# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 4 Methodology
### Normalized Difference Village Index (NDTI)  
To propose an index that can highlight built-up areas and separate them from bare land, the spectral profiles of these two land features were analyzed at several sample locations. It showed that the reflectance difference between the SWIR bands (bands 11 and 12) was higher for the pixels selected from the bare land than for the pixels selected from the built-up areas. This indicated the possible efficiency of these two SWIR bands for differentiating built-up area from bare land. The applicability of the NDTI on SWIR bands of the Sentinel-2A images for built-up area and bare land extraction was investigated. This index was first proposed by [PERSON] et al. (2017) for soil practices, tillage management, and crop residue mapping, and was successfully applied by [PERSON] et al. (2017) and [PERSON] et al. (2017) for agricultural practices and soil management. To our knowledge, this is the first time that the NDTI has been used as a component to discriminate and separate built-up areas and bare land.  
The NDTI data provided in Figure 4 and the existing built-up indices provided in Figure 3 show that the NDTI can highlight the urban areas and it increases the contrast between the bare land (red circle) and built-up area (blue circle). This visual inspection indicates the possible efficiency of the NDTI, compared to the existing built-up indices.  
As a second analysis, 80 random points (40 for each class) were selected and the mean index values were calculated to statistically evaluate the capability of the NDTI in separating the bare land from the built-up areas. As illustrated in Figure 5, the NDTI provides distinctive values for bare land and built-up area classes, whereas the existing built-up indices provide similar values.  
While the NDTI increases the contrast between bare land and built-up areas, it decreases the contrast between water bodies and other land covers (Figure 4). This drawback is overcome by the multi-index approach proposed in this research.  
Figure 6a shows an RGB composite multi-index image produced from the NDTI, the NDVIre, and the MNDWI. This multi-index image provides a good discrimination between the three major LC categories--vegetation cover in red, water bodies in blue, and the built-up areas in green. The spectral signature analysis shows that the multi-index dataset represents a more linear and simplified response for the main LC categories than the original image bands, which indicates a better separation capability (Figure 6b,c).  
Figure 4: Images of (**a**) the Normalized Difference Tillage Index (NDTI) and (**b**) a subset of NDTI exemplify the contrast between bare land and built-up regions.  
Figure 5: Comparison of the average index values of points on bare land and built-up classes.  
Figure 6: (**a**) RGB composite of the NDTI, red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI). (**b**) Spectral signatures represented by the mean of the three major categories of land cover for the thirteen bands of the Sentinel-2A image. (**c**) Simplified spectral signatures represented by the mean of the three major categories of land cover for the multi-index image.