# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 3 Methodology  
### UBD Estimation Model and Verification  
To achieve a small scale for research purposes and reduce the error in the research results, the estimation of the UBD is generally calculated in units of fixed-size grid cells. Considering the spatial resolution of the Luojia1-01, each sample area was divided into \(0.5\times 0.5\) km by testing the side length of the cell. Then, the radiance value of the study area was divided into 32 classes of NTL intensity by the natural breakpoint grading method, and 10 sample areas were set for each class of NTL intensity. According to the principle of equilibrium distribution, in this article we tried to avoid the boundary of the study area when selecting samples; finally, we set up 320 samples areas for modeling and randomly selected another 20 areas as test samples, as shown in Figure 4a. The area of a building's roof can usually be used instead of the area of the building for UBD calculations. Combined with the ZY3-02 orthophoto with a high resolution of 2.5 m, the building area can be obtained by digitizing all the buildings in each sampling area in turn. Figure 4b,c shows an example of building digitization. Through calculating the UBD of the 320 sample areas, we obtained a calculation result which can be regarded as the actual UBD of the sample area. The calculation formula is shown as follows:  
\[\rho_{a_{i}}=S_{a_{i}}/S \tag{2}\]  
Figure 3: Remote sensing image data of the study area: (**a**) Night light image from Luojia1-01; (**b**) Standard false-color composite images (made up of information from Bands 3–5) from Landsat8, where the vegetation is red, the waterbody is blue, and the white is roads and buildings; and (**c**) ZY3-02 Multi-spectral image from ZY3-02, where the vegetation is green, the waterbody is white, and the gray is buildings and roads. where \(\rho_{ai}\) is the UBD of the the \(i\)-th sample area; \(S_{a_{i}}\) is the building area of the \(i\)-th sample area; and \(S\) is the area of the sample area, which is \(0.5\times 0.5\) km. Among the various land use types, high-brightness lighting is widely used in the transportation field, while residential areas have low illumination; this can explain why the lighting along the roads is one factor in UBD estimation [43]. In recent years, researchers have used many methods to extract road information from high-resolution remote sensing images. In this paper, referring to the method in references [50; 51; 52], the road area information of the 320 modeling samples was extracted from ZY3-02 high-resolution images and overlapped with the Luojia1-01 NTL imagery to reduce the interference of road light sources in the UBD estimation model. Figure 5 is a schematic diagram of the entire process. Figure 4: Schematic diagram of plots and example of building digitalization: (**a**) Urban building density (UBD) sample area distribution; (**b**) Example of a sample area; and (**c**) Example of building digitization in a sample area. Figure 5: Flowchart of the procedure for eliminating the interference of road lighting in the Luojia1-01 images. Step 1: Perform grayscale processing on the original image from ZY3-02. The Mean-Shift algorithm was used to achieve the initial segmentation of the road. The setting of the dual-threshold can better distinguish the features of different gray levels and eliminate the interference of small-area shadows such as vegetation and vehicles. Finally, the binarized image containing road information is segmented. After binarization, dual-threshold segmentation can well overcome the differences caused by the different gray levels of various road materials since the spectral differences between shadows, green spaces, and roads are then apparent. In the process of Mean-Shift algorithm segmentation, combined with small-area segmentation and the dual-threshold segmentation method, most interfering factors can be eliminated.