This content shows the specification of DIWATA-1 (Philippine Microsatellite), create a new account, data downloading, image processing (NDVI and color composition) between pre- and post-flood.
Data Processing Using DIWATA-1 Microsatellite Imagery for Disaster Monitoring
1. Center for Research and Application for Satellite Remote Sensing
Yamaguchi University
Data Processing Using DIWATA-1 Microsatellite
Imagery for Disaster Monitoring
2. Diwata-1
• Diwata-1 is the Philipines’ first micro-satellite with a baseline design
of the 50 kilogram.
• The payload subsystem includes a high-precision telescope (HPT)
with 3 meter spatial resolution and bands R/G/B/NIR.
• The second payload is a space-borne multi-spectral imager (SMI)
with liquid crystal tunable filter (LCTF), which has an 80 meter
spatial resolution.
• The LCTF allows for image capture to be taken at 10 nanometer
increments from 420nm – 650nm for the visible range and 700nm –
1050nm for the near infrared region.
• With a spatial resolution of 7 kilometers, the wide field camera can
used to complement current weather forcasting capabilities. This
sensor can potentially image cloud formations and typhoons.
phl-microsat.upd.edu.ph/diwata1
NASA – space.skyrocket.de
3. Data Download Link
The following is the link for data downloading:
http://data.phl-microsat.xyz
4. Creating a New Account
Fill necessary information related to:
(1) Account information; (2) Personal information;
9. Criteria Selection for Data Searching
Criteria selection
Satellite: Diwata
Payload: SMI; MFC
Data of Acquisition: All
Available data location will be shown in color with information of the
data number in the period of selected date of acquisition
11. Selected area in 2016.
The graph below the image indicates the number
of available data.
• Area of interest can be selected by using “Draw a
rectangle” or “Draw a marker” option.
• The available data will be provided on the right
hand-side.
• Intended data can be checked from the “view
image details” option.
• Data can be downloaded directly by using
“Download image” option or collected first by
choosing “add image to download cart” option.
Area Selection
12. Visible band images of the downloaded raw images
Visible - 550 nm Visible - 710 nm
Visible - 440 nm
Blue band
450 - 495 nm
Green band
495 - 570 nm
Red band
620 - 750 nm
13. Near-infrared (NIR) band images of the downloaded raw data
NIR - 750 nm NIR - 760 nm NIR - 870 nm
14. • Vegetation index calculation using data sets from different acquisition date
Location: Sablayan, Occidental Mindoro, Philippines
Image acquisition date: 2016/04/03 and 2017/04/21
• Post-disaster analysis of flood event on March 12th, 2017
Location: Lamitan City, Basilan Province, Philippine
Image acquisition date: 2017/07/01
Image processing using DIWATA-1
15. Vegetation index calculation using data sets from different acquisition dates
• Location: Sablayan, Occidental Mindoro, Philippines
16. Data collection according to the location and availability
(1) 2016/11/10 → 1st Data set
(2) 2017/04/21 → 2nd Data set
17. Band selection for Visible (V) and Near-Infrared (N)
(1) 2016/11/10 → 1st Data set
Red (V)
NIR (N)
Red (V)
NIR (N)
Red (V)
NIR (N)
Red (V)
NIR (N)
NIR (N)
NIR (N)
Red (V)
Red (V)
• Band selection was conducted
according to the location and visibility
of the image.
• Selected bands are one Visible (Red
– 680 mm) band and one NIR (N –
740 mm) band.
Selected
bands for
NDVI
18. Band selection for Visible (V) and Near-Infrared (N)
(2) 2017/04/21 → 2nd Data set
NIR (V)
NIR (N)
NIR (N)
NIR (N)
NIR (N)
Green (V)
Green (V)
Blue (V)
Red (V)
Blue (V)
Selected
bands for
NDVI
• Visible bands selected from the
data set are Red – 710 mm, Green
– 530 mm, and Blue – 480 mm.
• Near-Infrared band selected from
the data set is NIR – 740 mm.
19. Build virtual raster from selected bands
• Raster → Miscellaneous → Build Virtual Raster (Catalog)
• Virtual Raster:
Band 1 : V440 (Blue band)
Band 2 : V530 (Green band)
Band 3 : V710 (Red band)
Band 4 : N740 (NIR band)
20. True-color composite
• Right click on the
virtual raster layer
→ Properties →
Style
R: V710; G: V530; B: V480
2017/04/21 – True Color
21. False color composite
• Right click on the virtual raster layer → Properties → Style →
R: N740; G: V710; B: V530
2017/04/21 –
False Color
22. Checking geographical boundary with available reference map
*Georectified should
be conducted in case
the image is not in
accordance with the
reference map.
