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As is known, chlorophyll is an important biophysical parameter used to monitor the overall physiological status of plants. The aim of this research was to study the potential of UAV multispectral images for estimating the contents of leaf chlorophyll in vineyards. For this purpose, a UAV flight was conducted (eBee SQ with Parrot Sequoia multispectral camera) and simultaneously in-situ measurements of leaf chlorophyll content of vine were performed using MC-100 Chlorophyll Meter. A total of 51 samples were collected: each sample representing the average of 5 measurements from the top of a single plant. Pearson correlation analysis was applied to test the relationships between spectral reflectance, Normalized Difference Vegetation Index (NDVI), Normalized Difference RedEdge Index (NDRE) and in-situ measured leaf chlorophyll content. A Partial Least Squares Regression (PLSR) model was also applied to predict chlorophyll content using 6 predictor variables: NDVI, NDRE and green, red, red edge, near infrared bands. The results showed that among spectral reflectance, the red band was most sensitive to chlorophyll variations (r = -0.46). Positive correlation between chlorophyll content and NDVI/NDRE also was found (r = 0.67 and r = 0.57, respectively). Promising results were obtained for the PLSR model (R2Val = 0.49, RMSEVal = 43.68), which proves the high potential of multispectral UAV imagery for chlorophyll monitoring in vineyards.

This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in 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, but 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.

The air temperature is a critical factor in many societal challenges to protect human health and the environment. Moreover, a vital climatic parameter, the temperature has a direct impact on evaporation, frost, and snow melting. Temperature predictions are based mainly on numerical and statistical models. Sometimes it is a challenge to improve the weather forecast accuracy. The article aims to implement a weather prediction technique based on machine learning methods and approaches to improve the hourly air temperature prediction for up to 24 hours in the Ararat valley (Armenia). Due to intense heat and low relative humidity, the high temperatures and hot winds occur between 120 and 160 days per year in Ararat valley, as one of the aridest regions of Armenia. The approach utilizes the earth observation data received from several meteorological stations and the large satellite analysis-ready datasets at different frequencies and resolutions. The experiments have been conducted with multiple neural networks to forecast air temperatures for 24 hours that happened over the Ararat valley. The suggested model has 87.31% and 75.57% accuracies to predict the temperature for the next 3 and 24 hours, which is sufficient to be used alongside the current state-of-the-art techniques.

The main goal of this research was to estimate heavy metals (HMs) (molybdenum (Mo), copper (Cu), nickel (Ni), cadmium (Cd)) contents in crop leaves through multispectral satellite imagery. During t...

Shoreline changes are important indicators of natural and manmade impacts on inland waters and particularly lakes. Man-induced changes in Lake Sevan water level during the 20th century affected not only the ecological status of the Sevan water but also near-shore areas. This article considers a long-term study of changes in Lake Sevan shoreline that occurred between 1973 and 2015. The Normalized Difference Water Index (NDWI) was applied to delineate the Sevan shoreline changes according to periods of lake water fluctuation from multi-temporal Landsat images and Historical changes in shorelines were analyzed with help of the Digital Shoreline Analysis System (DSAS) toolbox. Data obtained from the analysis have indicated that changes in the lake shoreline that occurred in different periods are similar to those in the lake water balance. Areas with the greatest shoreline changes have comparatively flat relief, so in the result of the lake water level raise vast forested areas were submerged. This study shows that application of multi-temporal spatial imagery and GIS methods can provide valuable information on time-and-space changes in the Sevan shoreline. Such information is important for monitoring Lake Sevan shoreline and nearshore changes.

In this paper, we study vegetation, temperature, and precipitation dynamics, as well as their correlations and lag-time effects in mountain ecosystems of Armenia using SPOT/VGT NDVI data over the period 1998–2013. Three study sites near the Sisian, Megri and Kajaran meteorological stations were chosen, representing steep, xerophytic and meadow vegetation types, respectively. The results showed a significant increase in NDVI across the growing season during the study period. Meanwhile, a significant increase in mean precipitation and temperature during the growing season was detected for the Kajaran station. A negative correlation was observed between temperature and xerophytic/steep vegetation NDVI and positive correlation between temperature and meadow vegetation NDVI, while correlations between precipitation and NDVI for all vegetation types were positive. Moreover, the response of vegetation to climatic factors exhibited a time-lag effect. In general, the temperature and precipitation in the 2nd and 3rd 10-day intervals of April have a strong impact on the vegetation growth in summer. This study suggests that for a better understanding of the impact and lag-time effect of climatic factors on mountain vegetation growth it is preferable to use 10-day data.

