StaMPS Process of Xuzhou City using Sentinel1A in LiDO3

Intro

To monitor the land subsidence inside Xuzhou city and to compare the resule of StaMPS-InSAR with SARscape PS process, and to understand the difference of these two methods.

Xuzhou City

Xuzhou, or known as Pengcheng in ancient times, is a major city in and the fourth largest prefecture-level city of Jiangsu province. It is known for its role as a transportation hub in northwestern Jiangsu, as it has expressways and railway links connecting directly to the provinces of Henan and Shandong, the neighboring port city of Lianyungang, as well as the economic hub Shanghai1.

Xuzhou is “a city of science and education” with a galaxy of talents. There are altogether 31 independent scientific research institutes, 335 R&D institutions and 14 colleges and universities, including China University of Mining & Technology, Xuzhou Medical College, Jiangsu Normal University, Xuzhou Institute of Technology, etc, which jointly provide Xuzhou with solid and strong intellectual support for its economic and social development.

Location

Xuzhou is located in the northwest of Jiangsu Province, the common boundary of Jiangsu Province, Shandong Province, Henan Province and Anhui Province. It is about 300 kilometers away from Nanjing, Jinan, Zhengzhou and Hefei. Covering a land of 1,125 square kilometers, Xuzhou has a population of 8.63 million.

The prefecture-level city of Xuzhou administers 10 county-level divisions, including five districts, two county-level cities and three counties.

Climate

By the influence of warm temperature zone semi-moist monsoon climate, Xuzhou has apparently different four seasons with cool summer and warm winter. The yearly average temperature is 14℃ with the yearly average rainfall volume of 866mm and the frost free period in Xuzhou is between 200 and 220 days. The yearly average sunshine period is 2,100 hours to 2,400 hours. The best time for traveling in Xuzhou is in fall.

Xuzhou

Economy

Xuzhou is rich in resources and diversified in agricultural and sideline produce. It has a huge reserve of high-quality mineral resources such as coal, iron, limestone, marble stone, etc.

The industrialization management of agriculture has achieved great progress. The guiding role of industrial economy has being continuously intensified. Coal, electronic, fabric, medicine and construction material industries have certain scope and level.

Xuzhou Construction Machinery Group (XCMG), Weiwei Group and Datun Coal and Electricity Corporation are listed the top 520 corporations in China. Tertiary industry has developed boomingly and the role of regional logistics center and tourism center has further intensified. Foreign-oriented economy has made great headway. Many world well-known companies such as Caterpillar, Rockwell and Haier Group settled here.

The construction machinery manufacturer XCMG is the largest company based in Xuzhou. It is the world’s 10th largest construction equipment maker measured by 2011 revenues, and the third-largest based in China (after Sany and Zoomlion).

History and Culture

With a civilization of over 5,000 years, Xuzhou was built 2,600 years ago and is one of the most ancient cities in China. Xuzhou was one of the nine states of the country over 3,000 years ago.

As a city of Han Dynasty Culture, Xuzhou was the hometown of Liu Bang (256-195BC), the first emperor of Western Han Dynasty. The Han Dynasty was divided into Eastern Han and Western Han periods (206BC-189AD) lasting for over 400 years. During which the local kings in Xuzhou left numerous historical heritages, including Han clay figurines, Han stone relief carvings and Han Tombs, which are called “Three Wonders of the Han Dynasty”. They are representatives of the Chinese Han culture.

Xuzhou

Dataset

134 Sentinel1-A SLC IW arcsending SAR data with path 142 and frame 106 from 2016-08-29 to 2021-11-07 has been acquired from ASF data search vertex2.

Preview of Sentinel 1A data

File name: S1A_IW_SLC__1SDV_20190428T101159_20190428T101227_026989_0309CA_8C7C

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Start Time • 04/28/19, 10:11:59Z
Stop Time • 04/28/19, 10:12:27Z
Beam Mode • IW
Path142
Frame106
Flight Direction • ASCENDING
Polarization • VV+VH
Absolute Orbit • 26989
Data courtesy of ESA
Citation

