Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R² = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops.

Nitrogen nutrition monitoring is crucial in agriculture and forestry. With the development of Unmanned Aerial Vehicle (UAV) imaging technology, its application in nitrogen nutrition monitoring has gained attention. Traditional regression methods often struggle to accurately capture the nonlinear relationships between image features and nitrogen nutrition parameters. This study introduces Gaussian regression models to better model the relationship between UAV image features and nitrogen nutrition in Ginkgo. UAV RGB imagery of three-year-old Ginkgo biloba L. seedlings was used to extract nitrogen-related image features. Gaussian regression models were employed to select and model these features, creating regression models for nitrogen accumulation and nitrogen content in Ginkgo. The accuracy of the models was validated. Results indicated that the optimal canopy type for monitoring nitrogen accumulation in Ginkgo was the shadowed canopy, with the color feature BMR being the most important feature. For monitoring nitrogen content, sunlight and shadow canopy types were suitable, with BMR and b* being the key features. Gaussian regression demonstrated superior accuracy and robustness compared to traditional regression models. This study emphasizes the potential of Gaussian regression models to improve nitrogen monitoring through UAV imagery, offering valuable applications in precision agriculture and forestry management, particularly in supporting nitrogen fertilization and nutrition management for Ginkgo.