Soils samples collected during different geochemical surveys of the city of Kajaran located near the biggest Cu-Mo mining area in Armenia were subjected to the multivariate geostatistical analysis and geochemical mapping in order to reveal soil heavy metals spatial distribution pattern and assess human health risk level under continuous impact of mining activities. In addition, human health risk assessment was done for the contents of Pb, Cu, Zn, Co, Mo, Mn, Ti, and Fe. The results of Principal Component Analysis and Cluster Analysis verify each other and were also complemented by the spatial distribution features of studied heavy metals indicating that two groups of elements have been generated. The first anthropogenically predominated group includes the main industrial elements Mo and Cu, and their accessories Pb and Zn while Ti, Mn, Fe and Co with the naturally predominant contents were observed in the second group. Moreover, the study reveals that the superposition of geogenic and anthropogenic components lead to the alteration of the shapes of areas with the high natural contents of heavy metals and formation of polluted areas with the intensive anomalies of elements. Health risk assessment showed that Mo was the only studied element which poses a non-carcinogenic risk to adult and children's health in some sampling sites during the whole period of investigations. Moreover, in all studied locations multi-elemental non-carcinogenic risk to children health from all studied heavy metals were detected. Special attention was given to the soils of kindergarten territories, and the results indicated that Hazard Index in kindergartens was >1 indicating an adverse health effect to children. The results obtained can serve as a basis for the development and implementation of risks reduction measures and systematic monitoring program planning.

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.

Lake Sevan being Armenia’s largest freshwater reservoir has a vital economic, recreational and cultural importance to both the catchment area and the nation as a whole. At present the Sevan which has seen the dramatic - some 20m drop - in water level entailing grave ecological consequences to the whole of its ecosystem, is at the stage of recovery. Hence, it is very important to study basic parameters describing the ecological status of the lake, and their annual and seasonal dynamics. The Sevan water surface temperature (WST) is a key parameter which influences all ecological processes that occur in the Lake. Declining lake level has brought to reduction of water volume and consequently to earlier warming of lake water in spring and its earlier cooling in the fall. Besides, more frequent becomes the complete surface freezing of Lake Sevan. Remotely sensed imagery makes it possible to get immediate information on a regular basis about WST across the entire surface of lakes. The purpose of this particular research was to study the space and time dynamics of Lake Sevan WST using Landsat 8 satellite imagery. The advantage of Landsat8 images is a regular frequency of capturing and availability of another thermal band that helps reduce the atmospheric refraction-induced errors/deviations. This research involved Landsat imagery for 2000-2018. The images underwent preprocessing steps (radiometric calibration, atmospheric correction, normalization etc) and then Lake Sevan WSTs and their monthly and annual changes over the mentioned periods were derived using both thermal bands (b10, b11). The research confirmed the fact, that Lake Sevan surface completely or partly freezing with periodicity of 2-3 years, whereas before the water drop the periodicity was 15-20 years. The study of spatial distribution of WST data derived from remote sensing shows that the temperature data corresponds to the overall general picture of temperature for Lake Sevan. This research has indicated that remotely sensed images and Landsat 8 imagery in particular allow derive both WST data on a regular basis and retrospective data (since 2013).

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, p3. 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.

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&rsquo, 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.

The city of Yerevan, Armenia has undergone major environmental and economic changes after the collapse of the Soviet Union. The objectives of this study were to: (i) investigate the changes of the Land Cover (LC) and Surface Urban Heat Island (SUHI) in Yerevan and analyze relations between them, (ii) study the relationships between land surface temperature (LST) and environmental factors/parameters, (iii) explore the accuracy of satellite derived LST. LC and SUHI were derived from Landsat TM/ETM+/OLI-TIRS images (years 1989, 2000, 2010 and 2018) by means of three Machine Learning algorithms and the Urban Thermal Field Variance Index (UTFVI) ecological evaluation index, respectively. The comparison between Unmanned Aerial Vehicle (UAV) and satellite LSTs showed that the overall spatial pattern of Landsat and UAV LSTs matched. It was found that the green and built-up areas were the main factors affecting LST variation in Yerevan. The results of the LC change analysis revealed an expansion of built-up areas and the reduction of green spaces. Yerevan shares almost an equal percentage of land for the excellent and the worst categories of the UTFVI. The transformations from excellent to the worst category of UTFVI were mainly related to the loss of green spaces, while the opposite transformations were associated with the gain of vegetation cover, the construction of new districts and the reduction/cessation of anthropogenic heat emission. It appeared that the urban construction had possibly led to the improvement of UTFVI index in the case of no/low anthropogenic heat emission.

