Estimation of flood water levels by merging DTM and satellite...
Transcript of Estimation of flood water levels by merging DTM and satellite...
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Estimation of flood water levels by merging DTM and satellite imagery
using hydraulics laws through AI to enhance the estimates
C PUECH *, R HOSTACHE ***, D RACLOT **, P MATGEN ****UMR TETIS CEMAGREF Montpellier
** UMR LISAH Montpellier *** CRPGL Luxembourg
Hydrospace, Geneva 12-14 nov 2007
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INTRODUCTION
Merging DTM and flood imagery can be used for water levels estimation.
A simple process generates independent estimations, spatially non-uniform over the plain
and often of poor quality : for instance, Brackenridge et al. (1998) obtain intervals of
remotely sensed water levels varying between 1 and 3m from ERS images.
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OUR PURPOSE To obtain an accuracy acceptable for hydraulic
modelling, we propose a methodology in two main steps:
(1) a remote sensing step : using DTM and Imagery to obtain a confidence interval
of independent estimates
(2) a dependence step to reduce the uncertainties using hydraulics laws through an AI constraining procedure
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Summary
1. Remote sensing : extraction of water bodies2. Merging DTM : set of independent water levels3. Hydraulic forcing : building the constraint system4. IA : eliminating conflicts and solving the CS
5. Some results and accuracy
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PART 1 :
Remote sensing step, extraction of water bodies
Here, an example from RADARSAT on Mosel River flooding
Flood Imagery : Aerial photograph or satellite images
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Flooded Mosel, with RADARSAT images(12-1997 event)
Black = Water (no return signal)
Grey = others items (return signal)
0 2 km
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radiometry
Nb
Radiometries HistogramClassification : water / no-water
NO-WATERWATER
Smin : only flooded pixels
Smax : all pixels partially flooded
Others pixels
Mixed Pixels
Water Pixels
Image RADAR
Threshold Smax
ThresholdSmin
rough extraction of water on RADARimage
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Useful signal Useful signalForest, cities …not available
RADAR signal : an incomplete information for waters
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0 1.5 km
Eliminating all the places with not useful signal(houses, trees …)
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Eliminating all the places with not useful signal(houses, trees …)
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Part 2. Merging DTM : estimation of local Water levels
Upstream Flooding limits
Downstream
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Areas for Z estimationFlood limits extracted from water bodies are uncertain. => transformed in patches where the limit is included
Useful patches including the flood limits
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First estimation of ZA merging procedure, patch by patch=> For each patch , a possible range of estimatefor the water level : [Zmin, Zmax] including the uncertainties on the limits ans on DTM Values
Max
Min
ExampleZ ∈[105.3, 107.6]
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First results along the river(independent values)
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A dependence step to reduce the uncertainties
A constraining procedure relates independent estimates applying a flow scheme over the flood
plain
Using hydraulics laws solved by AI constraining procedure
PART 3 : Dependence step
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Part 3: building the hydraulical constraints
•All the data obtained previouslyare independant
•There are local comparizons between DTM and image
•Local values
•BUT the water levels ARE NOT independant
•Useful to add« intelligence »
•To add Spatial relations between estimates
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Useful patches
Flow directions
Hydraulic relations :« hydraulic energy mustdecrease along the flow ! »
Part 3: building the hydraulical constraints
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Effect of ENERGY DECREASE from patch to patchon successive water levels estimates
MaxMax
Min
Min
Not compatible, not revised
FlowNot possible Revision
Conflicts on first estimates 1st case
Max Max
MinMin
Compatible, revisedAFTER
FlowMax Max
MinMin
Compatible, non revisedBEFORE
Flow2d case
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Successive water levels are assumed to decrease along the flow direction giving a
system of numerical linear constraintsto be applied
Example of constraint :Z min (upstream) cannot be < Zmin (downstream) + tolerance
Each not satisfied constraint needs a revision process
Part 4 : From Hydraulics dependanceto a constraint system
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MAX et MIN REVISION process
for an hydraulic “cascade”
FlowMax
The revision process build 2 envelop curves One for MAX values One for MIN values
Min
Solved by IA specific algorithm(dual scan algorithm)
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Final results along the river(dependent values)
Results show both a strong decrease in the Min Max interval width and bring a complementation in the distribution of the flood depth estimates over the flood plain.
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Part 5 Accuracy and Generalisation
All kinds of imageryProcedure applied with :
- aerial photographs to Mosel, Aisne and Herault River (France) - Radarsat imagery to Model river - ENVISAT imagery to Alzette River (Luxemburg)
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•
=> initial Min Max intervals range from 0.80 m for aerial photography to 1.60 m for satellite radar images. Conform to literature values
=> After procedure, Min Max intervals range from 0.40 m for aerial photography to 0.80 m for satellite radar images
Mean values of intervals on water levels estimates
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In fact this interval represents the envelop that includes the ‘true’ value of water levels.
The real accuracy is better :
a validation conducted on the Alzette River (Luxemburg) provides a RMS of 0.13 m with ENVISAT imagery
which seems acceptable for hydraulic needs.
Mean Interval – vs - accuracy
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CONCLUSION
• First step for assimilation of imagery into modelling
*Great interest of multi disciplinary approaches to better solve remote sensing issues
* Next step : run an hydraulic model with initial data obtained from imagery, to define the future of the water extent
Usefulness and objectives
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Thanks for your attention .