Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. This paper presents a semi-automatic method based on k-means unsupervised classification to analyze geomorphometric features as landform elements in Armenia. First, several data layers were derived from DEM: elevation, slope, profile curvature, plan curvature and flow path length. Then, k-means algorithm has been used for classifying landform elements based on these morphomertic parameters. The classification has seven landform classes. Overall, landform classification is performed in the form of a three-level hierarchical scheme. The resulting map reflects the general topography and landform character of Armenia.

Within the framework of the Russian-Armenian project “The Paleolimnological Aspect of Studying the Evolution of Ecosystems of High-Mountain Lakes of Russia and Armenia” in July-August 2018, we investigated four high-mountain lakes of Armenia. The research focuses on the lakes Kari, Umroi, Akna and Sev. All investigated lakes are located at the altitudes about 3000 m above sea level. We first time these lakes were investigated using a multi-proxy method that includes paleolimnological, geomorphological, hydrological, geochemical and biogeographic studies. The research offers the first statistical characteristics of lake depth distribution, water volume and other morphometrics. Lake sediments sequences and radiocarbon dates were received and analyzed for Armenian small lakes for the first time. We determined that all the studied lakes were formed during the Holocene. Sediments of Lake Kari were deposited in the last 4000 years, sediments of Lake Umroi – within the last 8000 years, while maximum thickness of sediments is around 1 m in both lakes. Hence, we assume low deposition rate in Armenian high-mountain lakes, however, it varied significantly in different periods of lake history.

This research was carried out as part of the "Copernicus assisted environmental monitoring across the Black Sea Basin – PONTOS" project, which aimed to support and enhance environmental monitoring in the Black Sea Basin region by utilizing Earth Observation products obtained from satellite, airborne, and ground sources. The project team evaluated the environmental monitoring system in pilot sites across Armenia, Greece, Georgia, and Ukraine. The current study focused on assessing changes in wetland and floating vegetation cover from 2009-2015 in Lake Sevan, the largest freshwater source in Armenia and one of the project's pilot sites. Monitoring spatio-temporal changes in aquatic vegetation is crucial for understanding the ecological and socioeconomic functions of lake ecosystems, and requires standardized methods. In order to identify floating aquatic vegetation in Lake Sevan, this study utilized Landsat TM and OLI imagery that were acquired during the main growing season from middle May to middle September of the years 2009-2015. To enhance the classification process, vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), and Normalized Difference Water Index (NDWI) were applied. The findings of this study indicate that medium-resolution Landsat and similar satellite images, which are freely available, can be effectively used to monitor spatiotemporal changes in lakes in a reproducible and continuous manner. However, it was also discovered that algal blooms can significantly hinder the accurate detection of floating vegetation from satellite imagery.