VERTIMAR-2005
Symposium on Marine Accidental Oil Spills

Detection, monitoring and forecasting of hydrocarbons spills in the ocean using remote sensing and artificial intelligence techniques

L. GONZÁLEZ1, J.M. TORRES1, J.M. CORCHADO2, A.M. TURIEL3
and E. GARCIA-LADONA3

1 Dept. de Física Aplicada, Universidade de Vigo, 36200. Vigo. ,

2 Dept. de Informática y Automática, Universidad de Salamanca (Spain).

3Dept. de Oceanografía Física, Institut de Ciencies del Mar -CMIMA (CSIC).

ABSTRACT In this study it is described an operational system for the detection, monitoring and forecasting of oil spills on the Galician coast. Remote sensing techniques are applied in order to detect the possible oil slicks, using mainly ENVISAT ASAR radar images although it was also studied the possible application of ultraviolet and visible sensors. The detection results are integrated into a GIS database jointly with other information, such us wind data or visual observations, with the aim of analyzing the spatial distribution and the evolution of the oil slicks. Finally, it was developed a forecasting systembased on meteorological and oceanographic models that uses the previous results in order predict the trajectory of the oil. Data derived of this system are finally added to a server and are used to assess the risk of the oil spill could reach the coastline.

1. INTRODUCTION

The Prestige tanker caused a big oil spill off the Galician coast on November 2002. The heavy fuel oil leaked from the vessel reached progressively the coastal areas during the next months occasioning a big environmental and economic catastrophe in the region. 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.

CONTINMAR is a project aimed to the development of elements, tools, action protocols and an information system for the design of contingency plans in the event of accidental marine pollution. It is a multidisciplinary action integrated by nine different projects that deal with different topics ranging from bioremediation to analysis of the environmental impacts of the proposed contingency plans.

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). The project is classified within the topic ‘Operational oceanography implementation on hydrocarbons spills’(PT2).

Regarding to the data, Advanced Synthetic Aperture Radar (ASAR) radar images were provided by the European Space Agency (ESA) within the framework of the Envisat AO-623 project. Envisat ASAR operates at C-band (5.331 GHz) and can acquire data in different modes and variable viewing geometry. 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 boardQuikScat, surface currents and wind data proceeding from the oceanographic model MERCATOR and visual observations data.

2. RESULTS AND DISCUSSION

The system described in this work involves several stages that are summarized in Figure 1. The first step is the processing of the ASAR images acquired over the affected area in order to detect radar signatures suspicious of being oil spills. Satellite radar data has proven very useful in large-scale oil spill detection due to its large coverage, in addition to other advantages as the ability to image the sea surface independently of light and cloud conditions (Fiscella et al, 2000). Different algorithms can be applied to carry out the detection, and the border of each possible oil slick is identified and stored as a geo-referenced vector layer.

Then, these layers are integrated into a GIS database together with other available information proceeding from different sources including wind data from scatterometer, wind and surface currents data derived from meteorological or oceanographic models, in-situ data and visual observations from helicopters, aircrafts or ships. Combining all this information by applying artificial intelligence techniques it is carried out a discrimination between the signatures with the highest probability of being real oil slicks and their look-alikes (Espedal and Wahl, 1999).

To develop the forecasting system, it is considered that a part of the spill travels over the surface and the other one more sunken due to the marine roughness. So, while the part travelling over the surface moves in the same direction that the wind blows, the sunken part suffers a delay because of the least effect of the wind and its movement depends on the surface currents, which in their turn are the result of the predominant winds and the Coriolis effect. Near the Galician coast, it is also considered the fluvial influence or the presence of a pole-ward current parallel to the coastline under certain wind conditions (Cabanas and Sanchez, 2003)

Figure 1: Diagram showing the main stages of the system for detection, monitoring and forecasting of oil spills on the Galician coast.

Positions of oil slicks proceeding from the radar images are used as starting point in order to predict the trajectory of the oil. The forecasting is carried out using wind and surface currents data derived from meteorological or oceanographic models by artificial intelligence techniques.

Data derived of this system are finally added to a server and could be used for scheduling flights to verify the oil spill or for helping to take decisions about the emergency procedures to be implemented in the event of a serious tanker accident in the area, specially if there is a real risk of the oil could reach the coast.

REFERENCES

Cabanas, J.M. and Sanchez F. (2003) Informe 04: Características Oceanográficas de la plataforma de Galicia en Diciembre de 2002”, Español de Oceanografía.

Fiscella, B. Giancaspro, A. Nirchio, F. Pavese, P. and Trivero, P. (2000) Oil spill detection using marine SAR images. Int. J. Remote Sensing, 21: 3561-3566.

Espedal, H.A. and Wahl, T. (1999). Satellite SAR oil spill detection using wind history information. Int. J. Remote Sensing, 20: 49-45.