Pigments are the biochemical material basis for energy and material exchange between vegetation and the external environment, therefore quantitative determination of pigment content is crucial. Unmanned Aerial Vehicle (UAV)-borne remote sensing data coupled with radiative transfer models (RTM) provide marked strengths for three-dimensional (3D) visualization, as well as accurate determination of the distributions of pigment content in forest canopies. In this study, Light Detection and Ranging (LiDAR) and hyperspectral images acquired by a multi-rotor UAV were assessed with the PROSAIL model (i.e., PROSPECT model coupled with 4SAIL model) and were synthetically implemented to estimate the horizontal and vertical distribution of pigments in canopies of Ginkgo plantations in a study site within coastal southeast China. Firstly, the fusion of LiDAR point cloud and hyperspectral images was carried out in the frame of voxels to obtain fused hyperspectral point clouds. Secondly, the PROSAIL model was calibrated using specific model parameters of Ginkgo trees and the corresponding look-up tables (LUTs) of leaf pigment content were constructed and optimally selected. Finally, based on the optimal LUTs and combined with the hyperspectral point clouds, the horizontal and vertical distributions of pigments in different ages of ginkgo trees were mapped to explore their distribution characteristics. The results showed that 22-year-old ginkgo trees had higher biochemical pigment content (increase 3.37–55.67%) than 13-year-old ginkgo trees. Pigment content decreased with the increase of height, whereas pigment content from the outer part of tree canopies showed a rising tendency as compared to the inner part of canopies. Compared with the traditional vegetation index models (R² = 0.25–0.46, rRMSE = 16.25–19.37%), the new approach developed in this study exhibited significant higher accuracies (R² = 0.36–0.60, rRMSE = 13.53–16.86%). The results of this study confirmed the effectiveness of coupling the UAV-borne LiDAR and hyperspectral image with the PROSAIL model for accurately assessing pigment content in ginkgo canopies, and the developed estimation methods can also be adopted to other regions under different conditions, providing technical support for sustainable forest management and precision silvicuture for plantations.

Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing the cultivation practices of Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis for determining chlorophyll content are labor-intensive and time-consuming, making them unsuitable for large-scale dynamic monitoring and high-throughput phenotyping. To accurately quantify chlorophyll content in Ginkgo seedlings under different nitrogen levels, this study employed a hyperspectral imaging camera to capture canopy hyperspectral images of seedlings throughout their annual growth periods. Reflectance derived from pure leaf pixels of Ginkgo seedlings was extracted to construct a set of spectral parameters, including original reflectance, logarithmic reflectance, and first derivative reflectance, along with spectral index combinations. A one-dimensional convolutional neural network (1D-CNN) model was then developed to estimate chlorophyll content, and its performance was compared with four common machine learning methods, including Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF). The results demonstrated that the 1D-CNN model outperformed others with the first derivative spectra, achieving higher CV-R² and lower RMSE values (CV-R² = 0.80, RMSE = 3.4). Furthermore, incorporating spectral index combinations enhanced the model’s performance, with the 1D-CNN model achieving the best performance (CV-R² = 0.82, RMSE = 3.3). These findings highlight the potential of the 1D-CNN model in strengthening the chlorophyll estimations, providing strong technical support for the precise cultivation and the fertilization management of Ginkgo seedlings.

