This content shows the specification of DubaiSat (UAE satellite), information of Hokkaido earthquake, data processing, pre-processing, pan-sharpening, Natural color composite, false color composite, NDVI calculation, image classification by clustering for the damaged area and landslide detection.

1. Center for Research and Application for Satellite Remote Sensing
Yamaguchi University
Data Processing Using DubaiSat Satellite Imagery
for Disaster Monitoring (Case Study: Earthquake in
Hokkaido, Japan)

2. DubaiSat
• DubaiSat-1 was the first ever earth
observation satellite launched by the
MBRSC. The satellite generates high-
resolution optical images at 2.5 m in
panchromatic and at 5 m in
multispectral bands [2].
• DubaiSat-2, the second satellite is
capable of providing satellite imagery
in both panchromatic (spatial
resolution of 1 m) and 4 multi-
spectral bands (4 m) across an area of
12.2 km[2].
[1] Arabian Aero Space, "UAE to Build New Space Research Centre" 26 May 2015.
[2] Mohammed Bin Rashid Space Centre, "DubaiSat-1 & DubaiSat-2” 15 November 2017
• The Mohammed Bin Rashid Space Centre (MBRSC),encompassing
the Emirates Institution for Advanced Science and Technology
(EIAST) is a Dubai government organization working on the UAE
space program[1].

3. Hokkaido Earthquake
• A powerful earthquake has rocked the city of Sapporo in northern
Japan killing at least four people, just hours after large parts of the
south of the country were battered by the biggest typhoon to hit in
25 years [3].
• The Magnitude 6.7 quake struck 70 miles south of Sapporo on the
island of Hokkaido on September 6, 2018, shortly after 7pm BST (3am
Thursday local time), according to the US Geological Survey (USGS) [3].
• At least 28 people were injured in the region and 20 residents in the
town of Atsuma may be unaccounted for, officials said. Twenty of
those injured were in the city of Sapporo [4].
• The earthquake was followed by multiple aftershocks, including one
registered at 5.4, NHK reported, citing the Japan Meteorological
Agency. The US Geological Survey said the earthquake registered at
6.6. The epicenter was just 16 miles from the city of Chitose, home to
Hokkaido's main airport[4].
[3] Scarsi, A, “Japan earthquake: Sapporo Rocked by 6.7 Earthquake - Landslide Traps People in Homes” Sep. 6, 2018.
https://www.express.co.uk/news/world/1013544/japan-earthquake-sapporo-hokkaido-earthquake-ring-of-fire-tsunami-warning
[4] CNN, “Magnitude 6.7 earthquake rocks Japan” Sep. 6, 2018.
https://edition.cnn.com/2018/09/06/asia/gallery/japan-earthquake-0906/index.html

4. Affected areas by the Hokkaido
earthquake:
Sapporo, Ebitsu, Yubari, Tomakomai, and Atsuma[5]
[5] NASA/ JPL-Caltech, “ARIA Product” 2018

5. Data collection: Sentinel Asia website
( https://sentinel.tksc.jaxa.jp/sentinel2/emobSelect.jsp )

6. Study area:
Atsuma, Hokkaido
Available Post-Disaster DubaiSat data in Reported damaged
areas for Hokkaido Earthquake :
Data Raw Data (.jpg)
DubaiSat-2
AEMBisis0001201809180002
Post-Disaster
2018/09/11 (10:45 AM)
DubaiSat-2
AEMBisis0001201809180003
Post-Disaster
2018/09/11 (10:45 AM)
AEMBisis0001201809180002
AEMBisis0001201809180003

7. Method of the Data Processing
Data processing is conducted using an open-source GIS application of QGIS[5].
Post-disaster DubaiSat-2
optical data
NDVI calculation
Thresholding and segmentation
Pansharpening
Pre-disaster Landsat 8
optical data
Plan curvature
Re-segmentation by masking two clusters image with five
clusters image
Refinement of damaged areas classification
Damaged areas including landslide, debris flow, damaged
road, damaged and destroyed house
Color compositing
Two clusters image
of damaged and non
damaged areas
Pre-processing
Color compositing
Visualization
of pre-disaster
image
Image comparison
[5]QGIS, “QGIS Tutorials and Tips” Apr. 30, 2018. https://www.qgistutorials.com/en/docs/
Visualization
of post-disaster
image
Five clusters image
of land cover
classes

8. Data Processing in Reported Damaged Areas
• DubaiSat-2: AEMBisis0001201809180002
• Post-Disaster: 2018/09/11 (10:45 AM)
• Reported damages: landslide, debris flow, damaged road, damaged and destroyed houses.

