Nighttime luminosity transitions are tightly spatiotemporally correlated with land use changes: A pixelwise case study in Beijing, China

Nighttime light data and land use data derived from remote sensing satellites are commonly used to monitor human activities on the Earth. A thorough understanding of the spatiotemporal interactions between these data enables better characterization and reconstruction of urbanization processes. In this work, a pixelwise fusion analysis using nighttime light data and land use data was performed to explore the spatiotemporal correlation between urban nighttime light luminosity transitions and land use changes. We found that the nighttime lu-minosity transitions are tightly related to the corresponding land use change types at the pixel level. A significant spatial correlation was identified between the nighttime light luminosity transitions and urban lands with intensive human activity. Relevant policies enacted in Beijing have greatly influenced the local light luminosity and land use type transitions. Pixels with change values in light luminosity in the same range have the same types of land use changes, the reasons for the changes in light luminosity are similar, and the spatial distribution characteristics are the same. This study provides a theoretical basis for quickly assessing changes in urban land use types through transitions in nighttime light luminosity. Simultaneously, the data in our study after inte-grating nighttime light luminosity and land use information perform well in urban development research and can provide valuable datasets and decision-making references for adjusting and optimizing urban sustainable development policies.

Automated summary

This study explores the relationship between urban nighttime light luminosity transitions and land use changes through the analysis of nighttime light data and land use data. It is found that there is a significant correlation between nighttime light luminosity transitions and urban lands with intensive human activity at the pixel level. This study provides a theoretical basis for quickly assessing changes in urban land use types through transitions in nighttime light luminosity.