Step-by-Step: Mudslides and Associated Flood Detection Using Sentinel-1 Data

Data Access: 

For this Recommended Practice, we used Sentinel-1 data freely available on the Copernicus Open Access Hub platform (https://scihub.copernicus.eu/dhus/#/home) as well as on the Alaska Satellite Facility (https://www.asf.alaska.edu/).

The two images used for this exercise were
S1B_IW_GRDH_1SDV_20190122T082905_20190122T082930_014604_01B364_2CB1 and
S1B_IW_GRDH_1SDV_20190203T082905_20190203T082930_014779_01B90C_150F, 1A level, ascending orbit, GRN product type and IW sensor mode.

 

Content:

Step 1: Preprocessing

Step 2: Processing

Step 3: Post processing

Data Preparation/Pre-processing: 

Step 1:

- Data preparation

This process must apply to both images.

Unzip the Sentinel-1 data in your working directory, Open SNAP software and call SAR images by clicking on File > Open Product and then selecting the manifest.safe file.

Recommended Practices: Mudslides and Floods, Figure 1

Figure 1


- Apply Orbit File 

This process must be applied to both images.

The orbit file provides an accurate position of SAR image and the update of the original metadata of SAR product, the orbit file is automatically downloaded from SNAP software. For this recommended practice, the "apply orbit file" procedure can be optional; nevertheless, it's highly recommended to ensure a successful spatial coregistration process.

Select Radar > Apply Orbit File, and then define all parameters according to Figure 2.

Recommended Practices: Mudflows and Floods, Figure 2

Figure 2


- Calibration

Select Radar > Radiometric > Calibrate, and set the “Processing Parameters”, select all polarizations and select Sigma0 as output band.

Recommended Practices: Mudslides and Floods, Figure 3

Figure 3

This process must be applied for both images.

Recommended Practices: Mudslides and Floods, Figure 4

Figure 4


- Speckle Filtering

Select Radar > Speckle Filtering > Single Product Speckle Filter, and set the “Processing Parameters” as shown in Figure 5.

You can select another type of filter according with your expertise; however, we recommend using Lee and Frost filters as they are less degrading to the SAR image.

Recommended Practice: Mudslides and Flood, Figure 5

Figure 5

This process must be applied for both images.

Recommended Practice: Mudslides and flood, Figure 6

Figure 6


- Geocoding

Click on Radar > Geometric > Terrain Correction > Range-Doppler Terrain Correction, and define all “Processing Parameters” according to Figure 7.

Recommended Pracitce: Mudslides and Flood, Figure 7

Figure 7

This processs must be applied for both image.

Recommened Practice: Mudslides and Flood, Figure 8

Figure 8


- Subset

To get the same spatial subset to both images, click on View and select the following menu options: Statusbar, Synchronise Image Cursors and Synchronise Image Views.

Recommened Practice: Mudslides and Flood, Figure 9

Figure 9

Go to Raster > Subset, specify the area and parameters of the region of interest as shown in Figure 9.

This process must be applied to both images.

To ensure the same spatial coverage for both images, double click on any of the bands (vv or hv) of the second image and start again the “subset processing” as mention above.

Recommended Practice: Mudslides and Flood, Figure 10

Figure 10

Now, the new subset products must be converted to BEAM-DIMAP format. For this, right click over the name of each new subset product and select Save Product As, rename it if desired, click ok.

Recommended Practice: Mudslides and Flood, Figure 11

Figure 11

Recommended Practice: Mudslides and Flood, Figure 12

Figure 12


- Corregistration stack

To generate a composite image from SAR data before and after the massive mudslide, a spatial coregistration procedure is necessary. For this, go to Radar > Corresgistration Stack > Tools > Create Stack, click the plus icon and select only the new subset products.

Recommended Practice: Mudslides and Flood, Figure 13

Figure 13

Define the rest of parameters as shown in Figure 14.

