Five degradation classes were defined as follows: degradation stage 1 (SC \(\leq\) 19%); degradation stage 2 (SC \(\leq\) 39%);degradation stage 3 (SC \(\leq\) 59%); degradation stage 4 (SC \(\leq\) 79%); and degradation stage 5 (SC \(\leq\) 95%). In another study aimed at assessing the grassland degradation in Inner Mongolia, China, the canopy coverage (CC) was selected as a criterion [23], and three degradation classes were defined based on it: dense grassland (CC \(>\) 50%); moderately dense grassland (CC \(>\) 20%); sparse grassland (CC \(>\) 5%). Similarly, in [24] the vegetation cover (VC) was used as the criterion for assessing degradation: none (VC \(>\) 80%); light to moderate (VC \(>\) 60%); moderate to severe (VC \(>\) 30%); and extreme (VC \(<\) 30%). From the performed analysis, it can be seen that numerous criteria and classifications exist, which could be used to assess the degradation of pastures, meadows and grasslands. In each individual study, the criterion and the scale/categories of degradation were chosen individually, depending on the local specifics of the investigated terrain: the observed problems, the species composition, the biodiversity, their impact on the farmers' production, etc. With the advancement of unmanned aerial vehicles (UAV) and satellite imaging technologies, the interest in management of grassland and pastures, their degradation, desertification, etc., has increased [25; 26; 27]. High-spatial-resolution drone and satellite-obtained data are becoming increasingly cheap and globally available, meaning that the use of these datasets for rangeland mapping, monitoring, and tracking is affordable [28; 29]. The results shown in [30] confirm the possibility of UAV imagery being applied as a tool for precision grassland surveys. The authors reached R\({}^{2}\) = 0.62 for their best-performing biomass estimation model and concluded that such an approach can be an alternative to conventional field methods. Different approaches are used when assessing the quality and efficiency of pastures with remote sensing, which are based mostly on vegetation indices and machine learning (ML). According to [31], the first Scopus publication on the application of ML in pasture evaluation is from 2013, and by 2020, the number of articles on the topic increased to 456, which shows that the application of ML algorithms with other metrics gives promising results. The study also stated that the most commonly used ML algorithms for this purpose are random forest (RF) and partial least squares regression (PLSR). Different studies have used ML regression models for the evaluation of aboveground biomass and dry matter of grasslands and pastures. In [32], machine learning-based predictive models were trained for the estimation of aboveground biomass using satellite and UAV-obtained data. For large-scale evaluation, the Huber model returned the best results with R\({}^{2}\) = 0.60, while for short-scale the best-performing models were K-nearest neighbours with R\({}^{2}\) = 0.76 and Extra trees with R\({}^{2}\) = 0.75. The grassland biomass was also estimated in [33], using a combination of Sentinel-2 imagery with machine learning algorithms. Even though the authors did not test their approach on large-scale grasslands, their best-performing model achieved R\({}^{2}\) = 0.6, which they consider enough to support farmers in the optimization of the biomass management of their pastures. In [34], a UAV-mounted multispectral camera was used, in order to estimate the grass stand of pastures. Using a combination of statistical models and machine learning allowed predicting the pre-grazing pasture biomass production with an error of less than 20%. In [35], computer vision algorithms were used to extract the biophysical parameters from images, which were then used to train machine learning algorithms that predict grassland height and biomass. The study achieved up to R\({}^{2}\) = 0.656 for pasture height estimation and R\({}^{2}\) = 0.496 for biomass estimation.