the numerical sea ice forecast in finland in the winter 1993-94 ...

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18 finally undelined areas are areas representing unknown factors mostly due high moisture andlor ice Crystals in the near air-layer above the ice (Fig. 4). 7” 35 30 25 20 15 10 5 0 No Ice/water Ice edge tt,i,!$e,,, Undefined areas Fig. 4. Obscurities in the ERS-1 FD SAR images in the winter 1993. N is the number. In the winter of 1993194 almost all of the images (- 80 %) were free of interpretation problems. However, some 16 % of the images revealed bared ice edge definition problems (Fig. 5. Appendix l- 2). The problems were strongly related into low toncentration of drift ice or/and new ice. During the experiment period the snow cover on the ice melted rapidly causing interpretation problems in the Gulf of Finland, however, the Arundu, making observations in the area, solved the problem. In table 1 the image and weather data are presented. Daily air temperatures and normal averages of the air temperatures are shown in table 2 and in figure 10. In table 3 ice and snow thicknesses are shown. Figure 6 shows the main weather and ice information points. No Edge Unclear Fig. 5. Obscurities in the ERS-1 FD SAR images in the winter 1994. N is the number.
19 1 ou•u, a tEp ort Fig. 6. The mentioned observation points. The image area identifications are in rectangles. 4. THE AUTOMATED SEA ICFJOPEN WATER DISCRIMINATION FROM ERS-1 SAR IMAGES The Finnish Institute of Marine Research participated during the years 1993- 1994 in the Application Oriented Pilot Project accepted by ESA talled “Operational sea ice charting using ERS-1 SAR images” (OSIC). An important part of the project has been to investigate the possibilities to automate SAR image interpretation. The first step in this direction has been to develop a classification algorithm to discriminate sea ice and open water from an ERS-1 SAR image. The algorithm has been examined in detail in Similä (1994a) and more theoretically in Similä (1994b). The classification algorithm is based on the physical fatt that the correlation structure of the backscatter is different for open water and ice fields. The open water pixels are practically uncorrelated whereas the ice field pixels are more or less strongly correlated in the Low Resolution images used with a resolution of 100 m by 100 m. Because the variance is big in the SAR images, the identification of open water or ice with the aid of autocorrelation demands a quite large pixel block size. According to the experiments the pixel block size of 15 by 15 pixels was reliable enough for the correlation calculations. For a pixel block of this size the directional autocorrelations to four directions were calculated and the mean autocorrelation was assigned as the correlation value of the pixel block. If the correlation value was very low the pixel was assigned with certainty to the open water elass. On the other hand if the correlation value was high it was interpreted to represent ice with a large probability. Because the threshold values to separate open water and ice classes can not be estimated from the image, they are given in advance, relying on past experience. Hence, the threshold values are somewhat arbitrary and there exists a relatively large range of correlation values where each elass is possible. Clustering the image according to the correlation values produces a crude image segmentation with resolution of 1500 m by 1500 m. This result is improved with a pixelwise classification.
Page 1 and 2: MER1 - Report Series of the Finnish
Page 3 and 4: THE NUMERICAL SEA ICE FORECAST IN F
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Page 7 and 8: 8 v”biiRICAL SEA ICE REDICTION :Q
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Page 14 and 15: 14 considerably affect navigation -
Page 16 and 17: 16 The maximum fast ice thicknesses
Page 20 and 21: 20 To make the pixelwise decision a
Page 22 and 23: 22 Fig. 9..a)EEi:S-1 SAR image 05.0
Page 24 and 25: 24 Table 1. ERS-1 images and weathe
Page 26 and 27: 26 VO Date Image Area Station 6 3 O
Page 28 and 29: l0 Date Image Area 136 26,Mar ers-9
Page 30 and 31: 30 HJLU, VAASA, RAUMA, MAARIANHAMIN
Page 32 and 33: 32 Table 3. Ice and snow thickness
Page 34 and 35: 34 Date: 10.0 1.1994 09:48 UTC Imag
Page 36 and 37: 36 Date: 09.02.1994 09:48 UTC Image
Page 38 and 39: 38 Obscurity: No. Date: 12.02.1994
Page 40 and 41: 40 Ice types in the image: Leve1 ic
Page 42 and 43: 42 Area: Off Kaskinen Ice thickness
Page 46 and 47: 46 Image: ers940304-a7 Area: Off Pi
Page 48 and 49: 48 Ice types in the image: Fast ice
Page 52 and 53: 52 Verification: Fast ice is visibl
Page 54 and 55: 54 Ice types in the image: Fast ice
Page 56 and 57: 56 Verification: Very good even in
Page 60 and 61: 60 Area: Off Kaskinen Ice thickness
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