Leaf nitrogen content (LNC) is a crucial indicator for assessing the nitrogen status of forest trees. The LNC retrieval can be achieved with the inversion of the PROSPECT-PRO model. However, the LNC retrieval from the commonly used leaf bidirectional reflectance factor (BRF) spectra remains challenging arising from the confounding effects of mesophyll structure, specular reflection, and other chemicals such as water. To address this issue, this study proposed an advanced BRF spectra-based approach, by alleviating the specular reflection effects and enhancing the leaf nitrogen absorption signals from Ginkgo trees and saplings, using 3 modified ratio indices (i.e., mPrior_800, mPrior_1131, and mPrior_1365) for the prior estimation of the N_struct structure parameter, combined with different inversion methods (STANDARD, sPROCOSINE, PROSDM, and PROCWT). The results demonstrated that the prior N_struct estimation strategy using modified ratio indices outperformed standard ratio indices or nonperforming prior N_struct estimation, especially for mPrior_1131 and mPrior_1365 yielding reliable performance for most constituents. With the use of the optimal approaches (i.e., PROCWT_S3 combined with mPrior_1131 or mPrior_1365), our results also revealed that the optimal estimation of LNC_area (normalized root mean square error [NRMSE] = 12.94% to 14.49%) and LNC_mass (NRMSE = 10.11% to 10.75%) can be further achieved, with the selected optimal wavebands concentrated in 5 common main domains of 1440 to 1539 nm, 1580 to 1639 nm, 1900 to 1999 nm, 2020 to 2099 nm, and 2120 to 2179 nm. These findings highlight marked potentials of the novel BRF spectra-based approach to improve the estimation of LNC and enhance the understanding of the impact of N_struct prior estimation on the LNC retrieval in leaves of Ginkgo trees and saplings.

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.

The photosynthetic pigments – chlorophyll a (Chl a), chlorophyll b (Chl b), and carotenoids (Car) – in juvenile ginkgo leaves are crucial for growth monitoring as they reflect physiological status and directly influence the biosynthesis of bioactive compounds such as flavonoids and terpene lactones. Traditional pigment measurement methods (acetone/ethanol extraction, SPAD, etc.) are inadequate for large-scale dynamic monitoring and high-throughput phenotyping analysis. To address this, this study developed a non-destructive prediction model for Chl a, Chl b, and Car contents in ginkgo seedlings using hyperspectral imaging combined with machine learning algorithms, which is applicable to seedlings with different genetic backgrounds and at various color development phases. A total of 3,460 seedlings from 590 families, sourced from ancient trees across 19 provinces in China, were analyzed using hyperspectral imaging and biochemical pigment quantification. A phased optimization strategy was implemented, including preprocessing method screening, model comparison, and feature wavelength selection. Among the four tested preprocessing methods (raw reflectance, normalization, first derivative, and second derivative), normalization significantly improved model accuracy. The Adaptive Boosting (AdaBoost) algorithm outperformed partial least squares regression (PLSR) and random forest (RF), achieving coefficients of determination (R²) above 0.83 and the ratio of performance to deviation (RPD) values exceeding 2.4 across all pigments. Compared with competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA) demonstrated more effective spectral dimensionality reduction while preserving predictive power. This framework enables efficient, accurate, and scalable pigment phenotyping in Ginkgo biloba, offering technical support for large-scale germplasm screening and precision breeding.

Individual tree structure and wood density are important indicators of forest quality and key parameters for biomass calculation. To explore the extraction accuracy of individual tree structure parameters based on LiDAR technology, as well as the correlation between individual tree structure parameters, resistance value and wood density can be beneficial for providing new ideas for predicting wood density. Taking a 23-year-old Ginkgo plantation as the research object, the tree QSM (Quantitative Structure Model) was constructed based on terrestrial and backpack LiDAR point clouds, and the individual tree structure parameters were extracted. The accuracy of estimating structure parameters based on two types of point clouds was compared. A wood density prediction model was constructed using principal component analysis based on the resistance, diameter, tree height, and crown width. The accuracy verification was carried out and it showed that the estimation accuracies of individual tree structure parameters (DBH, tree height, and crown width) extracted from tree QSM constructed based on TLS and BLS all had R² > 0.8. The estimation accuracy of DBH based on TLS was slightly higher than that based on BLS, and the estimation accuracy of tree height and crown width based on TLS was slightly lower than that based on BLS. BLS has great potential in accurately obtaining forest structure information, improving forest information collection efficiency, promoting forest resource monitoring, forest carbon sink estimation, and forest ecological research. The feasibility of predicting the wood basic density based on wood resistance (R² = 0.51) and combined with DBH, tree height, and crown width (R² = 0.49) was relatively high. Accurate and non-destructive estimation of the wood characteristics of standing timber can guide forest cultivation and management and promote sustainable management and utilization of forests.