Recommended Practice: Mudslides and Flood, Figure 14Recommended practice: Mudslides and Flood, Figure 14b

Figure 14

Open the new product as a composite image using the different polarizations. For this, right-click the name of the new product and select Open RGB Image Windows; choose the desired bands.

Recommended Practices: Mudslides and Flood, Figure 15

Figure 15

Example of the RGB image composite

Recommended Practice: Mudslides and Flood, Figure 16

Figure 16


 

Processing Steps: 

Step 2: Processing

- Change Detection

The Log Ratio is an algorithm used to the change detection procedure using mean ratio operator between two images of the same coverage area but taken at different times. To apply this procedure, click on Radar > SAR Applications > Change Detection, and define the processing parameters according to figure 17.

Recommended Practice: Mudslides and Floods, Figure 17

Figure 17

This procedure must be applied for all possible combinations between SAR images before and after the massive mudslide event. The following Log Ratio date combinations are required:

Recommended Pracitce: Mudslides and Flood, Figure 18

Figure 18

The massive mudslide and flooding area are identified by high-intensity pixels, identification is also possible by very low-intensity pixels. For this reason, PCA is used to regroup the pixel set optimally in order to achieve a digital enhancement of magnitude values associated with the affected area.

Recommended Practice: Mudslides and Flood, Figure 19

Figure 19


- Coregistration stack (Log Ratio results)

To apply principal components analysis, a composite image from all Log Ratio results must be generated. For this, go to Radar > Corresgistration Stack > Tools > Create Stack, click on plus icon and select the four Log Ratio images. It is important to keep the order of the image combinations as listed in the previous step, "change detection" (Log Ratio):

  1. Sigma0_VH_22Jan2019/Sigma0_VH_03Feb2019

  2. Sigma0_VV_22Jan2019/Sigma0_VH_03Feb2019

  3. Sigma0_VV_22Jan2019/Sigma0_VV_03Feb2019

  4. Sigma0_VH_22Jan2019/Sigma0_VV_03Feb2019

Recommended Practice: Mudslides and Flood, Figure 20

Figure 20

Define the processing parameters according to Figure 21. Rename the file if desired.

Recommended Practice: Mudslides and Flood, Figure 21

Figure 21

- Apply Principal Components Analysis (PCA)

Click on Raster > Image Analysis > Principal Components Analysis, and call the new product (Log Ratio stack), and select the four Log Ratio images. Define all “Processing Parameters” according to Figure 22.

Recommended Practice: Mudslides and Flood, Figure 22

Figure 22

The new product is created by four "out band components" and one "response band". As mention before, PCA implies a new data regrouping (a reversible orthogonal transformation), where towards the first "out band components" the variance is maximized. This means that the more significant pixel information is kept; while towards the last "out band component," all noise or redundant information is separated. The first three components can be used for the next steps. The “response band” represents each basis vector of the “out band components”, high values correspond to a better fit of data; so, this band can be used for the next steps as well.

Recommended Practice: Mudslides and Flood, Figure 23

Figure 23

“Out Band Components” results:

Recommended practice: Mudslides and Flood, Figure 24

Figure 24

“Response band” result:

Recommended practice: Mudslides and Flood, Figure 25

Figure 25


- Supervise image classification 

Each of the four final bands contains digital enhancement information about massive mudslide event and associated flooding. The differences in the spatial information provided by the final bands are linked to soil moisture conditions, differences between water bodies or by the large volume of sediments transported during a flood.

Thus the next step is to extract the affected area using a classification procedure. First, it is necessary to define the training polygons. Only two classes were defined: “no mudflow area” and “mudflow”; we recommend visualizing each of the final bands separately. 