Pre-process with snap2stamps

WSL and Xlaunch

In Xlaunch:
One large window -> Start no client -> Disable access control

Ubuntu on Windows

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yuchi@DESKTOP-C79LC47:~$ export DISPLAY=$(ip route list default | awk '{print $3}'):0
yuchi@DESKTOP-C79LC47:~$ export LIBGL_ALWAYS_INDIRECT=1
yuchi@DESKTOP-C79LC47:~$ startlxde
** Message: 17:46:13.991: main.vala:102: Session is LXDE
** Message: 17:46:13.991: main.vala:103: DE is LXDE
** Message: 17:46:14.060: main.vala:134: log directory: /home/yuchi/.cache/lxsession/LXDE
** Message: 17:46:14.060: main.vala:135: log path: /home/yuchi/.cache/lxsession/LXDE/run.log

SNAP Desktop

  • Select optimal master in SNAP using Radar / Interferometric / InSAR Stack Overview

Select optimal master in SNAP

  • Perform subsetting of whole image using TOPSAR Split via Radar / Sentinel-1 TOPS / S-1 TOPS Split. Set the processing parameters
    • subswath IW2
    • polarization Vv
    • bursts 6-10

Subsetting of whole image

  • Get LAT/LON MIN/MAX (bounding box) for PSI area of interest. This can be obtained e.g. from ROI polygon in QGIS Layer Properties | Metadata | Properties | Extent or ArcGIS.

Extent of Study area:
Top: 34.585279
Bottom: 34.022282
Left: 116.811255
Right: 117.713315

snap2stamps in WSL

  • Edit project.conf to set up configuration for your project.
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######### CONFIGURATION FILE ######
###################################
# PROJECT DEFINITION
PROJECTFOLDER=/mnt/i/SAR/Data/Xuzhou_city/
GRAPHSFOLDER=/home/yuchi/software/snap2stamps/graphs
##################################
# PROCESSING PARAMETERS
IW1=IW2
MASTER=/mnt/i/SAR/Data/Xuzhou_city/master/S1A_IW_SLC__1SDV_20190919T101207_20190919T101235_029089_034D30_8446_split.dim
# AOI BBOX DEFINITION
LONMIN=116.811255
LATMIN=34.022282
LONMAX=117.713315
LATMAX=34.585279
##################################
# SNAP GPT
GPTBIN_PATH=/home/yuchi/snap/bin/gpt
##################################
# COMPUTING RESOURCES TO EMPLOY
CPU=2
CACHE=16G
##################################
  • Move the master (zip + TOPS - Split Output) to the directory master in your PROJECTFOLDER /mnt/i/SAR/Data/Xuzhou_city/master/.

  • Make sure that all slave images (zip) are stored in the subfolder slaves in the PROJECTFOLDER /mnt/i/SAR/Data/Xuzhou_city/slaves/.

  • Check if all libraries are available for your Python 2 installation (you might need to pip install pathlib).

  • Run the python scripts of snap2stamp directly in your shell:

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# slave sorting
# (fast) 2-3 seconds
# in WSL
python slaves_prep.py project.conf

# slave splitting and orbit correction
# (this takes some time, approx. 50 seconds per slave) 120 seconds per slave
# in WSL

python splitting_slaves.py project.conf

# master-slave coregistration and interferometric generation
# (this takes some time, approx. 180 seconds per slave) 680+ seconds per slave, around 24 hours to process all
python coreg_ifg_topsar.py project.conf

# ouput data generation in StaMPS compatible format
# (approx. 30 seconds)
# python stamps_export.py project.conf
  • DO Copy /Xuzhou_city/coreg /graphs /ifg /logs folder to LiDO use Winscp and run the fellowing scripts in LiDO.

  • DO Edit the project.conf file in \work\smyumeng\snap2stamps\bin:

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    ######### CONFIGURATION FILE ######
    ###################################
    # PROJECT DEFINITION
    PROJECTFOLDER=/work/smyumeng/Sentinel_PS/xuzhou_city/
    GRAPHSFOLDER=/work/smyumeng/snap2stamps/graphs
    ##################################
    # PROCESSING PARAMETERS
    IW1=IW2
    MASTER=/work/smyumeng/Sentinel_PS/xuzhou_city/master/S1A_IW_SLC__1SDV_20190919T101207_20190919T101235_029089_034D30_8446_split.dim
    ##################################
    # AOI BBOX DEFINITION
    LONMIN=116.811255
    LATMIN=34.022282
    LONMAX=117.713315
    LATMAX=34.585279
    ##################################
    # SNAP GPT
    GPTBIN_PATH=/home/smyumeng/snap/bin/gpt
    ##################################
    # COMPUTING RESOURCES TO EMPLOY
    CPU=16
    CACHE=160G
    ##################################
  • DO cd to /work/smyumeng/project/snap2stamps_script/ and open in terminal

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sbatch snap2stamps_stamps_output.sh
  • The data final output structure after performing the last step should contain these four folders: rslc, diff0, geo and dem.

