# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 4 Results
Very good results have been achieved in coastal type discrimination (with a 90% success rate for Test site I and close to 100% for Test site II), as proved by aerial photos provided by the Maritime State Office of Apulia Region. The coastline classification has also been fruitfully exploited for the application of the segmentation algorithm in the next step. Three different segmentation algorithms have been tested on three \(165\times 130\) pixel matrices cut out of the original image, each of them representing a different coastal type (rock and sand) and one of them containing artifacts. In this step the performance of the tested segmentation algorithms has been visually inspected, overlaying the extracted shoreline on the original SAR image. In Figure 5, the land-sea interfaces extracted in the three sub-sites using three different segmentation approaches are displayed with different colors (red line for thresholding and region based, green line for LGDF). It can be seen from Figure 5 that SAR images have been correctly segmented in all the sub-sites, even on the sandy coast, by the LGDF algorithm, which is able to extract a \"visually correct\" land-sea interface. The results of all tests performed have been classified into two categories (Table 3): (i) Good if the SAR land-sea interface has no anomalies and is close to the visual boundary, and (ii) Poor if the SAR land-sea interface presents anomalies or significantly departs from the visual boundary. As reported in  
Figure 3: Scatter plot showing the correlation between the mean backscattering coefficient (computed offshore) and the significant wave height measured by a wave buoy (Test Site I). Table 3, LGDF has been the only method capable of providing accurate coastline for both the scenarios and to be also robust to handle SAR intensity artefacts. Since it is an adaptive method capable of exploiting local variability of the SAR images, it has been proven to be reliable and efficient even in the case of SAR images with high intensity dispersion and power noise. Hence, the LGDF approach has been clearly preferred over the other two and it has been integrated in the processing chain for land-sea boundary extraction. Figure 4: Coastal type classification over Test site I: rocky and sandy stretches are marked in brown and yellow, respectively. Figure 5: Segmentation results using thresholding, region-based algorithm and LGDF in the three examined sub-sites (rock coast, sandy coast and artifacts). The LGDF algorithm accuracy for land-sea interface extraction in the sandy areas have been tested on two wide sandy beaches, because in the case of narrow beaches, where beach width is smaller or equal to spatial resolution, the backscattering of the sand portion is indistinguishable from SAR echo signal of structures delimiting the beach. In selected SAR images, both the sandy beaches are represented by a good number of pixels (about 10 pixels in the widest part of the beach) even during the winter season (Figure 6). For each test site, a SAR acquisition has been taken for the segmentation assessment, selecting optimal weather and sea conditions (calm sea conditions and light winds) in the off-season in order to avoid the crowded coasts. Each SAR image has been segmented using an LGDF approach in order to extract the instantaneous shoreline. The LGDF algorithm, initialized in the first approximation by a \"coarse\" line, extracted applying a global thresholding, iteratively calculates, for subsequent steps, the dry/wet separation line using different segmentation parameters according to the coastal type. Extracted shorelines were affected by tidal fluctuations; they have been hence corrected by taking into account sea level values recorded by nearest tide stations and the local slopes derived from the available high resolution DEMs, and then referred to 0 m above sea level.