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Assessing Land Subsidence Using Remote Sensing Data

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A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 8937

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Special Issue Editors

Dr. Emre Havazli

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Interests: geophysical hazards; remote sensing; earth observation; InSAR; land subsidence; ground instability

Dr. Talib Oliver-Cabrera

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Interests: mapping; InSAR processing and applications; land subsidence; geophysical hazards; wetlands

Dr. Francesca Cigna

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Guest Editor

Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: landscape evolution; geophysical hazards; archaeology; cultural heritage; remote sensing; earth observation; InSAR; landslides; land subsidence; ground instability
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Special Issue Information

Dear Colleagues,

Land subsidence, the gradual sinking of the Earth's surface, is a widespread phenomenon that can occur due to various natural and human-related processes, including groundwater withdrawals, underground mining, permafrost thawing, and sediment compaction. The consequences of land subsidence are of global concern, with 45 states in the US alone being affected by this phenomenon. In China, for instance, land subsidence affects an area of approximately 79,000 square kilometers, mainly in heavily populated regions where it poses a continuous threat to infrastructure, buildings, and human lives and causes substantial economic losses.

Continuous monitoring of areas affected by land subsidence is vital to develop mitigation strategies and action plans that can prevent or minimize the associated hazards. To achieve this, the integration of various monitoring technologies and techniques has become increasingly important. Among these, remote sensing technologies such as LiDAR, synthetic aperture radar (SAR), radar interferometry (InSAR), and GPS provide high-resolution data that can detect Earth's changes with great precision (from centimeters to millimeters). Similarly, algorithmic and methodological developments, such as neural networks, machine learning, and principal component analysis, can analyze large and noisy datasets and extract relevant information for hazard mapping and risk assessment.

We invite submissions for a Special Issue on "Assessing Land Subsidence Using Remote Sensing". The Special Issue aims to advance our understanding of the use of remote sensing technologies for monitoring and quantifying land subsidence and its impacts on human societies and ecosystems. We welcome original research articles, reviews, and perspectives that cover various aspects of remote sensing for land subsidence assessment, from the theoretical basis to practical applications. Topics of interest include, but are not limited to:

Advances in remote sensing technologies for the assessment of land subsidence;
Methodologies for processing and analyzing remote sensing data for subsidence mapping and monitoring;
Integration of multiple remote sensing techniques for accurate and efficient subsidence assessment;
Machine learning and other advanced data analysis techniques for subsidence detection and prediction;
Case studies of subsidence assessment using remote sensing in various regions and contexts;
Mitigation planning and management based on remote sensing assessments;
Challenges and opportunities for the future of remote sensing in the assessment of land subsidence.

Dr. Emre Havazli
Dr. Talib Oliver-Cabrera
Dr. Francesca Cigna
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. Land is an international peer-reviewed open access monthly 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 2600 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
LiDAR
synthetic aperture radar
radar interferometry
GPS
groundwater withdrawals
mining
sediment compaction
hazard mapping
subsidence risk assessment

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Published Papers (8 papers)

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Research

20 pages, 6767 KiB

Open AccessArticle

Coastal Subsidence in Cape Canaveral, FL, and Surrounding Areas: Shallow Subsidence Induced by Natural and Anthropogenic Processes

