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

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.

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

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

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

Flooded Mosel, with RADARSAT images(12-1997 event)

Black = Water (no return signal)

Grey = others items (return signal)

0 2 km

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

Useful signal Useful signalForest, cities …not available

RADAR signal : an incomplete information for waters

0 1.5 km

Eliminating all the places with not useful signal(houses, trees …)

Eliminating all the places with not useful signal(houses, trees …)

Part 2. Merging DTM : estimation of local Water levels

Upstream Flooding limits

Downstream

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

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]

First results along the river(independent values)

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

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

Useful patches

Flow directions

Hydraulic relations :« hydraulic energy mustdecrease along the flow ! »

Part 3: building the hydraulical constraints

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

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

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)

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.

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)

•

=> 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

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

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

Thanks for your attention .