Examples of InSAR Norway in use

De ulike datasettene, stigende og synkende, viser to vidt forskjellige bevegelsesmønstre på grunn av satellittenes ulike sikteretning. — Figure 1: The two datasets, ascending and descending, show two vastly different movement patterns due to the different orientations of the satellites.

Although InSAR data is often presented and visualized with simple colour codes, it is not always easy to interpret. This is because there are many factors to consider. Here are some examples of how InSAR data is used and what is essential to keep in mind in different scenarios.

How do we interpret movement in urban areas?

Monitoring motion in urban areas and infrastructure can be useful for obtaining information about the movement before, during and after construction work. Construction work can affect the environment, and if you look at this together with movement measurements with InSAR, you can get new information about the course of events and causal relationships.

It is often difficult to predict how construction work and structures will affect the ground conditions. At the same time, it can be difficult to know whether settlement damage is a direct result of, for example, tunnelling or other construction activities in the vicinity.

Together with information about the ground conditions (rock type and surficial deposits) and groundwater conditions, you can confirm or deny whether there is a connection between movements and construction activities during the time you have InSAR data available.

Oslo

The area around the central train station in Oslo is characterized by movements. This is due to compaction of sediments due to increased loads and changed groundwater conditions. Read more about the movements in Bjørvika (PDF).

When investigating an area for movement, it is important to remember that InSAR data has several limitations.

Check several datasets to confirm that InSAR has detected motion. Read more about how to switch between different datasets.

If the patterns of movement are similar in several datasets that use the same geometry (ascending 1 and ascending 2, or descending 1 and descending 2), that confirms that real movement is taking place at the site of interest. If all the datasets - ascending and descending - show the same movement, it can be assumed that the movement at that site is vertical.

Fire ulike datsett viser innsynking i Oslo på kartet InSAR Norge. — The area around the central railway station in Oslo is characterized by movements of approximately 3−4 mm/year. All the data sets from the Sentinel-1 satellites (ascending 1, ascending 2, descending 1. descending 3) show approximately the same movement pattern over the same area in central Oslo. When several data sets from satellite geometry show the same movement pattern, it indicates that there are real movements. If the data sets from different geometries (ascending and descending) show the same movement pattern, one can assume that the movement is vertical (downwards when the values are negative and are shown in red).

When looking at the motion time series it is important to remember that the result varies depending on which single point is selected. Single points can contain noise; therefore, it is better to use the polygon tool to calculate the average motion of many points. It is also important to remember that although the graph often looks the same for different points or areas, the scale (y-axis) can be different. Read more about changing the visualization in the time series window.

Hvordan gjenomsnitthastighet på innsyking i Oslo kan sees i kartet InSAR Norge. — Different ways of viewing the average velocity of deformation. A) Using the polygon tool, calculate the average velocity for many points (here 873 points) for 2015 to 2019. The average velocity is 3 - 4 mm/year. B) The average speed for a single point, which shows approx. 0.5 mm/year. For low values, it is difficult to distinguish between real movement and noise. C) This point shows an average speed of about 8 mm/year. Also note that the scale on the y-axis is from -20 to + 20 mm/year.

Trondheim

The Port of Trondheim area shows movements of about 10 mm/year. This surface movement pattern is similar in the different InSAR datasets.

Fire datsett viser innsynking i Trondheim på kartet InSAR Norge. — In the Port of Trondheim area, the data sets from all the different geometries show a similar movement pattern, which strongly indicates that the movements are real. If you compare ascending and descending geometries carefully, you can see that descending shows somewhat higher movement in Brattøra areas. This may indicate that there is a somewhat larger westward movement.

In some areas of Norway, datasets from the Radarsat-2 satellite are available. This is the case for cities like Trondheim and can be useful if you want a longer motion time series and information about how movements have developed over a long period of time.

Historiske InSAR datasett (Radarsat-2 i perioden 2008-2018) viser at det har vært pågående bevegelser i havneområdet i Trondheim i lang tid. — Historical InSAR data sets (Radarsat-2 in the period 2008-2018) show that there have been ongoing movements in the port area in Trondheim for a long time.

When interpreting InSAR, it is a good idea to keep in mind the typical sources of error in the areas you are investigating. Read more about limitations and properties.

In urban areas, areas characterized by noise are often seen due to rapid changes on the surface, such as this construction area in Trondheim:

To bilder av ulike og feil målinger i kartet InSAR Norge. — Example of an urban area affected by errors. The area shows points with various different movement velocities. Construction activity with rapid changes, which lead to noise in the InSAR data.

How do we interpret movement in mountainous areas?

InSAR is a suitable method for detecting, mapping and monitoring unstable rock slopes.

One of the uses of InSAR Norway is to map unstable rock slopes. As in the case of urban areas and infrastructure, movements in mountainous areas are often monitored at regular intervals with more traditional methods using on-site motion measurement tools, such as GPS.

Although InSAR cannot directly replace on-site methods, vast nationwide datasets quickly provide users an overview of areas that are moving. With this information, priority areas are easier to identify, and resources can be used more efficiently.

Piggtind and Seidi

On either side Sørfjord in Tromsø and Finnmark are two unstable rock slopes. When investigating areas that display slope movement, begin by assuming the direction of motion. Without other information, it can be assumed that movements in mountain areas are parallel to slope.