Recommended Practice: Mudslides and Flood, Figure 26

Figure 26

  1. double click on the final band selected

  2. go to New Vector Data Container and a new window will be opened

  3. name the polygon class "no mudflow area" and you can start to draw the polygon contouring

  4. if you desired to add another polygon of the same class "no Mudflow area", click on Polygon Drawing Tool and then start to draw it

    Recommended Practice: Mudslides and Flood, Figure 26

    Figure 26

  5. to add a new polygon class, go to New Vector Data Container and a new window will be opened

  6. name the new polygon class "mudflow" and click ok

  7. click over the image and a new window will open, the two clases are listed, select "mudflow"; now draw the polygon contouring

  8. if another polygon of the "now Mudflow area" class is needed, click on Polygon Drawing Tool and start drawing it

Recommended Practice: Mudslides and Flood, Figure 27

Recommended Practice: Mudslides and Flood, Figure 27b

Figure 27

Go to Raster > Classification > Supervised Classification > Maximum Likelihood Classifier

Figure 28

Select the two “training vector” classes previously defined and the “feature bands” (out band components and response band) as shown in the Figure 29.

Recommended Practice: Mudslides and Flood, Figure 29

Supervised classification result:

Recommended practice: Mudslides and Flood, Figure 30

Figure 30


- Apply smooth filtering 

To smooth the classified image result and regroup the pixels in an optimal way, a filtering process is needed. For this, go to Raster > Image Analysis  > Texture Analysis > Grey Level Co-ocurrence Matrix, select only GLCM Variance option. Define all “Processing Parameters” according to Figure 31 and Figure 32.

Recommended Practice: Mudslides and Flood, Figure 31

Figure 31

Recommended Practice: Mudslides and Flood, Figure 32

Figure 32

Smooth filtering result:

Recommended Practice: Mudslides and Flood, Figure 33

Figure 33


 

Assessment: 

Step 3: Post Processing

- Getting a binary final image

After the smooth filtering procedure, the values of the final classified image convert from binary values to continuous raster cell values (stretch values); thus, converting a raster file to shape format can be complicated. An easy way to get a final binary image again is to apply a Supervised Classification a second time. For this, the last product from the “smooth filtering” procedure must be used to apply the Supervised Classification a second time.

Recommended Practice: Mudslides and Flood, Figure 34

Figure 34


- Convert raster to shape file

Go to File > Export > GeoTIFF and select the last product (Supervised Classification a second time).

Recommended practice: Mudslides and Flood, Figure 35

Figure 35

Open QGIS software and click on Raster >  Conversion > Poligonize, and call the GeoTiff file.

Recommended Practice: Mudslides and Flood, Figure 36

Figure 36

The shape file is generated.

  1. click the polygon that represents the spatial distribution of the massive mudslide; the polygone will change colour
  2. go to the layers menu and right-click on the name of the polygone file and go to Export > Save As > Selected Features, save the new shape file as Esri Format

Recommended practice: Mudslides and Flood, Figure 37

Figure 37


- Visualization of results

Open Snap Software and call the  RGB composite image generated in the procedure “Corregistration stack” (Step 1).

Go to File > Import > Vector Data, and call the new shape file as Esri Format. Thus, you can overlap the shape file associated to the affected flooding area with respect to the original SAR data.

Recommended practice: Mudslides and Flood, Figure 38

Figure 38

Recommended practice: Mudslides and Flood, GIF 1.

GIF 1

The result of this recommended practice was compared with previous information provided by the International Charter Space and Major Disasters, where the massive mudslide was mapped using RapidEye high resolution data acquired after the event.

It is important to mention that the optical satellite image (RapidEye) used to identify the affected area has a higher spatial resolution than Sentinel-1. Despite this, the final results are very similar in spatial terms. This speaks about the effectiveness of our method of mapping this type of disaster event using Sentinel-1.

Recommended practice: Mudslides and flood, Gif 2

GIF 2

 

Recommended practice: Mudslides and Flood, Figure 39

Recommended practice: Mudslide and Flood, Figure 39

Recommended practice: Mudslides and Flood, Figure 39

Figure 39

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