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Remote Sensing
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Mapping and Change Analysis Applications with Remote Sensing and GIS
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Mapping and Change Analysis Applications with Remote Sensing and GIS

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 11254

Special Issue Editors

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
Prof. Dr. Alex Hay-Man Ng

E-Mail Website
Guest Editor
1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
2. School of Civil and Environmental Engineering, UNSW Australia, Sydney 2052, Australia
Interests: InSAR; land subsidence; natural and human-induced hazards; subsidence modelling; monitoring/change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Zheyuan Du

E-Mail Website
Guest Editor
1. School of Civil and Environmental Engineering, UNSW Australia, Sydney 2052, Australia
2. School of Civil Engineering, University of Sydney, Sydney 2006, Australia
Interests: InSAR; land subsidence; land degradation; land use; atmosphere modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The goal of this Special Issue is to collect papers (original research articles and review papers) that provide insight into the use of remote sensing and GIS techniques for Earth observation, including elevation mapping, surface change detection, land deformation identification, etc.

Remote sensing data such as SAR, LiDAR, optical remote sensing data, etc., have played a very important role in observing the Earth’s surface, and its changes over time. Remote sensing techniques use aircraft, satellite, and ground sensors to collect information about a target or an area at a distance, providing an impressive amount of geospatial information in multiple dimensions. The Geographic Information System (GIS) is a method to visualise, manipulate, analyse, and display the spatial data collected from many sources. Recent technological advances in GIS techniques and methodologies, together with the analysis of remotely sensed data, have become powerful tools in fundamental and applied sciences, particularly in the fields of engineering, geology, geography, urban planning, forestry, and agriculture.

Remote sensing has been have drastically transformed in the last decade due to advancements in technology, e.g., a variety of new observation platforms have become available in both satellite and aerial platforms.

The availability of new remote sensing sensors and platforms allow scientists and researchers to not only improve the accuracy and efficiency of existing Earth observation applications, but also provide the opportunity to expand the applicability of remote sensing techniques for a wide range of new Earth observation applications, creating new opportunities for scientific discovery and revolutionized geophysics, space geodesy, and Earth science disciplines, among others.

In this Special Issue on “Mapping and Change Analysis Applications with Remote Sensing and GIS”, we invite authors to submit work making synergistic use of remote sensing (i.e., UAV-borne, airborne, spaceborne, and ground sensing) and GIS for Earth observation. We particularly welcome contributions exploiting novel remote sensing and GIS techniques and applications for mapping the Earth’s surface and its changes. Review articles are also welcome.

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

  • earth observations
  • geodetic monitoring
  • GNSS
  • remote sensing
  • GIS
  • deformation analysis
  • earth mapping
  • change analysis

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

Published Papers (5 papers)

