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Mapping and Monitoring of Geohazards with Remote Sensing Technologies

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 29906

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Special Issue Editors

Department of Geological Sciences, School of Mining and Metallurgical Engineering, The National Technical University of Athens (NTUA), Zografou Campus, GR-157 80 Athens, Greece
Interests: geohazard monitoring and modeling (landslides, land subsidence, erosion, floods); geotechnical engineering; engineering geology; computational geotechnical engineering; remote sensing data interpretation; natural hazards under climate change impacts; monitoring and protection of monuments
Special Issues, Collections and Topics in MDPI journals
Institute of Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, GR-118 10 Athens, Greece
Interests: earth observation; synthetic aperture radar; SAR interferometry; persistent scatterer interferometry; machine learning and information extraction; disaster management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation (EO) techniques have proven to be reliable and accurate for monitoring land surface deformations occurring naturally (landslides, earthquakes, and volcanoes) or due to anthropogenic activities (ground water overexploitation, extraction of oil and gas).

In cases where mitigation methods have to be put into practice, the detailed mapping, characterization, monitoring and simulation of the geocatastrophic phenomena have to precede their design and implementation. EO techniques possess high potential and suitability as alternative, cost-efficient methods for the management of geohazards, and have been proven to be a valuable tool for verifying and validating the spatial extent and the evolution of the deformations.

To this extent, in the current Special Issue, submissions are encouraged that cover innovative applications and case studies on the mapping and monitoring of all kinds of geohazards with remote sensing technologies. Submissions that make use of new tools and methodologies, including the use of data-driven machine learning methods, are encouraged.

Dr. Constantinos Loupasakis
Dr. Ioannis Papoutsis
Dr. Konstantinos G. Nikolakopoulos
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

  • Geohazards
  • InSAR
  • Remote sensing
  • Photogrammetry
  • Unmanned aerial vehicles
  • GNSS
  • TLS
  • Persistent scatterer interferometry
  • Machine learning

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Published Papers (15 papers)

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Editorial

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4 pages, 186 KiB  
Editorial
Special Issue “Mapping and Monitoring of Geohazards with Remote Sensing Technologies”
by Constantinos Loupasakis, Ioannis Papoutsis and Konstantinos G. Nikolakopoulos
Remote Sens. 2023, 15(17), 4145; https://doi.org/10.3390/rs15174145 - 24 Aug 2023
Viewed by 607
Abstract
Geohazard monitoring is crucial for building resilient communities [...] Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)

Research

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22 pages, 14761 KiB  
Article
Prediction of Mine Subsidence Based on InSAR Technology and the LSTM Algorithm: A Case Study of the Shigouyi Coalfield, Ningxia (China)
by Fei Ma, Lichun Sui and Wei Lian
Remote Sens. 2023, 15(11), 2755; https://doi.org/10.3390/rs15112755 - 25 May 2023
Cited by 3 | Viewed by 1549
Abstract
The accurate prediction of surface subsidence induced by coal mining is critical to safeguarding the environment and resources. However, the precision of current prediction models is often restricted by the lack of pertinent data or imprecise model parameters. To overcome these limitations, this [...] Read more.
The accurate prediction of surface subsidence induced by coal mining is critical to safeguarding the environment and resources. However, the precision of current prediction models is often restricted by the lack of pertinent data or imprecise model parameters. To overcome these limitations, this study proposes an approach to predicting mine subsidence that leverages Interferometric Synthetic Aperture Radar (InSAR) technology and the long short-term memory network (LSTM). The proposed approach utilizes small baseline multiple-master high-coherent target (SBMHCT) interferometric synthetic aperture radar technology to monitor the mine surface and applies the long short-term memory (LSTM) algorithm to construct the prediction model. The Shigouyi coalfield in Ningxia Province, China was chosen as a study area, and time series ground subsidence data were obtained based on Sentinel-1A data from 9 March 2015 to 7 June 2016. To evaluate the proposed approach, the prediction accuracies of LSTM and Support Vector Regression (SVR) were compared. The results show that the proposed approach could accurately predict mine subsidence, with maximum absolute errors of less than 2 cm and maximum relative errors of less than 6%. The findings demonstrate that combining InSAR technology with the LSTM algorithm is an effective and robust approach for predicting mine subsidence. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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24 pages, 9746 KiB  
Article
Landslides Triggered by the 2016 Heavy Rainfall Event in Sanming, Fujian Province: Distribution Pattern Analysis and Spatio-Temporal Susceptibility Assessment
by Siyuan Ma, Xiaoyi Shao and Chong Xu
Remote Sens. 2023, 15(11), 2738; https://doi.org/10.3390/rs15112738 - 24 May 2023
Cited by 7 | Viewed by 1944
Abstract
Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous area. From 5 to 10 May 2016, Sanming City in Fujian Province, China, experienced a heavy rainfall event that caused massive landslides, leading to significant loss [...] Read more.
Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous area. From 5 to 10 May 2016, Sanming City in Fujian Province, China, experienced a heavy rainfall event that caused massive landslides, leading to significant loss of life and property. Using high-resolution satellite imagery, we created a detailed inventory of landslides triggered by this event, which totaled 2665 across an area of 3700 km2. The majority of landslides were small-scale, shallow and elongated, with a dominant distribution in Xiaqu town. We analyzed the correlations between the landslide abundance and topographic, geological and hydro-meteorological factors. Our results indicated that the landslide abundance index is related to the gradient of the hillslope, distance from a river and total rainfall. The landslide area density, i.e., LAD increases with the increase in these influencing factors and is described by an exponential or linear relationship. Among all lithological types, Sinian mica schist and quartz schist (Sn-s) were found to be the most prone to landslides, with over 35% of landslides occurring in just 10% of the area. Overall, the lithology and rainfall characteristics primarily control the abundance of landslides, followed by topography. To gain a better understanding of the triggering conditions for shallow landslides, we conducted a physically based spatio-temporal susceptibility assessment in the landslide abundance area. Our numerical simulations, using the MAT.TRIGRS tool, show that it can accurately reproduce the temporal evolution of the instability process of landslides triggered by this event. Although rainfall before 8 May may have contributed to decreased slope stability in the study area, the short duration of heavy rainfall on 8 May is believed to be the primary triggering factor for the occurrence of massive landslides. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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25 pages, 25202 KiB  
Article
Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence
by Yuejuan Chen, Xu Dong, Yaolong Qi, Pingping Huang, Wenqing Sun, Wei Xu, Weixian Tan, Xiujuan Li and Xiaolong Liu
Remote Sens. 2023, 15(10), 2691; https://doi.org/10.3390/rs15102691 - 22 May 2023
Cited by 5 | Viewed by 1465
Abstract
Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 [...] Read more.
Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 were used as the data source in this study. Based on the high interference coherence of the permanent scatterer (PS) over a long period of time, the problem of the manual selection of ground control points (GCPs) affecting the monitoring results during refinement and re-flattening is solved. A DInSAR-PS-Stacking method combining the PS three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) is proposed as a means to select ground control points for refinement and re-flattening, as well as a means to obtain time-series deformation by weighted stacking processing. A SBAS-PS-InSAR method combining the PS three-threshold method to select PS points as GCPs for refinement and re-flattening is also proposed. The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are analyzed and verified. The results show that the subsidence location, range, distribution, and space–time subsidence law of surface deformation results obtained by DInSAR-PS-Stacking, SBAS-PS-InSAR, and GPS methods are basically the same. The deformation results obtained by these two InSAR methods have a good correlation with the GPS monitoring results, and the MAE and RMSE are within the acceptable range. The error showed that the edge of the subsidence basin was small and that the center was large. Both methods were found to be able to effectively monitor the coal mine, but there were also shortcomings. DInSAR-PS-Stacking has a strong ability to monitor the settlement center. SBAS-PS-InSAR performed well in monitoring slow and small deformations, but its monitoring of the settlement center was insufficient. Considering the advantages of these two InSAR methods, we proposed fusing the time-series deformation results obtained using these two InSAR methods to allow for more reliable deformation results and to carry out settlement analysis. The results showed that the automatic two-threshold (deformation threshold and average coherence threshold) fusion was effective for monitoring and analysis, and the deformation monitoring results are in good agreement with the actual situation. The deformation information obtained by the comparison, and fusion of multiple methods can allow for better monitoring and analysis of the mining area surface deformation, and can also provide a scientific reference for mining subsidence control and early disaster warning. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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10 pages, 1708 KiB  
Communication
EGMStream, a Desktop App for EGMS Data Downstream
by Davide Festa and Matteo Del Soldato
Remote Sens. 2023, 15(10), 2581; https://doi.org/10.3390/rs15102581 - 15 May 2023
Cited by 3 | Viewed by 1898
Abstract
The recent release of European Ground Motion Service (EGMS) products implemented under the responsibility of the Copernicus Land Monitoring Service (CLMS) guarantees free and accessible Europe-wide ground motion data for ground deformation analysis at the local and regional scales. The need for value-adding [...] Read more.
