# A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images
## 2 Materials
### UAV Image Processing  
Figure 3a shows the DJI Matrice 210 UAV equipped with the Micasense RedEdge MX sensor, which was used to obtain multispectral images. The Micasense RedEdge MX sensor is widely used to analyze vegetation, agricultural management practices, and water quality because it can acquire blue, green, red, and near-infrared spectral images [51; 52; 53; 54]. Its resolution is about 8 cm at an altitude of 120 m, providing very precise image information, and its weight is 231.9 g, which is very light [55], so it has the advantage of allowing sufficient flight time for the DJI Matrice 210, as shown in Table 1.  
Figure 3: The Unmanned Aerial Vehicle (UAV) system: (**a**) DJI Matrice 210, (**b**) Micasense RedEdge MX. Figure 2: The study area. The DJI GS Pro S/W was used to establish the UAV flight plan, as shown in Figure 4. The degree of longitudinal and lateral redundancy were designed to be 80% and 70%, respectively, and photography was performed at a flight altitude of about 100 m to acquire images with a resolution of 6 cm. Radiometric calibration was performed before the flight using a calibrated reflectance panel (CRP) and the incident light sensor of the Micasense RedEdge MX camera. To secure the positional accuracy of the UAV images, eight GCPs were selected, as shown in Table 2, and the transverse Mercator (TM) coordinates of the GRS80 ellipsoid were acquired through the VRS survey. Each captured image has Universal Transverse Mercator (UTM) coordinates on the WGS4 ellipsoid, obtained through a Global Positioning System (GPS) receiver attached to the UAV. To convert these coordinates into the TM coordinates of the GRS80 ellipsoid used in Korea, the survey results were matched to the GCPs. \begin{table}
\begin{tabular}{c c} \hline
**Items** & **Contents** \\ \hline Weight & 231.9 g \\ Dimensions & 8.7 cm \(\times\) 5.9 cm \(\times\) 3.4 cm \\ Spectral bands & Blue, Green, Red, Red Edge, Near Infrared \\ Ground sample distance & 8 cm per pixel at 120 m AGL \\ Field of view & 47.2\({}^{\circ}\) HFOV \\ Capture rate & 12-bit RAW \\ \hline \end{tabular}
\end{table} 
Table 1: Specifications of the Micasense RedEdge MX. Figure 4: UAV flight planning using the DJI GS Pro S/W. In addition, images from each band were merged using Pix4D Mapper S/W, and index maps were constructed for each red, green, blue, and near-infrared image to calculate the vegetation index (Figure 5). The following are the general steps of Pix4D Mapper S/W image processing: (1) Image alignment and sparse point cloud building, (2) Identification of GCPs and sparse point cloud optimization, (3) Dense point cloud building, and (4) Construction of an orthomosaic and Digital Surface Model (DSM). (**a**) Merging of UAV images  
(**b**) Index map of multispectral image  
**3. Vegetation Analysis Using Existing Vegetation Indices**  
In this study, various existing vegetation indices were analyzed using ArcGIS S/W from the multispectral images constructed through UAV image processing (Table 3). Figure 6 shows the vegetation index maps of NDVI, GNDVI, BNDVI, RGBVI, GRVI, and SAVI. Table 4 gives the statistical characteristics of each vegetation index. Figure 6: Maps produced using existing vegetation index equations. The target area contains various types of land cover, such as trees, grass, an artificial grass playground, long-run steel roofing, waterproof coated roofs, and urethane-coated areas. Therefore, as illustrated in Figure 7, a representative location was selected for each type of land cover, and three sample points were chosen for each location. Figure 7: _Cont._  
The UAV flight took place on 2 April 2020, in the early spring. At this time of year, the vegetation vitality of trees is high, but the vegetation vitality of grass is medium to low. Grass-covered positions showing medium vegetation vitality (Nos. 19-21) and slightly lower vitality (Nos. 22-24) were selected and compared. Table 5 shows the results of the six existing vegetation indices for each type of land cover.