# Vegetation Classification in Urban Areas by Combining UAV-Based NDVI and Thermal Infrared Image
## 1 Introduction  
The occurrence of natural disasters due to climate change is increasing worldwide. In particular, as the global temperature rises, heat waves' frequency, intensity, and duration increase causing casualties [1; 2; 3; 4; 5]. The frequency and intensity of heat waves are influenced not only by topographical characteristics but also by urban development [6]. Recently, the phenomenon of \"Urban Heat Islands\" (UHIs) on a regional scale is increasing due to the increase in artificial coverings such as concrete and asphalt, the decrease in vegetation, and the increase in high-rise buildings due to urbanization. The urban heat island (UHIs) effect is caused by changes in surface thermal properties due to artificial covering and coolingreduction due to lack of vegetation. It also accounts for hotter urban areas than nearby rural areas due to anthropogenic heat dissipation and air pollutants, which can increase significantly [7; 8; 9; 10]. Unlike concrete and asphalt, vegetation is a permeable surface and can control the temperature because rainwater cools it [11; 12; 13]. However, over the past few decades, urbanization has destroyed nearly 88% of the world's primary vegetation-covered land in urban areas and replaced it with artificial surfaces [14; 15]. Therefore, to create a pleasant residential environment in an urban area, it is necessary to understand the vegetation index accurately. The vegetation index quantifies vegetation by analyzing the difference between near-infrared light, which is strongly reflected by vegetation, and red light, which is strongly absorbed by vegetation [16; 17; 18; 19]. Additionally, it is mainly used in the field of remote sensing. The study of vegetation index using remote sensing technology uses the spectral characteristics of satellite images among the analysis methods using satellite images. The satellite data mainly used are Landsat satellites [20; 21; 22; 23; 24], and recently Sentinel satellites [25; 26; 27; 28] and MODIS satellites [16; 29; 30; 31] and images from SPOT satellites [32; 33] are also being used. The most commonly used vegetation index is the NDVI (Normalized Difference Vegetation Index), which was reported by [PERSON] et al. [34]. In addition, GNDVI (Green Normalized Difference Vegetation Index), BNDVI (Blue Normalized Difference Vegetation Index), RGBVI (Red Green Blue Vegetation Index), GRVI (Green Red Vegetation Index), NDRE (Normalized Difference Red Edge), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), NDMI (Normalized Difference Moisture Index), and other vegetation index have been reported. In particular, GNDVI is an index that responds sensitively to changes in chlorophyll in plants as one of the modified forms of NDVI. In SAVI, the soil adjustment factor L is used to compensate for the NDVI that may be affected by soil brightness in an area where vegetation density is not high. Recently, with the development of unmanned aerial vehicles (UAV) and high-resolution cameras, the application of environmental monitoring is increasing as an important supplement to the existing satellite remote sensing [35; 36; 37; 38; 39]. The vegetation index study using UAV is as follows. [PERSON] et al. [40] applied a UAV equipped with a multispectral camera for NDVI, GNDVI, and SAVI evaluation, and [PERSON] et al. [41] applied a UAV to predict rice grain yield and determine the optimal period and optimal VI. [PERSON] et al. [42] acquired high-resolution images through UAV to solve the problem of limited monitoring due to the low resolution of existing satellite images in specific agricultural applications. [PERSON] and [PERSON] et al. [43] determined the effective vegetation index for detecting aquatic plants in small reservoirs through multispectral UAV. [PERSON] et al. [44] determined the vegetation index detect disease in vine leaves using high-resolution UAV images. [PERSON] et al. [45] proposed a Soil Salinity Retrieval Index (SSRI) for monitoring the salinity of saline soils using UAVs. [PERSON] et al.