The recent release of European Ground Motion Service (EGMS) products implemented under the responsibility of the Copernicus Land Monitoring Service (CLMS) guarantees free and accessible Europe-wide ground motion data for ground deformation analysis at the local and regional scales. The need for value-adding services and tools for optimal dissemination of radar data from the Copernicus Sentinel-1 satellite mission urges the scientific community to find efficient solutions. A desktop R-based application with a user-friendly interface capable of automatically downloading and transforming EGMS products delivered as large .csv tiles, equivalent to a radar burst into geospatial databases, is presented here. EGMStream is a self-contained desktop app that enables users to systematically store, customize, and convert ground movement data into geospatial databases, burst per burst or for an area of interest directly selectable on the app interface. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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21 pages, 3991 KiB  
Article
An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph
by Luanjie Chen, Xingtong Ge, Lina Yang, Weichao Li and Ling Peng
Remote Sens. 2023, 15(8), 2126; https://doi.org/10.3390/rs15082126 - 17 Apr 2023
Cited by 3 | Viewed by 1772
Abstract
Landslides pose a significant threat to human lives and property, making the development of accurate and reliable landslide prediction methods essential. With the rapid advancement of multi-source remote sensing techniques and machine learning, remote sensing data-driven landslide prediction methods have attracted increasing attention. [...] Read more.
Landslides pose a significant threat to human lives and property, making the development of accurate and reliable landslide prediction methods essential. With the rapid advancement of multi-source remote sensing techniques and machine learning, remote sensing data-driven landslide prediction methods have attracted increasing attention. However, the lack of an effective and efficient paradigm for organizing multi-source remote sensing data and a unified prediction workflow often results in the weak generalization ability of existing prediction models. In this paper, we propose an improved multi-source data-driven landslide prediction method based on a spatio-temporal knowledge graph and machine learning models. By combining a spatio-temporal knowledge graph and machine learning models, we establish a framework that can effectively organize multi-source remote sensing data and generate unified prediction workflows. Our approach considers the environmental similarity between different areas, enabling the selection of the most adaptive machine learning model for predicting landslides in areas with scarce samples. Experimental results show that our method outperforms machine learning methods, achieving an increase in F1 score by 29% and an improvement in processing efficiency by 93%. Furthermore, by comparing the susceptibility maps generated in real scenarios, we found that our workflow can alleviate the problem of poor prediction performance caused by limited data availability in county-level predictions. This method provides new insights into the development of data-driven landslide evaluation methods, particularly in addressing the challenges posed by limited data availability. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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16 pages, 6244 KiB  
Article
A Method for Predicting Landslides Based on Micro-Deformation Monitoring Radar Data
by Weixian Tan, Yadong Wang, Pingping Huang, Yaolong Qi, Wei Xu, Chunming Li and Yuejuan Chen
Remote Sens. 2023, 15(3), 826; https://doi.org/10.3390/rs15030826 - 01 Feb 2023
Cited by 5 | Viewed by 1312
Abstract
Mine slope landslides seriously threaten the safety of people’s lives and property in mining areas. Landslide prediction is an effective way to reduce losses due to such disasters. In recent years, micro-deformation monitoring radar has been widely used in mine slope landslide monitoring. [...] Read more.
