Other studies have used ML classification algorithms for categorization of the types of grasslands/meadows based on different factors. In [36], the authors used support vector machine (SVM), RF, neural network, 3D fully convolutional network, 3D convolutional neural network, and the systematic classification method (SCM) with satellite-obtained multispectral images of grassland, in order to identify the types of vegetation. A large amount of ground-based reference data were used for training and validation, which allowed reaching accuracies between 43.42% to 99% for the different species with the SCM method. In [37], the authors tried to identify pastures in Brazil using Landsat 8 data and the RF algorithm. The study created a new pasture map for Brazil and reached an overall accuracy of 87%. In [38], an object-based image analysis was applied to Sentinel-2 data with seven vegetation indices in order to classify different types of pastures (herbaceous and shrubby). The authors used the simple linear iterative clustering (SLIC) algorithm for image segmentation and the random forest for image classification, which allowed them to achieve 87% accuracy. In another study, a pasture was classified into grazed and ungrazed categories, using ground-based camera images and the Modified Green Red Vegetation Index [39]. Different studies dealing specifically with the assessment of grasslands and pasture degradation exist, which are based on appropriate vegetation indices. In [40], shrub degradation of grasslands in Kyrgyzstan was identified based on Google Earth data. The study results showed that the shrubs' normalized difference vegetation index (NDVI) is higher, by approximately 0.20, than the grass NDVI during the summer months. In another study, the grassland degradation index (GDI) and satellite-obtained images were used to classify the degradation of grasslands [41]. The vegetable pasture cover was derived from NDVI, and with its help the GDI was estimated. NDVI and satellite-obtained data were also used in [42], in order to assess the degradation of pastures in Brazil over a five-level scale. The obtained degradation map was validated using ground samples, and 84.2% overall accuracy was achieved. In [16], pixel-based analysis was also applied, in order to estimate the degradation of pastures. LANDSAT-8 and Google Earth data were analyzed over a three-year period in order to obtain four vegetation index signatures of healthy and degraded pastures (NDVI and three RGB indices: total brightness quotient of blue, green and blue). Similarly, in [19], a degraded pasture land in Kazakhstan was investigated with the use of satellite imaging from Google Earth, Landsat 5 and 8 as well as other reference data. Thereafter, isolinear maps were created by dividing the area of herbaceous or woody vegetation by the total area. In [24], NDVI and the modified soil-adjusted vegetation index 2 (MSAVI2) were used on WorldView-2 satellite data to assess grassland degradation in the Georgian Caucasus. Thereafter, the random forest regression model was used to fit the results. Other studies have used machine learning to evaluate grassland degradation. In [22], the authors tried to assess it based on the percent of Stellera chameajsame coverage. Data from ground-based cameras and deep neural networks allowed a success rate between 84% and 96% to be achieved for the different classes. In another study, multispectral and multitemporal data from two satellites (RapidEye Ortho 3A and Landsat 5 TM L1T) were used to identify the degradation of a pasture [18]. The images were analyzed using the random forest classification algorithm, where a total of 210 regions of interest from the four degradation classes were identified. Furthermore, a total of 460 polygons were used for training and validation purposes. The obtained overall accuracy for the pasture degradation data reached 61% using the Landsat data, 75% using the RapidEye data and 77.5% using the combined data.