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19 pages, 6978 KiB  
Article
Relationship between Cephalometric and Ultrasonic Airway Parameters in Adults with High Risk of Obstructive Sleep Apnea
by Anutta Terawatpothong, Chidchanok Sessirisombat, Wish Banhiran, Hitoshi Hotokezaka, Noriaki Yoshida and Irin Sirisoontorn
J. Clin. Med. 2024, 13(12), 3540; https://doi.org/10.3390/jcm13123540 - 17 Jun 2024
Viewed by 772
Abstract
Background/Objectives: Polysomnography and cephalometry have been used for studying obstructive sleep apnea (OSA) etiology. The association between craniofacial skeleton and OSA severity remains controversial. To study OSA’s etiology, cephalometry, fiberoptic pharyngoscopy, polysomnography, and sleep endoscopy have been used; however, airway obstructions cannot be [...] Read more.
Background/Objectives: Polysomnography and cephalometry have been used for studying obstructive sleep apnea (OSA) etiology. The association between craniofacial skeleton and OSA severity remains controversial. To study OSA’s etiology, cephalometry, fiberoptic pharyngoscopy, polysomnography, and sleep endoscopy have been used; however, airway obstructions cannot be located. Recent research suggested ultrasonography for OSA screening and upper airway obstruction localization. Thus, this study aims to investigate the relationship between specific craniofacial cephalometric and ultrasonic airway parameters in adults at high risk of OSA. Methods: To assess craniofacial structure, lateral cephalograms were taken from thirty-three adults over 18 with a STOP-Bang questionnaire score of three or higher and a waist-to-height ratio (WHtR) of 0.5 or higher. Airway parameters were assessed through submental ultrasound. Results: NSBA correlated with tongue base airspace width, while MP-H correlated with oropharynx, tongue base, and epiglottis airspace width. SNA, SNB, and NSBA correlated with tongue width at the oropharynx. At tongue base, ANB and MP-H correlated with tongue width. SNB and NSBA were associated with deep tissue thickness at the oropharynx, while MP-H correlated with superficial tissue thickness at velum and oropharynx. Conclusions: Cephalometric parameters (SNA, SNB, ANB, NSBA, and MP-H) were correlated with ultrasonic parameters in the velum, oropharynx, tongue base, and epiglottis. Full article
(This article belongs to the Special Issue Current Challenges in Clinical Dentistry)
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Figure 1

Figure 1
<p>Study flow diagram. Subjects were considered high-risk if STOP-Bang scores were ≥3 according to the version used in the Department of Otorhinolaryngology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand [<a href="#B12-jcm-13-03540" class="html-bibr">12</a>].</p>
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<p>STOP-Bang questionnaire, BMI cutoff point was &gt;30 kg/m<sup>2</sup> adjusted for Thais [<a href="#B12-jcm-13-03540" class="html-bibr">12</a>].</p>
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<p>Cephalometric landmarks and parameters. Landmarks: S, sella; N, nasion; Ba, basion; A, subspinale; B, supramental; Go, Gonion; Gn, Gnathion; MP, mandibular plane; P, tip of soft palate; H, hyoid; PNS, posterior nasal spine; PAS, posterior airway space. Parameters: SNA (degree), antero-posterior position of the maxilla relative to the anterior cranial base; SNB (degree), antero-posterior position of the mandible relative to the anterior cranial base; ANB (degree), the difference between SNA and SNB; NSBA (degree), angle formed by nasion–sella–basion; MP-H (mm), perpendicular distance from hyoid bone to mandibular plane (vertical position of hyoid bone); PAS (mm), retroglossal posterior airway space, defined as the shortest distance between base of tongue and posterior pharyngeal wall; PNS-P (mm), soft palate length, measured from the posterior nasal spine (PNS) to the tip of soft palate (P).</p>
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<p>Submental ultrasonography equipment, laser alignment (AmCad BioMed Corporation).</p>
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<p>(<b>a</b>) The 30° segment of the upper airway; HM, hyoid-external meatus. (<b>b</b>) Transverse view of ultrasonographic images.</p>
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<p>The scatter plots of significant correlation between cephalometric parameters and ultrasound parameters (airspace width).</p>
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<p>The scatter plots of significant correlation between cephalometric parameters and ultrasound parameters (tongue width).</p>
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<p>The scatter plots of significant correlation between cephalometric parameters and ultrasound parameters (superficial tissue thickness).</p>
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<p>The scatter plots of significant correlation between cephalometric parameters and ultrasound parameters (deep tissue thickness).</p>
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29 pages, 26881 KiB  
Article
FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
by Franck Thollard, Dominique Clesse, Marie-Pierre Doin, Joëlle Donadieu, Philippe Durand, Raphaël Grandin, Cécile Lasserre, Christophe Laurent, Emilie Deschamps-Ostanciaux, Erwan Pathier, Elisabeth Pointal, Catherine Proy and Bernard Specht
Remote Sens. 2021, 13(18), 3734; https://doi.org/10.3390/rs13183734 - 17 Sep 2021
Cited by 15 | Viewed by 4323
Abstract
The purpose of the ForM@Ter LArge-scale multi-Temporal Sentinel-1 InterferoMetry service (FLATSIM) is the massive processing of Sentinel-1 data using multi-temporal interferometric synthetic aperture radar (InSAR) over large areas, i.e., greater than 250,000 km2. It provides the French ForM@ter scientific community with [...] Read more.
