## Materials and Methods
### Mapping of Land Use and Present-Day Shoreline on a Basis of Satellite and UAV Imagery  
Seamless mosaics of very high-resolution imagery provided via web-services Yandex.Maps, GoogleMaps, BingMaps (Microsoft), and ArcGIS.Imagery (ESRI) were used as data sources at the first stage of study for large-scale thematic mapping of the coastal zone. These mosaics were interpreted visually for recognition of buildings and land use large-scale mapping in the coastal zone. The following principal types of present-day land use were identified in coastal zone: man-made forests and shrulands, natural pastures (flood plain meadows and grasslands), coastal wetlands, residential built-up areas, industrial units, recreational facilities, arable lands, bare lands disturbed by human activities such as mining exploration and dumping sites, and road network. Key sites mapped in the course of UAV surveys and simultaneous field observations were used as a reference and ground data for interpretation of land cover and land use types. We also identified and mapped detached buildings in the coastal zone and livestock farms as objects under the risk of submerging in case of water level rising, potentially having adverse impacts on the ecological state of lacustrine waters as point sources of sewage.  
Additionally, we employed multi-seasonal and multi-spectral satellite data from Landsat 8 Operational Land Imager (OLI) to detect contemporary land cover and land use pattern throughout the entire drainage basin. From Landsat 8 Collection of Level 1 processing products (Courtesy of U.S. Geological Survey), we choose three scenes with different acquisition date of 2019: 15 May, 2 July, and 20 September. We converted digital numbers to the reflectance values at the top-of-atmosphere reflectance in red, near-infrared (NIR), and shortwave-infrared spectral bands. Then, we performed supervised classification of these multi-seasonal and multi-temporal data through a Support Vector Machine (SVM) algorithm in SAGA (System of Automated Geoscientific Analyses) GIS open source GIS software. Training and testing samples for nine general land cover classes were randomly chosen from ground data, high resolution satellite, and UAV imagery. As a result, the spatial distribution of nine land cover classes (built-up areas, arable lands, dry and alpine mountainous grasslands,  
Figure 2: Study site location and sites of repeated UAV surveys in 2018–2019.  
natural meadows, shrublands, forests, bare areas, and water) was acquired and analyzed for the whole area of the Lake Sevan drainage basin.  
Seamless mosaics of highly detailed satellite imagery acquired via web-services Yandex.Maps, GoogleMaps, BingMaps (Microsoft), and ArcGIS.Imagery (ESRI) were also used for mapping coastal types along the whole shoreline of Lake Sevan. The classification of present-day coastal types was verified by UAV surveys and ground truth data (reference points) along the entire lake shore. Ground surveys enabled identification of beach processes being active at specific locations, while UAV-derived ortho-imagery enabled the extrapolation of identified characteristics to other areas by inter-comparison of interpretation parameters.