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The digital transformation of forest inventories requires the validation of consumer-grade LiDAR technologies under varying environmental and phenological conditions. This study evaluates the seasonal performance and operational efficiency of stand-level basal area (BA) estimation obtained using the TreeScanner smartphone LiDAR application, compared with a professional personal laser scanning (PLS) system, FJD Trion P1. Data were collected in 50 circular plots (300 m 2 ) located in heterogeneous mountain forests near Brașov, Romania, characterized by complex terrain (slopes 8°–42°) and mixed species stands aged 45–170 years. A longitudinal repeated-measures design was implemented across four phenological stages: winter (WI), spring (SP), summer (SU), and autumn (AU). Inter-platform agreement was evaluated using bias, mean absolute error (MAE), root mean square error (RMSE), and Bland–Altman analysis, while statistical parity between platforms was assessed through paired t-tests. Operational efficiency was examined by comparing acquisition times between systems. Results showed that phenological conditions strongly influenced measurement agreement. The highest accuracy was obtained in WI ( RMSE = 0.093 m 2 ; Bias = − 0.059 m 2 ), whereas SP showed the lowest agreement ( RMSE = 0.391 m 2 ; Bias = − 0.184 m 2 ), likely due to increased vegetation occlusion during leaf emergence. AU was the only season in which no significant differences between platforms were detected ( p = 0.093). In terms of operational performance, TreeScanner maintained a relatively stable acquisition rate across seasons (around 16 s per tree; p 0.05). In contrast, the professional scanner exhibited significant seasonal variation ( p 0.05). These results suggest that smartphone-based LiDAR can produce reliable stand-level BA estimates while significantly improving overall efficiency.
Toaza et al. (2026) studied this question.