# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 5 Results and Discussion
### Results for the Main Study Region
Notably, the NDTI-based multi-index approach could not completely solve the problem of mixed pixels of bare land and built-up area, but showed an evident improvement, compared to other built-up indices and original image classifications. The sub regions from the classification results of Istanbul are provided in the following figures (Figures 8-10) to demonstrate the improvements of the proposed NDTI-based method, over other classification results. In addition, the accuracy assessment results of these sub regions are given in Tables 11-14. The accuracy metrics show the ten-band original image classification and NDBI-based multi-index set classification provided similar accuracies, whereas the NDTI-based multi-index set outperformed them with a 30% overall accuracy improvement and better consistency of the producer's and the user's accuracy. These results supported the accuracy assessment results of the whole study region. Figure 8 represents regions covered by water, asphalt road, bare land, vegetation covers, and built-up area land cover classes. All classified images accurately classified water bodies and asphalt road, but there was a misclassification of the bare lands as built-up regions in the multi-index images, using the NDBI (Figure 8c) and a misclassification issue and overestimation of the built-up area in the classified Sentinel-2A image (Figure 8b). The multi-index image of the NDTI, the NDVIre, and the MNDWI provided better results than the other two (Figure 8d). The NDTI-based image classification determined the built-up areas, more accurately, and separated the bare land near roads, which were classified as built-up areas in the classified Sentinel-2A image (Figure 8b). The multi-index combinations, including the NDVIre, determined vegetation cover better and improved the separation of different vegetation types in the study area. Different vegetation types can be recognized more obviously in both multi-index images (Figure 8c,d) than in the classified Sentinel-2A image (Figure 8b). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**(Sentinel-2A Image (10 Bands))**} & \multicolumn{2}{c}{**(NDBI+NDVIre+MNDWI)**} & \multicolumn{2}{c}{**(NDTI+NDVIre+MNDWI)**} \\ \hline
**Land Cover** & **Producer’s** & **User’s** & **Producer’s** & **User’s** & **Producer’s** & **User’s** \\
**Class** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline Bare land & 90.78 & 55.49 & 53.32 & 61.13 & 92.90 & 92.11 \\ Asphalt & 93.75 & 100 & 72.50 & 96.67 & 85.71 & 100.00 \\ Water & 100 & 97.50 & 100.00 & 97.50 & 100.00 & 97.50 \\ Industry & 96.77 & 85.71 & 92.68 & 54.29 & 98.28 & 81.43 \\ Built-Up & 62.77 & 95.20 & 40.79 & 34.32 & 93.28 & 92.25 \\ Forest & 62.89 & 100 & 81.90 & 86.00 & 94.23 & 98.00 \\ Veg. Cover & 78.57 & 45.71 & 81.82 & 77.14 & 93.33 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracies of the different approaches for the Istanbul region. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Overall Accuracy** & **Kappa** \\ \hline Sentinel-2A image (10 Bands) & 0.75 & 0.67 \\ Multi-index (NDBI+NDVIre+MNDWI) & 0.60 & 0.47 \\ Multi-index (NDTI+NDVIre+MNDWI) & 0.93 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the overall accuracy and Kappa statistics for the Istanbul region. In Figure 9, more complicated and heterogeneous parts of Istanbul were investigated. The area is covered with dense residential and industrial built-up patches and includes highways and urban green areas. According to visual inspection, the original ten-band image classification suffered from overestimation of residential areas and misclassification of industrial areas and bare land (Figure 9b). Additionally, the NDBI-based multi-index image could not separate the industrial areas from residential areas and there was a misclassification problem of the residential areas, due to the overestimation of bare land.