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Research

21 pages, 5805 KiB  
Article
Development of a Dynamic Prediction Model for Underground Coal-Mining-Induced Ground Subsidence Based on the Hook Function
by Huaizhi Bo, Guohong Lu, Huaizhan Li, Guangli Guo and Yunwei Li
Remote Sens. 2024, 16(2), 377; https://doi.org/10.3390/rs16020377 - 17 Jan 2024
Cited by 4 | Viewed by 1184
Abstract
Underground coal-mining-induced ground subsidence deformation is a common geological disaster impacting buildings, transportation and water supplies. Models predicting ground subsidence dynamically with high precision are important for the prevention of damage derived from ground subsidence. In this paper, the Hook function is utilized [...] Read more.
Underground coal-mining-induced ground subsidence deformation is a common geological disaster impacting buildings, transportation and water supplies. Models predicting ground subsidence dynamically with high precision are important for the prevention of damage derived from ground subsidence. In this paper, the Hook function is utilized to develop a model describing the velocity of ground subsidence due to underground coal mining. Based on the subsidence velocity model, a dynamic subsidence model is established by taking an integral of the velocity model. Coefficients of the model, which depend on maximum subsidence, maximum subsidence velocity and the time corresponding to the maximum subsidence velocity, are related to the geological and mining conditions of the coal seam being investigated. A Levenberg–Marquardt-algorithm-based method is also proposed to calculate the optimal model coefficients based on subsidence velocity observations. Four continuously operating Global Navigation Satellite System (GNSS) stations were constructed above a typical longwall coal mining working face in the Jining mining area, China. These GNSS stations collected subsidence observations over two years, which were used to validate the developed prediction model. The results show that the root-mean-square (RMS) of the model-predicted ground subsidence error is 56.1 mm, and the maximum relative error is 2.5% for all four GNSS stations, when the ground subsidence is less than 6000 mm. Full article
(This article belongs to the Special Issue Mapping and Change Analysis Applications with Remote Sensing and GIS)
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22 pages, 17603 KiB  
Article
A Geospatial Analysis-Based Method for Railway Route Selection in Marine Glaciers: A Case Study of the Sichuan-Tibet Railway Network
by Tao Deng, Abubakar Sharafat, Young Min Wie, Ki Gang Lee, Euiong Lee and Kang Hoon Lee
Remote Sens. 2023, 15(17), 4175; https://doi.org/10.3390/rs15174175 - 25 Aug 2023
Cited by 4 | Viewed by 1923
Abstract
Marine glaciers play a significant role in shaping landforms due to their erosive nature coupled with their surrounding environment. During this process, they pose a natural hazard threat to man-made infrastructure. The dynamic nature of these glaciers poses a particular threat, especially to [...] Read more.
Marine glaciers play a significant role in shaping landforms due to their erosive nature coupled with their surrounding environment. During this process, they pose a natural hazard threat to man-made infrastructure. The dynamic nature of these glaciers poses a particular threat, especially to railway infrastructure constructed in remote areas with glacial activity. Substantial research has been undertaken on the role of threats posed by marine glaciers to railway infrastructure. However, a detailed study of favorable glacier landforms prior to railway construction has yet to be explored. In this study, we propose a geospatial analysis-based method to determine the favorable most landforms shaped by marine glaciers for railway network route selection. This study provides a novel approach by first analyzing the availability of four major favorable landforms shaped by marine glaciers (glacier canyons, valley shoulders, moraine terraces, and ancient dammed lake basins), then proposes a railway route selection method for marine glacier distribution areas involving three steps. First, it is necessary to understand the basic situation of regional glaciers; then, to determine a feasible location for the railway based on judgment of the direct and indirect action areas of glaciers; and finally, through a thematic study of glacial geomorphology, to devise corresponding strategies for using glacial landforms to optimize the railway route. In order to verify the feasibility of the proposed method, it was implemented in the Palong Zangbo watershed of the Sichuan–Tibet railway network. Utilizing the power function method, the glacier basin areas of 22 glacier canyons along the Sichuan–Tibet railway line were identified and the maximum annual average velocity of 75 glaciers over the past ten years was calculated by offset tracking technology. The results indicate that the proposed optimization strategies utilizing glacier canyons for a short and straight route scheme and leveraging moraine terraces for a high-line scheme can provide comprehensive guidance for railway route selection in marine glacial areas. Full article
(This article belongs to the Special Issue Mapping and Change Analysis Applications with Remote Sensing and GIS)
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17 pages, 15159 KiB  
Article
Understanding the Spatiotemporal Characteristics of Land Subsidence and Rebound in the Lianjiang Plain Using Time-Series InSAR with Dual-Track Sentinel-1 Data
