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.

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.

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

Very-high-resolution thermal infrared data has a good potential to detect and monitor LST variations in urban areas. In this work, an attempt was made to estimate Land Surface Temperature (LST) from very high resolution (VHR) data using machine learning technics in an area of Botanical garden of Yerevan, Armenia; UAV-derived and in-situ measured high precision LST of various land cover (LC) patterns were then compared for September and October. The main purpose of this study was to explore the capabilities of UAV imagery (multispectral/TIR) in assessing LST of the different LC patterns. For this purpose, a UAV survey was performed, and VHR data were collected using multispectral and TIR thermal camera while in-situ measurements of temperature and surveying of the different LC patterns were performed. A significant correlation was detected between LSTs in situ measured and detected by UAV in 06 September (r=0.758; p-value <0.01) and in 27 October (r=0.686; p-value <0.01). When comparing the LSTs of separate land-cover patterns the best results were received for Bare soils (r=0.591; p-value <0.05) and Concrete (r=0.927; p-value <0.01) when the survey and measurements was done in September. Despite its limitations, it can be stated that UAV thermal survey has a good potential to detect and monitor LST variations in urban areas.

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.