by Anurag Sharma, Shimon Wdowinski and Randall W. Parkinson

Land 2025, 14(4), 735; https://doi.org/10.3390/land14040735 - 29 Mar 2025

Viewed by 178

Abstract

Cape Canaveral, home to critical space exploration infrastructure, is facing potential flooding hazards from land subsidence and sea-level rise. This study utilized three geodetic datasets, the Interferometric Synthetic Aperture Radar (InSAR), the Global Navigation Satellite System (GNSS), and precise leveling, to investigate the spatial and temporal patterns of vertical land motion (VLM) in Cape Canaveral and its surrounding areas. Our analysis revealed that Cape Canaveral experiences both long-term regional subsidence and localized subsiding areas, while Merritt Island and the Peninsular Mainland remain relatively stable. The long-term regional subsidence in Cape Canaveral is likely driven by the compaction of younger, unconsolidated siliciclastic sediments, with a small contribution from glacial isostatic adjustment (GIA). The three localized subsiding areas identified in Cape Canaveral are each driven by distinct mechanisms: wetland modification in the western area, runway infrastructure development in the central area, and the natural compaction of young siliciclastic sediments in the southeastern region. Historical leveling data indicated temporal variations in subsidence rates at Cape Canaveral, from 5 mm/yr during the 1950–70s to 2 mm/yr in the 2000s. These findings have significant implications for infrastructure resilience and flood hazard assessment, as the observed subsidence compounds with the projected accelerated sea-level rise in the region. Our results highlight the importance of integrating long-term datasets to better characterize VLM in the dynamic coastal region for effective planning and risk mitigation. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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25 pages, 10825 KiB

Open AccessArticle

Long-Term Subsidence Assessment by LiCSBAS and Emerging Hot Spot Analysis in Kathmandu Valley

by Sagar Rawal and Guoquan Wang

Land 2025, 14(4), 700; https://doi.org/10.3390/land14040700 - 26 Mar 2025

Viewed by 230

Abstract

Rapid urbanization in Kathmandu Valley has strained its aquifer system, causing significant land subsidence. This study employs LiCSBAS for InSAR processing of Sentinel-1 data (2017–2024) to map subsidence-prone areas. The significant subsidence was found in northwest (Baluwatar, Samakhusi, and Manmaiju), southern (Gwarko, Patan, and Koteshwor), and northeast (Madhapur Thimi and Gathhaghar) regions with a maximum subsidence rate ~21 cm/yr. Subsidence has also expanded towards the outskirts and open areas in the eastern and southern parts of Lalitpur and Bhaktapur districts. Emerging hot spot analysis reveals a slowing subsidence trend in high-risk zones, possibly linked to the MWSP project reducing groundwater extraction from 58 MLD (2021) to 26 MLD (2024). Many subsidence-affected areas are located over the Kalimati and Gokarna Formations in highly urbanized areas. The key contributing factors to subsidence are soil compaction, excessive groundwater use, and urban sprawl encroaching open areas and recharge zones. These findings underscore the urgent need for sustainable groundwater management and land-use planning to promote urban resilience. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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17 pages, 12868 KiB

Open AccessArticle

PSInSAR-Based Time-Series Coastal Deformation Estimation Using Sentinel-1 Data

by Muhammad Ali, Alessandra Budillon, Zeeshan Afzal, Gilda Schirinzi and Sajid Hussain

Land 2025, 14(3), 536; https://doi.org/10.3390/land14030536 - 4 Mar 2025

Cited by 1 | Viewed by 344

Abstract

Coastal areas are highly dynamic regions where surface deformation due to natural and anthropogenic activities poses significant challenges. Synthetic Aperture Radar (SAR) interferometry techniques, such as Persistent Scatterer Interferometry (PSInSAR), provide advanced capabilities to monitor surface deformation with high precision. This study applies PSInSAR techniques to estimate surface deformation along coastal zones from 2017 to 2020 using Sentinel-1 data. In the densely populated areas of Pasni, an annual subsidence rate of 130 mm is observed, while the northern, less populated region experiences an uplift of 70 mm per year. Seawater intrusion is an emerging issue causing surface deformation in Pasni’s coastal areas. It infiltrates freshwater aquifers, primarily due to excessive groundwater extraction and rising sea levels. Over time, seawater intrusion destabilizes the underlying soil and rock structures, leading to subsidence or gradual sinking of the ground surface. This form of surface deformation poses significant risks to infrastructure, agriculture, and the local ecosystem. Land deformation varies along the study area’s coastline. The eastern region, which is highly reclaimed, is particularly affected by erosion. The results derived from Sentinel-1 SAR data indicate significant subsidence in major urban districts. This information is crucial for coastal management, hazard assessment, and planning sustainable development in the region. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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22 pages, 15578 KiB

