**5. Discussion and Conclusions**

The present study confirms that the Molise coast has been affected by severe shoreline erosion in the long-term period (1954–2016), which mainly affected the coastal segments that include the Trigno and Biferno river mouths (S1 and S7, Table 4).

A comparison of the most up-to-date previous shoreline change data for the period of 1954–2014 [21] with those obtained in this study for 1954–2016 shows that annual retreat rates in coastal segments S1 and S7 have slightly decreased from 2.7 m/y to 2.2 m/y and from 2.9 m/y to 2.7 m/y, respectively.

Concerning the mid-term shoreline evolution of the Molise coast (for the periods 2004–2014 [21] and 2004–2016 in this study), the comparisons highlight that erosion has increasingly affected S2 (−0.7 m/y in [21], −1.2 m/y in this study) and has also begun to affect S9 (0.5 m/y in [21], −0.5 m/y in this study), thus evidencing a slight strengthening of the erosion trend.

Regarding specifically the mid-term shoreline changes in test areas A and B (for the period of 2004–2016, Figure 10 and Table 6), the data highlight their stability and slight trend toward erosion, which are in line with the mid-term shoreline evolution of the coastal segments S3 and S9 (Table 4), which include them.

Finally, concerning the period of 2016–2021 (Table 7), the acquired data show that test area A prograded overall. However, morpho-topographical features and shoreline positions measured along profiles P1–P7 give evidence of a slight trend toward shoreline retreat from 2019 to 2021, although without any effect on the overall positive balance. Test area B, however, has already been affected by shoreline retreat and dune erosion at least since 2004 (Table 6), and it continued to be affected by erosion until 2021 (Table 7), resulting in a progressive degradation of the beach–dune system.

Changing beach and shoreline conditions in the test areas from 2019 to 2021 has induced several changes in the values of the parameters used in the CVA assessment, and therefore changes in the resulting CVA values/levels. The CVA values obtained for 2019 and 2021 highlight a general although modest trend towards increasing coastal vulnerability for both test areas. In detail, the most significant negative changes in CVA values concern profile C3, pointing out the negative effect of human interventions on the coastal vulnerability of the central portion of test area B.

The obtained results highlight, according to other studies realized in similar coastal contexts [44–46], that beach–dune systems can undergo significant changes in the shortterm period and even from one year to the next, thus becoming part of a persisting trend or simply presenting as evidence of reversible fluctuations. Consequently, short-term/annual monitoring of shoreline dynamics and morphometric changes in the beach–dune system appears to be essential in order to detect in time and supervise erosion trends.

Moreover, the integration of traditional investigation methods—mainly those based on available photogrammetric and/or satellite imagery and GPS measurements—with the more innovative UAV survey technology allows for an increase of the scale of observation and monitoring as well as for the detection and updating of the most recent beach–dune and shoreline changes in an efficient, cheaper, and rapid way.

The demonstration that shoreline and beach morphology changes from 2019 to 2021 have caused variations in the indexes that enter in the CVA assessment highlights the need for and the opportunity to update such indexes in a rapid and efficient manner by using the proposed UAV approach, especially in critical erosion hotspot areas under monitoring.

In conclusion, the relatively simple use of UAV technology, along with the possibility to acquire DEMs and georeferenced images with high spatio-temporal resolution, allow this technology to excellently lend itself to the integration of existing coastal change and shoreline migration mapping methodologies and databases. Moreover, the integrated use of UAV and GIS approaches has proven to be an effective instrument, not only for a quick spatial data analysis, but also in order to offer an objective approach with consistent measurement and calculation processes.

**Author Contributions:** Conceptualization. G.D.P., A.M.A., G.R. and C.M.R.; methodology. G.D.P. and A.M.A.; software. G.D.P. and A.M.A.; validation. G.D.P., A.M.A., G.R. and C.M.R.; formal analysis. G.D.P., G.D., A.M.A., G.R. and C.M.R.; investigation. G.D.P., G.D., A.M.A., G.R. and C.M.R.; data curation. G.D.P. and A.M.A.; writing—original draft preparation. G.D.P., A.M.A., G.R. and C.M.R.; writing—review and editing. G.D.P. and C.M.R.; supervision. C.M.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