The purpose of the ForM@Ter LArge-scale multi-Temporal Sentinel-1 InterferoMetry service (FLATSIM) is the massive processing of Sentinel-1 data using multi-temporal interferometric synthetic aperture radar (InSAR) over large areas, i.e., greater than 250,000 km2. It provides the French ForM@ter scientific community with automatically processed products using a state of the art processing chain based on a small baseline subset approach, namely the New Small Baseline (NSBAS). The service results from a collaboration between the scientific team that develops and maintains the NSBAS processing chain and the French Spatial Agency (CNES) that mirrors the Sentinel-1 data. The proximity to Sentinel-1 data, the NSBAS workflow, and the specific optimizations to make NSBAS processing massively parallel for the CNES high performance computing infrastructure ensures the efficiency of the chain, especially in terms of input and output, which is the key for the success of such a service. The FLATSIM service is made of a production module, a delivery module and a user access module. Products include interferograms, surface line of sight velocity, phase delay time series and auxiliary data. Numerous quality indicators are provided for an in-depth analysis of the quality and limits of the results. The first national call in 2020 for region of interest ended up with 8 regions spread over the world with scientific interests, including seismology, tectonics, volcano-tectonics, and hydrological cycle. To illustrate the FLATSIM capabilities, an analysis is shown here on two processed regions, the Afar region in Ethiopa, and the eastern border of the Tibetan Plateau. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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Figure 1

Figure 1
<p>Distribution of bursts as a function of acquisition dates (<span class="html-italic">x</span>-axis) and latitude (<span class="html-italic">y</span>-axis). Empty circles represent bursts that have not been kept, whereas blue circles correspond to bursts that have been selected for processing. Pink circles are bursts that have not been selected, but belong to the beginning or the end of a partially selected SAFE product (standard product produced by ESA that typically includes 9 bursts per sub-swath).</p>
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<p>(<b>a</b>) perpendicular baseline versus time for the Sentinel-1 descending track D079 on the Afar region (red dot indicates the primary date); (<b>b</b>) coregistration network; (<b>c</b>) histogram showing the distribution of time interval between consecutive acquisitions; (<b>d</b>) small-baseline interferogram network; (<b>e</b>) histogram showing the distribution of temporal baselines between acquisitions used for the calculated interferograms.</p>
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<p>Network-inversion of spectral diversity (<b>a</b>) and overlap between sub-swath (<b>b</b>). Error bars correspond to the averaged network discrepancy per acquisition. These two figures are part of the delivered products.</p>
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<p>Example of a wrapped interferogram in 32 looks and in radar geometry, before (<b>a</b>) and after (<b>c</b>) correction by the prediction from ERA-5 atmospheric model (panel (<b>b</b>)). For the 250 km wide southern segment of track D135 at the border between the Tibetan plateau and the Sichuan basin. One color cycle = 2.6 cm of delay. First date: 8 June 2020; second date: 20 June 2020.</p>
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<p>Processing steps for each interferogram.</p>
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<p>Time-series processing. The time series inversion provides, from the input stack of interferograms, maps of cumulative displacement, together with quality indicators. The mean velocity, obtained by linear regression through the time series, is shown in a wrapped color scale.</p>
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<p>General architecture for production and delivery.</p>
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<p>Production module architecture and components.</p>
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<p>Delivery module for user access.</p>
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<p>Catalog access architecture.</p>
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<p>Map of the selected regions of interest.</p>
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<p>Sentinel-1 tracks footprint of (<b>a</b>) the Afar case study, (<b>b</b>) the Tibetan case study.</p>
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<p>LOS velocity maps provided by automated FLATSIM processing in eastern Tibet over 15 separated track segments.The color scale is in rad/yr, positive away from satellite. The color scale amplitude from minimum to maximum, of 2.8 rad/yr, corresponds 12.5 mm/yr. The maps are overlying each other, from north to south and from east to west, i.e., only the segment on the south-west corner is displayed in its entirety. A constant velocity is added to individual maps to enhance visual continuity across the segments.</p>
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<p>Examples of quality indicators merged for the two segments of track A070 in Tibet.</p>
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<p>Quality indicators for track A070 in Tibet: (<b>a</b>) percentage of unwrapped area for all interferograms; (<b>b</b>) RMS misclosure for all interferograms; (<b>c</b>) RMS misclosure per date. Black curves: northern section. Red curve: southern section.</p>
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<p>Proxy for temporal coherence, merged for all tracks that have been processed in eastern Tibet. (<b>a</b>) Global map. (<b>b</b>) Zoom on the rectangle shown in (<b>a</b>). (<b>c</b>) Associated optical image. The red arrow points toward a large geometrical field of stabilized sand dunes. The blue arrow shows how dirt roads increase the coherence in desert sand dunes.</p>
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<p>Mean velocity map for track D079 and D006 showing a number of geophysical signals.</p>
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<p>Zoom on three geophysical signals. Areas of interest are indicated by the black rectangles in <a href="#remotesensing-13-03734-f017" class="html-fig">Figure 17</a>.</p>
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22 pages, 11198 KiB  
Article
Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data
by Moidu Jameela Riyas, Tajdarul Hassan Syed, Hrishikesh Kumar and Claudia Kuenzer
Remote Sens. 2021, 13(8), 1521; https://doi.org/10.3390/rs13081521 - 15 Apr 2021
Cited by 18 | Viewed by 4669
Abstract
Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to [...] Read more.
Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017–2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km2 area of coal fires. Results reveal that nearly 10% of the coal fire area is newly formed, while 73% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Graphical abstract
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<p>Study area map showing a false-color composite of Jharia coalfield (JCF). Features having violet color in the map are opencast mines. Sub-plots showing the country map and the state map are also included in the figure. The swath of Sentinel-1 SAR data for ascending and descending paths are also shown in the map subset that portrays the state map of Jharkhand.</p>
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<p>Flow-chart representing the workflow for the computation of the subsidence time-series using Sentinel-1 data. Attributes of each shape are given at the bottom.</p>
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<p>Cumulative line-of-sight (LOS) surface deformation map of JCF from 2017 to 2020 computed using (<b>a</b>) Ascending and (<b>b</b>) Descending paths of Sentinel-1 data. Colliery boundary overlaid on top of the map is adapted with permission from ref. [<a href="#B69-remotesensing-13-01521" class="html-bibr">69</a>], Copyright 2021 Taylor &amp; Francis.</p>
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<p>Vertical surface deformation cumulative over 2017–2020.</p>
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<p>(<b>a</b>) Map showing the temporal changes in coal fires in JCF. The percentage of area covered by each category of temporal change is also mentioned in the map legend. The geology map of JCF is used as the basemap. (<b>b</b>–<b>f</b>) Graphs representing the time-series of subsidence at corresponding locations marked on the map (<b>b</b>–<b>f</b>). Blue points are the original subsidence values, and the best-fit lines for yearly surface deformation are shown in various colors.</p>
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<p>(<b>a</b>) Outlines of thermally anomalous regions overlaid on subsidence regions. Blue triangles represent GPS locations of coal fire sightings made in the field. (<b>b</b>) An opencast mine boundary. Smoke coming from sliced subsurface passages through which the hot gases from coal fires are transferred to the surface. (<b>c</b>) Surface crack induced by coal fires. Hot gases (by-products of coal fire) released through these cracks are also visible. (<b>d</b>) House wall cracked due to subsidence triggered by coal fires. Geo-location of the photographs shown in <a href="#remotesensing-13-01521-f006" class="html-fig">Figure 6</a>b–d are shown in <a href="#remotesensing-13-01521-f006" class="html-fig">Figure 6</a>a as 1,2,3 respectively.</p>
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<p>Spatial distribution of population in towns and villages within 1 km from subsidence zones (orange color). The population distributions of towns are portrayed in blue circles and villages in green circles. The diameter of a circle represents the spatial distribution of the population of the corresponding village/town. The scale used to represent the population distribution of villages and towns (in terms of circle diameter) is different, as shown in the map legend.</p>
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<p>The scatterplot of cumulative (from 2017 to 2020) surface deformations observed in ascending and descending path LOS. The ad-hoc plots on the top and right side are the histograms of respective LOS surface deformation. The black dashed line denotes the best-fit line for values less than −30mm, and the equation of this line has shown on the top left. The solid pink line represents the best-fit line of the original LOS surface deformations.</p>
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<p>Graph showing the bias value identified in 125 points locations that are geologically stable. Different colors are given to the sample points according to the asset from where it is taken. The mean bias and standard deviations are also shown in the plot.</p>
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<p>(<b>a</b>) A small part of the subsurface coal fire and associated smoke reaching the surface. (<b>b</b>) Smoke rising from exposed subsurface channels in an opencast mine boundary. (<b>c</b>) Rock deformations due to coal fire. (<b>d</b>) The thermal gun pointed towards the hot gases coming through a crack showing a temperature of 598 °C. (<b>e</b>) Thermal gun and Garmin GPS used in the field measurements. (<b>f</b>) Sample of surface collapse. (<b>g</b>) Measuring the temperature inside a surface crack. (<b>h</b>) A house abandoned due to surface collapse risk. (<b>i</b>) Surface collapsed region. (<b>j</b>) Residents gathered to observe a newly formed deep crack.</p>
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23 pages, 15581 KiB  
Article
Adjacent-Track InSAR Processing for Large-Scale Land Subsidence Monitoring in the Hebei Plain
by Xi Li, Li Yan, Lijun Lu, Guoman Huang, Zheng Zhao and Zechang Lu
Remote Sens. 2021, 13(4), 795; https://doi.org/10.3390/rs13040795 - 21 Feb 2021
Cited by 6 | Viewed by 2962
Abstract
Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed [...] Read more.
Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed the small baseline subset (SBAS) and NSBAS technique to monitor the land subsidence in the Hebei Plain (45,000 km2). The 166 Sentinel-1A data of adjacent-track 40 and 142 collected from May 2017 to May 2019 were used to generate the average deformation velocity and deformation time-series. A novel data fusion flow for the generation of land subsidence velocity of adjacent-track is presented and tested, named as the fusion of time-series interferometric synthetic aperture radar (TS-InSAR) results of adjacent-track using synthetic aperture radar amplitude images (FTASA). A cross-comparison analysis between the two tracks results and two TS-InSAR results was carried out. In addition, the deformation results were validated by leveling measurements and benchmarks on bedrock results, reaching a precision 9 mm/year. Twenty-six typical subsidence bowls were identified in Handan, Xingtai, Shijiazhuang, Hengshui, Cangzhou, and Baoding. An average annual subsidence velocity over −79 mm/year was observed in Gaoyang County of Baoding City. Through the cause analysis of the typical subsidence bowls, the results showed that the shallow and deep groundwater funnels, three different land use types over the building construction, industrial area, and dense residential area, and faults had high spatial correlation related to land subsidence bowls. Full article
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Graphical abstract

Graphical abstract
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<p>Hazards of land subsidence in Hebei Plain. (<b>a</b>,<b>b</b>) The Cangzhou People’s Hospital sank due to land subsidence. (<b>c</b>) Water accumulation caused by land subsidence in Cangzhou; (<b>d</b>) The Putong Temple tower skew caused by land subsidence in Nangong County, Xingtai City (provided by Hebei Geo-Environment Monitoring).</p>
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<p>Study area and coverage of SAR data tracks superimposed on one arc-second Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) shaded topography map over Hebei Plain, China. The right-top inset is the location of Hebei Province in China and the blue border is Hebei Province. Dark amethyst and fir green rectangles are the Sentinel-1A tracks 40 and 142 coverage, respectively. Red border is the study area. The six bench marks are marked with black triangles and the two benchmarks on the bedrock are marked with a black cross.</p>
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<p>Shallow groundwater depth in the plain area of Hebei Pin 2018 and 2019 (adapted from [<a href="#B46-remotesensing-13-00795" class="html-bibr">46</a>,<a href="#B47-remotesensing-13-00795" class="html-bibr">47</a>]).</p>
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<p>List of Sentinel-1A SAR data. Fir green and black cycles indicate the track 40 data list and track 142 data list, respectively.</p>
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<p>Flowchart of FTASA.</p>
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<p>Images of pre-registration and post-registration. The lighter section above the line is the amplitude image of track 40, and the darker section below the line is the amplitude image of track 142. Two delft blue ellipses show the effect of pre-registration and post-registration of tracks 40 and 142. (<b>a</b>) Pre-registration comparison chart of tracks 40 and 142; (<b>b</b>) Post-registration comparison chart of tracks 40 and 142.</p>
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<p>Combined singular value decomposition (SVD) solution of the deformation time-series results of the adjacent-track in an overlapping area. The bottom images are the partial deformation time-series results. The abscissa represents time and the ordinate represents two adjacent-tracks.</p>
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<p>Mean vertical displacement velocities generated by TS-InSAR techniques from May 2017 to May 2019. The black line is the overlapping area of tracks 40 and 142. (<b>a</b>) Vertical deformation velocity of tracks 40 calculated by SBAS; (<b>b</b>) Vertical deformation velocity of track 142 calculated by SBAS; (<b>c</b>) Vertical deformation velocity of track 40 calculated by NSBAS; (<b>d</b>) Vertical deformation velocity of track 142 calculated by NSBAS.</p>
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<p>Mean vertical displacement velocities map of Hebei Plain involving seven cities. Amethyst rectangles are representative of 26 subsidence bowls, named from d0 to d25. The four X marks (<b>A</b>–<b>D</b>) are the validation points of tracks 40 and 142.</p>
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<p>Distribution of 26 subsidence bowls in seven administrative zones. The six color and corresponding numbers stand for different administrative labelled with the total bowl numbers. Bold black line is our study area. Light black lines are the seven administrative zones.</p>
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<p>Vertical deformation velocity in FO, FTASA, and leveling. The black stars are the leveling results. The blue crosses are the FTASA results, and the black triangles are the FO results.</p>
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<p>Difference of the corresponding PS points of vertical deformation velocity between FTASA and FO.</p>
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<p>Comparison between the InSAR results and leveling results from May 2017 to May 2019, and the comparison between InSAR results and benchmark on bedrock results from December 2017 to June 2018. The black stars are the leveling results, the black cycles are the benchmark on bedrock results, and the blue crosses are the InSAR results. The location of the benchmark is shown in <a href="#remotesensing-13-00795-f002" class="html-fig">Figure 2</a>.</p>
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<p>The accuracy of the deformation results of tracks 40 and 142. (<b>a</b>) Blue bars represent the histograms of vertical deformation velocity difference in the overlapping region of tracks 40 and 142. The standard deviation was 7.37 mm/year; (<b>b</b>) The linear fitting results between tracks 40 and 142 of the overlapping area. The blue color represents the vertical deformation velocity, and the black line represents the linear fitting function. The linear fitting formula is shown in the figure.</p>
