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OTTAWA FLOODING 2019 APRIL/MAY PROCESSING OF SENTINEL-1 IMAGERY WITH PCI
GEOMATICA FOR HYDRAULIC RISK MANAGEMENTVera Costantini
▪ Sysdeco Italia can boast a twenty-years experience in GIS and Remote
Sensing fields.
▪ Sysdeco Italia represents the main companies that produce GIS & Remote
Sensing softwares and applications to optimize and manage human resources
as ; Airbus, ESRI, Extensis, PCI Geomatics, SII Imaging Services and Trimble
▪ FIELDS OF WORK
▪ Territory planning
▪ Environmental monitoring
▪ Landcover maps generation
▪ Urban plans
▪ Cadaster’s maps management
Sydeco Italia
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PCI Geomatics Company Profile
Geomatica
The flooding
■ Heavy rains hit eastern Canada from mid-April to early May,
causing Ottawa River to overflow many times. The first
flooding occurred on April 26th.
■ More than 6 thousand people were urgently evacuated in the
single night between Saturday 27 April and Sunday 28 April in
Sainte-Marthe-sur-le-Lac, west of Montreal, after a dam
suddenly broke
The flooding
■ The most affected areas go from Ottawa to Montréal, along
the Ottawa river which represents the natural border between
the provinces of Québec and Ontario.
■ The devastating flooding destroyed and damaged several
homes along a huge stretch of the Ottawa River
Input Data
■ Sentinel-1 multitemporal images acquired in the following
dates
– September 4, 2018 (dry period)
– Aprile 26, 2019 (first flood)
– May 2, 2019
– May 14, 2019
■ All images were acquired in INTERFEROMETRIC WIDE SWATH
mode, spatial resolution= 10 meters
Input Data
September 4, 2018 (dry period)
Input Data
April 26, 2019
Input Data
May 2, 2019
Input Data
May 14, 2019
Input Data
First processing and emergency response
■ One of the PCI Geomatics company's headquarters is in
Ottawa, so our partners from PCI rushed to generate a map of
the first flood and made it public online to help with the
emergency response, using 3 Sentinel-1 images with a simple
analysis
Video
http://www.pcigeomatics.com/Ottawa_Floods_2019/
First processing and emergency response
■ One of the PCI Geomatics company's headquarters is in
Ottawa, so our partners from PCI rushed to generate a map of
the first flood and made it public online to help with the
emergency response, using 3 Sentinel-1 images with a simple
analysis
Video
http://www.pcigeomatics.com/Ottawa_Floods_2019/
Data processing: Orthorectification
■ In Geomatica OrthoEngine
Sentinel 1 images in
manifest.safe were imported
and converted to .pix
■ Images were orthorectified
using RPC Math Model
▪ Rational Functions math model is a simple math model that builds acorrelation between the pixels and their ground locations.
▪ RPC is more suitable than Toutin rigorous model in case GCPs are notwell distributed over the area and are in low number, as in this case.
▪ With RPC Model in Geomatica we can achieve a good accuracy withouttoo many GCPs and we can extract a high resolution high detailed DSM
Data processing: Orthorectification
Data merge and display
■ Data merge of the dry season dataset with April 26
and May 2 imagery
■ In RGB the flooded areas appear in BLUE
Intensity Change DetectionWorkflow
■ CCDINTEN measures the change in total radar backscattering
between a test image and a reference image by comparing
the sum of the intensities of the input channels.
■ Given two registered detected or single-look complex SAR
images, CCDINTEN determines the overlap area, normalizes
the intensity values based on the total span of intensity
values, and calculates the change (represented as a
percentile).
Intensity Change DetectionWorkflow
■ The output file will consist of the overlap area, and four channels
that describe:
1. the intensity value (or sum of intensities) of the input test
data
2. the intensity value (or sum of intensities) of the reference
data
3. the absolute value of the intensity ratio (written in decibels)
between the test and reference average intensity
4. the change, as percentiles, ranked from 0% (no change) to
100% (maximum change)
Intensity Change DetectionWorkflow
■ The change detection was applied between the after flooding images and the dry
season image
■ the Ranked change metric in percentile layer was further analyzed
Intensity Change DetectionWorkflow
■ Only changes above 95
percentile were exported in
polygon shapefiles with
algorithm EXPOLRAS
■ This algorithm converts a
raster layer into a vector layer
and allows to choose only
specific values
Intensity Change DetectionWorkflow
OB Classification and GIS Analysis
CLASSIFICATION
-River in the dry season image
-Water in the 3 floodedimages
GIS ANALYSIS (1)
-ERASE on the 3 changespolygon shapefiles with River layer
- CLIP of resulting shapefileswith water polygons for eachimnages
- Select polygons bigger than10 ha
GIS ANALYSIS (2)
•Changes shapefilesobtained are overalyedto evaluate the flloodingevolution
OB Classification and GIS Analysis
■ The RIVER has beenclassified with the tool Object Analyst in the dry season image in order to use it as a mask
■ In the after floodingimages all water areasare also classified to take into account onlychanges related to flooding (low backscattering)
Petrie Island
Change Aprile 26 - September
Results: Flooding Evolution
Petrie Island
Change May 2 - September
Results: Flooding Evolution
Petrie Island
Change May 14 – September
Results: Flooding Evolution
Cumberland
Change Aprile 26 - September
Results: Flooding Evolution
Cumberland
Change May 2- September
Results: Flooding Evolution
Petrie Island
Change 14 maggio -
September
Results: Flooding Evolution
Cumberland
Change May 14- September
Rigaud
Change April 26 - September
Results: Flooding Evolution
Results: Flooding Evolution
Rigaud
Change May 2 - September
Results: Flooding Evolution
Rigaud
Change May 14 - September
Results and Conclusions
■ the peak of the flood seems to have occurred on May 2nd
– Aprile 26th ~ 2500 ha flooded areas
– May 2nd ~ 4800 ha flooded areas
– May 14th ~ 2000 ha flooded areas
■ Unfortunately there are no images between May 2 and 14
■ The analysis allowed to evaluate extension and evolution of
flooding
■ Automation is partially implemented (Modeler) but all workflow
could be automatized through Python scripts
Thank you for your attention!