Mine slope landslides seriously threaten the safety of people’s lives and property in mining areas. Landslide prediction is an effective way to reduce losses due to such disasters. In recent years, micro-deformation monitoring radar has been widely used in mine slope landslide monitoring. However, traditional landslide prediction methods are not able to make full use of the diversified monitoring data from these radars. This paper proposes a landslide time prediction method based on the time series monitoring data of micro-deformation monitoring radar. Specifically, deformation displacement, coherence and deformation volume, and the parametric degree of deformation (DOD) are calculated and combined with the use of the tangent angle method. Finally, the effectiveness of the method is verified by using measured data of a landslide in a mining area. The experimental results show that our proposed method can be used to identify the characteristics of an imminent sliding slope and landslide in advance, providing monitoring personnel with more reliable landslide prediction results. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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20 pages, 4905 KiB  
Article
High-Resolution Deformation Monitoring from DInSAR: Implications for Geohazards and Ground Stability in the Metropolitan Area of Santiago, Chile
by Felipe Orellana, Marcos Moreno and Gonzalo Yáñez
Remote Sens. 2022, 14(23), 6115; https://doi.org/10.3390/rs14236115 - 02 Dec 2022
Cited by 9 | Viewed by 3322
Abstract
Large urban areas are vulnerable to various geological hazards and anthropogenic activities that affect ground stability—a key factor in structural performance, such as buildings and infrastructure, in an inherently expanding context. Time series data from synthetic aperture radar (SAR) satellites make it possible [...] Read more.
Large urban areas are vulnerable to various geological hazards and anthropogenic activities that affect ground stability—a key factor in structural performance, such as buildings and infrastructure, in an inherently expanding context. Time series data from synthetic aperture radar (SAR) satellites make it possible to identify small rates of motion over large areas of the Earth’s surface with high spatial resolution, which is key to detecting high-deformation areas. Santiago de Chile’s metropolitan region comprises a large Andean foothills basin in one of the most seismically active subduction zones worldwide. The Santiago basin and its surroundings are prone to megathrust and shallow crustal earthquakes, landslides, and constant anthropogenic effects, such as the overexploitation of groundwater and land use modification, all of which constantly affect the ground stability. Here, we recorded ground deformations in the Santiago basin using a multi-temporal differential interferometric synthetic aperture radar (DInSAR) from Sentinel 1, obtaining high-resolution ground motion rates between 2018 and 2021. GNSS stations show a constant regional uplift in the metropolitan area (~10 mm/year); meanwhile, DInSAR allows for the identification of areas with anomalous local subsistence (rates < −15 mm/year) and mountain sectors with landslides with unprecedented detail. Ground deformation patterns vary depending on factors such as soil type, basin geometry, and soil/soil heterogeneities. Thus, the areas with high subsidence rates are concentrated in sectors with fine sedimentary cover and a depressing shallow water table as well as in cropping areas with excess water withdrawal. There is no evidence of detectable movement on the San Ramon Fault (the major quaternary fault in the metropolitan area) over the observational period. Our results highlight the mechanical control of the sediment characteristics of the basin and the impact of anthropogenic processes on ground stability. These results are essential to assess the stability of the Santiago basin and contribute to future infrastructure development and hazard management in highly populated areas. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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20 pages, 7890 KiB  
Article
Characterizing Spatiotemporal Patterns of Land Subsidence after the South-to-North Water Diversion Project Based on Sentinel-1 InSAR Observations in the Eastern Beijing Plain
by Yuanyuan Liu, Xia Yan, Yuanping Xia, Bo Liu, Zhong Lu and Mei Yu
Remote Sens. 2022, 14(22), 5810; https://doi.org/10.3390/rs14225810 - 17 Nov 2022
Cited by 3 | Viewed by 1476
Abstract
The eastern Beijing plain has been suffering severe subsidence for the last decades, mainly associated with the long-term excessive extraction of groundwater resource. Since the end of 2014, the annual water supply in Beijing plain has reached several hundred million cubic meters because [...] Read more.