It is also a good idea to look at various base maps available in the InSAR mapping service - aerial photos, shaded relief, geological maps: These maps provide additional information about other processes present in the area.

The data sets only show the component of movement that is parallel to the radar’s line-of-sight. Therefore, for a west-facing slope use descending data sets, since the radar looks obliquely down to the west, i.e., parallel to the slope and the assumed direction movement. On the other hand, if the slope is east facing, you will want to view from the ascending satellite: it can measure motion parallel to the line-of-sight. Read more about how the different geometries affect the InSAR data.

When interpreting InSAR data in mountainous areas, keep in mind possible sources of error. Vegetation can lead to data gaps or low measurement point density.

Point measurements may be missing in areas that are shaded by the satellite or in steep areas facing the satellite due to layover effect. Read more about sources of error.

To identify these problems, use the base map layers available in InSAR Norway.

Det er mange hull i dataene ved fjellet Piggtind fra kartet InSAR Norge. — Sources of error on Piggtind. Here you see that there are missing points where there is dense vegetation. Behind the highest and steepest peaks there is little coverage since this area is in the shadow of the satellite. There are also areas that lack measurement points. These gaps do not mean that there is no movement, but rather that the movement is too fast to be measured with the InSAR Norway technique.

Different symbolization can also have an effect on how an area is interpreted. With different palettes, or different scales, other movement patterns can emerge more clearly.

Ulik fargeskala viser ulike bevegelser på fjellet Seidi på kartet InSAR Norge. — The example from Seidi shows that different colour scales can give a different impression and make different movement patterns clearer. A) The colour scale is set from -10 mm/year to 10 mm/year. B) The colour scale is set from -20 mm/year to 20 mm/year.

Gámanjunni

Gámanjunni 3 is an unstable rock slope located in Manndalen in Tromsø. Today, the mountain section is continuously monitored by NVE.

Read more about the hazard and risk classification of Gámanjunni 3 (only in Norwegian) and about the monitoring and follow-up of the mountain section on NVEs webpage (only in Norwegian).

Fjellet Gamanunni med tidsserie på kartet InSAR Norge. — Gámanjunni 3 is an unstable rock slope that moves by about 5 cm a year.

Gámanjunni 3 is located on a west-facing slope, and without more information about the rock slope, it is appropriate to use the descending data sets (where the radar has a view down to the west).

Below one can see the difference between the ascending and descending datasets for this west-facing slope.

Stor forskjell på antall målinger med stigende og synkende målinger på fjellet Gamanjunni på kartet InSAR Norge. — Ascending og descending datasett ser svært ulike ut for området rundt Gámanjunni 3. Siden skråningen er bratt og vestvendt og dermed vendt mot sikteretningen for ascending satellitt, er mange målepunkter maskert vekk på grunn av effekten som kalles layover.

Keep in mind what other sources of error may occur in the area you are studying.

Ulike feil på fjellet Gamanjunni som skygge, tett vegetasjon, snø og for rask bevegelse kan skje i kartet InSAR Norge. — Ascending and descending datasets look very different for the area around Gámanjunni 3. Since the slope is steep and west-facing and thus facing the aiming direction for ascending satellite, many measuring points are masked away due to the layover effect. Read more about layover effect.

What is important to remember when interpreting InSAR data?

Look at velocity fields and time series from both ascending and descending orbits

Click on points to view entire time series. Remember the "draw polygon" tool to plot the average of multiple points.
Look at the direction of sight information at the top right of the time series window to understand the geometry of the measurement.

Check multiple InSAR datasets

Check if the movement pattern is visible in several data sets from the same satellite geometry. If so, you can assume that the movement is real.
Show both ascending and descending datasets motion in the same area: if the motion has the opposite direction of motion (red/blue colors), one can assume that there is a significant horizontal component in the motion. If both ascending and descending datasets are the same movement pattern, one can assume that the movement is approximately vertical.
Assume the direction of the movement in the area of interest and use the data set where the radar's viewing geometry is approximately parallel to the assumed direction of movement. If the movement is to the east, use the ascending dataset. If the movement is to the west, use the descending dataset.
Remember that the satellites are not sensitive enough to measure movement along towards north or south, therefore, the north or south movement is underestimated. Movement in north- and south-facing slopes is detected only if they have a clear vertical component.

Look at more than one point

Remember that a single points can contain noise and thus have the wrong value. Be careful interpreting individual points that show deviating values compared to the surrounding area.
Individual points are not always representative of larger areas. Rather use the polygon tool to average the motion of several points.

Use different basic maps

Without information on the ground conditions, it can be difficult to determine what process is behind the movement.
Therefore, refer to the various base maps like the shade relief map or aerial photos, to get information about the ground conditions (vegetation, surficial deposits, bedrock, bogs, construction areas or areas characterized by long snow seasons).

Test different symbolizations and scales

Try different map symbolizations (different colours or different colour scales) which can bring to light other possible movement patterns.
Test different x-/y-axis scales and limit values on the time series graph. The standard scale adapts to the value of selected points; that is, for points with very low velocities, the noise level will appear exaggerated.