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Remote Sensing for Landslide Investigation: From Ground Deformation Mapping to Hazard Assessment

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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: 31 October 2026 | Viewed by 2131

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Editors

Dr. Junwei Ma

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Guest Editor

Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan, China
Interests: geohazard prediction; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Ding Xia

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Guest Editor

1. College of Civil Engineering, Tongji University, Shanghai, China
2. PowerChina Huadong Engineering Corporation, Hangzhou, China
Interests: landslide susceptibility mapping; deep learning

Prof. Dr. Zhong Lu

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Guest Editor

Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA
Interests: SAR; InSAR; PSInSAR; geophysical modeling; volcanoes; landslides; geohazards
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Special Issue Information

Dear Colleagues,

Landslides are one of the most catastrophic geohazards globally, resulting in thousands of fatalities and billions of dollars in economic damages each year. Conventional field surveys and point sensors yield precise yet sparse data, complicating the capturing of spatial continuity and temporal dynamics of landslide processes. Remote sensing bridges this gap by providing comprehensive, reliable measurements across many scales—from individual landslide to region scale—facilitating a complete process from inventory and kinematics to hazard and risk evaluation.

This Special Issue intends to highlight advanced remote sensing techniques and applications in comprehensive landslide investigation, covering the entire hazard assessment process from first detection to risk assessments.

This planned Special Issue belongs to the Remote Sensing in Geology, Geomorphology and Hydrology section, and aims to promote remote sensing as a comprehensive framework that integrates Earth observation, process comprehension, and operational hazard evaluation for landslides.

We welcome submissions that strengthen the cutting edge of:

Advanced sensing and processing techniques—innovative applications of InSAR, optical, LiDAR, and UAV-based monitoring for the identification and measurement of landslide deformation across various spatiotemporal scales.
Multi-sensor data fusion and integration—Collaborative techniques utilizing SAR, optical, LiDAR, thermal, and hyperspectral data along with in situ measurements to make it more feasible to identify landslides and reduce uncertainties in landslide characterization.
Precursor identification and early warning—Techniques for detecting small deformation patterns, acceleration trends, and environmental triggers that facilitate prompt hazard alerts and inform threshold-based warning systems.
Machine learning and AI applications—Deep learning architectures, automated classification algorithms, and predictive models that leverage remote sensing data for landslide inventory mapping and susceptibility assessment.
Hazard and risk assessment frameworks—Quantitative approaches linking remote sensing observations to vulnerability analysis, exposure mapping, and risk scenarios for improved disaster risk reduction strategies.
Systematic reviews, intercomparisons, and best-practice guidelines that consolidate the state of the art and define future research directions.

Dr. Junwei Ma
Dr. Ding Xia
Prof. Dr. Zhong Lu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

space–air–ground–interior-based monitoring
multisource data
temporal and spatial deformation
multi-sensor data fusion
machine learning
explainable AI
landslide hazard and risk assessment

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Published Papers (3 papers)

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Research

26 pages, 7265 KB

Open AccessArticle

Landslide Susceptibility Assessment in a Complex Mountain Basin Transition Zone by Integrating Mamba and SBAS-InSAR Deformation Evidence: A Case Study of the Xining Basin, China

by Heming Yang, Wenhui Liu and Yabin Liu

Remote Sens. 2026, 18(13), 2170; https://doi.org/10.3390/rs18132170 - 3 Jul 2026

Viewed by 188

Abstract

Landslide susceptibility mapping (LSM) in mountain–basin transition zones remains challenging because conventional approaches rely mainly on historical inventories and static conditioning factors, whereas independent deformation evidence is seldom incorporated to refine susceptibility zonation. This study proposes an integrated LSM framework for the Xining Basin by coupling a Mamba-based model (Mamba-LSM) with SBAS-InSAR-based deformation-informed bidirectional reclassification, with the key innovation lying in the use of independent deformation evidence to refine susceptibility zonation after model prediction. Specifically, Mamba-LSM integrates six-channel neighborhood patches, CNN-based local spatial encoding, and Mamba-based latent feature transformation to improve the representation of local terrain context for landslide susceptibility assessment. Results show that Mamba-LSM achieved the highest AUC among the evaluated models, reaching 0.9011 with an F1-score of 0.7431. After deformation-informed bidirectional reclassification, the high- and very-high-susceptibility classes occupied only 25.31% of the study area but contained 69.84% of the mapped landslides, and were concentrated mainly in valley–mountain transition belts, river-incised slopes, and engineering-disturbed sectors where SBAS-InSAR deformation hotspots were also preferentially distributed. These findings demonstrate that integrating independent SBAS-InSAR deformation evidence can improve both the spatial concentration of landslides in high-susceptibility zones and the physical interpretability of susceptibility zonation. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Investigation: From Ground Deformation Mapping to Hazard Assessment)

