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Land
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Assessing Land Subsidence Using Remote Sensing Data
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Assessing Land Subsidence Using Remote Sensing Data

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2923

Special Issue Editors

Dr. Emre Havazli

E-Mail Website
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

E-Mail Website
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

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

Published Papers (2 papers)

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22 pages, 9561 KiB  
Article
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 - 04 Feb 2024
Viewed by 715
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 [...] Read more.
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  
Article
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
Viewed by 1297
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 [...] Read more.
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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