9. Assigning Coordinate Reference System
Set an appropriate Coordinate Reference System (CRS) for the project as well as adjust it for the image layers.
• CRS for the study area is JGD2000/
Japan Plane Rectangular CS XII
EPSG:2454.
• Project → Project Properties… →
CRS → Tick on “Enable ‘on the fly’
CRS transformation (OTF)” →
Type on “Filter” to search a
coordinate reference system →
Apply → OK
Japan Geodetic Datum (JGD) 2000
1
2

10. Georeferencing
Process of assigning real-world coordinates to each pixel of the raster..
• Georeferencing is conducted using 6 GCP points.
• Raster → Georeferencer → Add Point →
Transformation Settings → Transformation type
“Thin Plate Spline” and Resampling method “Nearest
neighbour” → Start Georeferencing
• Georeferencing for Panchromatic band:
1
2

11. • Raster → Georeferencer → Load GCP Points → Transformation Settings → Transformation type “Thin Plate
Spline” and Resampling method “Nearest neighbor” → Start Georeferencing
• Georeferencing for Red, Blue, Green, and Near-Infrared bands using the collected points:

12. Image Resizing
Change the size of the image to the intended area, in this case, the area suspected of being damaged by
an earthquake.
• Raster → Extraction → Clipper…
1
2
3

13. Band Set
Combining or merging several bands for color compositing, i.e., RGB bands.
• Raster → Build Virtual Raster (Catalog)… → Output file “Name of the output file” → Target SRS
“Selected CRS” → Separate
1 2

14. Pansharpening
Pan-sharpening is a process to acquire a high resolution multispectral (MS) image by combining a low
resolution MS image with a corresponding high resolution panchromatic (PAN) image.
• In this process, HSV fusion is used for
pansharpening that can be employed by
installing DSG tools from QGIS Plugins.
• Plugins → Manage and Install Plugins… →
DSG tools → Install plugin
• Processing → Toolbox → Scripts → DSG →
HSV fusion → “RGB Layer” and “Pan Layer”
• Raster → Miscellaneous → Build Virtual
Raster (Catalog)… → Select “Input files”,
“Output files”, and “Target SRS”

15. Natural Color Composite
A natural or true color composite is an image displaying a combination of the visible red, green and blue bands that
resulting composite resembles what would be observed naturally by the human eye.
• The natural color composite is produced using the combination of Red, Green, and Blue bands as
red, green, and blue components of the display.
• Right click on the layer → Properties → Band rendering → Render type “Multiband color” → RGB
123

16. False color composite
False color images are a representation of a multispectral image that allow us to visualize wavelengths that
the human eye can not see (i.e. near-infrared and beyond).
• The false color composite is produced using the combination of Near-Infrared, Red, and Green bands as
red, green, and blue components of the display.
• Right click on the layer → Properties → Band rendering → Render type “Multiband color” → RGB 432

17. NDVI Calculation
Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that can be used to analyze remote
sensing measurements, and quantify vegetation by measuring the difference between near-infrared and red light.
• NDVI = N (NIR) band – V (R) band / N (NIR) band + V (R) band
• Raster → Raster Calculator → NDVI = “(Band N – Band R) / (Band N + Band R)”

18. • Histogram of the NDVI value can be generated from the NDVI image result
• Right click on the layer → Properties → Histogram
• NDVI value ranges from – 0.324184 to 0.656901
0.656901
NDVI values
-0.324184
0.656901
NDVI values
-0.324184