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21 pages, 10681 KB

Open AccessArticle

Deformation Characteristics of Lumei Landslide in the Tibetan Plateau Combined with PS-InSAR and SBAS-InSAR

by Tao Wen, Xueqing Shi, Yankun Wang and Yunpeng Yang

Remote Sens. 2026, 18(8), 1128; https://doi.org/10.3390/rs18081128 - 10 Apr 2026

Cited by 2 | Viewed by 547

Abstract

Due to the highly complex geological environment of the Tibetan Plateau, landslides occur frequently, and signs of ancient landslide reactivation are widespread, posing significant threats to major infrastructure and local communities. Taking the Lumei landslide in Cuomei County as a case study, detailed field investigations were conducted, and Sentinel-1A SAR data (84 scenes from January 2017 to December 2023) were collected to characterize surface deformation. Both PS-InSAR and SBAS-InSAR methods were applied for long-term time-series monitoring, and the results of the two techniques were comparatively analyzed. Furthermore, the influencing factors of landslide deformation were explored on the basis of analyzing the deformation characteristics. The findings reveal that the surface deformation rate exhibits significant spatial heterogeneity, with deformation values decreasing progressively outward from the central region. The surface deformation rates obtained from PS-InSAR and SBAS-InSAR range from −36.55 to −21.81 mm/yr and from −30 to −10 mm/yr, respectively. Both methods indicate a general subsidence trend along the line-of-sight (LOS) direction and show strong spatial consistency and high correlation. By combining the high-precision point results obtained from PS-InSAR and the spatially continuous surface results derived from SBAS-InSAR, the fine spatial deformation characteristics of the Lumei landslide are revealed. The research results can provide an important reference for landslide monitoring, disaster prevention and mitigation in this region. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Investigation: From Ground Deformation Mapping to Hazard Assessment)

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29 pages, 123573 KB

Open AccessArticle

Dynamic Landslide Susceptibility Assessment Integrating SBAS-InSAR and Interpretable Machine Learning: A Case Study of the Baihetan Reservoir Area, Southwest China

by Hongfei Wang, Chuhan Deng, Ziyou Zhang, Zhekai Jiang, Qi Wei, Weijie Yi, Tao Chen and Junwei Ma

Remote Sens. 2026, 18(4), 578; https://doi.org/10.3390/rs18040578 - 12 Feb 2026

Cited by 1 | Viewed by 812

Abstract

Landslide susceptibility mapping (LSM) is a fundamental approach for identifying and predicting areas prone to slope failure. However, most conventional LSM methods are based on time-invariant conditioning factors or long-term-averaged predictors and seldom incorporate slope-kinematic information from deformation observations, thereby limiting their ability to capture evolving slope instability. Moreover, the black-box nature of many models limits interpretability and confidence in their predictions. In this study, we integrate small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) with interpretable machine learning (ML) methods to develop a dynamic LSM framework that improves the accuracy and reliability of susceptibility assessment. First, static LSM was performed using ML algorithms, and SHapley Additive exPlanations (SHAP) was used to quantify and visualize feature importance. Subsequently, SBAS-InSAR was applied to retrieve surface deformation rates. Finally, a dynamic LSM matrix was constructed to integrate InSAR-derived deformation with static susceptibility classes, producing time-varying landslide susceptibility maps. Application of the framework in the Baihetan Reservoir area, Southwest China, demonstrates its practical value. During the static LSM phase, the extreme gradient boosting (XGBoost) model achieved strong predictive performance (the area under the receiver operating characteristic curve (AUC) = 0.8864; accuracy = 0.8315; precision = 0.8947), outperforming the alternative models. SHAP analysis indicates that elevation and distance to rivers are the primary controls on landslide occurrence. Incorporating SBAS-InSAR deformation data into the dynamic LSM matrix effectively captures the spatiotemporal evolution of slope instability. Susceptibility upgrades are observed for multiple inventoried landslides, and the actively deforming Xiaomidi and Gantianba landslides are presented as representative case studies, further supported by multisource observations from satellite imagery, unmanned aerial vehicle (UAV) surveys, and ground-based global navigation satellite system (GNSS) monitoring. Consequently, the proposed dynamic LSM framework overcomes limitations of static approaches by integrating deformation information and enhancing interpretability through explainable artificial intelligence. Full article

(This article belongs to the Special Issue Remote Sensing for Landslide Investigation: From Ground Deformation Mapping to Hazard Assessment)

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