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<p>Deformation time-series at four points (A–D) derived from the Sentinel-1A of tracks 40 and 142 from May 2015 to May 2019 (<b>a</b>–<b>d</b>). The four points (A–D) are marked in <a href="#remotesensing-13-00795-f009" class="html-fig">Figure 9</a>. Black circle represents track 40 and the green circle represents track 142.</p>
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<p>The range of groundwater funnels and the distribution of land subsidence bowls. The areas of amethyst cycle and blue cycle are the areas of the groundwater funnels in 2018 and 2019, respectively. The groundwater depth contours have been marked on both sides of the line. (<b>a</b>,<b>b</b>) Amethyst and blue octagon represent the shallow groundwater funnels center in 2018 and 2019, respectively. (<b>c</b>) The Nangong deep groundwater funnel center in 2018 and 2019 is represented by the green octagon.</p>
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<p>(<b>a</b>) Mean vertical displacement velocities in subsidence bowl centers d0c, d14c, and d15c; (<b>b</b>) Land coverage maps of d0c, d14c, and d15c in May 2017 and May 2019. The images are from Google Earth. These subsidences are related to expansion or the demolition of buildings; (<b>c</b>) Accumulative time-series subsidence in p0, p8, and p9 points from May 2017 to May 2019.</p>
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<p>(<b>a</b>) Mean vertical displacement velocities of d2c, d4c, d12c, d16c, d22-1c, d22-2c; (<b>b</b>) The accumulative time-series subsidence of p1, p2, p6, p10, p11, and p12 points from May 2017 to May 2019; (<b>c</b>) Land coverage maps of d2c, d4c, d12c, d16c, d22c-1, and d22c-2 from May 2017 to May 2019. The images are from Google Earth. These subsidence are related to industrial clusters; (<b>d</b>) Horizontal and vertical subsidence (shown in (<b>a</b>)) of bowl center d22c-1.</p>
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<p>Accumulative time-series subsidence of d12c from May 2017 to May 2019 (only the partial time-series selected by the month).</p>
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<p>(<b>a</b>) Mean vertical displacement velocities of d5c, d7c, d8c, and d13c. (<b>b</b>) Land coverage maps of d5c, d7c, d8c, and d13c in 2019. The images are from Google Earth. These subsidences are related to dense residential areas; (<b>c</b>) Accumulative time-series subsidence of p3, p4, p5, and p7 points from May 2017 to May 2019, and monthly precipitation at the p5 point.</p>
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<p>Accumulative time-series subsidence of bowl center d8c from 2017 to 2019 (only the partial time-series selected by the month).</p>
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<p>Land subsidence rate map in the Hebei Plain with some main faults. (<b>a</b>) Land subsidence velocity map, and blue dotted lines are faults; (<b>b</b>–<b>d</b>) Deformation rates along profiles aa’, bb’, and cc’ in (<b>b</b>–<b>d</b>), and the positions of aa’, bb’, and cc’ are labeled in (<b>a</b>).</p>
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24 pages, 23511 KiB  
Article
Small-Baseline Approach for Monitoring the Freezing and Thawing Deformation of Permafrost on the Beiluhe Basin, Tibetan Plateau Using TerraSAR-X and Sentinel-1 Data
by Jing Wang, Chao Wang, Hong Zhang, Yixian Tang, Xuefei Zhang and Zhengjia Zhang
Sensors 2020, 20(16), 4464; https://doi.org/10.3390/s20164464 - 10 Aug 2020
Cited by 18 | Viewed by 2546
Abstract
The dynamic changes of the thawing and freezing processes of the active layer cause seasonal subsidence and uplift over a large area on the Qinghai–Tibet Plateau due to ongoing climate warming. To analyze and investigate the seasonal freeze–thaw process of the active layer, [...] Read more.
The dynamic changes of the thawing and freezing processes of the active layer cause seasonal subsidence and uplift over a large area on the Qinghai–Tibet Plateau due to ongoing climate warming. To analyze and investigate the seasonal freeze–thaw process of the active layer, we employ the new small baseline subset (NSBAS) technique based on a piecewise displacement model, including seasonal deformation, as well as linear and residual deformation trends, to retrieve the surface deformation of the Beiluhe basin. We collect 35 Sentinel-1 images with a 12 days revisit time and 9 TerraSAR-X images with less-than two month revisit time from 2018 to 2019 to analyze the type of the amplitude of seasonal oscillation of different ground targets on the Beiluhe basin in detail. The Sentinel-1 results show that the amplitude of seasonal deformation is between −62.50 mm and 11.50 mm, and the linear deformation rate ranges from −24.50 mm/yr to 5.00 mm/yr (2018–2019) in the study area. The deformation trends in the Qinghai–Tibet Railway (QTR) and Qinghai–Tibet Highway (QTH) regions are stable, ranging from −18.00 mm to 6 mm. The InSAR results of Sentinel-1 and TerraSAR-X data show that seasonal deformation trends are consistent, exhibiting good correlations 0.78 and 0.84, and the seasonal and linear deformation rates of different ground targets are clearly different on the Beiluhe basin. Additionally, there are different time lags between the maximum freezing uplift or thawing subsidence and the maximum or minimum temperature for the different ground target areas. The deformation values of the alpine meadow and floodplain areas are higher compared with the alpine desert and barren areas, and the time lags of the freezing and thawing periods based on the Sentinel-1 results are longest in the alpine desert area, that is, 86 days and 65 days, respectively. Our research has important reference significance for the seasonal dynamic monitoring of different types of seasonal deformation and the extensive investigations of permafrost in Qinghai Tibet Plateau. Full article
(This article belongs to the Section Remote Sensors)
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Figure 1
<p>(<b>a</b>) Geographic location of the study area. (<b>b</b>) Coverage of SAR images and the study area. The red box indicates the coverage of Sentinel-1 data. The blue box indicates the coverage of TerraSAR-X data, and the white box represents the overlay area of the study area. (<b>c</b>) Topographic map, which is extracted from a Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). (<b>d</b>) TerraSAR-X amplitude image in the blue box (<a href="#sensors-20-04464-f001" class="html-fig">Figure 1</a>c).</p>
