Automatic Analysis method
September 18 th, 2023
8 th Joint Project Team Meeting
Jakarta, Indonesia

Japan Aerospace Exploration Agency (JAXA)
Yuki TAKAKURASAR image-derived information
Information we can provide to users

ProcessTime

RGB composite image (★)

Auto

2 hourAuto

2.5 hour (Japan)
0.5 hour (Other)(from observation toend of processing)<Flooding>
Location of the flood extent (★)©JAXA<Flooding>- Location of the flood extent (★)(SM3 mode only)©JAXA- Area of flood by each municipality (Japan only)- Number of flooded buildings by eachmunicipality (Japan only)<Earthquakes> (Japan only)- Location of damaged-buildings- Number of damaged-buildings by eachmunicipality©JAXA(★) Information currently provided by JAXA to Sentinel Asia

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Optical image-derived information
Information we can provide to users

Process<Flooding>- Location of the flood extent (Under verification)

AutoTime(from observation toend of processing)

2 hour<Landslide>- Location of the landslide extent (Under verification)

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SAR imagery

4Automatic Analysis for Flood by SAR①JAXA has developed a system toautomatically detect the flood extent
using JAXA‘s ALOS-2 data for Sentinel
Asia.

 Benefit:

Flood in Pakistan on 17 August, 2022

When an EOR is activated and observations are made by ALOS-2, JAXA can now quickly
provide extracted flood-area information as well as ALOS-2 observation imagery.

 Analysis Method:

This analysis uses a simple and primitive SAR analysis method and is fully automated.
(Therefore, the accuracy may be lower than that of other DANs that use moresophisticated analysis methods.)
Notice••

The information is automatically estimated from satellite data and could differ from theactual flood extent.
It shows estimated flood extent at the time of satellite observation and does not indicate
the maximum extent.

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Automatic Analysis for Flood by SAR①
A simple and primitive method of extracting the flood extent byautomatically determining the threshold of water from the backscatter
intensity of the ALOS-2 (SAR) image.
SAR (before-disaster)

SAR (after-disaster)

Determine the thresholdof water body

Determine the thresholdof water body

Classify water body

Classify water body（L2.1）（L2.1）

LULC
Extract water bodydifference

DEM
Exclude areas with large slopes

Extract flood extent
smoothing
Output (flood extent)

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Automatic Analysis for Flood by SAR①
Storm, Flood and Landslide in Vietnam on 28 September, 2022
JAXA’s VAP is almost same as AIT’s and EOS’s

JAXA

ALOS-2, ScanSAR mode

AITEOS©JAXA©AIT, JAXA©EOS-RS, ARIA, NASA-JPL/Caltech, JAXA

©JAXA

©AIT, JAXA©EOS-RS, ARIA, NASA-JPL/Caltech, JAXA

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Automatic Analysis for Flood by SAR①
Cyclone MOCHA in Myanmar on 14 May, 2023

ALOS-2, ScanSAR mode

https://storymaps.arcgis.com/collections/6c2cdd4f4b92434cbb6d343eaa0006db?item=1

JAXA

AITEOS©JAXA©AIT, JAXA©EOS-RS, ARIA, NASA-JPL/Caltech, JAXA

©JAXA

©AIT, JAXA©EOS-RS, ARIA, NASA-JPL/Caltech, JAXA

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Automatic Analysis for Flood by SAR①
Flood, Landslide and Storm in India on 13 July, 2023

https://storymaps.arcgis.com/collections/9ee0925945ec4659b107b814c2f99b5e?item=1

JAXA

©JAXA

AIT

©AIT, JAXA

ALOS-2, ScanSAR mode

9Automatic Analysis for Flood by SAR②JAXA is developing another automated system with higher accuracy.
This system requires ALOS-2 L1.1 data, which is closer to raw data than
ALOS-2 L2.1 data used in the algorithm introduces earlier, for analysis.
This method is expected to be more accurate especially for urban area.
This method is currently being validated and may be replaced by this methodin the future.

Notice••

The information is automatically estimated from satellite data and may differ from theactual flood extent.
It shows estimated flood extent at the time of satellite observation and does not indicate
the maximum extent.

10Automatic Analysis for Flood by SAR②
Use L1.1 data and perform interference analysis to improve accuracy
especially in urban areas. Based on Ohki et al. (2020).
SAR
Pre-event(before-disaster)(L1.1) SLC
ALOS-2SAR(after-disaster)(L1.1)

DEM(AW3D)
slope < 5°beforeevent min.
amplitude

Coherencedifference

after-event
amplitude

Simplified
flood hazard
map

Initialclassification
Polygon simplification

Out put(Flood extent)
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Automatic Analysis for Flood by SAR②
Typhoon Mawar in Philippines on 27 May, 2023
JAXA (New method, L1.1)

ALOS-2, SM3 mode

JAXA (Conventional method, L2.1)

©JAXA

©JAXA

©JAXA

©JAXA

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Automatic Analysis for Flood by SAR②
Flood in Andhra Pradesh, India on 12 July, 2022

ALOS-2, SM3 mode

https://sentinel-asia.org/EO/2022/article20220712IN.html

JAXA (New method, L1.1)©JAXA

JAXA (Conventional method, L2.1)

©JAXA

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Optical imagery

14Automatic Analysis for Flood by Optical image
A simple and primitive method of extracting the flood extent byautomatically determining the threshold of water from the NDVI or NIR of
the optical image.
Optical image(before-disaster)

Optical image(after-disaster)

Determine the threshold ofwater body by NDVI/NIR

Determine the threshold ofwater body by NDVI/NIR

LULC
Extract water body

Classify water body

Classify water bodydifference

DEM
Exclude areas with large slopes

Extract flood extent
smoothing

NDVI: Normalized Difference Vegetation Index
NIR: Near infrared

Output15

Automatic Analysis for Flood by Optical image
NDVI: Normalized Difference Vegetation Index
NIR: Near infraredSatellite：SPOT
Before：2019/10/9
After：2019/10/13©Airbus DS

Precision

38.7%
NIR©Airbus DS

Blue: Estimated flood extent
Yellow: Grand truth by Geospatial Information Authorityof Japan（GSI） (as of 10/14）

Precision

80.7%
NDVI

©Airbus DS

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Summary
JAXA is developing automatic analysis systems to extract affectedareas using SAR and optical imagery.
SAR imagery :JAXA have already provided VAPs of the automatically estimated flood
extent to Sentinel Asia.
We are also working to improve its accuracy.
Optical imagery :Primitive and automatic methods have already been implemented.
Automatic analysis using deep learning is also under study.
Issue：
Lack of ground-truth data makes it difficult to assess the accuracy of VAPs.
As a DAN, I would appreciate it if anyone who has grand truth data such as
flood extent, location of landslides, location of building damage, etc., could
share it with Sentinel Asia, because it will help improve the accuracy of
DAN's VAP.
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