# A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images
## 4 A Novel Vegetation Index That Accurately Reflects Urban Land Cover  
Thus, in urban areas, use of existing vegetation indices (NDVI, GNDVI, BNDVI, RGBVI, GRVI, and SAVI) results in misclassification of long-run steel roofing, urethane-coated, and waterproof coated areas as vegetation. To address this problem, many equations were created by combining the blue, green, red, and mir bands, and three equations that can be used for urban vegetation analysis were finally selected through a manual process as Table 6. Finally, the optimal vegetation index was suggested by verifying the result of vegetation classification for each type of land cover. Table 6 shows the results of applying the novel vegetation index equation. Figures 8-10 show the vegetation index map for each equation and the results of vegetation classification according to the threshold value. The threshold value for vegetation classification was applied in consideration of the vegetation index of lower-vitality grass (Nos. 22-24). \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Vegetation Index Equation**} \\ \hline squared Blue-Green NDVI index & \(\text{Nir}^{2}\)Blue-Green \\ squared Red-Green NDVI index & \(\text{Nir}^{2}\)Blue-Green \\ squared Red-Blue NDVI index & \(\text{Nir}^{2}\)Red-Green \\ squared Red-Blue NDVI index & \(\text{Nir}^{2}\)Red-Blue \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A novel vegetation index equation. Figure 8: Vegetation index map and vegetation map based on the squared Blue-Green Normalized Difference Vegetation Index (NDVI) index. First, using the squared Blue-Green NDVI index, the vegetation indices of the red urethane-coated areas (Nos. 13-15) ranged from 0.72 to 0.74, which were slightly higher than the values of lower-vitality grass (Nos. 22-24). Therefore, as shown in Figure 8b, when 0.62 (the minimum value of lower-vitality grass) was set as the threshold value, the red urethane-coated areas were misclassified as vegetation. Second, using the squared Red-Green NDVI index, the vegetation indices of the long-run steel roofing (Nos. 1-3) were between 0.73 and 0.78, which were similar to the values of the medium-vitality grass (Nos. 19-21) and higher than those of the lower-vitality grass (Nos. 22-24). Therefore, as shown in Figure 9b, when 0.48 (the minimum value of the lower-vitality grass) was set as the threshold value, the areas with long-run steel roofing were misclassified as vegetation. Finally, using the squared Red-Blue NDVI index, the vegetation indices of the long-run steel roofing (Nos. 1-3), urethane-coated (Nos. 10-15), and waterproof-coated (Nos. 16-18) were misclassified as vegetation. Figure 10: Vegetation index map and vegetation map based on the squared Red-Blue NDVI index. Figure 9: Vegetation index map and vegetation map based on the squared Red-Green NDVI index. areas were all lower than the vegetation index of lower-vitality grass (Nos. 22-24). Therefore, as shown in Figure 10b, when 0.61 (the minimum value of the lower-vitality grass) was set as the threshold value, only areas with trees and grass were classified as vegetation areas, see Table 7.  
To quantitatively verify the novel vegetation index proposed in this study, five validation points were selected for each type of land cover through field surveys. Among these points, twenty validation points in grass and trees were classified as vegetation, while thirty validation points in the areas with long-run steel roofing, artificial playgrounds, playground tracks, urethane coatings, and waterproof coated roofs were classified as non-vegetation. Figures 11-13 show the validation points for each type of land cover on the vegetation map created by applying the novel vegetation index equation. Table 8 shows the kappa coefficient for the comparison between the novel vegetation map and the ground truth results. The kappa coefficients of the squared Blue-Green NDVI index and squared Red-Green NDVI index were both 0.8, and the kappa coefficient of the squared Red-Blue NDVI index was the highest, at 1.0.