19. Image Segmentation Into
Two Clusters
Divided an image into damaged and non-
damaged areas by using a threshold based on
NDVI values
• Processing → Toolbox → SAGA →
Image analysis → K-means clustering for
grids → Grids “Select NDVI raster layer”
→ Method “[2] Combined Minimum
Distance/ Hill-climbing → Clusters “2” →
Maximum Iterations “1000” →
Resampling method “Nearest
Neighbour”

20. Two clusters image result
Clusters ID Elements Std. Dev. NDVI Values Image Clusters
0 32975 0.780675 0.007258 1
1 122425 0.340675 0.542865 2
• Band rendering type Singleband
pseudocolor
• Band rendering type Singleband gray

21. Image Classification
Classified an image into clusters that represent
land cover, i.e., water, vegetation, built-up area,
bare land, as well as determining a class beside
the land cover classes as damaged areas.
• Processing → Toolbox → SAGA → Image
analysis → K-means clustering for grids → Grids
“Select NDVI raster layer” → Method “[2]
Combined Minimum Distance/ Hill-climbing →
Clusters “5” → Maximum Iterations “1000” →
Resampling method “Nearest Neighbour”

22. Five clusters image result
Clusters ID Elements Std. Dev. NDVI Values Image Clusters
0 20747 0.250419 0.046089 1
1 5876 0.277455 -0.361590 2
2 74345 0.132080 0.596846 3
3 39340 0.169234 0.486967 4
4 15092 0.260190 0.287453 5

23. The difference between two clusters and five clusters image results
• Damaged areas obtained in cluster 1 ( ) of the two clusters image and cluster 1
( ) of the five clusters image
• Nevertheless, damaged areas in the two clusters image result contain areas that are
classified as land cover classes. For instance, an area classified as water ( ) in the five
clusters image is classified as damaged areas ( ) in the two clusters image.

24. Image Masking
Subtracting two clusters image of damaged and non-damaged areas with five clusters image of land cover
classification.
• Raster → Raster Calculator… → Raster calculator expression “(Clusters 2) – (Clusters 5)”

25. • Rendering the image color of the masked image into pseudocolor
• Right click on the layer → Properties → Style → Render type “Singleband pseudocolor”
→ Color “ “ → Classify → Apply → OK
• According to the masked image classes of the two clusters and five clusters images,
values of damaged areas are in class “0” that is 0.007258 and 0.046089

26. Extracting damaged areas
Determine a range value that indicates damaged area both in two and five clusters images from the masked image result.
• Extracting values of damaged areas that is ≤ class 0
• Raster → Raster Calculator… → Raster calculator expression “(Clusters 2 – Clusters 5) ≤ 0”
• ” ” is damaged areas; and
• “ ” is non-damaged areas

27. Polygonize
Conversion of raster layer into vector (shapefile) layer.
• Convert image result of the extracted
damaged areas in raster format to vector
format.
• Raster → Conversions → Polygonize
(Raster to vector) → Input file (raster)
“Clusters 2 – Clusters 5)” → Output file for
polygons (shapefile) “Damaged areas” →
Field name “DN” → OK
• Right click on the layer → Properties →
Style → Fill “Transparent” → Outline
“Yellow” → →Fill style “Bold” →Outline
style “Solid Line” → Outline width “0.26”
→ Apply → OK

28. Combining Raster and Vector Layers
Overlaying image in vector layer on raster layer image to visualize the intended area distribution.
• Overlaying extracted damaged areas in vector layer on color composite raster image
to visualize the damaged areas distribution.
• Layer Panel → Activate “Vector layer of extracted damaged areas” and “Raster layer
of color composite image”
• Natural color composite • False color composite

29. Map Creation
Produce a map with necessary attributes as a final result of the image processing.
• Create a map of damaged area distribution obtained from the data processing with legend
and the other necessary information.
• Project → New Print Composer → Input “Name of the map”
Add new map
“Processed image
result”
Add image “Direction
symbol”
Add new label “Data
information”
Add new legend
“Extracted damaged
area information”
Add new scale bar
“Line” and “Numeric”
Add rectangle
“Boundary for the
map in rectangle”

30. Data Processing in Different Location Reported as Damaged Areas
• DubaiSat-2: AEMBisis0001201809180003
• Post-Disaster: 2018/09/11 (10:45 AM)
• Reported damages: landslide, debris flow, damaged road, damaged and destroyed houses.

