## 2 Materials and Methods
### Methodology of the Study  
Considering the available types of degradation on the investigated pasture, the following classes are defined as follows:  
1. Grass--this class corresponds to areas with no or insignificant degradation, which are covered by grass vegetation;
2. Shrubs--this class corresponds to areas which are covered with bushes and in the long term with trees;
3. Soil--this class represents areas with exposed soil and cattle tracks;
4. Stones--this class represents areas which are covered with stones and rocks. The methodology of this study is summarized in Figure 3. It includes the following steps:  
* Step 1. A UAV is used to make overlapping images of the investigated pasture;
* Step 2. The overlapping images are combined into one big high-quality (HQ) map, representing the investigated pasture area;
* Step 3. The HQ map is used to select numerous polygons (regions of interest) from each class, to be used for training and validation purposes;
* Step 4. The HQ map is used to generate a segmentation map, by manipulating three parameters of the pixels: spectral detail, spatial detail, and minimum segment size in pixels;
* Step 5. Using the combined HQ map, the training data, and the segmentation map, a classification map is created, using one of the available algorithms;
* Step 6. The accuracy of the classification map is assessed by using randomly selected pixels from the chosen polygons in Step 3 and comparing them with the reference class. Steps 4, 5, and 6 are repeated numerous times until the best-performing models are obtained;
* Step 7. Once the optimal classification models are obtained, they are used to evaluate and analyze the degradation of the pasture.  
In this study, two types of classification approaches are used and compared: pixel-based and object-based. Furthermore, for each one of them the following algorithms are applied as follows:  
* Maximum likelihood--the idea behind the maximum likelihood is to estimate the parameters of a probabilistic model by maximizing the likelihood function, which measures how well the model explains the observed data. By iteratively adjusting the model parameters, MxL finds the parameter values that make the observed data most probable under the model [44];
* Random trees--a very straightforward and easy-to-implement algorithm, based on the idea of using a random subset of features and samples from the training data. The  
Figure 3: Overview of the study methodology.  
randomness improves the models' accuracy and the overfitting, leading to a robust and more generalized model [45];
* Support Vector Machine--it is especially useful for models that work well with a small dataset or to separate the data into clearly distinguished classes. The goal of SVM is to find a hyperplane in multidimensional space that separates different classes in classification tasks. A hyperplane is a geometric representation of the dividing line in two-dimensional space or the plane in three-dimensional space, for example. The margin is the distance between the nearest points of the two classes and the hyperplane. The goal of SVM is to maximize this margin, because a wider margin usually leads to better generalization and fewer errors on new data. The support vectors are the data points that are closest to the hyperplane. They are key to defining the hyperplane and pattern, as the margin is calculated relative to them [46].