scholarly journals DEFORMATION MONITORING OF HIGH-LATITUDE PERMAFROST REGION OF NORTHEASTERN CHINA WITH TIME SERIES INSAR TECHNIQUE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1777-1780 ◽  
Author(s):  
T. Qu ◽  
Q. Xu ◽  
W. Shan ◽  
Z. Li ◽  
M. Shan ◽  
...  
Keyword(s):  
Time Series ◽  
High Latitude ◽  
Northeast China ◽  
Deformation Monitoring ◽  
Permafrost Region ◽  
Local Climate ◽  
Spatial Distribution Characteristics ◽  
Long Time ◽  
Multi Temporal ◽  
Local Climate Change

<p><strong>Abstract.</strong> Permafrost distributed in northeast China is the only high-altitude permafrost in China. The deformation monitoring over this permafrost region is of great importance to local climate change and ecological environments. This study focuses on the deformation monitoring of high-latitude permafrost in northeast China with time series InSAR technique. The spatial distribution characteristics, the annual deformation rates and the temporal deformation evolutions of permafrost could be retrieved from multi-temporal InSAR processing with Sentinel-1 TOPS datasets. This work concludes that time series InSAR technique could help to retrieve a comprehensive and reliable permafrost deformation, while a long time-series of displacements facilitated to better understand permafrost kinematics.</p>

scholarly journals Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 2898
Author(s):  
Filipe Lisboa ◽  
Vanda Brotas ◽  
Filipe Duarte Santos ◽  
Sakari Kuikka ◽  
Laura Kaikkonen ◽  
...  
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
High Latitude ◽  
Trophic State ◽  
Linear Models ◽  
P Value ◽  
Spatial And Temporal Variability ◽  
Eutrophic Lakes ◽  
Long Time

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using chlorophyll a (Chl) as a proxy over large areas, detection of Chl in small lakes is hindered by the low spatial resolution of conventional ocean color satellites. The short time-series of the newest generation of space-borne sensors (e.g., Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes’ Chl, it is still unclear how the spatial and temporal variability of Chl concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30 m resolution imagery to assess lakes’ Chl concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of Chl, taking advantage of the long-time span of the LT-5 and LT-7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50% of the Chl interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake’s trophic state, with models performing in average twice as better in lakes with higher Chl concentration (>20 µg/L) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes’ mean Chl concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of Chl provide a tool for assessing the impacts of environmental change.

scholarly journals An Improved Multi-Sensor MTI Time-Series Fusion Method to Monitor the Subsidence of Beijing Subway Network during the Past 15 Years

2020 ◽  
Vol 12 (13) ◽  
pp. 2125
Author(s):  
Li Duan ◽  
Huili Gong ◽  
Beibei Chen ◽  
Chaofan Zhou ◽  
Kunchao Lei ◽  
...  
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Mean Squared Error ◽  
Deformation Monitoring ◽  
Urban Rail Transit ◽  
Stable Operation ◽  
Urban Rail ◽  
Urban Safety ◽  
Multi Temporal ◽  
Beijing Subway

Land subsidence threatens the stable operation of urban rail transit, including subways. Obtaining deformation information during the entire life-cycle of a subway becomes a necessary means to guarantee urban safety. Restricted by sensor life and cost, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar (MTI) technology has been unable to meet the needs of long-term sequence, high-resolution deformation monitoring, especially of linear objects. The multi-sensor MTI time-series fusion (MMTI-TSF) techniques has been proposed to solve this problem, but rarely mentioned. In this paper, an improved MMTI-TSF is systematically explained and its limitations are discussed. Taking the Beijing Subway Network (BSN) as a case study, through cross-validation and timing verification, we find that the improved MMTI-TSF results have higher accuracy (R2 of 98% and, Root Mean Squared Error (RMSE) of 4mm), and compared with 38 leveling points, the fitting effect of the time series is good. We analyzed the characteristics of deformation along the BSN over a 15-year periods. The results suggest that there is a higher risk of instability in the eastern section of Beijing Subway Line 6 (L6). The land subsidence characteristics along the subway lines are related to its position from the subsidence center, and the edge of the subsidence center presents a segmented feature.

scholarly journals Land use and cover change in Northeast China and its impacts on the Xing'an permafrost in 1980-2020

2021 ◽  
Author(s):  
Hongwei Wang ◽  
Huijun Jin ◽  
Xiaoying Li ◽  
Ruixia He ◽  
Raul-David Șerban ◽  
...  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Northeast China ◽  
Structural Changes ◽  
Ordinary Least Squares ◽  
Permafrost Region ◽  
Unused Land ◽  
Degradation Rates ◽  
Long Time ◽  
Land Use And Cover