31. Bands Georeferencing Resizing
R
G
B
NIR
Pan
Data Processing
Data processing is conducted following the previous
processing steps.
• Georeferencing and resizing

32. Pansharpening, Band Set, and Color Compositing
• Natural color composite RGB 123
• False color composite RGB 423

33. NDVI Calculation
0.702827
-0.43609
NDVI values
• NDVI = N (NIR) band – V (R) band / N (NIR) band + V (R) band
• NDVI value ranges from -0.43609 to 0.702827
0.702827
-0.43609

34. Image Segmentation Into Two Clusters of Damaged and Non-Damaged Areas
Clusters
ID
Elements Std. Dev. NDVI
Values
Image
Clusters
0 79653 0.791765 0.131810 1
1 44733 0.417125 0.535056 2
• Band rendering type Singleband gray

35. Image Classification of Five Clusters Land Cover Classes
Clusters ID Elements Std. Dev. NDVI Values Image Clusters
0 195032 0.156794 0.516775 1
1 186635 0.170373 0.597322 2
2 70242 0.246666 0.406723 3
3 38588 0.511156 0.019729 4
4 36493 0.341215 0.226069 5
• Band rendering type Singleband gray

36. Differences between two clusters and five clusters image results
• Damaged areas obtained in cluster 1 ( ) of the two clusters image and cluster 1
( ) of the five clusters image
• Nevertheless, damaged areas in the two clusters image result contain areas that are
classified as land cover classes. For instance, an area classified as water ( ) in the five
clusters image is classified as damaged areas ( ) in the two clusters image.
Band rendering type Singleband pseudocolor Band rendering type Singleband pseudocolor

37. Image Masking • Singleband gray
• Singleband pseudocolor
• Subtracting two clusters image of damaged and non-damaged areas
“(Clusters 2)” with five clusters image of land cover classification
“(Clusters 5)” .
• Calculation expression = (Clusters 2) - (Clusters 5)

38. • Extracting values of damaged areas that is > class 4
and ≤ class 2.75
• Calculator expression “(Clusters 2 – Clusters 5) > 4
AND (Clusters 2 – Clusters 5) ≤ 2.75
• Convert image result of the extracted damaged areas
from raster to vector (shapefile) format.
• ” ” is damaged areas;
• “ ” is non-damaged areas
Image Masking and Polygonize

39. Combining Raster and Vector Layers
• Overlaying extracted damaged areas in vector layer on color composite raster image to visualize the
damaged areas distribution.
• Natural color composite • False color composite

40. Map Creation
• Create a map of damaged area distribution obtained from the data processing with legend and
the other necessary information.
• Project → New Print Composer → Input “Name of the map”
Add new map
“Processed image
result”
Add image “Direction
symbol”
Add new label “Data
information”
Add new legend
“Extracted damaged
area information”
Add new scale bar
“Line” and “Numeric”
Add rectangle
“Boundary for the
map in rectangle”

41. Data Processing of Pre-Disaster Image of Landsat 8
In particular for Landsat 8, data processing includes atmospheric correction, band set, resizing, and color compositing.
• SCP → Preprocessing → Landsat → “Apply DOS1 atmospheric
correction” and “Create Band set and use Band set tools”
• Raster → Miscellaneous → Build Virtual
Raster (Catalog)… → Input files “Band
RGBN 2345 → OK

42. • Raster → Extraction → Clipper…
• Right click on the layer → Properties →
Band rendering → Render type
“Multiband color” → Select “RGB 432
(False color composite)“ → Apply → OK

43. Pre- and Post-Disaster Images of Landsat 8 and DubaiSat-2
• Pre-Disaster Landsat 8 (2017/09/16) • Post-Disaster DubaiSat-2 (2018/09/04)
AEMBisis0001201809180002 AEMBisis0001201809180003
AEMBisis0001201809180002
AEMBisis0001201809180003
AEMBisis0001201809180002
AEMBisis0001201809180003

44. Thank you