# 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
Therefore, the squared Red-Blue NDVI index was found to be the most effective for vegetation analysis, reflecting the characteristics of urban land cover. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Land Cover**} & \multicolumn{3}{c}{**Squared Blue-Green**} & \multicolumn{3}{c}{**Squared Red-Green**} & \multicolumn{3}{c}{**Squared Red-Blue**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**NDVI Index**} & \multicolumn{2}{c}{**NDVI Index**} & \multicolumn{2}{c}{**NDVI Index**} \\ \cline{3-10}  & & & **Value** & **Mean** & **Value** & **Mean** & **Value** & **Mean** \\ \hline
1 & Long-run steel roofing & 0.42 & & 0.73 & & & 0.51 & \\ \cline{1-1} \cline{2-10}
2 & Long-run steel roofing & 0.46 & 0.75 & 0.76 & & 0.53 & 0.52 & \\ \cline{1-1} \cline{2-10}
3 & & 0.53 & & 0.78 & & & 0.52 & \\ \hline
4 & Artificial grass playground & 0.20 & & 0.21 & & & 0.23 & \\ \cline{1-1} \cline{2-10}
5 & & 0.23 & & 0.27 & 0.24 & & 0.25 & \\ \cline{1-1} \cline{2-10}
6 & & 0.20 & & 0.24 & & & 0.25 & \\ \cline{1-1} \cline{2-10}
7 & & 0.43 & & 0.18 & & & 0.19 & \\ \cline{1-1} \cline{2-10}
8 & & Playground track & 0.43 & & 0.17 & & & 0.19 & \\ \cline{1-1} \cline{2-10}
9 & & 0.45 & & 0.19 & & & 0.21 & \\ \cline{1-1} \cline{2-10}
10 & & Urethane-coated & 0.24 & & 0.37 & & & 0.48 & \\ \cline{1-1} \cline{2-10}
11 & & 0.22 & & 0.30 & & & 0.43 & \\ \cline{1-1} \cline{2-10}
12 & & 0.15 & & 0.26 & & & 0.37 & \\ \hline
13 & & 0.74 & & & 0.35 & & & 0.41 & \\ \cline{1-1} \cline{2-10}
14 & & 0.72 & & 0.73 & & 0.33 & & 0.41 & \\ \cline{1-1} \cline{2-10}
15 & & 0.72 & & & 0.32 & & & 0.39 & \\ \hline
16 & & Waterproof-coated roof & 0.27 & & & 0.38 & & & 0.46 & \\ \cline{1-1} \cline{2-10}
17 & & (Green color) & 0.27 & & & 0.38 & & & 0.41 & 0.46 & \\ \cline{1-1} \cline{2-10}
18 & & 0.36 & & & 0.47 & & & 0.54 & \\ \cline{1-1} \cline{2-10}
19 & & & 0.81 & & & 0.76 & & & 0.84 & \\ \cline{1-1} \cline{2-10}
20 & & 0.80 & & & 0.76 & & & 0.84 & \\ \cline{1-1} \cline{2-10}
21 & & 0.84 & & & 0.81 & & & 0.88 & \\ \cline{1-1} \cline{2-10}
22 & & & 0.71 & & & 0.61 & & & 0.73 & \\ \cline{1-1} \cline{2-10}
23 & & 0.64 & & & 0.52 & & & 0.63 & \\ \cline{1-1} \cline{2-10}
24 & & 0.62 & & & 0.48 & & & 0.61 & \\ \hline
25 & & Tree & 0.89 & & & & & 0.93 & \\ \cline{1-1} \cline{2-10}
26 & & 0.91 & & & 0.91 & & & 0.95 & \\ \cline{1-1} \cline{2-10}
27 & & 0.92 & & & 0.91 & & & 0.95 & \\ \cline{1-1} \cline{2-10}
28 & & 0.84 & & & 0.85 & & & 0.90 & \\ \cline{1-1} \cline{2-10}
29 & & 0.83 & & & 0.84 & & & 0.92 & \\ \cline{1-1} \cline{2-10}
30 & & 0.87 & & & 0.87 & & & 0.93 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of vegetation index values by type of land cover using the novel indices. Figure 11: Vegetation map and validation points based on the squared Blue-Green NDVI index. Figure 12: Vegetation map and validation points based on the squared Red-Green NDVI index. Existing studies using UAV have focused mainly on analyzing vegetation and topography in urban areas [41; 42; 43; 44; 45]. There have been studies to detect green-roof using the supervised classification method based on UAV images, but not to develop the vegetation index considering green-roof [41]. Compared with the existing UAV studies, the differences of the results performed in this study were added to the text. Therefore, the new index developed in this study will be a very effective method for classifying vegetation in urban areas with various land covers.