# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 4 Conclusions  
Vegetation is considered a very important factor in urban planning and landscaping, including urban regeneration projects, because vegetation plays a role in providing a comfortable living space for humans. Satellite images have been mainly used to construct vegetation information on a wide area. However, since the satellite moves only in a fixed orbit, it is difficult to obtain an image at a desired point of view. In particular, there is a limit to obtaining a good quality  
Figure 19: Vegetation map classified by applying NDVI and thermal infrared image around the basketball courts and the waterproof coating roofs. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{\begin{tabular}{c} **Field** \\ **Survey** \\ \end{tabular} } & \multicolumn{2}{c}{**Vegetation**} & \multicolumn{2}{c}{**Non-Vegetation**} & \multirow{2}{*}{**Sum**} & \multirow{2}{*}{\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} } \\ \hline \multirow{4}{*}{NDVI} & Vegetation & 2397 & 277 & 2674 & \\ \cline{2-6}  & \
\begin{tabular}{c} Non- \\ vegetation \\ \end{tabular} & 2 & 1647 & 1735 & 0.830 \\ \cline{2-6}  & Sum & 2485 & 1924 & 4409 & \\ \hline \multirow{2}{*}{NDVI} & Vegetation & 2643 & 31 & 2674 & \\ \cline{2-6}  &
\begin{tabular}{c} Non- \\ vegetation \\ \end{tabular} & 106 & 1629 & 1735 & 0.934 \\ \cline{2-6}  & Sum & 2749 & 1660 & 4409 & \\ \hline \end{tabular}
\end{table} 
Table 9: Analysis of the kappa coefficient for vegetation classification. image due to the influence of clouds. Recently, these problems have been greatly improved by utilizing the UAV system equipped with a multispectral camera. In this study, NDVI was analyzed using UAV multispectral images to construct vegetation information in urban areas, and in the process, it was accidentally found that NDVI values appeared high in basketball courts and waterproof coating roofs coated with urethane. In order to examine these problems, the reflectance of each land cover such as trees, grass, basketball court, waterproof coating roof, and road was investigated using the ASD FieldSpec4 spectrometer. As a result of the analysis, the NDVI values of basketball courts and waterproof coating roofs were similar to those of grass with slightly lower vegetation. To solve this problem, the temperature of the target area was analyzed using a UAV system equipped with a thermal infrared camera called themoMAP, and the final vegetation map was created by combining the temperature information and NDVI. In order to compare the vegetation map classified by using only NDVI and the vegetation map classified by combining the NDVI and thermal infrared images, 4409 validation points were selected and the kappa coefficients were calculated. The kappa coefficient analyzed by applying only the NDVI was 0.830, and the kappa coefficient analyzed by combining the NDVI and the thermal image was 0.934. Therefore, it was found that it was more accurate to classify vegetation by combining NDVI and thermal images than the result of classifying vegetation by applying only NDVI in urban areas where urethane coating was distributed, such as basketball courts and waterproof coating roofs. Recently, in Korea, waterproof coating roofs are coated with urethane paint having a waterproof effect, and basketball courts coated with urethane are also being constructed in order to provide living sports facilities to residents. Therefore, when analyzing the vegetation information of urban areas necessary for the promotion of work such as urban regeneration, landscaping, and the environment, it is necessary to first investigate the land cover characteristics of the target area. Additionally, if there is a lot of urethane-coated land cover in urban areas, it is suggested that the method of classifying vegetation by combining NDVI and thermal infrared images will be effective. Conceptualization, G.L., J.H. and S.C.; Investigation, G.L., G.K. and G.M. ; software. G.L. ; supervision, J.H.