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

1. Combined use of Electromagnetic Scattering Models, Fuzzy Logic and Mathematical Morphology for Flood Mapping 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

2. Introduction • Several overflows occurred in Italy in the recent years • ASI is presently funding some investigations about the use of Earth Observation data for civil protection from floods (e.g., OPERA). • 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-SkyMED images: • Veryhigh spatialresolution (especially in the spotlight configuration) →An accurate flood boundary delineation can be expected. • Short revisit time (constellation of 4 satellites) →A Multi-temporalanalysis can be performed.

3. Motivations • SAR data interpretation often not straightforward, especially in the presence of vegetation (challenging problem). • Need to rely on electromagnetic scattering models, simulating s0 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 for dealing with homogeneous areas. • Mathematical morphology allows identifying objects with different spatial extension (when used in a multi-scale manner).

4. Steps of the designed algorithm and case studies • Segmentation of a multi-temporal series of CSK observations of a flood. • Computation of the average s0 for each segment. • 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 used). • the flood occurred in Tuscany (central Italy) in December 2009 near the Massaciuccoli lake (5 CSK images used). • In both cases a portion of flooded area was vegetated.

5. Segmentation • 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. N

6. Z function S function 1 1 0 0 The fuzzysets • 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 soil: IEM • Vegetation: homogeneous half space overlaid by a layer filled with discrete dielectric scatterers representing stems and leaves. • Flooded conditions: simulated substituting the soil with a semi-infinite layer having the e of water and a negligible roughness.

7. s0HH SMC [%] Fuzzy set of flooded bare soils • Flooded bare soils generally much smoother than the surrounding dry land, thus acting as specular reflectors, giving low s0. 1 Flooded 0 s° X Band, HH pol

8. s0HH wheat plant height=70 cm SMC [%] Set of vegetated flooded areas • Protruding vegetation may produce large s0. • Reflections between water surface and upright vegetation may enhance backscattering → flooded vegetation may show a bright radar return in a SAR image. 1 0 Ds°

9. Multitemporalanalysis Tanaro river overflow. RGB color composite of the CSK observations of the flood Red:April, 29 2009Green: April, 30 2009 Blue: May, 1 2009 vegetated bare vegetated Mean (s 0) [dB] dry NDVI map(AVNIR-2 image acquired on April 23, 2009) April, 29 April, 30 May, 1 May, 16

10. Dependence of s0 on water level • Radar return predicted by the EM model (small leaves) versus the water level (hw). Large double buonce effect Small double buonce effect 75 cm plant 60 cm plant s 0 [dB] 25cm plant hw > h hw[cm]

11. 1 0 Otherfuzzyrules(multitemporalanalysis) • Non-flooded objects at time t are generally non-flooded at time t+1 • Flooded objects that at time t have small s0may be flooded at time t+1 if s0(t+1) considerably larger than s0(t) (decrease of hw) • Flooded objects that at time t have large s0may be flooded at time t+1 if s0(t+1) < s0(t) (decrease of hw) • Non-flooded objects surronded by flooded ones placed at higher altitude are probably flooded (DEM-based correction) 75 cm plant 60 cm plant s 0 [dB] 25cm plant hw[cm]

12. The 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).

13. Segmentationresults Tanaro flood: original images Tanaro flood: segmented image RGB color composite (3500x5000 pixels) Red:April 29, 2009Green: April 30, 2009 Blue: May 1, 2009 ~ 8000 objects

14. Flood evolution map (Apr. 29- May 1) Cyan :flooded Blue: water bodies

15. Flood map of April 29, 2009

16. The Tuscany flood • Occurred near the Massaciuccoli lake (Central Italy) on December 25-26, 2009.

17. Segmentationresults Tuscanyflood: original image Tuscanyflood: segmented image RGB color composite (3000x1500 pixels) Red:December, 20 2009Green: December, 30 2009 Blue: December, 31 2009 ~ 3700 objects (codes represented in grayscale)

18. A distinctive multi-temporal signature Dec. 27, 2009 dry

19. Floodevolutionmap Cyan :flooded Blue: water bodies

20. Conclusions • 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.