  • Check for empty interferograms. If any exist, remove files containing the date of the empty file from the folders rslc and diff0. Otherwise, this will throw warnings related to 0 mean amplitude during the final preparation step use in stamps (i.e. mt_prep_snap) and eventually screw up the selection of PS candidates.

  • copy INSAR_master_date folder from MATLAB workstation

  • prepare for StaMPS MATLAB process

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$ source /work/smyumeng/StaMPS/StaMPS_CONFIG.bash
$ mt_prep_snap 20190919 /work/smyumeng/Sentinel_PS/Xuzhou_city/INSAR_20190919 0.4 3 2 50 200
  • launch matlab to continue with StaMPS PS analysis

MATLAB

In LiDO, cd to shell scripts folder. In this case is cd /work/smyumeng/project/sentinel_scripts, edit script stamps13, stamps678 and run shell scripts.

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sbatch stamps1_4.sh
sbatch stamps5.sh
sbatch stamps6.sh
sbatch stamps7_8.sh
  • PS_info
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>> ps_info
1 05-Aug-2016 -31 m 53.765 deg
2 17-Aug-2016 15 m 51.862 deg
3 29-Aug-2016 24 m 49.975 deg
4 20-May-2017 -65 m 43.667 deg
5 13-Jun-2017 -4 m 40.435 deg
6 25-Jun-2017 30 m 39.724 deg
7 19-Jul-2017 -4 m 41.734 deg
8 31-Jul-2017 -51 m 44.778 deg
9 12-Aug-2017 -49 m 42.578 deg
10 24-Aug-2017 55 m 38.236 deg
11 05-Sep-2017 56 m 40.778 deg
12 17-Sep-2017 -42 m 37.375 deg
13 11-Oct-2017 -112 m 46.294 deg
14 23-Oct-2017 -58 m 35.192 deg
15 04-Nov-2017 18 m 35.254 deg
16 16-Nov-2017 -13 m 33.478 deg
17 28-Nov-2017 -83 m 33.531 deg
18 10-Dec-2017 -47 m 35.339 deg
19 22-Dec-2017 40 m 35.074 deg
20 03-Jan-2018 59 m 38.786 deg
21 15-Jan-2018 4 m 37.448 deg
22 27-Jan-2018 -32 m 42.990 deg
23 08-Feb-2018 -89 m 34.891 deg
24 20-Feb-2018 -76 m 32.894 deg
25 04-Mar-2018 -59 m 41.880 deg
26 28-Mar-2018 -10 m 29.675 deg
27 09-Apr-2018 -32 m 30.275 deg
28 21-Apr-2018 -9 m 42.681 deg
29 03-May-2018 45 m 28.915 deg
30 15-May-2018 -13 m 30.676 deg
31 27-May-2018 -21 m 30.958 deg
32 08-Jun-2018 -34 m 32.425 deg
33 20-Jun-2018 -19 m 32.944 deg
34 02-Jul-2018 58 m 28.058 deg
35 14-Jul-2018 60 m 30.617 deg
36 26-Jul-2018 32 m 48.992 deg
37 07-Aug-2018 1 m 34.364 deg
38 19-Aug-2018 -74 m 42.650 deg
39 31-Aug-2018 -49 m 32.299 deg
40 12-Sep-2018 14 m 27.427 deg
41 24-Sep-2018 55 m 26.934 deg
42 06-Oct-2018 19 m 26.798 deg
43 18-Oct-2018 -110 m 28.005 deg
44 11-Nov-2018 -37 m 29.667 deg
45 23-Nov-2018 34 m 29.855 deg
46 05-Dec-2018 -19 m 40.999 deg
47 17-Dec-2018 -11 m 31.237 deg
48 29-Dec-2018 -117 m 30.164 deg
49 10-Jan-2019 -37 m 40.075 deg
50 22-Jan-2019 -52 m 28.011 deg
51 03-Feb-2019 -12 m 35.595 deg
52 27-Feb-2019 -115 m 26.293 deg
53 11-Mar-2019 -111 m 24.771 deg
54 23-Mar-2019 -92 m 25.106 deg
55 04-Apr-2019 -59 m 24.716 deg
56 16-Apr-2019 -10 m 24.913 deg