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<p>Flowchart of the NSBAS method of deformation analysis with the main processing steps.</p>
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<p>Spatial and temporal baselines of the interferograms. (<b>a</b>) Sentinel-1 data. (<b>b</b>) TerraSAR-X data.</p>
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<p>2-m air temperature data in this study area from 7/1/2017 to 11/29/2019.</p>
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<p>Sentinel-1 InSAR results based on the seasonal and long-term deformation models over 08/07/2018–10/25/2019. (<b>a</b>) Amplitude of seasonal deformation. (<b>b</b>) Linear deformation rate. (<b>c</b>) DEM error. (<b>d</b>) Residual deformation.</p>
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<p>Sentinel-1 and TerraSAR-X InSAR results in the Beiluhe basin. (<b>a</b>) Amplitude of seasonal deformation of Sentinel-1 during the period 08/07/2018–10/25/2019. (<b>b</b>) Amplitude of seasonal deformation of TerraSAR-X during the period 12/15/2018–10/08/2019. (<b>c</b>) Linear deformation rate of Sentinel-1 during the period 08/07/2018–10/25/2019. (<b>d</b>) Linear deformation rate of TerraSAR-X during the period 12/15/2018–10/08/2019.</p>
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<p>Relation between the maximum (red), minimum (green), and mean (black) value and the elevation (blue) of the residual deformation. (<b>a</b>) Histogram of the DEM error. (<b>b</b>) Histogram of the residual deformation. (<b>c</b>) N1-N2 profile. (<b>d</b>) M1-M2 profile.</p>
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<p>Relation between the maximum (red), the minimum (green), mean (black) value and elevation (blue) of the amplitude of the seasonal deformation and linear deformation rate. (<b>a</b>) Amplitude of the seasonal deformation profile along N1 to N2. (<b>b</b>) Linear deformation rate profile along N1 to N2. (<b>c</b>) Amplitude of the seasonal deformation profile along M1 to M2. (<b>d</b>) Linear deformation rate profile along M1 to M2.</p>
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<p>Correlations between the height, the slope and amplitude of the seasonal deformation. (<b>a</b>) Height and amplitude of the seasonal deformation profile along N1 to N2. (<b>b</b>) Height and amplitude of the seasonal deformation profile along M1 to M2. (<b>c</b>) Slope and amplitude of the seasonal deformation profile along N1 to N2. (<b>d</b>) Slope and amplitude of the seasonal deformation profile along M1 to M2.</p>
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<p>Field photos of typical ground targets and the TerraSAR-X seasonal deformation map (<b>a</b>) Alpine Desert (D1). (<b>b</b>) QTH Region (H1). (<b>c</b>) Barren (B1). (<b>d</b>) Alpine Desert (D1). (<b>e</b>) QTR region (R1) (<b>f</b>) Floodplain (F1). (<b>g</b>) TerraSAR-X amplitude map.</p>
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<p>(<b>a</b>) Amplitude of the seasonal deformation along the P1-P2 profile. (<b>b</b>) Linear deformation rate along the P1-P2 profile. (<b>c</b>) Amplitude of the seasonal deformation along the Q1-Q2 profile. (<b>d</b>) Linear deformation rate along the Q1-Q2 profile.</p>
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<p>(<b>a</b>) Correlation of the amplitude of the seasonal deformation of TerraSAR-X and Sentinel-1 along the P1-P2 profile. (<b>b</b>) Correlation of the amplitude of the seasonal deformation of TerraSAR-X and Sentinel-1 along the Q1-Q2 profile. (<b>c</b>) Correlation of the linear deformation rate of TerraSAR-X and Sentinel-1 along the P1-P2 profile. (<b>d</b>) Correlation of the linear deformation rate of TerraSAR-X and Sentinel-1 along the Q1-Q2 profile.</p>
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<p>Time series deformation of the study area. The acquisition from 8 August 2019 is set as the reference image.</p>
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<p>Sentinel-1 displacement time series at six locations with six typical ground target regions on the Beiluhe basin. (<b>a</b>) Alpine meadow. (<b>b</b>) Alpine desert. (<b>c</b>) Barren. (<b>d</b>) Floodplain. (<b>e</b>) QTH Region. (<b>f</b>) QTR Region.</p>
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<p>Daily air temperature (2 m surface) and NSBAS time series displacement of the alpine meadow (point 1) alpine desert (point 2), barren (point 3), and floodplain (point 4) during the freezing and thawing periods. (<b>a</b>) Daily air temperature from 8/1/2018 to 11/1/2019. (<b>b</b>) Daily air temperature from 12/1/2018 to 11/1/2019. (<b>c</b>) Sentinel-1 NSBAS time series displacement. (<b>d</b>) TerraSAR-X NSBAS time series displacement.</p>
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<p>Interpreted GPR results of the ALT profile in Beiluhe basin. (<b>a</b>) Alpine meadow. (<b>b</b>) Alpine desert. (<b>c</b>) Barren. (<b>d</b>) Floodplain. (<b>e</b>–<b>h</b>) The site locations of GPR on Google earth images. (<b>i</b>–<b>l</b>) Amplitude of the seasonal deformation map of the Sentinel-1 data.</p>
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<p>The relationship between amplitude of seasonal deformation and ALT. (<b>a</b>) Alpine meadow. (<b>b</b>) Alpine desert. (<b>c</b>) Barren. (<b>d</b>) Flood plain.</p>
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15 pages, 3882 KiB  
Article
SBA-16 Cage-Like Porous Material Modified with APTES as an Adsorbent for Pb2+ Ions Removal from Aqueous Solution
by Viviana Palos-Barba, Abigail Moreno-Martell, Verónica Hernández-Morales, Carmen L. Peza-Ledesma, Eric M. Rivera-Muñoz, Rufino Nava and Barbara Pawelec
Materials 2020, 13(4), 927; https://doi.org/10.3390/ma13040927 - 19 Feb 2020
Cited by 18 | Viewed by 2974
Abstract
Tridimensional cubic mesoporous silica, SBA-16, functionalized with aminopropyl groups, were employed as adsorbents for Pb2+ ion removal from aqueous solution. The adsorption capacity was investigated for the effect of pH, contact time, temperature, and concentration of 3-aminopropyltriethoxysilane (APTES) employed for adsorbent functionalization. [...] Read more.
