Quantifying spatial heterogeneity and associated factors of Nanjing’s street green view index using urban vegetation structure and street view imagery

Urban street greenery is commonly evaluated using either street-level visual indicators or remote-sensing-based vegetation indices. However, the relationship between pedestrian-visible greenery and overhead vegetation greenness remains insufficiently understood across seasons, vegetation compositions, and heterogeneous urban contexts. Taking Nanjing, China, as a case study, this study develops a citywide analytical framework that integrates Baidu Street View imagery, semantic segmentation, NDVI-based remote sensing, vegetation-composition information, and spatial analysis. A total of 118,830 valid panoramic street-view images were used to calculate the Green View Index (GVI) along Nanjing’s road network. The analysis examined the spatial distribution of GVI, its relationship with NDVI, seasonal variation in GVI–NDVI correspondence, vegetation-composition characteristics, road-class and functional-zone differences, and local clustering patterns. The results show that roadside visible greenery in Nanjing exhibits substantial spatial heterogeneity, with a citywide mean GVI of 0.273 and a standard deviation of 0.208. High-GVI areas were mainly concentrated around major ecological and waterfront landscapes, including the southern foothills of Purple Mountain and the Qinhuai River corridor, whereas low-GVI areas were more frequently observed in dense commercial centers, transportation hubs, and older industrial–residential mixed districts. GVI and NDVI showed a moderate positive correlation, indicating that street-level visible greenery and remotely sensed vegetation greenness are related but not interchangeable. Their relationship also varied seasonally: the correlation was weakest in spring, strengthened in summer, reached its highest level in autumn, and declined slightly in winter. Vegetation composition further contributed to differences in GVI–NDVI correspondence, as trees and grasslands dominated roadside green space while shrubs accounted for a much smaller proportion. Road-class and functional-zone analyses showed that sidewalks, internal roads, ecological zones, and public zones generally had higher GVI values, whereas traffic-priority roads and highly urbanized functional areas tended to show lower visible greening levels. These findings demonstrate the value of combining street-view-based and remote-sensing-based indicators in urban greenery assessment. They also highlight the need to consider seasonal context, vegetation composition, road hierarchy, and functional zoning in citywide evaluations of street greenery. The proposed framework can support more differentiated assessment and planning of visible street greenery in Nanjing and other spatially heterogeneous cities.