TS Explorer

In the Single TS Selection panel you can choose a case study. The map is automatically adjusted to show the data of the case study. In the map, you can click on each marker (colored points) and a plot of its deformation time series will be plotted in the Single TS Selection panel. The number of the selected point is also shown below the plot, this number may be interesting for use in TS Comparison.
To add a vertical line of an event of interest to the TS plot choose a date within the time line of the TS plot.

Adding Costume Case Study Data

To explore and plot costume data, you have to export a dataset from StaMPS/MTI-Matlab in a specific .csv table, create a folder with the name of the case study in the stusi folder of the application and put the .csv table in this folder. After that, reload StaMPS/MTI-Visualizer to choose the new dataset.

To prepare the dataset in StaMPS/MTI-Matlab run your process in Matlab until step 6 or further. Create a ‘ts’ plot where you choose a region of interest you want to export. I recommend a search radius of 500m but this depends on point density and study site. If you have trouble because of an error telling something about different dimension during the export, use a search radius that is large enough to fit all your PS in it. We can use this huge table later in R and make a subset there which is easier. In both cases, this action will create some needed objects in Matlab. Which ps_plot you create depends on you, ‘V-DO’, ‘V-D’, ‘V-O’ etc. but be sure that you repeat the same plot without ‘ts’ but with -1 argument after that, see line 1, 3 and 4 in the chunk below. In this example the ‘v-do’ argument must be the same in all lines, adapt it to your chosen method. Additional data is prepared in this step, similar to the googleearth-kml export in StaMPS/MTI.
The last adaption is optional, you can change the name of the outpufile.csv in the last line. The file will be written into your SMALL_BASELINES directory. After the export, move the .csv file to your case study folder in stusi directory of the StaMSP-Visualizer application.

ps_plot('v-doa', 'ts');
load parms.mat;
ps_plot('v-doa', -1);
load ps_plot_v-doa.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);
export_res = [lon2 lat2 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')

If the export is not a subset of some measurement points but all points, because you encountered errors during exporting and set the search radius very high, use this R script to spatially subset the csv. table afterwards:

###########################
###subset ts plot export###
###########################
library(sp)
library(rgdal)
library(rgeos)

#read subsetpolygon
roi <- readOGR("/home/user/studysite/roi.kml")
#read exported .csv
pnts <- read.csv("/home/user/ISNAR_20170310/20170310_all.csv")
#create spatial object
lon <- pnts$export_res_.1[2:nrow(pnts)]
lat <- pnts$export_res_.2[2:nrow(pnts)]
loc <- data.frame(lon, lat)
pnts.geo <- SpatialPointsDataFrame(loc, pnts[2:nrow(pnts), ],
            proj4string = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"))
#spatial subset
pnts.sub <- pnts.geo[roi, ]
#create table from spatial subset
sub.csv <- rbind(pnts[1, ], pnts.sub@data)
#export csv to StaMPS-Visualizer application
write.table(sub, file = "/home/user/stamps_visualizer/stusi/newstudysite/20170310.csv",
            row.names = F, col.names = T, sep = ",")

TS Comparison

After you have explored your points, you might want to compare some of the single time series. During exploration you can see the point number after you have clicked on a marker on the map. Note your points of interest and change to TS Comparison here you can compare up to five time series, by choosing the case study and the numbers of points you have noted.