# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 4 Results
During both experiments, an accuracy assessment of shorelines extracted by the COSMO-Beach system has been performed by comparing the outcomes of the processing chain to ground truths collected through GPS survey campaigns carried out simultaneously with the SAR acquisition. The surveys have been conducted using two high accuracy GPS receivers (Leica GX1230) operating in Real Time Kinematic (RTK) mode and real-time corrected through the permanent GNSS network of the Apulia Region. Within the in situ campaigns, regularly spaced shore-perpendicular transects have been surveyed and the 0 m contour has been extrapolated. The comparison between the corrected SAR shorelines and the observed 0 m GPS contour highlights a good performance in coastline detection using the proposed procedure (Figure 7a,b). For the statistical error analysis, 50 transects in test site I and 160 transects in test site II, located at a fixed distance of 10 m between each other, have been considered. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Rocky Coast** & **Sandy Coast** & **Artifact** \\ \hline Thresholding & GOOD & GOOD & POOR \\ Region-based & GOOD & POOR & GOOD \\ LGDF & GOOD & GOOD & GOOD \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performances of different segmentation algorithms on the selected test site. Figure 6: Synthetic Aperture Radar (SAR) Pixels belonging to sandy beach region. The overall performances of the LGDF algorithm have been estimated using a series of statistical indicators. Mean error, standard deviation and Root Mean Square Error (RMSE) have been calculated for the sample of distances between SAR and GPS shorelines for each transect. Results have shown an overall mapping accuracy (RMSE) of 1.35 m for Test Site I and 2.2 m for Test Site II, i.e., lower than image pixel resolution (Table 4). The algorithm performance has also been assessed using a criterion based on neighbourhood pixels [40], estimating the pixel distance between the SAR shoreline and the GPS shoreline (Table 5) \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Test Site** & **Mean (m)** & **Standard Deviation (m)** & **RMSE (m)** \\ \hline Test Site I & 1.12 & 0.78 & 1.35 \\ Test Site II & 1.36 & 1.47 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Shoreline accuracy assessment. Figure 7: Accuracy assessment of SAR extracted shoreline: (**a**) Test site I; (**b**) Test site II. The accuracy analysis carried out on Test Site I shows that 87% of extracted shoreline segments lie within 1 pixel distance from the surveyed shoreline, and, with a tolerance of 2 pixels, the accuracy rises to 100%. Test Site II experiment, even with a greater number of transects, has achieved similar results: 85% of SAR coastline lies within 1 pixel distance from the GPS coastline, 98% of SAR coastline has a tolerance of 2 pixels, and the accuracy rises to 100% with a tolerance of 3 pixels. It has to be highlighted that these results have been achieved by excluding, from the statistical analysis, transects where organic deposits of _Posidonia oceanica_ were detected during on-site inspections. Larger errors have been detected only over those areas, since the intensity of the radar signal backscattered by the stockpiles hinders the reliable estimation of the shoreline through any segmentation algorithm (Figure 8). In such cases, as expected, the extracted land-sea interface lies along the algal deposit on the shore in the zone extending seaward from the \"real\" shoreline. After the accuracy assessment of shoreline detection, the coastal evolution trend has been analyzed in both target areas. Shorelines have been extracted from each available SAR image and have been corrected taking into account the tidal oscillations. Shoreline movements have been measured along user-specified orthogonal transects and rates-of-change and associated statistics have been estimated.