by Yangfan He, Alex Hay-Man Ng, Hua Wang and Jianming Kuang
Remote Sens. 2023, 15(13), 3236; https://doi.org/10.3390/rs15133236 - 23 Jun 2023
Cited by 3 | Viewed by 1573
Abstract
The Lianjiang Plain, renowned for its position as ‘China’s textile hub’ and characterized by its high population density, has experienced considerable subsidence due to excessive groundwater extraction in recent years. Although some studies have investigated short-term subsidence in this plain, research on long-term [...] Read more.
The Lianjiang Plain, renowned for its position as ‘China’s textile hub’ and characterized by its high population density, has experienced considerable subsidence due to excessive groundwater extraction in recent years. Although some studies have investigated short-term subsidence in this plain, research on long-term subsidence and rebound remain understudied. In this paper, the characteristics of surface deformation in the Lijiang Plain during two periods (2015–2017 and 2018–2021) have been investigated using the time-series interferometric synthetic aperture radar (TS-InSAR) technique, and the correlation with the changes in groundwater level, geological factors, and urban construction are discussed. The InSAR-derived results are cross-validated with the adjacent orbit datasets. Large-scale and uneven subsidence ranging from −124 mm/year to +40 mm/year is observed from 2015 to 2017. However, a significant decrease in the subsidence rate during 2018–2021, with local rebound deformation up to +48 mm/year in three regions, is also observed. Groundwater level changes are found to be the major cause of the ground deformation, and the intercomparison between groundwater level and ground displacement time series from TS-InSAR measurements also indicates a clear relationship between them during 2018–2021. Geological factors control the range of deformation area over the study period. The impact of urban construction on surface subsidence is evident, contributing to high deformation. Our findings could improve the understanding of how deformation is affected by groundwater rebound and offer valuable insights into groundwater management, urban planning, and land subsidence mitigation. Full article
(This article belongs to the Special Issue Mapping and Change Analysis Applications with Remote Sensing and GIS)
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27 pages, 23704 KiB  
Article
A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means
by Francesco Giuseppe Figliomeni, Francesca Guastaferro, Claudio Parente and Andrea Vallario
Remote Sens. 2023, 15(12), 3181; https://doi.org/10.3390/rs15123181 - 19 Jun 2023
Cited by 6 | Viewed by 3401
Abstract
The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue [...] Read more.
The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance. Full article
(This article belongs to the Special Issue Mapping and Change Analysis Applications with Remote Sensing and GIS)
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21 pages, 6053 KiB  
Article
Satellite Imaging Techniques for Ground Movement Monitoring of a Deep Pipeline Trench Backfilled with Recycled Materials
by B. Teodosio, A. Al-Taie, E. Yaghoubi and P. L. P. Wasantha
Remote Sens. 2023, 15(1), 204; https://doi.org/10.3390/rs15010204 - 30 Dec 2022
Cited by 4 | Viewed by 2107
Abstract
The damage to pipeline infrastructures caused by reactive soils has been a critical challenge for asset owners. Sustainable backfilling materials have recently gained interest to stabilize highly reactive zones as a pre-emptive approach towards sustainability. In this study, two adjacent sections of a [...] Read more.
The damage to pipeline infrastructures caused by reactive soils has been a critical challenge for asset owners. Sustainable backfilling materials have recently gained interest to stabilize highly reactive zones as a pre-emptive approach towards sustainability. In this study, two adjacent sections of a sewer pipeline trench in Melbourne, Australia were backfilled with two blends of 100% recycled aggregates. The sites were monitored for ground deformations during October 2020–February 2022 (17 months) using surveying techniques. Interferometric synthetic aperture radar (InSAR) techniques and algorithms were also employed to estimate the ground movements of the sites and surrounding regions. The cross-validation of deformation results achieved from both techniques enabled an in-depth analysis of the effectiveness of the recycled aggregates to address reactive soil issues in urban developments. Observational deformation data and their spatiotemporal variation in the field were satisfactorily captured by the InSAR techniques: differential InSAR (DInSAR), persistent scatterer interferometry (PSI), and small baseline subset (SBAS). The SBAS estimations were found to be the closest to field measurements, primarily due to the analysis of zones without well-defined geometries. This study’s contribution to existing knowledge defines the spatiotemporal influence of sustainable backfill in areas with reactive soil through field data and satellite imaging. The relationship between InSAR techniques and actual field behavior of sustainable backfill can be a baseline for the growing construction that may be challenging to perform field monitoring due to resource constraints. Full article
(This article belongs to the Special Issue Mapping and Change Analysis Applications with Remote Sensing and GIS)
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