## 1 Introduction  
Natural grasslands are one of the most important terrestrial ecosystems on earth, covering more than 40% of the land area [1] and 70% of the world's agricultural area [2, 3] and providing 30% of the terrestrial biomass [1, 4, 5]. Grasslands are the main source of livestock feed and solve several human food security problems [6, 7], especially in mountainous and crop-poor regions [8, 9, 10, 11, 12]. In addition, grasslands also carry out important ecological functions that contribute to the protection of the environment, such as strengthening the soil layer, - water and carbon accumulation [1], and regulating the circulation of vital elements, water, and nitrogen in nature [7, 13, 14]. However, nowadays, like other environmental ecosystems, natural grasslands are also under the threat of degradation and desertification triggered by many natural and anthropogenic factors [15, 16, 17, 18, 19]. All these threats lead to a significant decrease in productivity and to a disruption of the grassland's ecosystem functions which then create serious challenges for grassland management and sustainable development [14, 20, 21, 22]. Therefore, studying and timely and accurate monitoring of changes in the state of grasslands [23, 24, 25, 26] have great economic and environmental importance [15]. The mountainous natural grasslands of Armenia have not been spared from these threats of grassland mismanagement, overgrazing, and natural factors (erosion,landslides, mudslides), which are all even more acute and more strongly expressed in mountainous areas [27]. There are two main methods for grassland monitoring and study: traditional (field) and remote sensing. Traditional methods include biomass harvesting, eddy covariance tower measurements, and phenocams [28], and are the most reliable and accurate methods of obtaining information on the condition of grasslands. However, these are time-consuming and labor-intensive methods, effective only at a local scale. Moreover, for large-scale research, these methods have severe limitations in both spatial and temporal scales [29; 30]. In contrast to traditional measurements, satellite and aerial remote sensing have an overall advantage of convenience, efficiency, and cost-effectiveness [1]. Furthermore, in the last 20 years, the role of remote sensing technologies in the study and monitoring of grasslands has greatly increased [3], and these technologies have the potential to produce rapid and automated measurements needed to quantify both structural and biochemical properties of vegetation [25; 31; 32]. Satellite remote sensing, such as LANDSAT, MODIS, SENTINEL, Planet Scope etc., has a primary role in the field of grassland monitoring and time series analysis. Although this type of sensing does have limitations related to low spatial and temporal resolution [33] and to external influences such us cloud cover, shadows, position, and the angle of incidence of solar radiation [1], it can be complemented mostly by aerial images such as UAV data, likewise validating the results derived from the satellite images [34; 35]. The dynamic growth in the use of unmanned aerial vehicles (UAVs) over the last decade, supported by a large number of scientific articles, proves the viability of these devices for monitoring and assessing pasture biomass condition, measuring biophysical parameters [36], and validating the satellite data [34]. These devices are flexible, simple, and affordable, and, most importantly, they provide high-resolution spatial and temporal data related to the state of grassland biomass and spatial distribution as needed [20; 25; 37]. However, UAVs also have several limitations, such as covering relatively small areas, climatic conditions, possible financial inaccessibility, etc. Vegetation spectral indices are principal for remote sensing data analysis, particularly for assessing the quantity and quality of vegetation cover, monitoring terrestrial ecosystems, studying vegetation dynamics, and identifying environmental stresses [38; 39].