Vegetation plays important roles in the development and protection of permafrost; it is one of the main local and ecosystemic factors that affect the thermal stability of the underlying soil strata. Multi-period land use and cover change (LUCC) data and long-time series of air temperature were chosed. Based on these data, spatiotemporal changes in mean annual air temperature (MAAT) were simulated by the Ordinary Least Squares (OLS) method and Ordinary Kriging (OK) model in the 1980s-2010s in Northeast China. The influences of LUCC on MAAT in Northeast China and distribution of the Xing’an permafrost were analyzed and the results showed that: (1) Decadal average of MAAT increased from 4.60oC (1980s) to 5.38oC (2010s) in Northeast China, with an upward trend of 0.25oC/10a. (2) During the 1980s to 2010s, the total permafrost area showed a decreasing trend (3.668×104 km2/10a). (3) In permafrost regions, LUCC had undergone significant structural changes: forested land showed a consistent decreasing trend and other lands showed an overall increasing trend. (4) The effects of different LUCC on MAAT in the permafrost region varied substantially. The mean MAAT of forested land was the lowest (2.33oC), and; that of unused land, the highest (0.37oC). The change rate in MAAT of cultivated land was the highest (0.37oC/10a), and; that of unused land, the lowest (0.28oC/10a). (5) The degradation rates of permafrost in forested land (1.822×104 km2/10a) and grassland (1.397×104 km2/10a) were the largest from 1980s to 2010s.

scholarly journals Statistically-Based Trend Analysis of MTInSAR Displacement Time Series

2021 ◽  
Vol 13 (12) ◽  
pp. 2302
Author(s):  
Fabio Bovenga ◽  
Guido Pasquariello ◽  
Alberto Refice
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
High Rate ◽  
Automatic Identification ◽  
Warning Signals ◽  
Temporal Sampling ◽  
Long Time ◽  
Multi Temporal ◽  
Minimum Number ◽  
Linear Trends

Current multi-temporal interferometric Synthetic Aperture Radar (MTInSAR) datasets cover long time periods with regular temporal sampling. This allows high-rate and non-linear trends to be observed, which typically characterize pre-failure warning signals. In order to fully exploit the content of MTInSAR products, methods are needed for the automatic identification of relevant changes along displacement time series and the classification of the targets on the ground according to their kinematic regime. This work reviews some of the classical procedures for model ranking, based on statistical indices, which are applied to the characterization of MTInSAR displacement time series, and introduces a new quality index based on the Fisher distribution. Then, we propose a procedure to recognize automatically the minimum number of parameters needed to model a given time series reliably within a predefined confidence level. The method, though general, is explored here for polynomial models, which can be used in particular to approximate satisfactorily and with computational efficiency the piecewise linear trends that are generally used to model warning signals preceding the failure of natural and artificial structures. The algorithm performance is evaluated under simulated scenarios. Finally, the proposed procedure is also demonstrated on displacement time series derived by the processing of Sentinel-1 data.

Detection and deformation monitoring of landslides by InSAR: applications along Jinsha River, China

2020 ◽  
Author(s):  
Ruya Xiao ◽  
Yongsheng Li ◽  
Chen Yu ◽  
Zhenhong Li ◽  
Xiufeng He
Keyword(s):  
Time Series ◽  
Deformation Monitoring ◽  
Large Area ◽  
General Survey ◽  
Jinsha River ◽  
Area Of Interest ◽  
Sensing Technology ◽  
Short Period ◽  
Wide Range ◽  
Multi Temporal

&lt;p&gt;In recent years, massive landslides and the related secondary hazards such as the dammed lakes occurred in the mountainous areas of southwestern China, e.g., the Wenchuan earthquake-triggered landslide dammed lake at Tangjiashan in 2008 and the Jinsha River Baige landslide in October and November 2018 near the junction of Sichuan and Tibet Province, has attracted wide attention of the geoscience community. Geologists and disaster scientists have recognized the important role of remote sensing technology in the early detection and deformation monitoring of geohazards. Some leading countries, such as Italy and Norway, have completed nationwide InSAR monitoring projects and the results have been well applied in the field of geohazards prevention and monitoring.&lt;br&gt;We applied InSAR technology in the detection and deformation monitoring of geological hazards in the Jinsha River, mainly including 1) General survey: the mean deformation rate from InSAR stacking with atmospheric corrections conducted for a wide-range area would be helpful to narrow down the area of detailed investigation, as well as to initially establish a geological hazard inventory. 2) Detailed investigation: For potential geohazards delineated in the general survey, or the areas require special attention, multi-temporal, multi-band and high-resolution InSAR should be utilized. The exhaustive deformation time series and the retrospect results provide information for geologists to carry out risk assessments. 3) Field monitoring: For the key areas, or in the rapid response for hazards, ground-based radar equipment can be used to carry out monitoring work to quickly obtain deformation over a relatively large area of interest in a short period of time.&lt;br&gt;In this work, we will provide general survey results of landslides on the scale of hundreds of kilometres along the Jinsha River, as well as detailed results of InSAR time series analysis of Baige Landslide, Woda Landslide, and some other potential landslide failures with rapid moving trends. The deformation monitoring results of Baige landslide using ground-based radar after the first failure will also be included in this work. Finally, we will also list several challenges at this stage and the possible solutions.&lt;/p&gt;

