widespread application. At present, a new cloud-based online platform, i.e., Alaska Satellite Facility’s Hybrid Pluggable Processing Pipeline (ASF HyP3) was developed for massive SAR data processing. In this study, combining the HyP3 online platform and Stacking-InSAR method, we constructed a new easy-to-use processing chain for rapidly identifying slow-moving landslides over large areas. With this processing chain, a total of 923 interferometric pairs covering an area of over 1800 km2 were processed within a few hours (about 4 to 5 h). A total of 81 slow-moving landslides were immediately detected and mapped using Stacking-InSAR method, of which 65 landslides were confirmed by previous studies and 16 landslides were newly detected. Results show that the new processing chain can greatly improve the efficiency of wide-area landslide mapping and is expected to serve as an effective tool for rapid updating the existing landslide inventories and contribute to the prevention and management of geological hazards. Full article
(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)
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