Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
Interpretation and Attribution of Land Subsidence: A Remote Sensing and...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Interpretation and Attribution of Land Subsidence: A Remote Sensing and Machine Learning Perspective

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 August 2026 | Viewed by 2568

Special Issue Editors

Prof. Dr. Alex Hay-Man Ng

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: InSAR; land subsidence; natural and human-induced hazards; subsidence modeling; monitoring/change detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Linlin Ge

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: InSAR; GPS; GIS; UAV; optical remote sensing; geodetic surveying
Special Issues, Collections and Topics in MDPI journals
Dr. Hsing-Chung Chang

E-Mail Website
Guest Editor
School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
Interests: InSAR; land cover and land deformation mapping; bushfire and vegetation recovery monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Zheyuan Du

E-Mail Website
Guest Editor
Geoscience Australia, Canberra, ACT 2061, Australia
Interests: InSAR; land subsidence; land degradation; land use; atmosphere modeling

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring together original research articles and review papers that focus on the interpretation and attribution of land subsidence, leveraging advanced remote sensing techniques and machine learning methodologies.

Land subsidence is a critical global issue with far-reaching consequences, including increased vulnerability to flooding, damage to infrastructure, compromised groundwater systems, and the triggering of geological hazards such as fault reactivation. These impacts make it essential to understand, monitor, and mitigate subsidence effectively. Remote sensing technologies, combined with the power of machine learning, offer new opportunities to enhance the monitoring, modeling, and analysis of land subsidence phenomena.

This Special Issue seeks to explore the integration of cutting-edge remote sensing techniques, such as InSAR, optical satellite imagery, LiDAR, and UAV-based surveys, with machine learning algorithms for the identification, interpretation, and prediction of land subsidence patterns. Papers that focus on novel approaches to subsidence detection, the attribution of causes, and predictive modeling are highly encouraged. Additionally, submissions exploring the potential of machine learning for data fusion, anomaly detection, and the development of automated subsidence monitoring systems are welcome.

We invite contributions that present advancements in algorithms, methodologies, and applications, particularly those demonstrating case studies of subsidence in urban, agricultural, coastal, and mining regions. By understanding the drivers and consequences of land subsidence through an integrated remote sensing and machine learning approach, this Special Issue aims to provide valuable insights into effective mitigation strategies and policy recommendations.

We look forward to receiving your contributions to this Special Issue, which will highlight the state-of-the-art techniques and future directions in land subsidence research.

Prof. Dr. Alex Hay-Man Ng
Prof. Dr. Linlin Ge
Dr. Hsing-Chung Chang
Dr. Zheyuan Du
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-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

