To achieve a micro-destructive and rapid measurement of the wood density of standing trees, this study investigated the possibility of the unified modeling of multiple tree species, the reliability of the micro drilling resistance method for measuring wood density, the relationship between drilling needle resistance and wood density, and whether moisture content has a significant impact on the model. First, 231 tree cores and drill resistance data were sampled from Pinus massoniana, Cunninghamia lanceolate, and Cryptomeria fortunei. The basic density and moisture content of each core were measured, and the average value of each resistance data record was calculated. Second, the average drill resistance, the natural logarithm of average drill resistance, and absolute moisture content were used as independent variables, while the basic wood density was used as the dependent variable. Third, the total model of the three tree species and sub-model for each tree species were established through a stepwise regression method. Finally, the accuracy of each model was compared and analyzed with that of using the average basic density of each tree species as an estimated density. The estimated accuracy of the total model, sub model, and average wood density modeling data were 90.070%, 93.865%, and 92.195%, respectively. The results revealed that the estimation accuracy of the sub-model was 1.670 percentage points higher than that of the average wood density modeling data, while the estimation accuracy of the total model was 2.125 percentage points lower than that of the average wood density modeling data. Additionally, except for Cryptomeria fortunei, the natural logarithm of drill resistance significantly influenced the wood density model at a significance level of 0.05. Moreover, moisture content significantly affected the total model and sub-models of Pinus massoniana at a significance level of 0.05. The results indicated the feasibility of using the micro-drilling resistance method to measure the wood density of standing trees. Moreover, the relationship between wood density and drill resistance did not follow a linear pattern, and moisture content slightly influenced the drill needle resistance. Furthermore, the establishment of a mathematical model for each tree species was deemed essential. This study provides valuable guidance for measuring the wood density of standing trees through the micro-drilling resistance method.

To improve the accuracy of tree age estimation by accounting for variations in radial growth, this study developed a diameter-age model that incorporates radial growth rate for seven typical tree species across subtropical to cold temperate regions. For each tree species, six trees were selected, including 2 dominant trees, 2 intermediate trees, and 2 suppressed trees. A total of 646 disks were collected at 1-meter intervals along the stems, starting at 0.3 m height. Disks diameters and tree-rings were measured, and the radial growth rate of each disk over the past two years was calculated. For each tree species, 2/3 of the data were randomly selected as the modeling dataset, while the remaining 1/3 served as the testing dataset. Based on scatter plots, select linear models, logarithmic models, and exponential models as candidate models. A logarithmic function best described the diameter-age relationship, while an exponential model best fit the radial growth rate -age relationship. A dual-factor nonlinear model combining both variables achieved the highest estimation accuracy (77.95%), significantly outperforming single-factor models based solely on diameter (52.72%) or growth rate (70.78%). These results demonstrate that integrating radial growth rate substantially enhances the precision of tree age estimation.

To improve the accuracy of tree age estimation by accounting for variations in radial growth, this study developed a diameter/age model that incorporates the radial growth rate for seven typical tree species across subtropical to cold temperate regions. For each tree species, six trees—representing dominant, intermediate, and suppressed trees—were selected. A total of 646 disks were collected at 1 m intervals along the stems, starting at 0.3 m height. Disk diameters and tree rings were measured, and the radial growth rate of each disk over the past two years was calculated. For each tree species, two-thirds of the data were randomly selected as the modeling dataset, while the remaining one-third served as the testing dataset. Based on scatter plots, we selected linear models, logarithmic models, and exponential models as candidate models. A logarithmic function best described the diameter/age relationship, while an exponential model best fit the radial growth rate/age relationship. A dual-factor nonlinear model combining both variables achieved the highest estimation accuracy (80.29%), significantly outperforming single-factor models based solely on diameter (50.76%) or growth rate (73.01%). These results demonstrate that integrating radial growth rate substantially enhances the precision of tree age estimation.