Earth Science community depends on the exploration, analysis and reprocessing of high volumes of data as well as the modeling and simulation of complex coupled systems on multiple scales. The main aim of this article is to introduce a new hydrological modeling service based on the Soil and Water Assessment Tool (SWAT) (Arnold et al. J American Water Resour Assoc 34(1), 73–89, 1998 ; Arnold and Fohrer Hydrol Process 19(3), 563–572, 2005) model using high efficiency, resource sharing and low cost cloud computing resources (Astsatryan et al. International Journal of Scientific & Engineering Research 1(1), 1130–1133, 2014). Such a Desktop as a Service (DaaS) approach allowing users to work from anywhere, and gives centralized desktop management and great performance. Within the Spatial Data Infrastructure (SDI) and cloud platform, the DaaS service gives secure access to the model and a centralized data storage to get a SWAT model input. The article illustrates the analyses of the implementation of the SWAT model for the Sotk watershed of Lake Sevan in Armenia (Sargsyan 2007).

Processing of high-resolution time series satellite images typically requires a large amount of computational resources and time. We introduce here a scientific gateway for computing the Normalized Difference Vegetation Index (NDVI) time series data. Based on a distributed workflow using the Web Processing Service (WPS) standard, the gateway aims to be completely interoperable with other standardized tools. The availability of this gateway may help researchers to acquire knowledge of land cover changes more efficiently over very large spatial and temporal extents, which is especially important in the context of Armenia for which timely decision-making is needed. A scientific gateway for computing the NDVI time series data based on a distributed workflow using the WPS standard.An optimal NDVI times series geoprocessing services based on cloud infrastructures.Experimental results in the study area that include some part of the territory of Armenia.

Honey is a highly nutritious natural product widely produced and consumed by people in Shirak and Syunik regions of Armenia. Unlike Shirak, Syunik is under the impact of mining industry. Since the environmental pollution can adversely impact the safety of honey and entail a probable risk to human health, it is important to evaluate the presence of potentially toxic trace elements in honey samples from both regions and draw comparisons. This study assesses the dietary exposure to trace elements and persistent organic pollutants through the intake of honey for the first time among people in Shirak and Syunik regions. 24-hour dietary recall method was used to investigate the consumption of honey. The presence of seven trace elements (Pb, Cd, As, Hg, Cu, Zn, and Ni) and persistent organic pollutants (hexachlorocyclohexane (HCH), dichlorodiphenyltrichloroethane (DDT) and its metabolites) were determined in honey samples using atomic absorption spectrometry and gas chromatography-mass spectrometry, respectively. In several samples, the concentrations of Cu were above the maximum allowable level. Non-carcinogenic risk values did not exceed the acceptable level, while carcinogenic risk values for Ni and As exceeded the risk level of 10-6 in both regions. Moreover, among the persistent organic pollutants, only the concentration of DDT in honey from Shirak was above the European Union maximum residue level.

Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p 2 ~0.9, RPD~2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R 2 ~0.7, RPD~1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R 2 , rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.

A small urban park in Yerevan, Armenia, was studied using biogechemical analysis of the tree canopy, field spectral reflectance measurements of tree leaves, simulated WorldView-2 multispectral data generated from the leaf spectra, and two summer images of real WorldView-2 data. The tree canopy of the park is dominated by two trees, Robinia pseudoacacia L. (locust) and Fraxinus excelsior L. (ash). The Highest values of lead, nickel, molybdenum, copper and zinc were found in leaves harvested from trees adjacent to the streets, whereas most of the lowest values for those metals were found in the interior of the park. A t-test of the field spectral measurements indicated that the green and red edge spectral reflectance of leaves from trees near the streets was significantly higher than that of leaves of trees in the interior (p

Space limitations arising from human activities affect demographic structure and performance of mammalian populations and thus reduce their viability. This is especially true for wide-ranging wild cats (family Felidae) which generally lead solitary lives and require large tracts of good-quality habitats for survival (Sunquist and Sunquist 2001). As human activities leave more and more mosaics of modified lands behind, felid populations become fragmented and further impaired by the small and often unviable size of patches necessitating more complicated dispersal of individuals between patches (Reed 2004).