Mapping coastal erosion using UAVs, GNSS, and GIS: the case of Rio Greece

Coastal zones are defined as the areas of interface between land and sea, hosting 41% of the world's population. These areas are exposed to various risks cause by either natural processes or anthropogenic activities (i.e. erosion, heat waves, sea level changes, etc.). Nowadays coastal monitoring can be performed with different remote sensing data such as satellite, airborne or Unmanned Aerial Vehicle (UAV) images, exhibiting enormous possibilities for observation. However, in emergencies the prompt and accurate mapping and assessment of structural damage are of utmost importance for effective post-disaster response. In light of this, UAVs display exceptional capabilities in post-disaster mapping since they provide almost near real time data with high spatial resolution and low operational costs, they have a flexible survey planning, as well as they are able to collect data in hazardous environments. Moreover, it is noteworthy that it usually takes less than 45 minutes to one hour in the research area to receive the necessary information, while the generated processing products (i.e. orthophotos and Digital Surface Models) can be made available within 24 hours to the local authorities or stakeholders. In this framework, the current research focuses on the utilization of UAVs along with GNSS measurements to create high-resolution and high-precision maps of the damaged areas after the occurrence of weather extremes. The coastline of Rion in Western Greece was selected as case study. Specifically, the specific area has been affected by recurrent storms causing severe damages to the coastline since 2017. Hence, repeated UAV campaigns following the same photogrammetric grid were carried out in 2016, 2017, 2018, 2021 and 2024. UAV data were processed using SfM (Structure-from-Motion) photogrammetry resulted in the generation of multitemporal orpthophotos and DSMs. These products were compared in GIS environment. Meteorological data from the three nearest station we also integrated in the analysis of the results.