# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 5 Discussion  
In this study, we applied a modified version of the Canny edge detection method to Landsat images in order to delineate the coastline of the Antarctica. The high resolution of the images was an essential factor for improvement in the precision of coastline products. Compared to existing coastline products from coarse-resolution MODIS data (Figure 4), Landsat-7/8 images reveal finer details and more accurate coastline results. The high deviation between the MOA coastline and our results in Table 2 is mainly caused by the data resolution, differences in acquisition dates, and the procedures of the extraction method. The manually delineated MOA-2004 coastline is derived from MODIS data acquired from November 2003 to February 2004, and the data we used were acquired from January 2005 to early March 2005. The resolution of MOA-2004 is 125 m and that of our result is 30-75 m.  
We demonstrated the virtue of the improved coastline extraction algorithm, including its high resolution and the derived scientific and operational applications based on this method. By basing coastline mapping solely on remotely sensed data, the coastline extraction may be updated with little effort as more circum-Antarctic imagery becomes available. While we sought to completely automate coastline extraction, there are a few instances when there is near-coastal fast ice attached to the glacial ice sheets or ice sheets shelves. Under these conditions, the edge of off-shore sea ice is automatically extracted as the coastline, and the uncertainty of coastline extraction using our method increases. Results in Table 4 show that the percentage of pixel points with deviation between the automatically and manually extracted coastlines \(>\)100 m is 17.5% and 21.3%, in Figure 4c,g. The corresponding percentage of deviation (\(>\)100 m) for the total 9000 points is 7.68%. The main cause of this deviation is that the automated processing cannot detect the differences in the gradient histograms between glacial ice and the adjacent fast ice. This consequently assigned the oceanic fast-ice area to the continental ice area, and the edge of fast ice was misjudged as the coastline. This issue is more crucial along the Antarctic Peninsula, where the intensity contrast between fast ice and ice shelves is weak. In this case, one has to rely on expert knowledge to post-process the extracted coastlines. Such misclassification errors may be corrected using the graphic editor tools within the ArcGIS software environment. Therefore, further improvements, especially in the image-handling procedure, are recommended. For example, our processing algorithm requires a certain amount of compute time. Hence to maintain acceptable processing time, high-resolution optical images, such as the Landsat scenes, have to be split into sub-images for parallel processing. This could possibly impact the coastline displacement. Also, the spring tide, derived by data assimilation in the Filchner Ronne and Larsen Ice Shelves, could range 1-2 m and even exceed 3 m [40], but the impact on the horizontal movement of ice shelves with height exceeding over tens of meters is minor. Therefore, the tide may have virtually no influence on the location of coastlines. Also, for regions with low contrast, we intend to revise our algorithm by taking other data--such as ice velocity and in-situ observations--into consideration to yield higher extraction accuracy. Finally, we propose to combine a range of high-resolution imagery to feed into the automated algorithm with the aim to improve the accuracy of the extracted coastlines. This would include the integration of optical and radar data for improved interpretation of the spectral and structural coastline characteristics. According to the fractal geometry theory, coastlines extracted from remote sensing images with different spatial resolution are discrepant [41].