Climate change may induce regional climate shifts, profoundly affecting plant growth, distribution, and ecosystems. This study collected 37 Sabina chinensis (Sabina chinensis (L.) Ant. cv. Kaizuca) tree cores (74 samples) from a site in the Yangtze River Delta (YRD) coastal region. Utilizing traditional dendrochronological principles and methods, a standardized tree-ring width chronology was developed to detect climate shift points and explore the differences in Sabina chinensis radial growth responses to climate factors, before and after these shifts. The findings are as follows: (1) Between 1967 and 2020, temperature emerged as the main climate factor influencing the radial growth of Sabina chinensis in the study area. (2) There are differences in the correlations between the tree radial growth of Sabina chinensis and climate factors in different months and seasons, before and after climate change. (3) Moving correlation analysis indicated that the relationships between radial growth and precipitation and temperature gradually altered. The study reveals the intricate influencing mechanisms of different climate factors on Sabina chinensis radial growth, before and after climate shifts, offering valuable references for other similar dendrochronological studies.

While LiDAR is widely used in tree measurement, its high cost and complexity remain limiting factors. With advances in photogrammetry and deep learning, efficient and accurate alternatives are gaining importance in forest resource surveys. Therefore, this study proposes a fully automated framework for tree stem volume measurement based on virtual measurement from a single photograph. This framework integrates the Segformer architecture, Generative Adversarial Network inverse mapping technology, and Sobel operator (SGS-3DRecon), enabling multi-level analysis from image segmentation to 3D reconstruction and measurement. To validate its potential application, we collected images of 5,192 individual trees across multiple scenarios (Natural forests, Afforested area, and Urban parks) in Beijing. Additionally, point cloud data of 250 trees were acquired using handheld mobile laser scanning (HMLS) technology for comparative evaluation. Results indicate that SGS-3DRecon achieved IoU and Recall accuracies of 86.40% and 92.61%, respectively, in the combined scenarios. The average accuracy fluctuation between different scenarios was 3.41%, with the highest accuracy observed in the park scenario. Compared to the complex processing required by HMLS, the SGS-3DRecon method achieved an 85.5% improvement in efficiency for volume measurement. While ensuring high efficiency, the average rRMSE was 17.91%, and the average rBias was -0.40%. In summary, this framework provides an efficient and low-cost solution for forest resource surveys. It shows significant potential, especially in monitoring tasks with limited resources and large-scale requirements. This innovative approach has the potential to further advance forest resource survey technologies.

The accurate assessment of tree volume is crucial for developing forest management plans, and this can be achieved using tree volume models. In this study, data on individual trees were collected and calculated, including the diameter at breast height (D), ground diameter (DG), tree height (H), and tree volume (V). A total of 400 Populus × tomentosa Carrière, 400 Populus tomentosa Carr, and 400 Populus × canadensis Moench trees were sampled. Two compatible volume model systems were established using corresponding methods. The models consisted of the following five types: V-DH, V-D, V-DG, H-D, and DG-D. In our calculations, before the horizontal line was the dependent variable, and behind the horizontal line was the independent variable. Variations in preferences for the H-D models were observed among the tree species, with the logistic function proving the most suitable for Populus × tomentosa Carrière, Chapman–Richard function for Populus tomentosa Carr, and power function for Populus × canadensis Moench. Among the three volume models, the V-DH model exhibited a superior performance, with its R² values ranging from 0.965 to 0.984 and mean estimated error (MPE) values ranging from 1.26% to 1.78%. Following this was the V-D model, with R² values between 0.9359 and 0.9704 and MPE values between 1.71% and 2.46%. The V-DG model ranked third, with R² values ranging from 0.8746 to 0.9501 and MPE values ranging from 2.33% to 3.16%. In the H-D model, the R² and MPE values ranged from 0.4998 to 0.7851 and from 1.31% to 1.45%, respectively. For the DG-D model, the R² values ranged from 0.9563 to 0.9868 and the MPE values ranged from 0.47% to 0.68%. Comparing both compatible methods, the nonlinear seemingly uncorrelated regression (NSUR) was more effective. The three volume models demonstrated high levels of accuracy and compatibility, providing a reliable scientific foundation for forest management and the formulation of harvesting plans in Beijing, with significant practical implications.

Accurate stem-volume estimation is fundamental for urban tree inventory and management, but equations developed for forest-grown trees may not be directly suitable for open-grown urban trees with altered stem form and height–diameter relationships. This study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China: Quercus mongolica, Sophora japonica, Ginkgo biloba, and Populus davidiana. A total of 2679 standing trees from 535 plots were used for model development and evaluation. The diameter at breast height and ground diameter were field-measured, whereas tree height was obtained as a photogrammetry-derived non-destructive measurement using a handheld tree-measurement superstation. Bivariate DBH–height models, DBH-based linked models, and ground-diameter-based chained models were fitted using weighted nonlinear least squares. Model performance was assessed using validation statistics, 10-fold cross-validation, Monte Carlo uncertainty propagation, and an independent destructive reference dataset of 55 felled trees with section-measured stem volume. Across species, the bivariate models performed best, with mean percent standard errors of 8.68%–16.24%, compared with 9.76%–20.25% for DBH-based linked models and 15.13%–28.56% for ground-diameter-based models. Destructive reference validation showed acceptable agreement within the available validation dataset, with relative RMSE values of 2.30%–5.03% and relative bias values of 0.51%–2.51%. Monte Carlo simulation indicated species-specific propagation of photogrammetric height error, with the lowest average volume fluctuation in Ginkgo biloba. These results suggest that handheld photogrammetry combined with species-specific modelling provides a practical and uncertainty-aware basis for urban stem-volume estimation. This study directly estimates stem volume rather than biomass or carbon stock, and the equations may support future biomass- and carbon-related assessments when combined with appropriate conversion parameters.