Comparing Luojia 1-01 and VIIRS Nighttime Light Data in Detecting Urban Spatial Structure Using a Threshold-Based Kernel Density Estimation

Nighttime light (NTL) data are increasingly used in urban studies and urban planning owing to their strong connection with human activities, although the detection capacity is limited by the spatial resolution of older data. In the present study, we comparedthe results of extractions of urban built-up areas using data obtained from the first professional NTL satellite Luojia 1-01 with a resolution of 130 m and the Visible Infrared Imaging Radiometer Suite (VIIRS). We applied an analyzing framework combing kernel density estimation (KDE) under different search radii and threshold-based extraction to detect the boundary and spatial structure of urban areas. The results showed that: (1) Benefiting from a higher spatial resolution, Luojia 1-01 data was more sensitive in detecting new emerging urban built-up areas, thus better reflected the spatial structure of urban system, and can achieve a higher extraction accuracy than that of VIIRS data; (2) Combining with a proper threshold, KDE improves the extraction accuracy of NTL data by making use of the spatial autocorrelation of nighttime light, thus better detects the scale of the spatial pattern of urban built-up areas; (3) A proper searching radius for KDE is critical for achieving the optimal result, which was 1000 m for Luojia 1-01 and 1600 m for VIIRS in this study. Our findings indicate the usefulness of the KDE method in applying the upcoming high-resolution NTL data such as Luojia 1-01 data in urban spatial analysis and planning.