# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
## 3 Results  
### Land Cover Accuracy Assessment and Change Analysis  
The land cover maps over the study area for the epochs of 2001 and 2019 were produced at a 30-m spatial resolution using Landsat satellite data and the RF method. The produced maps differentiate six land cover classes: water, artificial surfaces, forest, shrubs, grassland, and agriculture. All available Landsat channels were used in the classification model. The usefulness of the spectral indices in the land cover classification process were proved by several studies [70; 71]; therefore, an additional set of spectral indices was included in the classification as well. These include Normalized Difference Vegetation Index (NDVI) [72], Enhanced Vegetation Index (EVI) [73] and from a group of water spectral indices proposed by [PERSON] [74] the Modified Normalized Difference Water Index (MNDWI). The training polygons for each LC class were created by manual vectorization, separately, for each epoch. The additional independent set of polygons was also vectorized for validation purposes. The validation polygons are created for each epoch so that they are equally distributed over the study area (Figure 2). Table 1 shows the basic information for the created training and validation datasets. RF models were trained and applied to the study area separately for the 2001 and 2019 epochs (Figure 3). Accuracy assessment is a key component of any properly conducted remote sensing study [75; 76; 77; 78]. Visual inspection of the produced LC maps showed meaningful distributions of the classes over the study area, which was additionally confirmed through statistical analyses. This was done by comparing outputs maps with the vectorized \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**2001**} & \multicolumn{4}{c}{**2019**} \\ \cline{2-10}
**Land Cover** & **Training Count** & **Area (km\({}^{2}\))** & **Validation Count** & **Area (km\({}^{2}\))** & **Training Count** & **Area (km\({}^{2}\))** & **Validation Count** & **Area (km\({}^{2}\))** \\ \hline Artificial surfaces & 119 & 25.40 & 9 & 1.09 & 120 & 21.80 & 12 & 0.98 \\ Water bodies & 14 & 5.23 & 8 & 1.24 & 11 & 7.47 & 9 & 1.90 \\ Forests & 67 & 14.08 & 11 & 2.85 & 74 & 12.42 & 15 & 3.32 \\ Grasslands & 10 & 1.05 & 15 & 0.93 & 14 & 1.55 & 10 & 1.09 \\ Agricultural areas & 76 & 17.88 & 16 & 1.13 & 46 & 14.09 & 15 & 0.72 \\ Shrubs & 14 & 1.08 & 11 & 9.59 & 23 & 1.89 & 11 & 10.06 \\ Total & 300 & 64.72 & 70 & 16.83 & 288 & 59.22 & 72 & 18.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of vectorized training and validation polygons of land cover classification. Figure 2: Samples of the polygons for training and land cover validation for 2001 **(a)** and 2019 **(b)**. validation polygons, pixel-wise, and producing confusion matrixes [70; 79; 80]. The overall accuracy (OA), producer's accuracy (PA), user's accuracy (UA) and kappa coefficient of agreement (Kappa) metrics were calculated and analyzed as well as these are commonly used metrics for evaluation of land cover classification results [77; 80; 81]. The results shown in Table 2 show an accuracy assessment for the 2001 land cover classification. User's accuracy for each category separately ranges from 70.39% (grasslands) to 99.82% (water bodies). Analyzing the production accuracy for each category separately, the values range from 76.65% (grasslands) to 100% (water bodies). The overall accuracy for all categories in the analyzed image is 94.52%, while the Kappa is 0.92. An assessment of the accuracy of the classification of the image from 2019 is shown in Table 3. According to the given table and confusion matrix, the user's accuracy shows the lowest values for the grassland category (71.26%), while the highest values were recorded for water bodies (99.82%). The producer's accuracy according to each category ranges from 66.67% (artificial surfaces) to 100% (water bodies). The overall accuracy according to all categories is 96.91%, while the Kappa is 0.95.