The eastern Beijing plain has been suffering severe subsidence for the last decades, mainly associated with the long-term excessive extraction of groundwater resource. Since the end of 2014, the annual water supply in Beijing plain has reached several hundred million cubic meters because of the South-to-North Water Diversion (SNWD) Project, which has reduced the groundwater exploitation and changed the status of land subsidence. In this work, we first obtain the current spatiotemporal variations of land subsidence in the eastern Beijing plain by using progressive small baseline subsets (SBAS) InSAR time series analysis method with Sentinel-1 SAR data acquired from July 2015 to December 2021. Then, we analyze the correlations between InSAR-derived subsidence and groundwater level change by applying the cross wavelet method. The results show that two major subsidence zones are successfully detected with the maximum deformation rate of −150 mm/yr and maximum cumulative deformation of −950 mm. Besides, the ground deformation at different stages from 2016 to 2021 reveal that the area and magnitude of major deformation significantly slow down, even in the regions with severe subsidence, especially in the year of 2017, which is about two years later than the start time of SNWD Project in Beijing. Further, we find the InSAR-derived subsidence lags groundwater level change with about 1–2-month lagging time, indicating that the dynamic variation of groundwater level fluctuation may be the main factor affecting the uneven subsidence in the severe subsiding zones. Last, differential subsidence rates are identified at both sides of geological faults, such as Nankou-Sunhe fault and Nanyuan-Tongxian fault, from the observed deformation map, which could be explained that the groundwater flow is blocked when a fault is encountered. These findings can provide significant information to reveal the deformation mechanisms of land subsidence, establish the hydrogeological models and assist decision-making, early warning and hazard relief in Beijing, China. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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21 pages, 18192 KiB  
Article
Timely and Low-Cost Remote Sensing Practices for the Assessment of Landslide Activity in the Service of Hazard Management
by Aggeliki Kyriou, Konstantinos G. Nikolakopoulos and Ioannis K. Koukouvelas
Remote Sens. 2022, 14(19), 4745; https://doi.org/10.3390/rs14194745 - 22 Sep 2022
Cited by 10 | Viewed by 2261
Abstract
Landslides are among the most dangerous and catastrophic events in the world. The increasing progress in remote sensing technology made landslide observations timely, systematic and less costly. In this context, we collected multi-dated data obtained by Unmanned Aerial Vehicle (UAV) campaigns and Terrestrial [...] Read more.
Landslides are among the most dangerous and catastrophic events in the world. The increasing progress in remote sensing technology made landslide observations timely, systematic and less costly. In this context, we collected multi-dated data obtained by Unmanned Aerial Vehicle (UAV) campaigns and Terrestrial Laser Scanning (TLS) surveys for the accurate and immediate monitoring of a landslide located in a steep and v-shaped valley, in order to provide operational information concerning the stability of the area to the local authorities. The derived data were processed appropriately, and UAV-based as well as TLS point clouds were generated. The monitoring and assessment of the evolution of the landslide were based on the identification of instability phenomena between the multi-dated UAV and TLS point clouds using the direct cloud-to-cloud comparison and the estimation of the deviation between surface sections. The overall evaluation of the results revealed that the landslide remains active for three years but is progressing particularly slowly. Moreover, point clouds arising from a UAV or a TLS sensor can be effectively utilized for landslide monitoring with comparable accuracies. Nevertheless, TLS point clouds proved to be denser and more appropriate in terms of enhancing the accuracy of the monitoring process. The outcomes were validated using measurements, acquired by the Global Navigation Satellite System (GNSS). Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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29 pages, 11292 KiB  
Article
Evaluation of SAR and Optical Data for Flood Delineation Using Supervised and Unsupervised Classification
by Fatemeh Foroughnia, Silvia Maria Alfieri, Massimo Menenti and Roderik Lindenbergh
Remote Sens. 2022, 14(15), 3718; https://doi.org/10.3390/rs14153718 - 03 Aug 2022
Cited by 11 | Viewed by 2578
Abstract
Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a [...] Read more.
Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a new stepwise sequence of unsupervised and supervised classification methods using both SAR and MS data. The MS and SAR signatures of land and water targets in the study area were evaluated prior to the classification to identify the land and water classes that could be delineated. The delineation based on a simple thresholding method provided a satisfactory estimate of the total flooded area but did not perform well on heterogeneous surface water. To deal with the heterogeneity and fragmentation of water patches, a new unsupervised classification approach based on a combination of thresholding and segmentation (CThS) was developed. Since sandy areas and emergent vegetation could not be classified by the SAR-based unsupervised methods, supervised random forest (RF) classification was applied to a time series of SAR and co-event MS data, both combined and separated. The new stepwise approach was tested for determining the flood extent of two events in Italy. The results showed that all the classification methods applied to MS data outperformed the ones applied to SAR data. Although the supervised RF classification may lead to better accuracies, the CThS (unsupervised) method achieved precision and accuracy comparable to the RF, making it more appropriate for rapid flood mapping due to its ease of implementation. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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16 pages, 9054 KiB  
Article
Using a Lidar-Based Height Variability Method for Recognizing and Analyzing Fault Displacement and Related Fossil Mass Movement in the Vipava Valley, SW Slovenia
by Tomislav Popit, Boštjan Rožič, Andrej Šmuc, Andrej Novak and Timotej Verbovšek
Remote Sens. 2022, 14(9), 2016; https://doi.org/10.3390/rs14092016 - 22 Apr 2022
Cited by 3 | Viewed by 1437
Abstract
The northern slopes of the Vipava Valley are defined by a thrust front of Mesozoic carbonates over Tertiary flysch deposits. These slopes are characterized by a variety of different surface forms, among which recent and fossil polygenetic landslides are the most prominent mass [...] Read more.
The northern slopes of the Vipava Valley are defined by a thrust front of Mesozoic carbonates over Tertiary flysch deposits. These slopes are characterized by a variety of different surface forms, among which recent and fossil polygenetic landslides are the most prominent mass movements. We used the height variability method as a morphometric indicator, which proved to be the most useful among the various methods for quantifying and visualizing fossil landslides. Height variability is based on the difference in elevations derived from a high-resolution lidar-derived DEM. Based on geologic field mapping and geomorphometric analysis, we distinguished two main types of movements: structurally induced movement along the fault zone and movements caused by complex Quaternary gravitational slope processes. The most pronounced element is the sliding of the huge rotational carbonate massif, which was displaced partly along older fault structures in the hinterland of fossil rock avalanches and carbonate blocks. In addition to the material properties of the lithology, the level of surface roughness also depends on the depositional processes of the individual sedimentary bodies. These were formed by complex sedimentary events and are intertwined in the geological past. The sedimentary bodies indicate two large fossil rock avalanches, while the smaller gravity blocks indicate translational–rotational slides of carbonate and carbonate breccia. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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21 pages, 24745 KiB  
Article
Seasonal Ground Movement Due to Swelling/Shrinkage of Nicosia Marl
by Ploutarchos Tzampoglou, Dimitrios Loukidis and Niki Koulermou
Remote Sens. 2022, 14(6), 1440; https://doi.org/10.3390/rs14061440 - 16 Mar 2022
Cited by 5 | Viewed by 2181
Abstract
This research investigates the seasonal ground heave/settlement of an area covered by an expansive soil of Cyprus called Nicosia marl, highlighting the degree of influence of the main causal factors. For this purpose, existing geotechnical data from the archives of the Cyprus Geological [...] Read more.