57 28-Apr-2019 -122 m 28.848 deg
58 10-May-2019 -18 m 25.191 deg
59 22-May-2019 -24 m 27.777 deg
60 03-Jun-2019 -3 m 27.778 deg
61 15-Jun-2019 60 m 27.487 deg
62 27-Jun-2019 -9 m 27.436 deg
63 09-Jul-2019 -19 m 27.882 deg
64 21-Jul-2019 -36 m 30.697 deg
65 02-Aug-2019 59 m 33.582 deg
66 14-Aug-2019 -42 m 30.115 deg
67 26-Aug-2019 -78 m 29.975 deg
68 07-Sep-2019 -98 m 29.634 deg
69 19-Sep-2019 0 m 27.047 deg
70 01-Oct-2019 -84 m 28.263 deg
71 13-Oct-2019 49 m 28.745 deg
72 25-Oct-2019 43 m 26.720 deg
73 06-Nov-2019 -88 m 23.890 deg
74 18-Nov-2019 -130 m 26.777 deg
75 30-Nov-2019 -53 m 37.977 deg
76 12-Dec-2019 3 m 27.751 deg
77 24-Dec-2019 0 m 29.870 deg
78 05-Jan-2020 -44 m 41.667 deg
79 17-Jan-2020 -90 m 34.344 deg
80 29-Jan-2020 -74 m 29.120 deg
81 10-Feb-2020 -37 m 25.491 deg
82 22-Feb-2020 -25 m 26.369 deg
83 05-Mar-2020 -62 m 25.282 deg
84 17-Mar-2020 -114 m 24.210 deg
85 29-Mar-2020 -85 m 24.555 deg
86 10-Apr-2020 -24 m 27.543 deg
87 22-Apr-2020 2 m 26.744 deg
88 04-May-2020 -2 m 24.821 deg
89 16-May-2020 -24 m 26.998 deg
90 28-May-2020 -35 m 30.625 deg
91 09-Jun-2020 43 m 28.305 deg
92 21-Jun-2020 14 m 28.372 deg
93 03-Jul-2020 75 m 30.817 deg
94 15-Jul-2020 2 m 31.607 deg
95 27-Jul-2020 -88 m 33.561 deg
96 08-Aug-2020 -43 m 34.077 deg
97 20-Aug-2020 89 m 33.414 deg
98 01-Sep-2020 -4 m 33.438 deg
99 13-Sep-2020 -74 m 29.420 deg
100 25-Sep-2020 -115 m 29.306 deg
101 07-Oct-2020 -126 m 27.926 deg
102 19-Oct-2020 -27 m 26.992 deg
103 31-Oct-2020 5 m 27.406 deg
104 12-Nov-2020 -59 m 26.295 deg
105 24-Nov-2020 -26 m 33.760 deg
106 06-Dec-2020 -96 m 27.112 deg
107 18-Dec-2020 -95 m 28.680 deg
108 30-Dec-2020 -6 m 58.614 deg
109 11-Jan-2021 24 m 37.044 deg
110 23-Jan-2021 6 m 32.530 deg
111 04-Feb-2021 -54 m 28.520 deg
112 16-Feb-2021 -65 m 29.469 deg
113 28-Feb-2021 -46 m 41.582 deg
114 12-Mar-2021 -27 m 31.155 deg
115 24-Mar-2021 -44 m 27.754 deg
116 05-Apr-2021 -35 m 30.524 deg
117 17-Apr-2021 -45 m 31.035 deg
118 29-Apr-2021 101 m 33.577 deg
119 11-May-2021 4 m 30.074 deg
120 23-May-2021 -25 m 30.407 deg
121 04-Jun-2021 -37 m 35.760 deg
122 16-Jun-2021 -76 m 34.532 deg
123 28-Jun-2021 23 m 34.516 deg
124 10-Jul-2021 36 m 35.456 deg
125 22-Jul-2021 24 m 34.594 deg
126 03-Aug-2021 -25 m 36.501 deg
127 15-Aug-2021 -15 m 36.154 deg
128 27-Aug-2021 -57 m 44.687 deg
129 08-Sep-2021 -12 m 35.791 deg
130 20-Sep-2021 34 m 40.224 deg
131 02-Oct-2021 -69 m 36.497 deg
132 14-Oct-2021 -92 m 34.842 deg
133 26-Oct-2021 -104 m 34.270 deg
134 07-Nov-2021 -43 m 45.862 deg
Number of stable-phase pixels: 654086
  • PS_plot

phase unwrap results

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>> ps_plot('v-do', 'ts');
Deramping computed on the fly.
**** z = ax + by+ c
654086 ref PS selected
Color Range: -5.53427 to 8.19528 mm/yr
  • Result

TS plot for default parameter.

