nd (lower 
ssimilated 
) to each 
Figure 1 illustrates a typical ebb tide surface current field in 
Juan de Fuca Strait, British Columbia. The measured field 
using two SeaSonde radars on opposite sides of the strait is 
shown in the upper panel. The modelled field, without 
assimilation, is shown in the centre panel, and the blended 
field is shown in the lowest panel. The averagé x-field for 3 
weeks of hourly assimilations is shown in Figure 2 and 
illustrates the expected decrease in k towards the edges of the 
coverage area and along the baseline zone where surface 
currents cannot be estimated from the radial components. 
  
  
  
  
  
  
  
  
  
Figure 2 Spatial Distribution of the Mean Weight « for 500 
SeaSonde Current Assimilations. 
Oil Slicks 
Previous studies using ERS-1 synthetic aperture radar (SAR) 
indicated that hydrocarbon oil slicks could be detected in the 
imagery under suitable low-wind conditions (Bern et al., 
1992). Using recent RADARSAT SAR images of the Sea 
Empress oil spill at Milford Haven in 1996 and the Nakhodka 
fuel oil spill in the Sea of Japan in January 1997, Hodgins et 
al. (1997a, 1997b) have shown that it is possible to monitor 
oil distributions on the sea and classify the images for areas 
that are heavily oiled, areas that are lightly oiled and areas 
that are free of oil. A composite 3-scene SAR image obtained 
on January 11, 1997, for the Nakhodka spill in the Sea of 
Japan is shown in Figure 3. The main source of the spilled 
fuel oil was from the grounded bow section of the tanker 
approximately 200 m offshore of Mikuni. This image 
suggests that the heavy oil patch originating at Mikuni is 
surrounded by a transition zone of lighter oiling, consistent 
with the application of dispersants used by the response 
agencies. It also displays low-brightness areas associated 
with unstable atmospheric conditions behind a front passing 
over the area. 
  
Figure 3 Composite SAR Image of the Nakhodka spill, Jan. 
11, 1997. 
Image classification involves speckle reduction using a 15x15 
Lee adaptive filter, followed by calculation of frequency 
distributions of reflectance from selected training areas 
spanning heavy oil to background sea and selection of 
thresholds to delineate areas with differing oil coverage. 
Applying three thresholds to the digital values (DV) over the 
entire SAR scene image yields a classified image as shown in 
Fig. 4. Matching the thresholds with categories in 
Environment Canada's oil observing scheme provides a 
relative scaling to oil thickness (Table 1). 
The classified images contain the slick features, but also false 
features that are atmospheric in origin. Moreover, the area 
associated with each slick class will be sensitive to the 
thresholds set for the class. In order to provide for human 
judgment in the assimilation process, an interactive scheme 
allowing the SEACAST modeller to view the SAR image, the 
classified SAR image and the oil distribution predicted by the 
model within the oil spill model's geographic canvas has been 
developed. The software also provides a graphical editor to 
map and re-classify the delineated slicks as a set of polygons 
linked to oil classes (Fingas et al., 1979). Once the polygon 
set has been derived, the volume of oil on the water V(x;,t) is 
mapped into the polygons weighting the volumes by the 
relative thickness of the classes. The final result is a new 
allocation of volume matched to the polygon locations. This 
reinitializing editor provides the man-machine link between 
SPILLSIM and either the enhanced SAR image, or the 
classified SAR image, both of which are viewed as data 
layers through the interface. 
Intemational Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 431