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Understanding the contributions of stream bank and bed erosion will allow us to implement the most effective management practices. The objective of this study was to assess different methods to measure bank and bed erosion at different scales, specifically the watershed, reach and plot. Innovative and traditional methods were utilized. At the watershed scale, indices based on free satellite images were used. For the reach scale, indices were used, but the images with higher accuracy were purchased and captured by unmanned aerial vehicles (UAVs). At the plot scale, erosion pins, cross-sections and laser scanning were applied. The watershed scale analysis showcased “hot spots”. These “hot spots” were reaches vulnerable to erosion and deposition. The indices of the purchased images were applied to these “hot spots” and allowed us to narrow the length of the reaches where UAV flights took place. These flight images located where erosion and deposition occurred. Finally, at the plot scale, laser scanning provided more detailed and accurate data at a greater scale compared to the traditional methods. The implementation of these methods allows us to find the areas vulnerable to erosion and deposition. These are the areas where nature-based solutions should be implemented to effectively mitigate erosion problems.

The alleviation of landslide impacts is a priority since they have the potential to cause significant economic damage as well as the loss of human life. Mitigation can be achieved effectively by using warning systems and preventive measures. The development of improved methodologies for the analysis and understanding of landslides is at the forefront of this scientific field. Identifying effective monitoring techniques (accurate, fast, and low cost) is the pursued objective. Geographic Information Systems (GISs) and remote sensing techniques are utilized in order to achieve this goal. In this study, four methodological approaches (manual landslide delineation, a segmentation process, and two mapping models, specifically object-based image analysis and pixel-based image analysis (OBIA and PBIA)) were proposed and tested with the use of Unmanned Aerial Vehicles (UAVs) and data analysis methods to showcase the state and evolution of landslides. The digital surface model (DSM)-based classification approach was also used to support the aforementioned approaches. This study focused on streamside landslides at research sites in three different countries: Greece, Romania, and Turkey. The results highlight that the areas of the OBIA-based classifications were the most similar (98%) to our control (manual) classifications for all three sites. The landslides’ perimeters at the Lefkothea and Chirlesti sites showed similar results to the OBIA-based classification (93%), as opposed to the Sirtoba site, where the perimeters of the landslides from OBIA-based classification were not well corroborated by the perimeters in the manual classification. Deposition areas that extend beyond the trees were revealed by the DSM-based classification. The results are encouraging because the methodology can be used to monitor landslide evolution with accuracy and high performance in different regions. Specifically, terrains that are difficult to access can be surveyed by UAVs because of their ability to take aerial images. The obtained results provide a framework for the unitary analysis of landslides using modern techniques and tools.