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Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques

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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: closed (28 July 2024) | Viewed by 21868

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Special Issue Editors

Dr. Giulia Tessari

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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

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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

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Guest Editor

Institute for Earth Observation, Eurac Research, Bolzano, Italy
Interests: InSAR; remote sensing; geomorphology; slope instabilities; landslides; geohazards

Dr. Anna Barra

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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

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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

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Keywords

remote sensing techniques
landslide mapping
landslide monitoring
natural hazards
earth observation

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Published Papers (13 papers)

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20 pages, 10531 KiB

Open AccessArticle

Geomorphological Insights to Analyze the Kinematics of a DSGSD in Western Sicily (Southern Italy)

by Chiara Cappadonia, Pierluigi Confuorto, Diego Di Martire, Domenico Calcaterra, Sandro Moretti, Edoardo Rotigliano and Luigi Guerriero

Remote Sens. 2024, 16(21), 4040; https://doi.org/10.3390/rs16214040 - 30 Oct 2024

Viewed by 420

Abstract

Deep-Seated Gravitational Slope Deformations (DSGSDs) are common in many geological environments, and due to their common limited displacement rate, they can remain unrecognized for a long time. Among the most significant events in Sicily is the Mt. San Calogero DSGSD. To contribute to a better understanding of its characteristics, including the geologic setting promoting its development, ongoing kinematics, and mechanism, a specific analysis was completed. In this paper, the results of this analysis, based on a three-folded strategy, are provided and interpreted in the context of DSGSD predisposing conditions and controlling factors. Especially, field observations associated to visual interpretation of aerial imagery were used for the identification and mapping of main geological features and landforms, high-resolution X-Band DInSAR data enabled researchers to fully characterize the deformational behavior of the slope, while a reduced complexity slope stability analysis allowed them to reconstruct the deep geometry of the DSGSD. Results from the analysis indicate that the DSGSD of Mt. San Calogero is composed of three blocks corresponding to fault-bounded tectonic elements and characterized by a specific kinematics and sensitivity to external forcing (i.e., rainfall), multiple landslides are associated to the DSGSD in the area and the deep geometry of the DSGSD is concave upward and resemble the characteristics of a rotational slide. The interpretation of the results suggests that the formation and the deformation of the Mt. San Calogero DSGSD are linked with the local and regional fault systems related to the Sicilian orogen, while shallow landslides are triggered, in clayey terrains, mostly by rainfalls. In addition, the integrated approach reveals that active tectonics and rainfalls in the San Calogero massive relief are the main driving forces of its different deformation behavior. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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30 pages, 6012 KiB

Open AccessArticle

A Remote-Sensing-Based Method Using Rockfall Inventories for Hazard Mapping at the Community Scale in the Arequipa Region of Peru

by Cassidy L. Grady, Paul M. Santi, Gabriel Walton, Carlos Luza, Guido Salas, Pablo Meza and Segundo Percy Colque Riega

Remote Sens. 2024, 16(19), 3732; https://doi.org/10.3390/rs16193732 - 8 Oct 2024

Viewed by 790

Abstract

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R² value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R² of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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23 pages, 17408 KiB

Open AccessArticle

InSAR Integrated Machine Learning Approach for Landslide Susceptibility Mapping in California

by Divya Sekhar Vaka, Vishnuvardhan Reddy Yaragunda, Skevi Perdikou and Alexandra Papanicolaou

Remote Sens. 2024, 16(19), 3574; https://doi.org/10.3390/rs16193574 - 25 Sep 2024

Viewed by 1364

Abstract

Landslides pose significant threats to life and property, particularly in mountainous regions. To address this, this study develops a landslide susceptibility model integrating Earth Observation (EO) data, historical data, and Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) ground movement results. The model categorizes areas into four susceptibility classes (from Class 1 to Class 4) using a multi-class classification approach. Results indicate that the Xtreme Gradient Boosting (XGB) model effectively predicts landslide susceptibility with area under the curve (AUC) values ranging from 0.93 to 0.97, with high accuracy of 0.89 and a balanced performance across different susceptibility classes. The integration of MT-InSAR data enhances the model’s ability to capture dynamic ground movement and improves landslide mapping. The landslide susceptibility map generated by the XGB model indicates high susceptibility along the Pacific coast. The optimal model was validated against 272 historical landslide occurrences, with predictions distributed as follows: 68 occurrences (25%) in Class 1, 142 occurrences (52%) in Class 2, 58 occurrences (21.5%) in Class 3, and 4 occurrences (1.5%) in Class 4. This study highlights the importance of considering temporal changes in environmental conditions such as precipitation, distance to streams, and changes in vegetation for accurate landslide susceptibility assessment. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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22 pages, 16283 KiB

Open AccessArticle

Estimating Reactivation Times and Velocities of Slow-Moving Landslides via PS-InSAR and Their Relationship with Precipitation in Central Italy

by Ebrahim Ghaderpour, Claudia Masciulli, Marta Zocchi, Francesca Bozzano, Gabriele Scarascia Mugnozza and Paolo Mazzanti

Remote Sens. 2024, 16(16), 3055; https://doi.org/10.3390/rs16163055 - 20 Aug 2024

Viewed by 918

Abstract

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at

0 . 1^{\circ} \times 0 . 1^{\circ}

spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation (

r \geq 0.7

) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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41 pages, 33882 KiB

Open AccessArticle

Assessing Many Image Processing Products Retrieved from Sentinel-2 Data to Monitor Shallow Landslides in Agricultural Environments

by Rosa Maria Cavalli, Luca Pisano, Federica Fiorucci and Francesca Ardizzone

Remote Sens. 2024, 16(13), 2286; https://doi.org/10.3390/rs16132286 - 22 Jun 2024

Viewed by 1263

Abstract

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (O_BARPs) are often comparable to those of the pixels surrounding them (S_BARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of O_BARPs and S_BARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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18 pages, 7831 KiB

Open AccessEditor’s ChoiceArticle

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

by Nicola Angelo Famiglietti, Pietro Miele, Marco Defilippi, Alessio Cantone, Paolo Riccardi, Giulia Tessari and Annamaria Vicari

Remote Sens. 2024, 16(9), 1610; https://doi.org/10.3390/rs16091610 - 30 Apr 2024

Cited by 3 | Viewed by 2149

Abstract

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons. Full article

(This article belongs to the Special Issue Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques)

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27 pages, 10021 KiB

Open AccessArticle

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

Cited by 7 | Viewed by 2058

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1279

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 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

Open AccessArticle

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 1794

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 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

Open AccessArticle

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 24 | Viewed by 2130 | 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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1552

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 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

Open AccessArticle

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 4 | Viewed by 1635

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 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

Open AccessTechnical 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 7 | Viewed by 2880

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 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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Landslide Inventory Mapping and Monitoring Using Remote Sensing Techniques

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