## 4 Results and Discussion
### Land Use and Land Cover Change
#### 4.2.2 Mapping of Changes from 1991 to 2019  
This subsection presents the results of the LULC maps for the epochs 1991-2019. Since the area of interest is mostly inside the Dilijan National Park where conversation practices are put in place, the area is very stable in terms of land cover change. Figure 16 depicts the area breakdown for four selected epochs (1991, 2002, 2010 and 2019) and clearly shows that most class areas remained almost the same over the entire period of time. The biggest change can be observed in the forest class that dropped from 50.18% in 1991 to 49.71% in  
Figure 14: Breakdown of the area of land cover land use classes for the 2019 epoch. Figure 15: Comparison of the 10 and 30 m LULC map product for 2019. The Sentinel-2-based (10 m) product on the left and the Landsat 8-based product (30 m) on the right. 2019. The main driver behind this loss of forest is either natural degradation, small scale urbanization or minor agricultural expansion. The class \"other vegetation\" (than forest) increased by 0.41% over the entire period and is the main driver behind the loss in forest, this is followed by a minor increase in agricultural activity of 0.04% and lastly by the extension of settlements which grew by 0.01% of the total project area from 1991 until 2019. All other classes (water and bare soil) did not show any significant change. The validation results for epochs 2019, 2002 and 1991 are presented in Tables 5-7 below and range between 85 and 89% in terms of overall accuracy. Generally speaking, the selected method together with the input data yields good results and most classes show both high user and producers' accuracies (usually above 80%). The class with the lowest  
Figure 16: Breakdown of the area of land cover land use classes for the epochs 1991, 2002, 2010 and 2019.  
accuracies is \"Other vegetation\" whose producers' accuracy only ranges from 58%-63%. When looking at the validation matrices, it can be seen that this class often gets confused with agriculture. This probably arises from the fact that the training data for agriculture do not stem from a very reliable source (Global Crop Extent Layer) and the very broad class definition \"Other vegetation\" seems to be too vague for the classifier to be able to find sharp class boundaries which yields high confusion with other classes. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Ground Truth**} & \multirow{2}{*}{**Rivers**} & \multirow{2}{*}{**User**} \\  & & & & & & & & & & & **Accuracy** \\ \cline{5-10}  & & & & & & & & & & & & & **Accuracy** \\ \hline \multirow{10}{*}{**Classification**} & **Forest** & **Agriculture** & **Settlement** & **Primary** & **Bare** & **Other** & **Standing** & **Rivers** & **\# Samples** & **User** \\  & & & & & & & & & & & **Accuracy** \\ \cline{1-1}  & **Forest** & **26** & 0 & 0 & 0 & 0 & 4 & 0 & 0 & 30 & 86.67\% \\  & **Agriculture** & 0 & **24** & 0 & 0 & 0 & 6 & 0 & 0 & 30 & 80.00\% \\  & **Settlement** & 0 & 0 & **28** & 1 & 0 & 1 & 0 & 0 & 30 & 93.33\% \\  & **Primary** & 0 & 0 & **26** & 0 & 0 & 0 & 0 & 28 & 92.86\% \\  & **roads** & 2 & 0 & 0 & **26** & 0 & 0 & 0 & 0 & 28 & 92.86\% \\  & **Bare soil** & 0 & 0 & 0 & 5 & **22** & 3 & 0 & 0 & 30 & 73.33\% \\  & **Other** & 3 & 1 & 0 & 0 & 0 & **26** & 0 & 0 & 30 & 86.97\% \\  & **vegetation** & 3 & 1 & 0 & 0 & 0 & 0 & **28** & 1 & 30 & 93.33\% \\  & **Standing** & 1 & 0 & 0 & 0 & 0 & **28** & 0 & 30 & 93.33\% \\  & **Rivers** & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 28 & 30 & 93.33\% \\ \hline \multirow{10}{*}{**Classification**} & **Totals** & 33 & 25 & 28 & 32 & 22 & 41 & 28 & 29 & **238** & \\  & **Producer** & 78.79\% & 96.00\% & 100.00\% & 81.25\% & 100.00\% & 63.41\% & 100.00\% & 96.55\% & & \\ \cline{1-1}  & **Overall** & **87.39\%** & \multicolumn{1}{c}{} & & & & & & & & \\ \cline{1-1}  & **accuracy** & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation Matrix for Service 1: LULC classification for epoch 2002.