Open AccessArticle

Analysis of Ground Subsidence Evolution Characteristics and Attribution Along the Beijing–Xiong’an Intercity Railway with Time-Series InSAR and Explainable Machine-Learning Technique

by Xin Liu, Huili Gong, Chaofan Zhou, Beibei Chen, Yanmin Su, Jiajun Zhu and Wei Lu

Land 2025, 14(2), 364; https://doi.org/10.3390/land14020364 - 10 Feb 2025

Viewed by 409

Abstract

The long-term overextraction of groundwater in the Beijing–Tianjin–Hebei region has led to the formation of the world’s largest groundwater depression cone and the most extensive land subsidence zone, posing a potential threat to the operational safety of high-speed railways in the region. As a critical transportation hub connecting Beijing and the Xiong’an New Area, the Beijing–Xiong’an Intercity Railway traverses geologically complex areas with significant ground subsidence issues. Monitoring and analyzing the causes of land subsidence along the railway are essential for ensuring its safe operation. Using Sentinel-1A radar imagery, this study applies PS-InSAR technology to extract the spatiotemporal evolution characteristics of ground subsidence along the railway from 2016 to 2022. By employing a buffer zone analysis and profile analysis, the subsidence patterns at different stages (pre-construction, construction, and operation) are revealed, identifying the major subsidence cones along the Yongding River, Yongqing, Daying, and Shengfang regions, and their impacts on the railway. Furthermore, the XGBoost model and SHAP method are used to quantify the primary influencing factors of land subsidence. The results show that changes in confined water levels are the most significant factor, contributing 34.5%, with strong interactions observed between the compressible layer thickness and confined water levels. The subsidence gradient analysis indicates that the overall subsidence gradient along the Beijing–Xiong’an Intercity Railway currently meets safety standards. This study provides scientific evidence for risk prevention and the control of land subsidence along the railway and holds significant implications for ensuring the safety of high-speed rail operations. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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21 pages, 9480 KiB

Open AccessArticle

Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques

by Niloofar Alizadeh, Yasser Maghsoudi, Tayebe Managhebi and Saeed Azadnejad

Land 2024, 13(12), 2237; https://doi.org/10.3390/land13122237 - 20 Dec 2024

Viewed by 931

Abstract

Urban areas face an imminent risk of collapse due to structural deficiencies and gradual ground subsidence. Therefore, monitoring surface movements is crucial for detecting abnormal behavior, implementing timely preventive measures, and minimizing the detrimental effects of this phenomenon in residential regions. In this context, interferometric synthetic aperture radar (InSAR) has emerged as a highly effective technique for monitoring slow and long-term ground hazards and surface motions. The first goal of this study is to explore the potential applications of persistent scatterer interferometry (PSI) and small baseline subset (SBAS) algorithms in collapse hotspot detection, utilizing a dataset consisting of 144 Sentinel-1 images. The experimental results from three areas with a history of collapses demonstrate that the SBAS algorithm outperforms PSI in uncovering behavior patterns indicative of collapse and accurately pinpointing collapse points near real collapse sites. In the second phase, this research incorporated an additional dataset of 36 TerraSAR-X images alongside the Sentinel-1 data to compare results based on radar images with different spatial resolutions in the C and X bands. The findings reveal a strong correlation between the TerraSAR-X and Sentinel-1 time series. Notably, the analysis of the TerraSAR-X time series for one study area identified additional collapse-prone points near the accident site, attributed to the higher spatial resolution of these data. By leveraging the capabilities of InSAR and advanced algorithms, like SBAS, this study highlights the potential to identify areas at risk of collapse, enabling the implementation of preventive measures and reducing potential harm to residential communities. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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34 pages, 90974 KiB

