Institute of Forest Resource Information Techniques

Transitioning from monocultures to mixed-species plantations is a key strategy for enhancing soil organic carbon (SOC) sequestration. However, the specific mechanisms by which tree species interactions, particularly through root exudation and morphological traits, shape rhizosphere SOC stability remain poorly understood. This study investigated the effects of introducing broad-leaf species into coniferous plantations on rhizosphere SOC dynamics.We examined a near-mature Pinus massoniana monoculture and two paired plantations interplanted with Erythrophleum fordii (a nitrogen-fixing species) and Castanopsis hystrix. We quantified root exudation rates, root morphological traits, microbial biomass carbon, and rhizosphere physicochemical properties to identify the controlling factors of SOC stability.Our results revealed divergent stabilization pathways depending on the companion species. Interplanting with C. hystrix significantly stimulated the root exudation of P. massoniana. This increase in exudates was positively correlated with the mass proportion and carbon content of both large and small macro-aggregates, suggesting that exudate-mediated physical protection is the primary driver of SOC stability in this mixture. Conversely, interplanting with the N-fixing E. fordii did not significantly alter root exudation rates or their relationship with aggregation. Instead, SOC stability in the P. massoniana rhizosphere was primarily attributed to increased nitrogen availability.Our findings highlight that root exudates play a conditional role in SOC stabilization, heavily dependent on the identity of neighbor species. We conclude that selecting appropriate companion species is critical for managing specific SOC sequestration pathways in mixed-species plantations.

IntroductionAccurate estimation of individual tree aboveground biomass (AGB) is essential for tree species selection, carbon accounting, and precision forestry. Unmanned aerial vehicle (UAV) LiDAR provides rapid access to detailed tree structural information, offering a promising tool for high-frequency biomass assessment.MethodsIn this study, a nonlinear mixed-effects (NLME) model integrating UAV LiDAR and field measurements was developed to quantify the influence of genetic heterogeneity and environmental factors on AGB estimation of Catalpa bungei. Data from 2,941 trees across 79 genotypes were collected in Henan Province, including LiDAR-derived tree height (LH), LiDAR-derived crown diameter (LCD), and AGB. By incorporating genotype as a random effect and planting density as a dummy variable, the NLME model significantly outperformed traditional dummy-variable models.ResultsGenotype effects explained significant AGB variation, achieving high accuracy (R²=0.7916, RMSE = 3.7095) and reducing TRE by 23.29% compared to the basic power function model. Leave-one-genotype-out cross-validation confirmed robustness. Calibration with the four largest trees yielded the best performance (TRE = 13.09%), while a simplified scheme using only two trees per genotype maintained high accuracy (TRE = 13.24%), markedly reducing field effort.DiscussionThese results highlight the superiority of NLME AGB models over linear approaches and demonstrate that accounting for genotype effects is critical for reliable biomass estimation. The proposed framework provides an efficient and cost-effective solution for biomass monitoring, tree breeding, carbon sink assessment, and precision forestry.

Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R2 = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement.

The integration of unmanned aerial vehicle-based laser scanning (ULS) and mobile laser scanning (MLS) enables the detection of forest three-dimensional structure in high-density canopy areas and has become an important tool for monitoring and managing forest ecosystems. However, MLS faces difficulties in positioning due to canopy occlusion, making integration challenging. Due to the variations in observation platforms, ULS and MLS point clouds exhibit significant structural discrepancies and limited overlapping areas, necessitating effective methods for feature extraction and correspondence establishment between these features to achieve high-precision registration and integration. Therefore, we propose a registration algorithm that introduces a Feature Deviation Metric to enable feature extraction and correspondence construction for forest point clouds in complex regional environments. The algorithm first extracts surface point clouds using the hidden point algorithm. Then, it applies the proposed dual-threshold method to cluster individual tree features in ULS, using cylindrical detection to construct a Feature Deviation Metric from the feature points and surface point clouds. Finally, an optimization algorithm is employed to match the optimal Feature Deviation Metric for registration. Experiments were conducted in 8 stratified mixed tropical rainforest plots with complex mixed-species canopies in Malaysia and 6 structurally simple, high-canopy-density pure forest plots in anorthern China. Our algorithm achieved an average RMSE of 0.17 m in eight tropical rainforest plots with an average canopy density of 0.93, and an RMSE of 0.05 m in six northern forest plots in China with an average canopy density of 0.75, demonstrating high registration capability. Additionally, we also conducted comparative and adaptability analyses, and the results indicate that the proposed model exhibits high accuracy, efficiency, and stability in high-canopy-density forest areas. Moreover, it shows promise for high-precision ULS-MLS registration in a wider range of forest types in the future.

Camellia chekiangoleosa is a significant oil-bearing tree species, known for its high oleic acid content and shorter reproductive cycle compared to traditional oil-tea plants. However, there are few studies on the molecular mechanism and compatibility of the interaction between oil-Camellia scion and rootstock, which poses certain challenges to the cultivation and promotion of oil-Camellia. This study systematically evaluates the effects of hetero-grafting Camellia chekiangoleosa scions onto divergent rootstocks (Camellia chekiangoleosa, Camellia oleifera, and Camellia yuhsienensis). Then the research investigates how rootstock selection alters scion growth and development through phenotypic, biochemical, and transcriptomic analyses. Our findings reveal that the combination of C. oleifera scion grafted onto C. yuhsienensis suppresses auxin (IAA) and cytokinin (ZR) levels while elevating abscisic acid (ABA). Transcriptomic analysis identified that the PYL1, AMY, and INV1 screened by transcriptome data were mainly enriched in starch and sucrose metabolic pathways and plant hormone signal transduction, which collectively prioritize carbon allocation toward growth over storage. Meanwhile, hetero-grafting improved photosynthetic capacity by upregulating light-harvesting complex (LHC) genes and carotenoid biosynthesis enzymes (ZEP), optimizing light energy conversion and photoprotection. These findings provide novel insights into the molecular mechanisms underlying rootstock-scion interactions in oil-Camellia, bridging a critical knowledge gap in this economically important genus.

Leaf Area Index (LAI) is a critical biophysical parameter for characterizing vegetation canopy structure and function. However, fine-scale LAI estimation remains challenging due to limitations in spatial resolution and structural detail in traditional remote sensing data and the insufficiency of single-index models like the LiDAR Penetration Index (LPI) in capturing canopy complexity. This study proposes a multi-scale LAI estimation approach integrating high-density UAV-based LiDAR data with LPI and point cloud texture features. A total of 40 field-sampled plots were used to develop and validate the model. LPI was computed at three spatial scales (5 m, 10 m, and 15 m) and corrected using a scale-specific adjustment coefficient (μ). Texture features including roughness and curvature were extracted and combined with LPI in a multiple linear regression model. Results showed that μ = 15 provided the optimal LPI correction, with the 10 m scale yielding the best model performance (R2 = 0.40, RMSE = 0.35). Incorporating texture features moderately improved estimation accuracy (R2 = 0.49, RMSE = 0.32). The findings confirm that integrating structural metrics enhances LAI prediction and that spatial scale selection is crucial, with 10 m identified as optimal for this study area. This method offers a practical and scalable solution for improving LAI retrieval using UAV-based LiDAR in heterogeneous forest environments.