1. Detection, monitoring and forecasting of hydrocarbons spills in the ocean using remote sensing and artificial intelligence techniques VERTIMAR-2005 SYMPOSIUM for monitoring of Accidental Oil Spills Projects in Marine Environment related with VEM2003 Program of Ministry of Education and Science (Spain) González L., Torres J.M., Corchado J.M., Turiel A.M. and Garcia-Ladona E. Dept. de Física Aplicada, Universidade de Vigo, 36200. Vigo. luisgv@uvigo.es, jesu@uvigo.esluisgv@uvigo.esjesu@uvigo.es Dept. de Informática y Automática, Universidad de Salamanca (Spain). Dept. de Oceanografía Física, Institut de Ciencies del Mar -CMIMA (CSIC).

2.   This study has been done in the framework of the CONTINMAR project within the Strategic Action against Marine Pollution of the Science and Technology ministry related to the Prestige catastrophe   Within CONTINMAR, our group is involved in the project of detection, monitoring and forecasting of hydrocarbon spills in the ocean using remote sensing and artificial intelligence techniques. In addition to the Applied Physics department of the University of Vigo, there are other groups participating in the project from Autonomous University of Barcelona, University of Burgos, University of Salamanca and CMIMA-CSIC (Mediterranean Marine and Environmental Research Centre).   Advanced Synthetic Aperture Radar (ASAR) radar images were provided by the European Space Agency (ESA) within the framework of the Envisat AO-623 project. The scenes processed it this study were collected in wide- swath mode using the ScanSAR technique during the Prestige catastrophe. It was also used wind data from NASA scatterometer SeaWinds on board QuikScat, surface currents and wind data proceeding from the oceanographic model MERCATOR and visual observations data.

3. STUDY PHASES:  Detection  Vectorization  Classification Algorithms  Forecasting Systems

4. ASAR (Advanced Synthetic Aperture Radar ) ENVISAT Envisat ASAR operates at C-band (5.331 GHz) and can acquire data in different modes and variable viewing geometry.Effect of the sea surface roughness upon the radar backscattering: (a)Smooth surface, specular reflection. b)Rough surface, diffuse reflection  Detection Envisat

5. PRE-PROCESSING ASAR Product Header Analysis Full Resolution Extraction Full Resolution Image Backscattering Image Amplitude To Power Backscattering generation ·Incident angle ·Calibration constant Oil spill Detection

6. MASKING Backscattering ImageMasked Image Mask -Coastline -Islands -Land -Invalid Pixels Create Mask Apply Mask ©ESA- ENVISAT

7. Detected slicks:   Natural slicks   Oil spill   Wind smoothed   Human activity

8. Natural slicks   Atmospheric effects ©ESA- ENVISAT

9.   Atmospheric effects

10.   Currents, internal waves, eddies, thermal fronts etc.. ©ESA- ENVISAT

11.   Topographic effects ©ESA- ENVISAT

12.   Algal blooms ©ESA- ENVISAT

13. Wind smoothed Winds 14 m/s ©ESA- ENVISAT

14. Human activity Dark patches inside the Rías Baixas due to the presence of mussel culture rafts. The photograph shows these floating structures. ©ESA- ENVISAT

15. AATSR Scatterometer data MERISDEM Cloud mask Thermal fronts Winds masks Winds <2m/s Winds> 14 m/s Topographic effects Algal blooms  Previous discrimination of slicks

16. Automatic Detection Algorithms Multi-scale techniques on contour coherence Fourier transform and low-pass filtering Adaptive thresholding

17.  Vectorization: -Identification of the border of the possible oil spill Example of georeferenced vector layer 2 December 2003

18. -Derivation of different characteristics for each possible oil spill   Position (lat/lon)   Area(km 2 )   Perimeter(km)   Average backscattering inside the dark area   Average backscattering outside the dark area   Gradient   Form Factor   Average incident Angle. Geographic Information System

19.  Classification using Neural Network

20. GIS Parameter Masks Visual Observation & Field Data   Natural slicks   Oil spill   Wind smoothed Data Input Geographic Information System

21.  Prediction Physic Model Environmental data Classification results Development of Expert/Distribute d Systems Forecasting of the evolution of contaminated water masses CBS

22. Wind direction Current Spreading Evaporation Emulsification DispersionProperties   Density   Viscosity   Water Fraction Processes   Dispersion   Evaporation   Emulsification   Spreading Physic Model

23. Environmental Models Data from SeaWinds on board QuikScat Oceanographic model MERCATOR Waves propagation from ERS images

24. Physic Model Environmental Models GIS Problem description [w1,w2,w3,w4...wn][t1][w1,w2,w3,w4...wn][t2][w1,w2,w3,w4...wn][t3]*[w1,w2,w3,w4...wn][tn] CASE BASE

25. 4 steps which are recalled every time a problem needs to be solved (Kolodner, 1993; Aamodt y Plaza, 1994; Watson, 1997) CASE BASE Newproblem K similar cases cases ProposedSolutionConfirmedSolution (1) RETRIEVE (2) REUSE (3) REVISE (4) RETAIN RETRIEVE Select the most similar case(s) to the new problem. REUSE Adaptation of these cases to generate a proposed solution REVISE The proposed solution is revised RETAIN Retain the new solution as a part of a new case Case-based reasoning systems

26. Estimation NOAA-AVHRR or Envisat’s AATSR Ocean temperature Oceanographic cruises Temperature (ºC) Salinity (sn) Wind direction Wind strength ( nudes ) Exchange rate ofExchange rate of CO 2 in a given point/time (micro atmospheres) Etc. [Temp, Sal, Wind strength, etc.] estimation Prediction of trajectories

27. Neural net techniques together with GIS are a good strategy for the classification of slicks in ASAR images  Conclusions : The use of AATSR, MERIS and scatterometer data has provided very useful for the discrimination of slicks

28. CBR systems are especially suitable when the rules that define a knowledge system are difficult to obtain CBR systems have the capacity to update their memory dynamically, based on new information (new cases)

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