Open AccessArticle

Multi-Decadal Land Subsidence Risk Assessment at Major Italian Cities by Integrating PSInSAR with Urban Vulnerability

by Michelle Lenardón Sánchez, Celina Anael Farías and Francesca Cigna

Land 2024, 13(12), 2103; https://doi.org/10.3390/land13122103 - 5 Dec 2024

Cited by 1 | Viewed by 968

Abstract

This study assesses subsidence-induced risk to urban infrastructure in three major Italian cities—Rome, Bologna, and Florence—by integrating satellite-based persistent scatterer interferometric synthetic aperture radar (PSInSAR) ground displacement data with urban vulnerability metrics into a novel risk assessment workflow, incorporating land use and population data from the Copernicus Land Monitoring Service (CLMS)—Urban Atlas. This analysis exploits ERS-1/2, ENVISAT, and COSMO-SkyMed PSInSAR datasets from the Italian Extraordinary Plan of Environmental Remote Sensing, plus Sentinel-1 datasets from CLMS—European Ground Motion Service (EGMS), and spans a 30-year period, thus capturing both historical and recent subsidence trends. Angular distortion is introduced as a critical parameter for assessing potential structural damage due to differential settlement, which helps to quantify subsidence-induced hazards more precisely. The results reveal variable subsidence hazard patterns across the three cities, with specific areas exhibiting significant differential ground deformation that poses risks to key infrastructure. A total of 36.15, 11.44, and 0.43 km² of land at high to very high risk are identified in Rome, Bologna, and Florence, respectively. By integrating geospatial and vulnerability data at the building-block level, this study offers a more comprehensive understanding of subsidence-induced risk, potentially contributing to improved management and mitigation strategies in urban areas. This study contributes to the limited literature on embedding PSInSAR data into urban risk assessment workflows and provides a replicable framework for future applications in other urban areas. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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22 pages, 9561 KiB

Open AccessArticle

Associations between Surface Deformation and Groundwater Storage in Different Landscape Areas of the Loess Plateau, China

by Zhiqiang Liu, Shengwei Zhang, Wenjie Fan, Lei Huang, Xiaojing Zhang, Meng Luo, Shuai Wang and Lin Yang

Land 2024, 13(2), 184; https://doi.org/10.3390/land13020184 - 4 Feb 2024

Cited by 1 | Viewed by 1585

Abstract

The Loess Plateau is an important grain-producing area and energy base in China and is an area featuring dramatic changes in both surface and underground processes. However, the associations between surface deformation and groundwater storage changes in different landscape types in the region are still unclear. Based on Sentinel-1 and GRACE (Gravity Recovery and Climate Experiment) data, this study monitored and verified the surface deformation and groundwater storage changes in different landscape types, such as those of the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area, in the Loess Plateau of China from 2020 to 2021. Through time series and cumulative analysis using the same spatial and temporal resolution, the associations between these two changes in different regions are discussed. The results show that: (1) the surface deformation rates in different landscape types differ significantly. The minimum surface deformation rate in the Kubuqi Desert is −5~5 mm/yr, while the surface deformation rates in the Hetao Irrigation District, the open-pit mine recovery area in the Jinbei Mining Area, and the Shendong Mining Area are −60~25 mm/yr, −25~25 mm/yr, and −95.33~26 mm/yr, respectively. (2) The regional groundwater reserves all showed a decreasing trend, with the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area declining by 359.42 mm, 103.30 mm, 45.60 mm, and 691.72 mm, respectively. (3) The surface elasticity deformation had the same trend as the temporal fluctuation in groundwater storage, and the diversion activity was the main reason why the temporal surface deformation in the Hetao Irrigation District lagged behind the change in groundwater storage by 1~2 months. The measure of “underground water reservoirs in coal mines” slows down the rate of collapse of coal mine roof formations, resulting in the strongest time-series correlation between mild deformation of the surface of the Shendong mine and changes in the amount of groundwater reserves (R = 0.73). This study analyzes the associations between surface deformation and groundwater storage changes in different landscape areas of the Loess Plateau of China and provides new approaches to analyzing the dynamic associations between the two and the causes of changes in both variables. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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22 pages, 3579 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers

by Joe Yazbeck and John B. Rundle

Land 2023, 12(11), 1977; https://doi.org/10.3390/land12111977 - 26 Oct 2023

Cited by 6 | Viewed by 2329

Abstract

The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations. Full article

(This article belongs to the Special Issue Assessing Land Subsidence Using Remote Sensing Data)

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