# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 5 Results and Discussion  
### Results for the Main Study Region  
The classification results and accuracy assessment metrics showed that the NDTI, in combination with NDVIre and the MNDWI, provided the highest accuracy, in comparison to other combinations including the existing built-up indices and the original image classification (Tables 4 and 5). Visual analysis of the resulting images in Figure 7 shows that the NDTI solves the mixing problem of the built-up and the bare land, which is obvious in other built-up indices. In addition, the problems \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Training Sample**} & \multicolumn{4}{c}{**Number of Accuracy**} \\  & \multicolumn{2}{c}{**Assessment Points**} \\ \hline \multirow{2}{*}{**Class Name/ Region**} & \multicolumn{2}{c}{**Istanbul**} & \multicolumn{2}{c}{**Ankara**} & \multicolumn{2}{c}{**Konya**} & \multicolumn{2}{c}{**Istanbul**} & \multicolumn{2}{c}{**Ankara**} & \multicolumn{2}{c}{**Konya**} \\  & **Poly.** & **Pix.** & **Poly.** & **Pix.** & **Poly.** & **Pix.** & **Point** & **Point** & **Point** \\ \hline Bare Land & 215 & 1874 & 67 & 12198 & 72 & 42070 & 250 & 185 & 250 \\ Asphalt & 47 & 473 & 25 & 360 & 19 & 1033 & 60 & 40 & 50 \\ Water & 18 & 1587 & 2 & 4543 & 1 & 636 & 40 & 15 & 10 \\ Industry & 26 & 198 & 4 & 72 & 12 & 731 & 70 & 30 & 50 \\ Built-up & 333 & 1326 & 295 & 629 & 84 & 1373 & 250 & 185 & 250 \\ Forest & 173 & 5184 & 8 & 565 & 6 & 85 & 70 & 25 & 20 \\ Veg. Cover & 81 & 4458 & 4 & 118 & 17 & 2206 & 70 & 45 & 25 \\ Total & 893 & 15100 & 405 & 18485 & 211 & 48134 & 810 & 525 & 655 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training sample and accuracy assessment point distribution for the study regions. related to overestimation of built-up regions and underestimation of bare land improved significantly, using this combination. The second highest accuracy was achieved with the combination of NDBI, NDVIre, and MNDWI, for all multi-index combinations. This result was in accordance with NDBI's reported performance in built-up area detection [76]. However, this combination provided lower overall accuracies and a low rate of bare land detection, compared to the NDTI-based combination and original image classification results. These results strengthened the findings regarding the built-up indices described in Section 4.3. According to the per-class accuracy results presented in the Table 4, the multi-index image of the NDBI, the NDVIre, and the MNDWI, provided lower accuracies than the ten-band original Sentinel-2A image for most land cover classes, except for improvements in the vegetation cover class. This improvement can be explained by the use of the NDVIre index, which includes the red-edge band of the Sentinel-2A, for vegetation monitoring. However, this combination could not provide a reliable separation between the built-up area and the bare land. The multi-index image of the NDTI, the NDVIre, and the MNDWI provided better accuracies for most of the land cover classes, especially the built-up and the bare land classes. Similarly, the red-edge band included in the NDVIre provided superior information about vegetation cover class. The overall accuracy and kappa metric presented in Table 5 indicated that the NDTI-based combination improved the accuracy metrics with consistent ranges for each individual LCU class, compared to the classified Sentinel-2A image and classified multi-index image of the NDBI, the NDVIre, and the MNDWI. Figure 7: Classification results of: (**a**) NDBI; (**b**) BUI; (**c**) BAEI; (**d**) NBI; (**e**) VIBI; (**f**) IBI; (**g**) UI; (**h**) BSI; (**i**) NDTI-based multi index sets; and (**j**) original spectral bands. As an additional experiment, three indices, including the NDTI, the NDVIre, and the MNDWI were stacked with the original Sentinel-2A ten bands and the resultant image with thirteen layers (ten original bands and three index images) was classified using the same ROIs; however, the classification result did not show a satisfying performance.