Preliminary Result of Permafrost Roadbed Settlement Measurement Using Satellite D-InSAR Technology

2011 ◽  
Vol 105-107 ◽  
pp. 1912-1915
Author(s):  
Qu Lin Tan ◽  
Chou Xie
Keyword(s):  
Safety Management ◽  
Test Site ◽  
Deformation Monitoring ◽  
Ground Subsidence ◽  
Crushed Rock ◽  
Stable Phase ◽  
Permafrost Region ◽  
Time Deformation ◽  
Deformation Amount ◽  
Long Time

Monitoring ground subsidence of roadbed in permafrost region is very important for stability evaluation and safety management. To test its applicability to deformation monitoring for railway subgrade settlement in plateau permafrost region, satellite differential interferometric synthetic aperture radar technology (D-InSAR) has been applied to detect subgrade deformation in one test site along the Qinghai-Tibet railway with gathered interferometric SAR images. The preliminary experimental results showed that satellite D-InSAR technology can effectively acquire long-time deformation sequence through analyzing the ground scatterers with relative stable phase and are in accordance with the results obtained by conventional in-situ ground leveling. Along the Qinghai-Tibet railway in permafrost regions, it is found that settlement is the main behavior of subgrade deformation and the deformation amount of railway bridge is less than the sliced or the crushed rock embankment. It has very important engineering significance for state key infrastructure long-term monitoring system in plateau permafrost region.

scholarly journals Spatial Variability and Detection Levels for Chlorophyll-A Estimates in High Latitude Lakes Using Landsat Imagery

2020 ◽  
Author(s):  
Filipe Lisboa ◽  
Vanda Brotas ◽  
Filipe Duarte Santos ◽  
Sakari Kuikka ◽  
Laura Kaikkonen ◽  
...  
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
High Latitude ◽  
Trophic State ◽  
Linear Models ◽  
Eutrophic Lakes ◽  
Algae Biomass ◽  
Chl A ◽  
Long Time

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. Satellite imagery can monitor algae biomass over large areas. The detection of chlorophyll a (chl-a) concentrations in small lakes is hindered by the low spatial resolution of conventional ocean colour satellites. The short time-series of the newest generation of space-borne sensors (e.g. Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes' chl-a, it is still unclear how the spatial and temporal variability of chl-a concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30-m resolution imagery to assess lakes' chl-a concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of chl-a, taking advantage of the long-time span of the LT 5 and 7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50 % of the chl-a interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake's trophic state, with models performing in average twice as better in lakes with higher chl-a concentration (&gt; 20 &micro;g/l) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value &lt; 0.05) in determining lakes' annual mean chl-a concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of chl-a provide a tool for assessing the impacts of environmental change.

scholarly journals Time Series InSAR Three-Dimensional Displacement Inversion Model of Coal Mining Areas Based on Symmetrical Features of Mining Subsidence

2021 ◽  
Vol 13 (11) ◽  
pp. 2143
Author(s):  
Longkai Dong ◽  
Chao Wang ◽  
Yixian Tang ◽  
Fuquan Tang ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Three Dimensional ◽  
Mining Area ◽  
Deformation Monitoring ◽  
Mining Subsidence ◽  
Surface Movement ◽  
Mining Areas ◽  
Multi Temporal ◽  
Movement Parameters ◽  
Mean Square Errors

The three-dimensional (3-D) displacements of mining areas is the basis of studying the mining subsidence law and obtaining surface movement parameters. The traditional multi-temporal interferometry synthetic aperture radar (InSAR) technology can only obtain the surface deformation in line-of-sight (LOS) direction, even if some methods can obtain the 3-D displacements of mining area based on InSAR. However, it has high data requirements for data types, which are not conducive to the inversion of 3-D displacements. In this paper, the symmetry of the surface basin caused by mining subsidence under different mining degrees is analyzed. According to the basic symmetrical features of mining subsidence—that the surface vertical displacement and horizontal displacement in near horizontal coal seam is symmetrical with respect to the main section of the basin, combined with time series InSAR technology (TS-InSAR)—a novel method for retrieving the 3-D displacement results from a single-geometry InSAR dataset based on symmetrical features (hereafter referred to as the SGI-SF method) is proposed. The SGI-SF method first generates multi-temporal observations of LOS displacement from a single-geometry InSAR dataset, and then transforms them into multi-temporal observations of 3-D displacement datasets according to symmetrical features. There is no necessity to obtain the surface movement parameters from the measured data to calculate 3-D displacement fields. Finally, the time series of 3-D displacements are estimated from multi-temporal 3-D displacements using the singular value decomposition (SVD) method. Nine descending Sentinel-1 images from the Yulin mining area of China are used to test the proposed SGI-SF method. The results show that the average root mean square errors (RMSE) in the vertical and horizontal direction of the three-dimensional deformations are approximately 9.28 mm and 13.10 mm, respectively, which are much smaller than mining-induced displacements and can provide support for deformation monitoring in mining areas.

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