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Land
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Ground Deformation Monitoring via Remote Sensing Time Series Data
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Ground Deformation Monitoring via Remote Sensing Time Series Data

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 5828

Special Issue Editors

Dr. Ebrahim Ghaderpour

E-Mail Website
Guest Editor
1. Department of Earth Sciences, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
2. Department of Geomatics Engineering, University of Calgary, Calgary, AB 2TN 1N4, Canada
Interests: artificial intelligence; big data analytics; remote sensing; hydrology; climate change; geoscience
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Mazzanti

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Rome “Sapienza”, Rome, Italy
Interests: landslide monitoring; photomonitoring; interferometry; geological risks; geological hazards; satellite images; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring ground deformation is a crucial task in geohazard management to ensure the safety of lives and infrastructure. Many factors can cause the land surface or ground to deform, such as earthquakes, slow-moving landslides, subsidence due to groundwater exploitation or underground mining, volcanic unrest, and others. Recent advances in remote sensing techniques have created a great opportunity to effectively and continuously monitor the land surface. These techniques include Interferometric Synthetic Aperture Radar (InSAR), Persistent Scatterer InSAR (PS-InSAR), Light Detection And Ranging (LiDAR), Global Navigation Satellite Systems (GNSS), Close-Range Photogrammetry (CRP), Robotic Total Station (RTS), etc. Spatio-temporal land surface monitoring can be rigorously carried out by analyzing the time series acquired from these techniques. Processing such time series can also be very challenging for several reasons, such as non-uniform sampling, biases as a result of preprocessing, and atmospheric/environmental noise.

The aim of this Special Issue is to collect papers (original research articles and review papers) that offer insights into effectively monitoring and measuring land deformation using remotely sensed time series data.

This Special Issue will welcome manuscripts that link the following themes:

  • New time series analysis methods for ground deformation monitoring;
  • Applications of existing time series or data processing methods in Earth’s surface monitoring;
  • A combination of different techniques, such as InSAR, LiDAR, GNSS, CRP, etc., for ground deformation monitoring and change detection using advanced artificial intelligence models.

We look forward to receiving your original research articles and reviews.

Dr. Ebrahim Ghaderpour
Prof. Dr. Paolo Mazzanti
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

  • artificial intelligence
  • change detection
  • ground deformation
  • PS-InSAR
  • monitoring
  • synthetic aperture radar
  • time series
  • trend analysis

Published Papers (4 papers)

