A meta-analysis for the nighttime light remote sensing data applied in urban research: Key topics, hotspot study areas and new trends

Abstract

Nighttime light (NTL) data have become an essential tool for urban remote-sensing research in the past 25 years because of its ability to intuitively detect human activities. With new data and technologies constantly emerging leading to accumulated research, there is an urgent need for a comprehensive review of this subject. Although there are currently some review articles focusing on NTL-based urban studies, they lack visual analysis of research keywords based on co-occurrence analysis, as well as the research topics and changes of global countries and regions. Furthermore, they not yet delved into research methods and their relationship with research topics. Addressing these gaps, this study thoroughly investigated 545 relevant publications from 1992 to 2023 via comprehensive meta-analysis and visual co-occurrence analysis. The results indicate an increasing trend in NTL-based urban studies. ‘China’ appears as the most frequently mentioned keyword. Based on the co-occurrence clustering results, this study categorized the research topics into 4 groups. The most attention was given to identifying urban spatial dynamics, especially urban expansion. We found that the research topics of the 6 most frequently studied countries/regions varied across different time stages and were correlated with the urbanization levels of those regions at that time. Regarding the research methods, we observed an increase in the use of machine learning and index-based evaluation methods, with the former most commonly applied to urban area extraction and environmental variable prediction. We also highlighted emerging trends including: (1) Growing significance of machine learning models; (2) Transition of NTL from a leading role to an auxiliary tool; (3) An increased focus on the physical modelling of NTL, and challenges including: (1) Difficulties faced when applying medium-high resolution NTL imagery; (2) Limited applications of deep learning models; (3) Unable to genuinely reflect the urban artificial light information; (4) Inadequate temporal flexibility and consistency of observations. This study expects to systematically demonstrate the current status, trends and challenges of NTL-based urban research through Meta-analysis, so as to provide scientific references for more future innovative research and the management of urban nighttime environment.