Calibrated general-purpose smartphone augmented reality for urban street-tree structural data acquisition

Urban street trees are key to urban climate regulation, carbon storage and habitat provision, yet many cities still lack fine-scale, up-to-date field data on their structure and site conditions. Conventional tape and laser measurements are accurate but slow, labour-intensive and often impractical along narrow, obstacle-rich sidewalks. This study develops and tests a smartphone-based augmented-reality (AR) framework for measuring three ecologically critical geometric parameters of street trees—diameter at breast height (DBH), planting-pit area and tree spacing. Using general-purpose AR ruler apps on three consumer smartphones, we implemented a standardized field protocol and a generic geometric-plus-statistical calibration workflow that converts basal rectangular measurements into calibrated DBH. Field validation along a 1.5-km street corridor in Guangzhou, China (125 trees) shows that, after calibration, mean absolute percentage errors are 6–8% for DBH and below 3% for planting-pit area and spacing, satisfying Class B precision under the Chinese Regulations on Main Technical Specifications for Forestry Professional Surveys . AR workflows reduced survey time by 35–40% and required only one operator. The framework thus provides an operationally efficient, low-cost and transferable pathway to generate standardized structural data for urban-forest inventories and ecological-informatics applications. • Evaluates general-purpose smartphone AR apps for DBH, planting-pit area and tree spacing in dense urban streetscapes. • Develops an open geometric–statistical calibration workflow converting basal AR rectangles into calibrated DBH under forestry precision standards. • Using 125 street trees, shows calibrated AR achieves ≈6–8% DBH error and < 3% planar error (Class B). • Demonstrates 35–40% reductions in field time and single-operator capability compared with tape-based measurements. • Positions calibrated smartphone AR as a scalable, low-cost source of structural inputs for urban ecological informatics.