Characterizing the spectral composition of artificial light at night (ALAN) within urban green spaces (UGS) is vital for ecological conservation, yet traditional sensors often lack the requisite spatial and spectral resolution for fine-scale analysis. To address this gap, this study leverages high-resolution multispectral nighttime light (NTL) data from the SDGSAT-1 to perform a fine-scale characterization of lighting across diverse UGS typologies. We developed UGS-STUNet, a semantic segmentation framework based on Swin Transformer architecture, to accurately extract five UGS categories from Google Earth imagery. Two specialized spectral indices, blue-to-green (B/G) and green-to-red (G/R) ratios, were derived from SDGSAT-1 NTL data to quantify the lighting’s spectral composition. Application in Shanghai demonstrated that UGS-STUNet achieved a precision of 85.72%, significantly outperforming existing methods. Our findings reveal that street trees are subjected to the highest red-light intensity and the lowest B/G and G/R ratios due to their proximity to roadway illumination. In contrast, forest patches and belts exhibit higher spectral ratios, indicating a relatively higher exposure to blue and green wavelengths. This study provides a robust and scalable method for monitoring the spectral quality of urban nightscapes, offering critical insights for sustainable urban planning and lighting mitigation strategies to safeguard global biodiversity and public health.
Yuan et al. (Sat,) studied this question.