The importance of the height-diameter (H-D) relationship in forest productivity is well known. The general nonlinear regression model, based on the mean regression technical, is not able to give a complete description of the H-D relationship. This study aims to evaluate the H-D relationship among relative competition levels and develop a quantile regression (QR) model to fully describe the H-D relationship. The dominance index was applied to determine the relative competition levels of trees for the Chinese fir. Based on the basic Weibull growth model, the mean regression for five relative competition levels and 11 QR models was constructed with 10-fold cross-validation. We have demonstrated that the H-D relationship for the Chinese fir strongly correlated with relative competition states, but the five curves from mean regression models did not show a notable difference between the trends of H-D relationship under different competition levels. Similar regression results were found in QR models of the specific quantiles; the average tree height of five competition levels varied between 5.78% and 17.65% (i.e., about 0.06 and 0.18 quantiles). In addition, some special curves of the H-D relationship such as the QR models of the 0.01 and 0.99 quantile showed the H-D relationship under certain conditions. These findings indicate that the QR models not only evaluated the rates of change of the H-D relationships in various competition levels, but also described their characteristics with more information, like the upper and lower boundary of the conditional distribution of responses. Although the flexible QR curves followed the distribution of the data and showed more information about the H-D relationships, the H-D curves may not intersect with each other, even when the trees reached their maximum height. Hence, the QR model requires further practice in assessing the growth trajectory of the tree’s diameter or tree height to gain better results.

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Pine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

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Background Water storage is a significant physiological index of vegetation growth. However, information on water storage at the individual tree level and its relationship to climatic conditions and productivity is scarce. Methods We performed a comparative analysis of water storage using field measurements acquired three age classes of Chinese fir (Cunninghamia lanceolata) and Korean larch (Larix olgensis). The distributions of water storage, water content ratio and dry mass were presented, and regression analyses were used to confirm the relationships of water storage and water content ratio to dry mass components, respectively. Results Our results indicated that water was mostly concentrated in the stem xylem, which aligned well with the distribution of dry mass in both conifer species. However, the water storage of the stem xylem was always higher in Chinese fir than in Korean larch. The average water content ratio of both conifer species decreased with age, but that of Chinese fir was always higher than that of Korean larch. There was a significant difference in the water storage proportion in the components of Chinese fir (P < 0.001) and Korean larch (P < 0.001). The effects of age class on the water storage of Chinese fir (P = 0.72) and Korean larch (P = 0.077) were not significant. Interestingly, significant positive linear correlations were found between fine root water and leaf water and mass in Chinese fir (P < 0.001, R2 ≥ 0.57) and Korean larch (P < 0.001, R2 ≥ 0.74). The slopes showing that the linear relationship between tree size and water content ratio of stem xylem were always steeper than that of other components for the two conifers. Conclusion Our study indicates the similar water related characteristics and their close relations to biomass accumulation and growth in both fast growing species at contrasting climates, illustrating the same coherent strategies of fast growing conifers in water utilization.