## 3 Results  
### A Spatial Correlation Analysis of the Space-Born and UAV-Born Spectral Vegetation Indices  
Spatial correlation of the obtained results was carried out in order to assess the possibility of using different remote sensing images, including medium-resolution open-access sensors for the assessment of the ecological state of mountain grasslands for small areas.  
The correlation analysis showed significant correlation \(\alpha\) = 0.01 ** between all VIs derived from air- and space-born data, however, with different intensity. As a result of the correlation analysis, we identified two levels of positive correlation strength: weak and moderate, according to Table 4. Moderate positive correlations [76] were detected between the GLI index derived from UAV and the GLI index from Planet Scope where r = 0.69; in the case of Landsat, r = 0.50 and for Sentinel, r = 0.66 (Figure 2).  
The SAVI correlation matrix showed weak positive correlations with Planet Scope (r = 0.49) and Sentinel (r = 0.43) and a weak correlation with Landsat (r = 0.38). A similar trend was observed with MSAVI: weak positive correlations with Planet Scope (r = 0.49) and Sentinel (r = 0.43) and a weak positive correlation with Landsat (r = 0.38). However, in the case of the GSAVI vegetation index, UAV had a weak positive correlation with Planet Scope (r = 0.15) and no correlation with Landsat (r = 0.04) and Sentinel (r = 0.09). The correlation of the NDWI-derived UAV was weak with Planet Scope's NDWI (r = 0.14) and no correlation was observed with Landsat (r = 0.01) and Sentinel (r = 0.04).  
As shown in the correlation analysis for NDVI, there was a moderate correlation (r = 0.50) between UAV VIs and Planet Scope VIs, a weak correlation (r = 0.40) between UAV VIs and Landsat VIs, and a weak correlation (r = 0.43) between UAV VIs and Sentinel VIs. For GNDVI, there were weak correlations among satellite VIs and UAV VIs. Specifically, weak positive correlations were observed between UAV VIs and Planet Scope (r = 0.14), Landsat (r = 0.04), and Sentinel (r = 0.08) VIs. Weak positive correlations for DVI were observed with Planet Scope (r = 0.43), Landsat (r = 0.34), and Sentinel (r = 0.38). For EVI,moderate positive correlations were detected: r = 0.61 with Planet Scope, r = 0.54 with Landsat, and r = 0.57 with Sentinel.  
The analysis of the correlation tables shows that UAV VIs are comparable to other sensors for vegetation monitoring because, in all cases, regardless of power and type, a significant correlation was observed (\(\alpha\) = 0.01). In particular, the GLI, EVI, and NDVI VIs stand out, in the cases of which relatively strong correlations were obtained. However, the strength of the correlation indicates that there may be some differences in the information received due to sensor variations or other environmental factors.  
According to the results of the regression model, in the case of Landsat, R\({}^{2}\) = 0.22, RMSE = 0.05, ad RPD = 1.13. For Sentinel, R\({}^{2}\) = 0.39, RMSE = 0.04, and RPD = 1.28; for Planet Scope, R\({}^{2}\) = 0.37, RMSE = 0.04, and RPD = 1.26. From here, it is clearly seen that the best results are recorded for Sentinel, where a higher value of R\({}^{2}\) is recorded, RMSE trends to the minimum, and weak RPD is observed, according to Table 5 [77]. However, the  
Figure 2: Correlations between satellite Planet Scope (**a**), Sentinel-2 (**b**) and Landsat 8 (**c**) and UAV data (at the 0.01 * significance level).  
obtained results indicate that the absolute values of the indices obtained from satellite and UAV images do not correspond because, even with the index showing the best correlation, there is a weak RPD, which may be due to the difference of the sensor.