Satellite monitoring of oil spills in the Mediterranean Sea for

Transcription

1 Satellite monitoring of oil spills in the Mediterranean Sea for Konstantinos Topouzelis, O. Muellenhoff, G. Ferraro, B. Bulgarelli E.C. Joint Research Centre Institute for the Protection and Security of the Citizen Maritime Affairs Unit 1

2 List of contents The need for oil spills statistics of sea-based oil pollution; Methodological approach and statistical analysis; Results for the Mediterranean Sea; Case studies; Discussion and Conclusions. 2

3 Legal perspective The International Convention for the Prevention of Pollution from Ships (MARPOL 73/78) introduces the idea of Special Areas, which are considered to be so vulnerable to pollution that discharges within them have been completely prohibited, with minor and well-defined exceptions. 3

4 Marine oil pollution from vessels ships in distress Massive spills. Important impact on the local ecosystems and economic activities. Immediately reported to responsible Authorities. About 25 % of the oil released by vessel is reported to come from this source. 4

5 Operational pollution What are we looking for? Besides accidental pollution, caused by ships in distress, there are three types of routine ship operations, which pollute the sea: [Ballast water]; Tank washing residues (discharges from slop); Engine room effluent discharges (bilges and sludge discharges). The tank washing residues concerns mainly tankers, while the engine room effluent discharges all types of ships. 5

6 The JRC approach Objective: systematic mapping of illicit oil discharges in European Seas. Why? Increase the awareness of the problem. Verify and analyze trends. Identify hot spots. Assess on the effectiveness of legislative measures taken at regional and local scale. 6

7 Examples of SAR images Oil spills Look-alikes 7

8 Methodological approach -filename, -dimensions, -acquisition time and day of year, -longitude & latitude of corner coordinates and center pixel 8 - type, - area, - centroid, perimeter, -ship presence, -quicklook

9 Output (I) Creation of a synoptic map for each year 9

10 Output (II) Merging information on spatial frame density 10

11 Output (III) Creation of a oil spill density map for each year 11

12 Output (IV) Creation of added value products 12

13 Output (V) statistical analysis Spill lengths < < Km Spill widths Monthly distribution < < Km Spilled area < < 11 Km 2 13

14 Output (V) Oil spills classification Amorphous spill Discharged by stopped ship 5 Old zig-zag spill Slow ship speed +distortion by currents Discharging during maneuvering 4 Fresh zig-zag spill Discharging during maneuvering 3 Old straight spill Discharged en-route by fast moving ship few hrs before satellite pass Fresh straight spill Discharged en-route by fast moving ship almost on time of satellite pass

15 MONTHLY CHANGE Results (VI) Monthly variations 15

16 Analysis of satellite images from archives SAR images analyzed possible oil spills detected 16

17 17 Identification of hot spots

18 Trends of operational pollution Possible spills or SAR coverage Pollution density (spill/square degree) Possible spills Area observed by SAR (coverage in square degrees) Pollution density (spill / square degree of SAR) 18

19 Trend analysis an example (I) Zone de Protection Ecologique declared by France in

20 Trend analysis an example (II) Within ZPE Outside ZPE Possible oil spills Satellite coverage 20

21 East Mediterranean case study (I) East Mediterranean basin, covering the sea area between Egypt, Greece and Turkey. 21

22 East Mediterranean case study (II) East Mediterranean basin, covering the sea area between Egypt, Greece and Turkey. 22

23 East Mediterranean case study (III) Analysis of satellite images from archives SAR images analyzed possible oil spills detected 23

24 24 East Mediterranean case study (IV)

25 The way forward Space-borne sensors will have to secure their role in support to aerial and naval means providing: Sustainability of the statistical analysis; Improved reliability of the identification of oil slicks (reduction of the false positives and false negatives rate); Integration with AIS data; Integration with additionally ancillary data (risky maps, optical data); Improved prediction of oil slicks movement; Assess new satellite capabilities (Radarsat-2, ALOS, ). EMSA s real valuable dataset worldwide! (service validation, verification cases, false negatives-positives, trends, statistics, special case studies) 25

26 26 Thank-you