Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in the years 1984–2018. NDVI is derived from MODIS and LANDSAT data, temperature and precipitation data are from meteorological stations. Two study sites were selected, representing arid and semi-arid grassland vegetation types, respectively. Various trend estimators including Mann–Kendall (MK) and derivatives were combined for vegetation change analysis at different time scales. Results suggest that temperature and precipitation had negative and positive impacts on vegetation growth, respectively, in both areas. NDVI-to-precipitation correlation was significant but with an apparent time-lag effect that was further investigated. No significant general changes were observed in vegetation along the observed period. Further comparisons between results from corrected and uncorrected data led us to conclude that MODIS and LANDSAT data with BRDF, topographic and atmospheric corrections applied are best suited for analyzing relationships between NDVI and climatic factors for the 2000–2018 period in grassland at a very local scale; however, in the absence of correction tools and information, uncorrected data can still provide meaningful results. Future refinements will include removal of anthropogenic impact, and deeper investigation of time-lag effects of climatic factors on vegetation dynamics.

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

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.

Machine learning (ML) was used to assess and predict urban air temperature (T_air) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact T_air prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R²_Val = 0.77, RMSE_Val = 1.58) between the predicted and measured T_air from the test set. It was concluded the remote sensing is an effective tool to estimate T_air distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques.

The Sevan is one of the world’s largest highland lakes and the largest drinking water reservoir to the South Caucasus. An intensive drop in the level of the lake that occurred over the last decades of the 20th century has brought to eutrophication. The 2000s were marked by an increase in the level of the lake and development of fish farming. To assess possible effect of these processes on water quality, creating a state-ofthe- art water quality monitoring system is required. Traditional approaches to monitoring aquatic systems are often time-consuming, expensive and non-continuous. Thus, remote sensing technologies are crucial in quantitatively monitoring the status of water quality due to the rapidity, cyclicity, large-scale and low-cost. The aim of this work was to evaluate potential applications of the Landsat 8 Operational Land Imager (OLI) to study the spatio-temporal phytoplankton biomass changes. In this study phytoplankton biomasses are used as a water quality indicator, because phytoplankton communities are sensitive to changes in their environment and directly correlated with eutrophication. We used Landsat 8 OLI (30 m spatial resolution, May, Aug, Sep 2016) images converted to the bottom of atmosphere (BOA) reflectance by performing standard preprocessing steps (radiometric and atmospheric correction, sun glint removal etc.). The nonlinear regression model was developed using Landsat 8 (May 2016) coastal blue, blue, green, red, NIR bands, their ratios (blue/red, red/green, red/blue etc.) and in situ measurements (R²=0.7, p<0.05) performed by the Scientific Center of Zoology and Hydroecology of NAS RA in May 2016. Model was applied to the OLI images received for August and September 2016. The data obtained through the model shows that in May the quantity of phytoplankton mostly varies from 0.2 to 0.6g/m³. In August vs. May a sharp increase in the quantity of phytoplankton around 1-5 g/m³ is observable. In September, very high contents of phytoplankton are observed for almost entire surface of the lake. Preliminary collation between data generated with help of the model and in-situ measurements allows to conclude that the RS model for phytoplankton biomass estimation showed reasonable results, but further validation is necessary.