This research investigates the seasonal ground heave/settlement of an area covered by an expansive soil of Cyprus called Nicosia marl, highlighting the degree of influence of the main causal factors. For this purpose, existing geotechnical data from the archives of the Cyprus Geological Survey were first collected and processed to compile maps of the key geotechnical parameters in the study area. In order to estimate the ground movements in the area, Earth Observation (EO) techniques for the period between 16 November 2002–30 December 2006 were processed. The correlation of these movements with the existing geotechnical data indicates that there is a statistically significant correlation between plasticity index and the ground movements. Multivariate linear regression analysis using Lasso revealed that the plasticity index ranks first in importance among the examined variables. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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20 pages, 8775 KiB  
Article
Kinematics of Active Landslides in Achaia (Peloponnese, Greece) through InSAR Time Series Analysis and Relation to Rainfall Patterns
by Varvara Tsironi, Athanassios Ganas, Ioannis Karamitros, Eirini Efstathiou, Ioannis Koukouvelas and Efthimios Sokos
Remote Sens. 2022, 14(4), 844; https://doi.org/10.3390/rs14040844 - 11 Feb 2022
Cited by 18 | Viewed by 3022
Abstract
We studied the kinematic behaviour of active landslides at several localities in the area of Panachaikon Mountain, Achaia (Peloponnese, Greece) using Sentinel (C-band) InSAR time series analysis. We processed LiCSAR interferograms using the SBAS tool, and we obtained average displacement maps for the [...] Read more.
We studied the kinematic behaviour of active landslides at several localities in the area of Panachaikon Mountain, Achaia (Peloponnese, Greece) using Sentinel (C-band) InSAR time series analysis. We processed LiCSAR interferograms using the SBAS tool, and we obtained average displacement maps for the period 2016–2021. We found that the maximum displacement rate of each landslide is located at about the center of it. The average E-W velocity of the Krini landslide is ~3 cm/year (toward the east) and 0.6 cm/year downward. The line-of-sight (LOS) velocity of the landslide (descending orbit) compares well to a co-located GNSS station within (±) 3 mm/yr. Our results also suggest a correlation between rainfall and landslide motion. For the Krini landslide, a cross-correlation analysis of our data suggests that the mean time lag was 13.5 days between the maximum seasonal rainfall and the change in the LOS displacement rate. We also found that the amount of total seasonal rainfall controls the increase in the displacement rate, as 40–550% changes in the displacement rate of the Krini landslide were detected, following to a seasonal maximum of rainfall values at the nearby meteorological station of Kato Vlassia. According to our results, it seems that large part of this mountainous region of Achaia suffers from slope instability that is manifested in various degrees of ground displacement greatly affecting its morphological features and inhabited areas. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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18 pages, 9079 KiB  
Technical Note
A High-Precision Remote Sensing Identification Method on Saline-Alkaline Areas Using Multi-Sources Data
by Jingyi Yang, Qinjun Wang, Dingkun Chang, Wentao Xu and Boqi Yuan
Remote Sens. 2023, 15(10), 2556; https://doi.org/10.3390/rs15102556 - 13 May 2023
Cited by 1 | Viewed by 1038
Abstract
Soil salinization is a widespread and important environmental problem. We propose a high-precision remote sensing identification method for saline-alkaline areas using multi-source data, a method which is of some significance for improving ecological and environmental problems on a global scale which have been [...] Read more.
Soil salinization is a widespread and important environmental problem. We propose a high-precision remote sensing identification method for saline-alkaline areas using multi-source data, a method which is of some significance for improving ecological and environmental problems on a global scale which have been caused by soil salinization. Its principle is to identify saline-alkaline areas from remote sensing imagery by a decision tree model combining four spectral indices named NDSI34 (Normalized Difference Spectral Index of Band 3 and Band 4), NDSI25 (Normalized Difference Spectral Index of Band 2 and Band 5), NDSI237 (Normalized Difference Spectral Index of Band 3 and Band 4) and NDSInew (New Normalized Difference Salt Index) that can distinguish saline-alkaline areas from other features. In this method, the complementary information within the multi-source data is used to improve classification accuracy. The main steps of the method include multi-source data acquisition, adaptive feature fusion of multi-source data, feature identification and integrated expression of the saline-alkaline area from multi-source data, fine classification of the saline-alkaline area, and accuracy verification. Taking Minqin County, Gansu Province, China as the study area, we use the method to identify saline-alkaline areas based on GF-2, GF-6/WFV and DEM data. The results show that the overall accuracy of the method is 88.11%, which is 7.69% higher than that of the traditional methods, indicating that it could effectively identify the distribution of saline-alkaline areas, and thus provide a scientific technique for the quick identification of saline-alkaline areas in large regions. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)
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