The paper presents the range and applications of thematic tasks for ultra-high spatial resolution data from small unmanned aerial vehicles (UAVs) in the integral system of environmental multi-platform and multi-scaled monitoring of Lake Sevan, which is one of the greatest freshwater lakes in Eurasia. From the 1930s, it had been subjected to human-driven changing of the water level with associated and currently exacerbated environmental issues. We elaborated the specific techniques of optical and thermal surveys for the different coastal sites and phenomena in study. UAV-derived optical imagery and thermal stream were processed by a Structure-from-Motion algorithm to create digital surface models (DSMs) and ortho-imagery for several key sites. UAV imagery were used as additional sources of detailed spatial data under large-scale mapping of current land-use and point sources of water pollution in the coastal zone, and a main data source on environmental violations, especially sewage discharge or illegal landfills. The revealed present-day coastal types were mapped at a large scale, and the net changes of shoreline position and rates of shore erosion were calculated on multi-temporal UAV data using modified Hausdorff’s distance. Based on highly-detailed DSMs, we revealed the areas and objects at risk of flooding under the projected water level rise to 1903.5 m along the west coasts of Minor Sevan being the most popular recreational area. We indicated that the structural and environmental state of marsh coasts and coastal wetlands as potential sources of lake eutrophication and associated algal blooms could be more efficiently studied under thermal UAV surveys than optical ones. We proposed to consider UAV surveys as a necessary intermediary between ground data and satellite imagery with different spatial resolutions for the complex environmental monitoring of the coastal area and water body of Lake Sevan as a whole.

The article is based on an integrated approach that includes joint consideration of shrinkage of traditional agricultural land use, an increase in the share of small and abandoned villages, and expansion of new types of recreational use of rural areas around Moscow and Moscow oblast. The use in the article of statistical indicators for federal subjects and municipal districts, as well as satellite images, gives a multiscale and fractional picture of the shrinkage of land use, which in recent years more and more clearly geographically replicates the historical process of development of these territories, only with the opposite sign. Agricultural production is “shifting” to areas with better natural conditions, including within regions. The tendency of its concentration in the suburbs with accumulated investments, labor resources, sales opportunities, and infrastructure is gradually changing to development in the areas with better soils, sometimes remote from cities. The population, however, continues to concentrate in cities and suburbs of regional centers, which contributes to shrinkage of the inhabited space. These multidirectional trends are confirmed by statistical calculations for municipal districts for the period from 1990 to 2017. The results are illustrated and confirmed by maps, based on satellite images. The maps show arable agricultural and fallow lands, as well as the spread of abandoned and small villages. Calculations for municipal districts based on information obtained from maps make it possible to correct statistical indicators and see the real changes in the rural settlement pattern and land use. The cartographic method also made it possible to identify the possibilities and limitations of redevelopment of rural areas by urban residents, including not only the expanding areas of second home settlements (dacha settlements), but also the dachas of urban residents in depopulated villages.

Remote sensing (RS) is a compulsory component in studying and monitoring ecosystems suffering from the disruption of natural balance, productivity, and degradation. The current study attempted to assess the feasibility of multisource RS for assessing and monitoring mountainous natural grasslands in Armenia. Different spatial resolution RS data (Landsat 8, Sentinel-2, Planet Scope, and multispectral UAV) were used to obtain various vegetation spectral indices: NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI, and the relationships among the indices were assessed via the Spearman correlation method, which showed a significant positive correlation for all cases (p < 0.01). A comparison of all indices showed a significant high correlation between UAV and the Planet Scope imagery. The comparisons of UAV with Sentinel and Landsat data show moderate and low significant correlation (p < 0.01), correspondingly. Also, trend analysis was performed to explore the spatial–temporal changes of these indices using Mann–Kendall statistical tests (MK, MKKH, MKKY, PW, TFPW), which indicated no significant trend. However, Sen’s slope as a second estimator showed a decreasing trend. Generally, it could be proved that, as opensource data, Sentinel-2 seemed to have better alignment, making it a reliable tool for the accurate monitoring of the ecological state of small mountainous grasslands.