# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 5 Discussion  
A coastal monitoring system requires some key characteristics such as cost effectiveness, high-speed data acquisition and processing and the accuracy of the collected information. Traditional methods for coastal monitoring, generally ensuring higher accuracy than EO systems, determine a significant financial burden due to the extension of the areas, the survey frequency and the long-term continuity. Since the aim of our research has been to set up an operational tool for coastal observation ensuring high accuracy, the experimental analysis has been carried out on CSK SAR data due to their high spatial resolution, low-cost, short revisit period and operational continuity. Moreover, the CSK satellite constellation has been operating since 2007, providing a large archive of SAR images that can be exploited for short and long-term analysis. The fast revisit/response time makes it possible to employ these data even for risk situation assessment. Besides, COSMO-SkyMed Second Generation is  
Figure 9: Shoreline changes detected from SAR images (blue line for the oldest SAR shoreline, red line for the most recent): (**a**) Test site I (Porto Cesareo); (**b**) Test site II (Torre Canne). planned to become operational in 2019-2020, thus ensuring continuity of service for many more years. Currently, there are very few SAR satellite missions that combine all these features. The direct comparison of accuracy results in shoreline extraction with previous studies is not straightforward since they depend on several factors including sensor characteristics, spatial resolution, sensor acquisition geometry, shoreline morphology, sea state, and processing methodology [76]. Moreover, it has to be underlined that in most studies the algorithm performance is estimated by an overall eye inspection [42; 43; 50] or a comparison with a manually traced coastline [45; 48; 77]. Few studies comply with ground truth data for accuracy assessment [46; 47; 51; 78]. Focusing attention only on shoreline identification performance from CSK data estimated by comparing the extracted coastline with ground truths, our experiments are consistent with previous results [47], that found accuracies of 1 pixel for shorelines extracted by the CSK Stripmap HIMAGE data using a procedure based on image segmentation using locally adaptive thresholding method. CSK polarimetric data produce less accurate results in shoreline detection than those that can be achieved using CSK Stripmap images, providing a 7 pixel accuracy [51]. Interesting results have also been reported in the recent literature with high-resolution X-band SAR images acquired by the TSX mission. In particular, [PERSON] et al. [78] monitored a beach reenourishment in the Netherlands. Thanks to the high accuracy of the timing information and orbital state vectors, TSX allows a sub-decimetric ranging accuracy and a high-quality geocoding [79; 80], thus potentially ensuring a very precise georeferencing of the shoreline. Nevertheless, the authors have stated in [78] that the errors in the shoreline estimate may amount to some tens of meters, which is well above the TSX spatial resolution (\(\sim\)3 m). Although the spatial resolutions of CSK and TSX stripmap data are similar, the results detailed in [78] are not directly comparable with the outcomes of the present study, because of the very different settings of the selected test sites. In particular, the significant discrepancies between the performances achieved in [78] and in the present study, respectively, can be explained in terms of different environmental conditions, such as meteorological, tidal and topographic factors among the areas of interest. In particular, results reported in [78] are heavily affected by the local exposure of the test site, a mega scale beach reenourishment (known as \"Sand Motor\") on the Dutch coast facing the North Sea, and exposed to severe winds, wave and tide conditions.