TS plot

Selected point coordinates (lon,lat):116.8939, 34.0225

Time series plot for #points

Selected point coordinates (lon,lat):116.9868, 34.132

![Time series plot for #points

Process 2

Change parameter to below and restart process.

Parameter Default Description
scla_deramp ‘n’ ‘y’
unwrap_gold_n_win 32 8
unwrap_grid_size 200 50
unwrap_time_win 730 60
scn_time_win 365 120

Rerun step6

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sbatch stamps6.sh

Post-StaMPS

After getting the result of stamps, copy the files to windows through Winscp, furthur analysis is in Matlab.

Write csv file in Matlab

Make a csv file with all data vor visualizations:

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ps_plot('v-do', 'ts');
% after the plot has appeared magically, set radius and location by clicking into the plot
% set radius to 50000 m, and set a point in plot
% Please select a point on the figure to plot time series (TS)
% Selected point coordinates (lon,lat):117.161, 34.1283
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load parms.mat;
ps_plot('v-do', -1);
load ps_plot_v-do.mat;
lon2_str = cellstr(num2str(lon2));
lat2_str = cellstr(num2str(lat2));
lonlat2_str = strcat(lon2_str, lat2_str);

lonlat_str = strcat(cellstr(num2str(lonlat(:,1))), cellstr(num2str(lonlat(:,2))));
ind = ismember(lonlat_str, lonlat2_str);

disp = ph_disp(ind);
disp_ts = ph_mm(ind,:);
export_res = [lon2 lat2 disp disp_ts];

metarow = [ref_centre_lonlat NaN transpose(day)-1];
k = 0;
export_res = [export_res(1:k,:); metarow; export_res(k+1:end,:)];
export_res = table(export_res);
writetable(export_res,'stamps_tsexport.csv')

Figure v-do

StamPS_visualizer

  • open the StaMPS_Visualizer.Rproj file with Rstudio

  • run install.packages("renv") in the R Console

  • run renv::restore() in the R Console to restore the complete environment (this might take some time)

  • go to File --> Open File… and open ui.R

  • click on Run App in the upper left panel in the upper right corner

Resample of stamps results

  • Create roi.kml in Google Earth

  • Open R.Studio

  • Open file subset_ts_export.R and edit inputs and outputs

  • Run script

Redo snap2stamps and steps after

Due to some errors in PS_result, we redo all the processes in Lido.

  • Copy folder I:\SAR\Data\Xuzhou_city\master and \slaves to LiDO work\smyumeng\Sentinel_PS\xuzhou_city_new

  • Rerun snap2stamps

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######### CONFIGURATION FILE ######
###################################
# PROJECT DEFINITION
PROJECTFOLDER=/work/smyumeng/Sentinel_PS/Xuzhou_city_new/
GRAPHSFOLDER=/work/smyumeng/snap2stamps/graphs
##################################
# PROCESSING PARAMETERS
IW1=IW2
MASTER=/work/smyumeng/Sentinel_PS/Xuzhou_city_new/master/S1A_IW_SLC__1SDV_20190919T101207_20190919T101235_029089_034D30_8446_split.dim
##################################
# AOI BBOX DEFINITION
LONMIN=116.42
LATMIN=33.81
LONMAX=117.61
LATMAX=34.81
##################################
# SNAP GPT
GPTBIN_PATH=/home/smyumeng/snap/bin/gpt
##################################
# COMPUTING RESOURCES TO EMPLOY
CPU=28
CACHE=256G
##################################
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python slaves_prep.py project.conf

python splitting_slaves.py project.conf

python coreg_ifg_topsar.py project.conf

python stamps_export.py project.conf
  • Rerun StaMPS

  • prepare for StaMPS MATLAB process

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$ source /work/smyumeng/StaMPS/StaMPS_CONFIG.bash
$ mt_prep_snap 20190919 /work/smyumeng/Sentinel_PS/Xuzhou_city/INSAR_20190919 0.4 3 2 50 200
  • In LiDO, cd to shell scripts folder. In this case is cd /work/smyumeng/project/sentinel_scripts, edit script stamps13, stamps678 and run shell scripts.
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sbatch stamps1_4.sh
sbatch stamps5.sh
sbatch stamps6.sh
sbatch stamps7_8.sh

To be continued!