This content shows IRS specification (Indian satellite), color composition, NDVI calculation, extracting damaged areas and comparison of Landsat-8 and IRS for the case study of earthquake in Indonesia.

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

2. IRS
• India's remote sensing programme under the Indian Space Research
Organization (ISRO) started off in 1988 with the IRS-1A, the first of the
series of indigenous state-of-art operating remote sensing
satellites,which was successfully launched into a polar sun-synchronous
orbit on March 17, 1988 from the Soviet Cosmodrome at Baikonur.
• IRS is the integrated LEO (Low Earth Orbit) element of India's NNRMS
(National Natural Resources Management System) with the objective to
provide a long-term spaceborne operational capability to India for the
observation and management of the country's natural resources
(applications in agriculture, hydrology, geology, drought and flood
monitoring, marine studies, snow studies, and land use)[1].
[1] National Remote Sensing Centre, “IRS Data Products” 2019. https://www.nrsc.gov.in/IRS_Data_Products

3. Earthquake in Indonesia
• On 28 September 2018, a shallow, large earthquake struck in
the neck of the Minahasa Peninsula, Indonesia, with
its epicentre located in the mountainous Donggala
Regency, Central Sulawesi.
• The magnitude 7.5 quake was located 77 km (48 mi) away
from the provincial capital Palu and was felt as far away
as Samarinda on East Kalimantan and also
in Tawau, Malaysia.
• This event was preceded by a sequence of foreshocks, the
largest of which was a magnitude 6.1 tremor that occurred
earlier that day[2].
[2] BBC, “Indonesia earthquake: Hundreds dead in Palu quake and tsunami”, Sept. 29, 2016.
https://www.bbc.com/news/world-asia-45683630

4. Affected areas by the
earthquake and tsunami:
Center Sulawesi, Sulawesi Island,
Indonesia[2]

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

6. Study area: Center Sulawesi, Sulawesi
Island, Indonesia
Available Post-Disaster IRS Data for
Indonesia Earthquake:

7. Post-Disaster THEOS Data in the Reported Damaged Areas (2018/10/04)
• INSRisis0001201810030006
Satellite ID IRS-R2
Repeat Coverage 5-24 Days
Sensors High Resolution Linear
Imaging Self-Scanning
System IV (LISS-IV)
Resolution (m) 5.8
Swath Width (km) 24 – 70
Sensor Channels LISS-IV-2
LISS-IV-3
LISS-IV-4
Spectral bands Raw images
BAND2_RPC
Green (G)
0.52 – 0.59 μm
BAND3_RPC
Red (R)
0.62 – 0.68 m
BAND4_RPC
Near-Infrared (NIR)
0.77 – 0.86 μm
• Product ID 1850681410
Spectral bands TIFF format image
Band 123 (Red,
Green, Blue)

8. Method of the Data Processing
Data processing is conducted using an open-source GIS application of QGIS[3].
Post-disaster IRS
optical data
NDVI calculation
Thresholding and segmentation
Georeferencing
Pre-disaster Landsat 8
optical data
Plan curvature
Re-segmentation by masking two clusters image with five
clusters image
Refinement of damaged areas classification
Extracted damaged areas due to earthquake and tsunami
Color compositing
Two clusters image
of damaged and non
damaged areas
Pre-processing
Color compositing
Visualization
of pre-disaster
image
Image comparison
[3]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

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
Makassar EPSG:4257
• Project → Project Properties…
→ CRS → Tick on “Enable ‘on
the fly’ CRS transformation
(OTF)” → Type on “Filter” to
search a coordinate reference
system “Makassar EPSG:4257”
→ Apply → OK

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 Green (BAND2_RPC) band:
1
2

11. • Georeferencing for Red and
Near-Infrared bands using the
collected points.
• Raster → Georeferencer →
Load GCP Points →
Transformation Settings →
Transformation type “Thin
Plate Spline” and Resampling
method “Nearest neighbor”
→ Start Georeferencing
• Georeferencing for Red
(BAND3_RPC) band:
• Georeferencing for Near-
Infrared (BAND4_RPC) band:

12. 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” →
Source No Data “0” → Target SRS “Selected CRS” → Separate → OK
1
2

13. Image Resizing
Change the size of the image to the intended area, in this case, the area suspected of being damaged.
• Raster → Extraction → Clipper…
• Input file (raster) “Virtual raster image” → Output file “Name of the image clip” →
No data value”0” → Extent → Select the extent by drag on canvas → OK
1
2

14. 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

15. 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

16. 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)”

17. • Histogram of the NDVI value can be generated from the NDVI image result
• Right click on the layer → Properties → Histogram
• NDVI value ranges from -0.154832 to 0.633683
0.633683
NDVI values
-0.154832
NDVI values
• Band rendering type
Singleband gray
• Band rendering type
Singleband pseudocolor
0.633683
-0.154832

18. Image Segmentation Into Two
Clusters
Divided an image into flooded and non-flooded
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”

19. Two clusters image result
Clusters ID Elements Std. Dev. NDVI Values Image Clusters
0 4109472 0.535937 0.007828 1
1 7121328 0.545083 0.402636 2
• Band rendering type Singleband
pseudocolor
• Band rendering type Singleband gray

20. 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 flooded 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”

21. Five clusters image result
Clusters ID Elements Std. Dev. NDVI Values Image Clusters
0 1878310 0.226205 0.570192 1
1 2596146 0.187728 0.409741 2
2 2790744 0.181486 0.272822 3
3 1828890 0.216425 0.117686 4
4 2136710 0.195385 -0.099162 5
• Band rendering type Singleband gray
• Band rendering type Singleband
pseudocolor

22. The difference between two clusters and five clusters image results
• Damaged areas obtained in cluster 1 ( ) of the two clusters image and cluster 3 ( ) of the
five clusters image
• Nevertheless, flooded 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.

23. 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)”

24. Rendering the image color of the masked image into singleband 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 “-1” that is 0.007828 and 0.117686.

25. 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 non-damaged areas;
• “ ” is damaged areas

26. Polygonize
Conversion of raster layer into vector 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

27. 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”

28. 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”

29. 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

30. • Raster → Extraction → Clipper…
• Right click on the layer → Properties → Band rendering → Render type
“Multiband color” → Select “RGB“ → Apply → OK
“RGB 432
(False color
composite)”
“RGB 123
(Natural color
composite)”

31. Pre- and Post-Disaster Images of Landsat 8 and IRS
False color composite
• Pre-Disaster Landsat 8 (2017/10/06) • Post-Disaster IRS (2018/10/04)
False color composite

32. Thank you