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Combined use of Electromagnetic Combined use of Electromagnetic Scattering Models, Fuzzy Logic and Scattering Models, Fuzzy Logic and Mathematical Morphology for Flood Mathematical Morphology for Flood Mapping from COSMO-SkyMED dataMapping from COSMO-SkyMED data

L. Pulvirenti 1, M. Chini 2, N. Pierdicca 1, L. Guerriero 3

(1) Sapienza, University of Rome

(2) Istituto Nazionale di Geofisica e Vulcanologia

(3) Tor Vergata University of Rome

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IntroductionIntroduction

Several overflows occurred in Italy in the recent years

– ASIASI is presently funding some investigations about the use of Earth Observation data for civil protection from floods (e.g., OPERAOPERA).

Potential of SAR for flood monitoring– The synoptic view, the good spatial resolution, the

capabilities to operate in almost all-weather conditions and both during daytime and nighttime are the key features of radar sensors.

Possible advantages of using X-band COSMO-X-band COSMO-SkyMED imagesSkyMED images:

– VeryVery high spatialhigh spatial resolutionresolution (especially in the spotlight configuration) → An accurate flood boundary delineation can be expected.

– Short revisit timeShort revisit time (constellation of 4 satellites) → A Multi-Multi-temporaltemporal analysis analysis can be performed.

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MotivationsMotivations

SAR data interpretation often not straightforward, especially in the presence of vegetationin the presence of vegetation (challenging challenging problemproblem).– Need to rely onNeed to rely on electromagnetic scattering models electromagnetic scattering models, simulating 0

under flooded conditions, to correctly interpret the SAR observations.

Fuzzy logic suitable for representing the set of flooded pixels in SAR images for which the definition of a criterion of membership is a difficult task.

Spatial details of high resolution images generally smaller than the dimensions of the targets → large within-class variances (also because of the speckle noise)

– Need to segment imagery Need to segment imagery for dealing with homogeneous areas.– Mathematical morphology Mathematical morphology allows identifying objects with different

spatial extension (when used in a multi-scale manner).

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Steps of the designed algorithm Steps of the designed algorithm and case studies and case studies

1. Segmentation of a multi-temporal series of CSK observations of a flood.

2. Computation of the average 0 for each segment.

3. Application to the segmented images of the fuzzy-logic-based approach – allowing us to account for different scattering mechanisms.

Methodology tested on two case studies (OPERA team activated):– the overflow of the Tanaro River, close to the city of

Alessandria (Northern Italy) in April 2009 (4 CSK images 4 CSK images usedused).

– the flood occurred in Tuscany (central Italy) in December 2009 near the Massaciuccoli lake (5 CSK images used5 CSK images used).

– In both cases a portion of flooded area was vegetated.In both cases a portion of flooded area was vegetated.

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The opening (erosion followed by dilation) and closing morphological operators are applied with structuring elements (se) of different sizes.

– Structures in a SAR image may have a high response for a specific selected se size and a lower response for other sizes.

The morphological profile is built for each image of the available multi-temporal series.

A K-means clustering is applied to the multi-temporal profile.

The final segmentation (extraction of contiguous objects belonging to the same class) is performed.

SegmentationSegmentation

N

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S function

0

1

0

1

Z function

The fuzzy setsThe fuzzy sets

Degree of membership to a fuzzy set defined through the standard S and Z functions.

fuzzy thresholds

Default values of the fuzzy thresholds based on the outputs of the EM scattering model developed at the Tor Vergata University of Rome. It assumes:

Bare soilBare soil: IEMVegetationVegetation: homogeneous half space overlaid by a layer filled with discrete dielectric scatterers representing stems and leaves.

Flooded conditionsFlooded conditions: simulated substituting the soil with a semi-infinite layer having the of water and a negligible roughness.

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Fuzzy set of flooded bare soilsFuzzy set of flooded bare soils

Flooded bare soils generally much smoother than the surrounding dry land, thus acting as specular reflectors, giving low 0.

0H

H

SMC [%]

Flooded

0

1

X Band, HH polX Band, HH pol

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Set of vegetated flooded areasSet of vegetated flooded areas

wheat plant height=70 cm

0H

H

SMC [%]

Protruding vegetation may produce large 0.

