## 3 Results
### Generated LUILC Maps  
The confusion matrices obtained from the performance evaluation on the test data of the composite Landsat 7 images classified using the RF algorithm on the GEE platform, along with the accuracy values, are presented in Tables 4 and 5, respectively. According to the results, the classification of satellite images for the years 2007, 2012, 2017, and 2022 achieved the desired performance in terms of user accuracy and producer accuracy. Both the class-specific accuracy and overall accuracy values were greater than 0.80, indicating an acceptable level of classification performance. The Kappa coefficients for the classification of images for the years 2007, 2012, 2017, and 2022 were calculated as 0.89, 0.90, 0.86, and 0.88, respectively, while the overall accuracy values were calculated as 0.90, 0.93, 0.88, and 0.89, respectively. The hyperparameters and their values that achieved this performance with the RF classifier are presented in Table 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Built-Up**} & \multicolumn{2}{c}{**Agricultural**} & \multicolumn{2}{c}{**Forest**} & \multicolumn{2}{c}{**Water**} & \multicolumn{2}{c}{**Agricultural**} & \multicolumn{1}{c}{**User’s**} \\  & **Areas** & **Areas** & **Areas** & **Surfaces** & **and** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{2022} & Built-up Areas & 61 & 3 & 0 & 0 & 6 & 96 \\  & Agricultural Areas & 0 & 56 & 14 & 0 & 0 & 80 \\  & Forest Areas & 0 & 0 & 70 & 0 & 0 & 99 \\  & Water Surfaces & 5 & 4 & 0 & 61 & 0 & 87 \\  & Non-Agricultural and & 3 & 1 & 0 & 0 & 66 & 94 \\  & Producer’s Accuracy (\%) & **96** & **93** & **95** & **97** & **98** & \\ \hline \multirow{8}{*}{2017} & Built-up Areas & 56 & 1 & 1 & 0 & 12 & **80** \\  & Agricultural Areas & 2 & 58 & 9 & 1 & 0 & **85** \\ \cline{1-1}  & Forest Areas & 0 & 0 & 69 & 1 & 0 & **96** \\ \cline{1-1}  & Water Surfaces & 3 & 0 & 2 & 65 & 0 & **49** \\ \cline{1-1}  & Non-Agricultural and & 3 & 4 & 0 & 0 & 63 & **90** \\ \cline{1-1}  & Producer’s Accuracy (\%) & **94** & **96** & **96** & **83** & **80** & \\ \hline \multirow{8}{*}{2012} & Built-up Areas & 58 & 4 & 0 & 0 & 8 & **83** \\ \cline{1-1}  & Agricultural Areas & 0 & 68 & 2 & 0 & 0 & **97** \\ \cline{1-1}  & Forest Areas & 0 & 0 & 69 & 1 & 0 & **99** \\ \cline{1-1}  & Water Surfaces & 0 & 1 & 0 & 69 & 0 & **99** \\ \cline{1-1}  & Non-Agricultural and & 4 & 0 & 0 & 0 & 66 & **93** \\ \cline{1-1}  & Non-Forest & **95** & **98** & **99** & **99** & **96** & \\ \hline \multirow{8}{*}{2007} & Built-up Areas & 62 & 1 & 0 & 0 & 7 & **89** \\ \cline{1-1}  & Agricultural Areas & 9 & 57 & 3 & 1 & 0 & **81** \\ \cline{1-1}  & Forest Areas & 0 & 3 & 67 & 67 & 0 & **96** \\ \cline{1-1}  & Water Surfaces & 1 & 2 & 0 & 0 & 0 & **96** \\ \cline{1-1}  & Non-Agricultural and & 6 & 2 & 0 & 0 & 62 & **89** \\ \cline{1-1}  & Producer’s Accuracy (\%) & **93** & **94** & **98** & **98** & **95** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrices for each composite image generated from the test data after training the RF classifier. After demonstrating successful performance on the test samples of the composite Landsat 7 images for four different years, the trained RF model was employed for predicting LULC classes for all remaining pixels. Following these prediction processes, a LULC class was assigned to each pixel, thereby obtaining a LULC map from each image. The spatial resolution of these LULC maps was consistent with Landsat 7 images, at 30 m. LULC maps for the four years are depicted in Figure 11.  
The proportional distribution of each LULC class in these maps is shown in Figure 12. Upon examination of these LULC maps, it is observed that the proportion of built-up area class to the study area decreased from 21.01% in 2007 to 19.15% in 2022. Agricultural land witnessed an increase over the years, with its proportion rising from 53.40% in 2007 to 60.45% in 2022. Forest area decreased from 15.6% in 2007 to 13.07% in 2022.