Objectives Most of the remotely sensed night light data is acquired by satellites, and the spatial resolution is always coarse and the overpass time is fixed. These limitations hinder understanding night light patterns with insight. Compared to satellite images, camera image data has higher temporal and spatial resolution, and web camera images provide more information on night light and details of urban economic activities. However, such data has rarely been reported for academic researches.
Methods For real-time video data, we design a specific Crawler program for downloading images from the Earth Camera website. Consequently, the acquired time series camera images are analyzed to find different trend components and their spatial distribution by using principal component analysis (PCA).
Results Generally, the Crawler program can be run stably to obtain public camera image data from the Earth Camera website. Through analyzing city light in a region inside Tokyo based on the proposed PCA method, we find that the city light dynamic is complex and random in some extent, and the revealed pattern shows a general and significant decreasing trend, while some regions have more complicated temporal patterns such as increasing at first and then decreasing. In addition, the different building façades have different city light dynamic as well.
Conclusions This study proposes a technical framework for acquiring and analyzing urban public camera images at night, and it suggests that urban camera can effectively provide city light change information from a micro perspective, which will provide new data source for supporting quantitative remote sensing of night light.