Reflections between water surface and upright vegetation may enhance backscattering → flooded vegetation may show a bright radar return in a SAR image.

0

1

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Multitemporal analysisMultitemporal analysis

Tanaro river overflowTanaro river overflow. RGB color composite

of the CSK observations of the

flood

Red: April, 29 2009 Green: April, 30 2009 Blue: May, 1 2009

NDVI map NDVI map (AVNIR-2 image acquired on April

23, 2009)

barevegetated

vegetated

Mea

n (

0)

[dB

] April, 29 April, 30 May, 1 May, 16

dry

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Dependence of Dependence of 00 on water level on water level

Radar return predicted by the EM model (small leaves) versus the water level (hw).

60 cm plant

25cm plant

75 cm plant

0

[dB

]

hw [cm]

Large double buonce effect

Small double buonce effect

hw > h

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60 cm plant

25cm plant

75 cm plant

0

[dB

]

hw [cm]

Other fuzzy rules Other fuzzy rules (multitemporal analysis)(multitemporal analysis)

Non-flooded objects at time t are generally non-flooded at time t+1

Flooded objects that at time t have small 0 may be flooded at time t+1 if 0(t+1) considerably larger than 0(t) (decrease of hw)

Flooded objects that at time t have large 0 may be flooded at time t+1 if 0(t+1) < 0(t) (decrease of hw)

Non-flooded objects surronded by flooded ones placed at higher altitude are probably flooded (DEM-based correction)

0

1

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The Tanaro overflowThe Tanaro overflow

Occurred near the town of Alessandria (Northern Italy) on April 27-28, 2009.

~ 6000 people were evacuated for precaution. Some agricultural fields were inundated. These

fields were either bare or covered by wheat at different stage of growth (early – intermediate).

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Segmentation resultsSegmentation results

Tanaro flood: original imagesRGB color composite RGB color composite

(3500x5000 pixels)(3500x5000 pixels)

Red: April 29, 2009 Green: April 30, 2009

Blue: May 1, 2009

Tanaro flood: segmented image

~ 8000 objects

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Flood evolution map (Apr. 29- Flood evolution map (Apr. 29- May 1)May 1)

Cyan :flooded

Blue: water bodies

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Flood map of April 29, 2009

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The The TuscanyTuscany flood flood

Occurred near the Massaciuccoli lake (Central Italy) on December 25-26, 2009.

CSK databaseAcquisition

timeIncidence

angleOrbit Mode Pol.

Dec. 20, 2009 30.6° RA Stripmap HH

Dec. 30, 2009 32.1° LD Stripmap HH

Dec. 31, 2009 43.9° RD Stripmap HH

Jan. 01, 2010 24.1° RD Stripmap HH

Jan. 04, 2010 29.1° RA Stripmap HH

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Segmentation resultsSegmentation results

Tuscany flood: original image

Tuscany flood: segmented image

~ 3700 objects

(codes represented in grayscale)

RGB color composite RGB color composite (3000x1500 pixels)(3000x1500 pixels)

Red: December, 20 2009 Green: December, 30 2009 Blue: December, 31 2009

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A distinctive multi-temporal signature

Dec. 27, 2009

-12

-10

-8

-6

-4

-2

0

Dec 20, 2009 Dec, 30 2009 Dec 31, 2009 Jan 01, 2010 Jan 04, 2010

mea

n

0 [d

B]

dry

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Flood evolution mapFlood evolution map

Cyan :flooded

Blue: water bodies

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ConclusionsConclusions

The COSMO-SkyMed mission offers a unique opportunity to obtain radar images characterized by short revisit time

– Potential usefulness for monitoring the temporal evolution of floods.

A combined approach using an advanced segmentation technique and a well-established surface scattering model has been presented

The objects with distinctive multi-temporal trends have been identified by the segmentation algorithm.

Simulations has allowed us to explain COSMO-SkyMed multi-temporal signatures of different surface types (vegetated or bare).

This work has been supported by the Italian Space Agency (ASI) under contract No. I/048/07/0.