The perceived quality of street lighting influences pedestrians’ perceptions of safety and visual comfort, as well as outdoors activities at night. This study explores the association between street lighting attributes, such as illuminance and wavelength, and pedestrians’ feeling of safety (FoS) and perceived lighting quality (PLQ) in eight residential districts in Dalian, China. To achieve this goal, we combine remote sensing technology with ground investigation. The ground research includes physical measurements of lighting attributes, such as intensity, color temperature, and glare, as well as survey evaluations of pedestrians’ perceptions of safety and visual comfort. We also analyze the influence of several environmental factors, such as traffic volumes and vegetation, while accounting for personal characteristics of the observers, such as gender and age. Findings from the remote sensing reveal that Dalian’s residential districts differ substantially by their nighttime light emissions, with high concentration of strong red band (i.e., long wavelength) emissions occurring in Zhongshan and Jinzhou, and strong blue band (i.e., short wavelength) emissions found in central Zhongshan. Results from the ground surveys further indicate that a satisfactory level of FoS reaches at the illumination levels of 5–17 lx, and that people feel safer if nighttime light is warm and uniform. From a multiple regression analysis, it is also found that illuminance and uniformity are the main factors affecting PLQ under conditions of low or high illuminance, while glare and color temperature play a more significant role under high illuminance. In addition, a satisfactory level of PLQ is found at illuminance levels of 25–35 lx and light color temperature of 4000 K–5500 K.
With the continuously growing city size and the increasingly complex and changeable light environment in the city, remote sensing and ground-measured technologies have certain limitations in the research of urban night light environment. The ground-measured data are accurate but low in efficiency and small in scale, while the night-light remote sensing data have the characteristics of high accuracy and large coverage. In this paper, high-resolution night-light remote sensing data and high-accuracy ground-measured data were used to establish an urban ground light environment inversion method with the advantages of remote sensing and ground-measured data in a “space-ground collaboration” approach. A ground database is constructed in GIS based on 26,000 ground measurement data of 4 blocks, 3 spatial perspectives, and 3 light environment parameters. Based on the comparison of the numerical relationship between the measured data of each light environment parameter and each window, the horizontal window is selected as the target window for the ground night light environment inversion research. The urban night light environment inversion method based on the correlation between telemetry and ground- measurement is used to construct and compare the correlation between Luojia night light radiance data and 9 sets of measured data of different ground windows and different light environment parameters. The illuminance measured data of horizontal window and Luojia radiance data, both of which are highly correlated, are selected for regression analysis. The mathematical inversion model of ground illuminance is constructed based on the cubic polynomial model with the lowest RMSE among the six regression models. The inversion result not only has photometric calibration, but also is superior to the original data in terms of population data relevance and accuracy.
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