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Research

16 pages, 12646 KiB  
Article
Application of Time Series INSAR (SBAS) Method Using Sentinel-1 for Monitoring Ground Deformation of the Aegina Island (Western Edge of Hellenic Volcanic Arc)
by Ioanna-Efstathia Kalavrezou, Ignacio Castro-Melgar, Dimitra Nika, Theodoros Gatsios, Spyros Lalechos and Issaak Parcharidis
Land 2024, 13(4), 485; https://doi.org/10.3390/land13040485 - 09 Apr 2024
Viewed by 903
Abstract
This study employs advanced synthetic aperture radar (SAR) techniques, specifically the small baseline subset (SBAS) method, to analyze ground deformation dynamics on Aegina, a volcanic island within the Hellenic Volcanic Arc. Using Sentinel-1 satellite data spanning January 2016 to May 2023, this research [...] Read more.
This study employs advanced synthetic aperture radar (SAR) techniques, specifically the small baseline subset (SBAS) method, to analyze ground deformation dynamics on Aegina, a volcanic island within the Hellenic Volcanic Arc. Using Sentinel-1 satellite data spanning January 2016 to May 2023, this research reveals different deformation behaviors. The towns of Aegina and Saint Marina portray regions of stability, contrasting with central areas exhibiting subsidence rates of up to 1 cm/year. The absence of deformation consistent with volcanic activity on Aegina Island aligns with geological records and limited seismic activity, attributing the observed subsidence processes to settlement phenomena from past volcanic events and regional geothermal activity. These findings reinforce the need for continuous monitoring of the volcanic islands located in the Hellenic Volcanic Arc, providing important insights for local risk management, and contributing to our broader understanding of geodynamic and volcanic processes. Full article
(This article belongs to the Special Issue Ground Deformation Monitoring via Remote Sensing Time Series Data)
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21 pages, 71891 KiB  
Article
Susceptibility of Landslide Debris Flow in Yanghe Township Based on Multi-Source Remote Sensing Information Extraction Technology (Sichuan, China)
by Hongyi Guo and A. M. Martínez-Graña
Land 2024, 13(2), 206; https://doi.org/10.3390/land13020206 - 08 Feb 2024
Viewed by 607
Abstract
The extraction of real geological environment information is a key factor in accurately evaluating the vulnerability to geological hazards. Yanghe Township is located in the mountainous area of western Sichuan and lacks geological survey data. Therefore, it is important predict the spatial and [...] Read more.
The extraction of real geological environment information is a key factor in accurately evaluating the vulnerability to geological hazards. Yanghe Township is located in the mountainous area of western Sichuan and lacks geological survey data. Therefore, it is important predict the spatial and temporal development law of landslide debris flow in this area and improve the effectiveness and accuracy of monitoring changes in landslide debris flow, this article proposes a method for extracting information on the changes in landslide debris flows combined with NDVI variation, which is based on short baseline interferometry (SBAS-InSAR) and optical remote sensing interpretation. In this article, we present relevant maps based on six main factors: vegetation index, slope, slope orientation, elevation, topographic relief, and formation lithology. At the same time, different remote sensing images were compared to improve the accuracy of landslide debris flow sensitivity assessments. The research showed that the highest altitude of the region extracted by multi-source remote sensing technology is 2877 m, and the lowest is 630 m, which can truly reflect the topographic relief characteristics of the region. The pixel binary model’s lack of regional restrictions enables a more accurate estimation of the Normalized Difference Vegetation Index (NDVI), bringing it closer to the actual vegetation situation. The study uncovered a bidirectional relationship between vegetation coverage changes and landslide deformation in the study area, revealing spatial–temporal evolution patterns. By employing multi-source remote sensing technology, the research effectively utilized changes in multi-period imagery and feature extraction methods to accurately depict the development process and distribution characteristics of landslide debris flow. This approach not only offers technical support but also provides guidance for evaluating the vulnerability of landslide debris flow in the region. Full article
(This article belongs to the Special Issue Ground Deformation Monitoring via Remote Sensing Time Series Data)
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22 pages, 10018 KiB  
Article
Monitoring and Analysis of Land Subsidence in Cangzhou Based on Small Baseline Subsets Interferometric Point Target Analysis Technology
by Xinyue Xu, Chaofan Zhou, Huili Gong, Beibei Chen and Lin Wang
Land 2023, 12(12), 2114; https://doi.org/10.3390/land12122114 - 28 Nov 2023
Viewed by 897
Abstract
Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this [...] Read more.
Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this area is significant. In this paper, we used the Small Baseline Subsets Interferometric Point Target Analysis (SBAS-IPTA) technique to process the Envisat-ASAR, Radarsat-2, and Sentinel-1A data and obtained the land subsidence of Cangzhou from 2004 to 2020. Additionally, we obtained winter wheat distribution information in Cangzhou using the Pixel Information Expert Engine (PIE-Engine) remote sensing cloud platform. On this basis, we analyzed the relationship between ground water level, winter wheat planting area, and the response of land subsidence according to the land use type and groundwater level monitoring data near the winter wheat growing area. The results show that during 2004–2020, the average annual subsidence rate of many places in Cangzhou was higher than 30 mm/year, and the maximum subsidence rate was 115 mm/year in 2012. From 2004 to 2020, the area of the subsidence funnel showed a trend of first increasing and then decreasing. In 2020, the subsidence funnel area reached 6.9 × 103 km2. The winter wheat planting area in the urban area showed a trend of first decreasing, then increasing and then decreasing, and it accounted for a large proportion in the funnel area. At the same time, we studied the relationship between the land subsidence rate and the water level at different burial depths and the response of winter wheat planting area. The results showed that the change of confined water level had a stronger response with the other two variables. Full article
(This article belongs to the Special Issue Ground Deformation Monitoring via Remote Sensing Time Series Data)
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18 pages, 14960 KiB  
Article
U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model
by Lirong Yin, Lei Wang, Tingqiao Li, Siyu Lu, Jiawei Tian, Zhengtong Yin, Xiaolu Li and Wenfeng Zheng
Land 2023, 12(10), 1859; https://doi.org/10.3390/land12101859 - 29 Sep 2023
Cited by 50 | Viewed by 2395
Abstract
Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing [...] Read more.
Change detection of natural lake boundaries is one of the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons in each layer can only be propagated to the upper layer, and the processing of samples is independent at each moment. However, for time-series data with transferability, the learned change information needs to be recorded and utilized. To solve the above problems, we propose a lake boundary change prediction model combining U-Net and LSTM. The ensemble of LSTMs helps to improve the overall accuracy and robustness of the model by capturing the spatial and temporal nuances in the data, resulting in more precise predictions. This study selected Lake Urmia as the research area and used the annual panoramic remote sensing images from 1996 to 2014 (Lat: 37°00′ N to 38°15′ N, Lon: 46°10′ E to 44°50′ E) obtained by Google Earth Professional Edition 7.3 software as the research data set. This model uses the U-Net network to extract multi-level change features and analyze the change trend of lake boundaries. The LSTM module is introduced after U-Net to optimize the predictive model using historical data storage and forgetting as well as current input data. This method enables the model to automatically fit the trend of time series data and mine the deep information of lake boundary changes. Through experimental verification, the model’s prediction accuracy for lake boundary changes after training can reach 89.43%. Comparative experiments with the existing U-Net-STN model show that the U-Net-LSTM model used in this study has higher prediction accuracy and lower mean square error. Full article
(This article belongs to the Special Issue Ground Deformation Monitoring via Remote Sensing Time Series Data)
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