## 3 Results and Discussion
### Landscape Metrics Analysis  
Land-cover maps for 2010 and 2020 were derived using the random forest classifier (using 20 trees). Corine Land Cover level 3 classes were aggregated to the following 6 classes (urban, cropland, grassland, woodland and forest, heathland and shrub, sparsely vegetated areas) to simplify the analysis of change. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Scenario 1** & **Scenario 2** & **Scenario 5** & **Scenario 6** \\ \cline{2-4}  & **R-Factor Derived from GPM Data** & **R-Factor Derived from Rain Gauge Data** \\ \cline{2-4}  & **C-Factor Derived from Literature** & **C-Factor Derived from NDVI (vk)** & **C-Factor Derived from NDVI (vk)** \\ \hline Mean annual & & & \\ soil erosion & 2.59 & 4.63 & 1.95 & 3.24 \\ (t ha\({}^{-1}\) year\({}^{-1}\)) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Mean annual soil loss rates for scenarios 1, 2, 5, and 6. Figure 21: Soil loss map with R-factor values derived from in situ data and C-factor values derived NDVI(vk) data. The classification results were subsequently validated using Google earth imagery, with accuracies exceeding 75% for the two dates with Kappa statistics of 68% and 64% for 2010 and 2020, respectively. After classification, change-detection analysis was carried out. The classified images are shown in Figure 22. The selected metrics were calculated at the class level using an 8 \(\times\) 8 m cell neighborhood rule in FRAGSTAT. Table 11 illustrates the values for the calculated landscape metrics. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Land Cover Classes**} & \multicolumn{2}{c}{**Total Area**} & \multicolumn{2}{c}{**NP**} & \multicolumn{2}{c}{**PD**} & \multicolumn{2}{c}{**ED**} \\ \cline{2-10}  & **2010** & **2020** & **2010** & **2020** & **2010** & **2020** & **2010** & **2020** \\ \hline
1 & Urban & 71.28 & 97.74 & 140 & 243 & 0.93 & 1.61 & 2.79 & 4.56 \\ \hline
2 & Cropland & 3218.58 & 4673.43 & 2259 & 3887 & 15.04 & 25.89 & 100.45 & 157.88 \\ \hline
3 & Woodland and forest & 247.41 & 377.37 & 480 & 837 & 3.19 & 5.57 & 10.23 & 20.18 \\ \hline
4 & Grassland & 2947.68 & 2768.31 & 1458 & 3336 & 9.71 & 22.22 & 61.47 & 95.58 \\ \hline
5 & Heathland and shrub & 8527.05 & 7077.69 & 1253 & 3977 & 8.34 & 26.49 & 142.23 & 202.5 \\ \hline
6 & Sparsely vegetated areas & \multicolumn{2}{c}{\(18.72\)} & \multicolumn{2}{c}{\(46\)} & \multicolumn{2}{c}{\(0.3\)} & \multicolumn{2}{c}{\(1.12\)} \\ \cline{2-10}  & **Land Cover Classes** & \multicolumn{2}{c}{**LPI**} & \multicolumn{2}{c}{**ENN\_AM**} & \multicolumn{2}{c}{**PLAND**} & \multicolumn{2}{c}{**SPLIT**} \\ \cline{2-10}  & **2010** & **2020** & **2010** & **2020** & **2010** & **2020** & **2010** & **2020** \\ \hline
1 & Urban & 0.15 & 0.14 & 140 & 243 & 0.47 & 0.65 & 337133.02 & 231397.27 \\ \hline
2 & Cropland & 8.21 & 18.56 & 2259 & 3887 & 21.44 & 31.12 & 110.97 & 26.40 \\ \hline
3 & Woodland and forest & 0.12 & 0.43 & 480 & 837 & 1.64 & 2.51 & 51675.1 & 41194.35 \\ \hline
4 & Grassland & 4.1 & 2.81 & 1458 & 3336 & 19.63 & 18.43 & 312.88 & 444.27 \\ \hline
5 & Heathland and shrub & 49.18 & 20.64 & 1253 & 3977 & 56.8 & 47.14 & 4.12 & 20.22 \\ \hline
6 & Sparsely vegetated areas & \multicolumn{2}{c}{0.039} & \multicolumn{2}{c}{\(46\)} & \multicolumn{2}{c}{\(0.12\)} & \multicolumn{2}{c}{\(4719625.27\)} \\ \hline \hline \end{tabular}  
* _NP_: Number of Patches; _PD_: Patch Density; _ED_: Edge Density; _LPI_: Largest Patch Index; ENN_AM: Euclidean Nearest Neighbor Distance Area-Weighted Mean; _PLAND_: Percentage of Landscape; SPLIT: Split index.  
\end{table} 
Table 11: Results for the landscape metrics. Figure 22: Classified LULC maps of Paleochora for 2010 (**left**) and 2020 (**right**). Land cover classes across the two dates (2010 and 2020) showed that heathland and shrub (5) was the dominant class in the study area according to PLAND metric values.