  • land subsidence
  • remote sensing
  • machine learning
  • deep learning
  • subsidence monitoring
  • predictive modeling
  • data fusion
  • ground deformation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 21614 KB  
Article
Insights into Spatial Heterogeneity of Land Subsidence Susceptibility Using InSAR and Explainable Machine Learning
by Min Shi, Xiaoyu Wang, Chenghong Gu, Mingliang Gao, Chaofan Zhou and Huili Gong
Remote Sens. 2026, 18(9), 1298; https://doi.org/10.3390/rs18091298 - 24 Apr 2026
Viewed by 241
Abstract
Land subsidence (LS) is a widespread geoenvironmental problem driven by both natural processes and human activities. Identifying the main factors controlling LS susceptibility and their spatial contribution patterns is essential for LS management and mitigation. In this study, an interpretable earth observation framework [...] Read more.
Land subsidence (LS) is a widespread geoenvironmental problem driven by both natural processes and human activities. Identifying the main factors controlling LS susceptibility and their spatial contribution patterns is essential for LS management and mitigation. In this study, an interpretable earth observation framework was developed for the North China Plain (NCP) to quantify both spatial and non-spatial contributions of dominant LS drivers. Land displacement was derived from Sentinel-1A SAR images using Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) processing. The displacement map was then combined with nine geoenvironmental variables to construct an LS susceptibility model using the eXtreme Gradient-Boosting (XGBoost) algorithm. The model performed well, with an R2 of 0.96, an EVS of 0.96, and an MAE of 2.25 mm/yr. SHapley Additive exPlanations (SHAP) analysis was employed to quantify feature contributions and their effects on LS susceptibility. The results show that a deep groundwater level (DGL) was the dominant factor, followed by elevation and a shallow groundwater level (SGL). The effect of DGL was strongest when it ranged from −75 to 20 m. Elevation showed a clear effect on LS occurrence when values fall between 30 and 50 m. Relatively high subsidence sensitivity was mainly observed in areas where SGL was below −7 m. Interaction effects, particularly those between DGL and elevation and between DGL and SGL, further increased LS susceptibility in specific areas. The highest predicted susceptibility occurred in areas with DGL below −20 m and elevations below 30 m. Higher susceptibility was also identified where DGL was high and SGL ranged between −20 and −10 m, and where DGL was low and SGL ranged from 15 to 20 m. In contrast, factors such as slope and aspect had limited influence at the regional scale. The contributions of the predominant factors show obvious marginal effects and significant spatial heterogeneity to LS susceptibility. The results clarify where and how key factors shape subsidence and can inform targeted mitigation measures and urban planning by local authorities. Full article
(This article belongs to the Special Issue Interpretation and Attribution of Land Subsidence: A Remote Sensing and Machine Learning Perspective)
►▼ Show Figures

Figure 1

22 pages, 3215 KB  
Article
Spatiotemporal Evolution Monitoring of Small Water Body Coverage Associated with Land Subsidence Using SAR Data: A Case Study in Geleshan, Chongqing, China
by Tianhao Jiang, Faming Gong, Qiankun Kong and Kui Zhang
Remote Sens. 2026, 18(4), 644; https://doi.org/10.3390/rs18040644 - 19 Feb 2026
Viewed by 429
Abstract
Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has [...] Read more.
Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article
(This article belongs to the Special Issue Interpretation and Attribution of Land Subsidence: A Remote Sensing and Machine Learning Perspective)
►▼ Show Figures

Figure 1

27 pages, 8643 KB  
Article
Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model
by Wojciech Gruszczyński, Edyta Puniach, Paweł Ćwiąkała and Wojciech Matwij
Remote Sens. 2025, 17(18), 3259; https://doi.org/10.3390/rs17183259 - 21 Sep 2025
Cited by 1 | Viewed by 1031
Abstract
This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike conventional ground filters that rely on point cloud classification, the proposed [...] Read more.
This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike conventional ground filters that rely on point cloud classification, the proposed approach employs heteroscedastic regression. The U-Net model predicts the conditional expected values of the elevation corrections, aiming to reduce the impact of vegetation on determined ground surface elevations. Concurrently, it estimates the logarithm of the elevation correction variance, allowing for direct quantification of the uncertainty associated with each elevation correction value. The algorithm was evaluated using three metrics: the root mean square error (RMSE) of vertical displacements, the percentage of nodes with determined displacement values, and the percentage of outliers among those values. Performance was assessed using the technique for order of preference by similarity to ideal solution (TOPSIS) method and compared against several ground-filter-based algorithms across four datasets, each including at least two time intervals. In most cases, the U-Net-based approach demonstrated a slight performance advantage over traditional ground filtering techniques. For example, for the U-Net-based algorithm, for one of the test datasets, the RMSE of the determined subsidences was 6.1 cm, the percentage of nodes with determined subsidences was 80.5%, and the percentage of outliers was 0.2%. For the same case, the algorithm based on the next best model (SMRF) allowed an RMSE of 7.7 cm to be obtained; for 77.3% of nodes, the subsidences were determined; and the percentage of outliers was 0.3%. Full article
(This article belongs to the Special Issue Interpretation and Attribution of Land Subsidence: A Remote Sensing and Machine Learning Perspective)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop