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Nighttime lights (NTLs) provide an unparalleled view for understanding urban environments. NTL images have been widely used to investigate the dynamics of urban extent and socio-economic variables at multiple scales. However, the high-frequency NTL intensity changes (NTLICs) and their land drivers in megaregions are ignored. Here, monthly Visible Infrared Imaging Radiometer Suite-Day Night Band (VIIRS-DNB) NTL images from 2014 to 2020 were regarded as time series modeling input data. Furthermore, the 7-year mean values of five types of Points of Interest Density (POID), Road Density (RD), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) were used to extract features as land drivers of NTLIC. A simplified version of the Breaks For Additive Season and Trend (BFAST01) algorithm was adopted to detect gradual and abrupt changes in monthly NTL intensity at the pixel level in the Beijing-Tianjin-Hebei (BTH) megaregion, China. Moreover, the geographically weighted regression (GWR) model was used to address the spatial nonstationarity of the land drivers. The results showed that (1) increasing trends (63.35&#x0025;) and decreasing trends (4.57&#x0025;) were mainly observed in central and coastal cities, respectively. This illustrates that the spatial characteristics of the NLTIC were unbalanced; (2) reversal change type 8 (37.29&#x0025; of abrupt changes) was the dominant type, mainly occurring around April 2017, which may be related to the implementation of the non-capital function redistribution policy; (3) NDVI and RD were the best one of two physical drivers and six human activity drivers, respectively; and (4) GWR considers the spatial heterogeneity of response processes between NTLIC and two land drivers, and the R2 reached 0.60, a 20&#x0025; improvement over ordinary least squares regression. The findings of this study provide an insightful understanding of the dynamics of NTL intensity and its response mechanisms in megaregions and promote the urban application of NTL remote sensing.

Evolution of gradual and abrupt trends in nighttime lights and responses to land drivers via BFAST01 and geographically weighted regression