# A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images
###### Abstract  
Unmanned aerial vehicles (UAVs) equipped with high-resolution multispectral cameras have increasingly been used in urban planning, landscape management, and environmental monitoring as an important complement to traditional satellite remote sensing systems. Interest in urban regeneration projects is on the rise in Korea, and the results of UAV-based urban vegetation analysis are in the spotlight as important data to effectively promote urban regeneration projects. Vegetation indices have been used to obtain vegetation information in a wide area using the multispectral bands of satellites. UAV images have recently been used to obtain vegetation information in a more rapid and precise manner. In this study, multispectral images were acquired using a UAV equipped with a Micasense RedEdge MX camera to analyze vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Blue Normalized Difference Vegetation Index (BNDVI), Red Green Blue Vegetation Index (RGBVI), Green Red Vegetation Index (GRVI), and Soil Adjusted Vegetation Index (SAVI). However, in the process of analyzing urban vegetation using the existing vegetation indices, it became clear that the vegetation index values of long-run steel roofing, waterproof coated roofs, and urethane-coated areas are often similar to, or slightly higher than, those of grass. In order to improve the problem of misclassification of vegetation, various equations were tested by combining multispectral bands. Kappa coefficient analysis showed that the squared Red-Blue NDVI index produced the best results when analyzing vegetation reflecting urban land cover. The novel vegetation index developed in this study will be very useful for effective analysis of vegetation in urban areas with various types of land cover, such as long-run steel roofing, waterproof coated roofs, and urethane-coated areas. v 2021 22 February 2  
the distribution of vegetation in urban areas and to effectively manage and maintain this vegetation. Various vegetation indices are used to classify vegetation area from satellite or Unmanned Aerial Vehicle (UAV) images. The Normalized Difference Vegetation Index (NDVI), reported by [PERSON] et al. [12], is one of the most commonly used vegetation indices [13; 14; 15; 16; 17]. Many other vegetation indices are also used in research, including the Green Normalized Difference Vegetation Index (GNDVI), Blue Normalized Difference Vegetation Index (BNDVI), Red Green Blue Vegetation Index (RGVI), Green Red Vegetation Index (GRVI), and Soil Adjusted Vegetation Index (SAVI). GNDVI effectively represents the properties of chlorophyll in green plants [18; 19], and BNDVI may help to analyze the spatial heterogeneity and distribution of chlorophyll [20; 21]. Soil background is a major surface component that has a significant impact on the spectral behavior of vegetation canopies in arid grasslands with sparse vegetation cover. To reduce the soil background effect, [PERSON] used a soil adjustment factor to account for first-order soil background variations, and proposed the SAVI [22]. Recently, unmanned aerial vehicles (UAVs) equipped with high-resolution cameras have increasingly been used in environment monitoring as an important complement to traditional satellite remote sensing systems [23]. Since RGVI and GRVI can monitor vegetation using optical sensors [23; 24; 25], these two indices are often employed for vegetation and biomass analysis. Such research uses UAVs equipped with inexpensive optical cameras. Vegetation indices, which assess the spectroscopic properties of plant chlorophyll using the optical and near-infrared bands, have been used in the field of remote sensing to evaluate vegetation distribution and growth characteristics [26; 27]. Vegetation indices have been evaluated using Landsat, SPOT, and MODIS satellite images [16; 17; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. Satellite imagery is effective in analyzing vegetation in a large area.