Tridimensional cubic mesoporous silica, SBA-16, functionalized with aminopropyl groups, were employed as adsorbents for Pb2+ ion removal from aqueous solution. The adsorption capacity was investigated for the effect of pH, contact time, temperature, and concentration of 3-aminopropyltriethoxysilane (APTES) employed for adsorbent functionalization. The textural properties and morphology of the adsorbents were evaluated by N2 physisorption, small-angle X-ray diffraction (XRD), diffuse reflectance spectroscopy (UV-vis), and transmission electron microscopy (TEM). The functionalization of the SBA-16 was evaluated by elemental analysis (N), thermogravimetric analysis (TG), Fourier transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS). Batch adsorption studies show that the total Pb2+ ions removal was archived on adsorbent having an optimized amount of aminopropyl groups (2N-SBA-16). The maximum of Pb2+ ions removal occurred at optimized adsorption conditions: pH = 5–6, contact time 40 min, and at a low initial lead concentration in solution (200 mg L−1). Under the same adsorption conditions, the amino-functionalized SBA-16 with cubic 3D unit cell structure exhibited higher adsorption capability than its SBA-15 counterpart with uniform mesoporous channels. Full article
(This article belongs to the Special Issue Mesoporous Silica and Their Applications)
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Graphical abstract

Graphical abstract
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<p>Small-angle X-ray diffraction patterns of pure SBA-16 and amine-functionalized SBA-16 adsorbents.</p>
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<p>Transmission electron microscopy (TEM) image of the 3.8N/SBA-16 adsorbent showing a well-ordered cubic array of unit cells.</p>
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<p>Non-functionalized SBA-16 solid and amino-functionalized adsorbents: (<b>a</b>) N<sub>2</sub> adsorption-desorption isotherms, and (<b>b</b>) Pore size distribution.</p>
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<p>Fourier transform infrared spectra of the framework vibration region of the SBA-16 substrate before and after its grafting with aminopropyl groups.</p>
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<p>Thermogravimetric curves of SBA-16, 2.6N/SBA-16, 3.8N/SBA-16, and 5.1N/SBA-16 adsorbents.</p>
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<p>UV-vis spectra for the 3.8N/SBA-16 adsorbent after adsorption (initial Pb<sup>2+</sup> concentrations in aqueous solution: 200 and 400 mg L<sup>−1</sup>). The scheme of the proposed van der Walls interaction of Pb<sup>2+</sup> with amino group [<a href="#B1-materials-13-00927" class="html-bibr">1</a>] is shown in the inlet of this figure.</p>
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<p>Core level spectra of 3.8N/SBA-16 adsorbate after Pb2+ adsorption: (<b>a</b>) N 1s, and (<b>b</b>) Pb4f (0.1 g of adsorbent, 20 mL of 200 mg L<sup>−1</sup> aqueous Pb<sup>2+</sup> solution, pH = 5.0, contact time of 60 min, and T = 30 °C).</p>
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<p>Influence of adsorbent/ligand molar ratio on the Pb<sup>2+</sup> adsorption onto SBA-16-based adsorbents. For comparison purposes, the SBA-15-based adsorbent prepared with tetraethyl orthosilicate/3-aminopropyltriethoxysilane (TEOS/APTES) molar ratio of 3.3 is included (from [<a href="#B1-materials-13-00927" class="html-bibr">1</a>]). Conditions were: 20 mL of 200 mg L<sup>−1</sup> aqueous Pb<sup>2+</sup> solution, 0.10 g of adsorbent, pH = 5.0, contact time of 60 min, and T = 30 °C.</p>
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<p>Influence of (<b>a</b>) temperature, (<b>b</b>) contact time, (<b>c</b>) pH, and (<b>d</b>) initial lead concentration on the Pb<sup>2+</sup> removal from aqueous solution by the adsorption onto 3.8N/SBA-16. The SBA-15-based counterpart prepared with the same TEOS/APTES molar ratio of 3.3 is used as a reference (from [<a href="#B1-materials-13-00927" class="html-bibr">1</a>]).</p>
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19 pages, 8191 KiB  
Article
Time-Series InSAR Monitoring of Permafrost Freeze-Thaw Seasonal Displacement over Qinghai–Tibetan Plateau Using Sentinel-1 Data
by Xuefei Zhang, Hong Zhang, Chao Wang, Yixian Tang, Bo Zhang, Fan Wu, Jing Wang and Zhengjia Zhang
Remote Sens. 2019, 11(9), 1000; https://doi.org/10.3390/rs11091000 - 26 Apr 2019
Cited by 56 | Viewed by 5613
Abstract
Permafrost is widely distributed in the Tibetan Plateau. Seasonal freeze–thaw cycles of permafrost result in upward and downward surface displacement. Multitemporal interferometric synthetic aperture radar (MT-InSAR) observations provide an effective method for monitoring permafrost displacement under difficult terrain and climatic conditions. In this [...] Read more.