• “Layers Panel” → 🗹 Philippine administration shapefile (PHL_adm0)
🗹 Virtual raster layer of the image
23. NDVI calculation
N (NIR) band – V (R) band / N (NIR) band + V (R) band
• Raster → Raster Calculator
1st Data set (2016/11/10)
NDVI = (ρ740 – ρ680) / (ρ740 + ρ680)
2nd Data set (2017/04/11)
NDVI = (ρ740 – ρ680) / (ρ740 + ρ680)
25. NDVI image results
1st Data set (2016/11/10)
NDVI = (ρ740 – ρ680) / (ρ740 + ρ680)
2nd Data set (2017/04/11)
NDVI = (ρ740 – ρ680) / (ρ740 + ρ680)
1
-1 -1 1
-1 1 -1 1
26. Post-disaster analysis of flood event
Location: Lamitan City, Basilan Province, Philippine
As of March 12th, 2017, over 11,000 people in Lamitan City (Basilan Province) and Tambulig municipality
(Zambonga del Sur province) are displaced by flooding. The floods damaged 127 houses, mostly in Lamitan
City (OCHA, 2017).
28. Band selection for Visible (V) and Near-Infrared (N)
NIR (N)
NIR (N)
NIR (N)
NIR (N)
Red (V)
Red (V)
Blue (V)
Red (V)
Red (V)
Red (V)
Green (V)
Blue (V)
Green (V)
Visible bands: Red – 690 mm; Green – 550 mm; Blue – 490 mm
Near-Infrared band: NIR – 760 mm
Selected
bands for
analysis
29. Build virtual raster from selected bands
• Raster → Miscellaneous → Build Virtual Raster (Catalog)
Virtual Raster:
Band 1 : V490 (Blue band)
Band 2 : V550 (Green band)
Band 3 : V690 (Red band)
Band 4 : N760 (NIR band)
30. True-color composite
• Right click on the virtual raster layer → Properties → Style
R: V690; G: V550; B: V490
2017/07/01
– True Color
31. False color composite
• Right click on the virtual raster layer → Properties → Style →
R: N760; G: V690; B: V550
2017/07/01 –
False Color
32. Checking geographical boundary with available reference map
*Georectified should be conducted in
case the image is not in accordance
with the reference map.
• “Layers Panel” → 🗹 Philippine administration shapefile (PHL_adm0)
🗹 Virtual raster layer of the image
33. NDWI calculation
V (G) band – N (NIR) band / V (G) band + N (NIR) band
• Raster → Raster Calculator
• NDWI = (ρ550 – ρ760) / (ρ550 + ρ760
1 -1
34. Changing NDVI color
1 -1
• Properties → Style ”Color” is changed into “bgyr” color type
35. NDVI calculation
N (NIR) band – V (R) band / N (NIR) band + V (R) band
• Raster → Raster Calculator
• NDVI = (ρ760 – ρ690) / (ρ760 + ρ690)
1 -1
37. Histogram of the NDVI image result as reference for image segmentation
• Right click on the NDVI image’s raster layer → Properties → Histogram
Histogram of DIWATA-1
Post-disaster NDVI image
result
2017/07/01
38. Kmeans clustering for grids
Processing → Toolbox → SAGA (2.3.1) [geoalgorithms] → Image analysis
→ Kmeans clustering for grids
40. Polygonize raster layer to vector layer
• Raster → Conversion → Polygonize (Raster to vector)
41. Changing properties of vector layer
• Right click on the sieved cluster’s vector layer → Properties → Style
42. Save vector layer as shapefile
• Right click on the sieved cluster’s vector layer → Save vector layer as
43. Landsat-8 data analysis for the flood event
• Pre-Disaster (2016/04/03) • Post-Disaster (2017/04/22)
True-color composite RGB – 432 True-color composite RGB – 432
False-color composite RGB – 543 False-color composite RGB – 543
Obtained changes between pre- and post-disaster images
44. NDVI comparison between pre- & post-disaster of Landsat-8 and post-disaster of DIWATA-1
-1 1 -1 1
2016/04/03 – Pre-Disaster 2017/04/22 – Post-Disaster 2017/07/01 – Post-Disaster
Landsat-8 Landsat-8 -1 1 Diwata-1
Flood line
45. Flood line on pre- and post-disaster images
Post-Disaster
DIWATA-1 (2017//07/01)
Pre-Disaster
Landsat-8 (2016/04/03)
Flood line
• Overlaying raster and vector layers by activating the layers → Check the layers in the “Layers Panel”
• Active layers:
🗹 True-color composite raster layer
🗹 Cluster polygon shapefile of flood line
46. Map preparation
Project → New Print Composer
Add map → Add legend, draw grid, draw north arrow, and add label