Permafrost is widely distributed in the Tibetan Plateau. Seasonal freeze–thaw cycles of permafrost result in upward and downward surface displacement. Multitemporal interferometric synthetic aperture radar (MT-InSAR) observations provide an effective method for monitoring permafrost displacement under difficult terrain and climatic conditions. In this study, a seasonal sinusoidal model-based new small baselines subset (NSBAS) chain was adopted to obtain a deformation time series. An experimental study was carried out using 33 scenes of Sentinel-1 data (S-1) from 28 November 2017 to 29 December 2018 with frequent revisit (12 days) observations. The spatial and temporal characteristics of the surface displacements variation combined with different types of surface land cover, elevation and surface temperature factors were analyzed. The results revealed that the seasonal changes observed in the time series of ground movements, induced by freeze–thaw cycles were observed on flat surfaces of sedimentary basins and mountainous areas with gentle slopes. The estimated seasonal oscillations ranged from 2 mm to 30 mm, which were smaller in Alpine deserts than in Alpine meadows. In particular, there were significant systematic differences in seasonal surface deformation between areas near mountains and sedimentary basins. It was also found that the time series of deformation was consistent with the variation of surface temperature. Based on soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data, the deformation analysis influenced by soil moisture factors was also carried out. The comprehensive analysis of deformation results and auxiliary data (elevation, soil moisture and surface temperature et al.) provides important insights for the monitoring of the seasonal freeze-thaw cycles in the Tibetan Plateau. Full article
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Figure 1
<p>(<b>a</b>) Google Earth image of study area. (<b>b</b>) Shuttle radar topography mission (SRTM) digital elevation model (DEM) map of study area. (<b>c</b>) Sentinel-1 amplitude image of study area and location of Qinghai–Tibetan Plateau (QTR). (<b>d</b>) TerraSAR-X amplitude image in the blue box of (c).</p>
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<p>The flow chart of multitemporal interferometric synthetic aperture radar (MT-InSAR) applied in this study.</p>
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<p>(<b>a</b>) The amplitude of seasonal oscillation in the study area. (<b>b</b>) DEM error in the study area. (<b>c</b>) Frequency distribution histogram of DEM error.</p>
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<p>(<b>a</b>) The amplitude of seasonal oscillation in the study area. (<b>b</b>) DEM error in the study area. (<b>c</b>) Frequency distribution histogram of DEM error.</p>
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<p>(<b>a</b>) Profiles of amplitude of seasonal deformation (blue) and elevation (green). (<b>b</b>) Correlation between slope and seasonal amplitude for profile zone B1−B2. (<b>c</b>) Correlation between slope and seasonal amplitude for profile zone C1−C2.</p>
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<p>The amplitude of seasonal oscillation under different land covers. (<b>a</b>) The amplitude map of high-resolution TerraSAR-X image (11 December 2015); (<b>b</b>) the classification result of TerraSAR-X image. (<b>c</b>) the amplitude of seasonal oscillations along profile A−B.</p>
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<p>(<b>a</b>) Amplitude variations along QTR using a 1 km buffer. (<b>b</b>) and (<b>c</b>) Zoomed-in images over region A and Region B from (a), respectively.</p>
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<p>Maps showing the deformation time series in the study area, relative to the first acquisition on 28 November 2017.</p>
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<p>(<b>a</b>) Deformation time series statistics results over alpine meadow (P1); (<b>b</b>) Deformation time series statistics results over alpine desert (P2) (the positions of P1 and P2 are shown in <a href="#remotesensing-11-01000-f005" class="html-fig">Figure 5</a>a). Time-series surface soil moisture (<b>c</b>) and root zone soil moisture (<b>d</b>) for soil moisture active/passive (SMAP) L4 data over the study area( Presented are the median (red line), the first quantile Q1 and third quantile Q3 (as indicated by box), and the Q1 − 1.5(Q3 − Q1) and Q3 + 1.5(Q3 − Q1) value(whiskers) and outliers (red point)).</p>
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<p>Time series correlation analysis between root zone soil moisture and relative deformation in thawing season from 9 April 2018 to 6 October, 2018.</p>
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<p>Time series of surface temperature of SMAP L4 data in the study area.</p>
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<p>(<b>a</b>) In situ active layer thickness (ALT) profile of Alpine meadow based on ground-penetrating radar (GPR) (August 28, 2018). (<b>b</b>) In situ ALT profile of Alpine desert based on GPR (August 28, 2018). (<b>c</b>) The amplitude of seasonal oscillation surrounding (a). (<b>d</b>) The amplitude of seasonal oscillation surrounding (b).</p>
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