The leaf area index (LAI) is an essential indicator for assessing vegetation growth and understanding the dynamics of forest ecosystems and is defined as the ratio of the total leaf surface area in the plant canopy to the corresponding surface area below it. LAI has applications for obtaining information on plant health, carbon cycling, and forest ecosystems. Due to their price and portability, mobile devices are becoming an alternative to measuring LAI. In this research, a new method for estimating LAI using a smart device with a fisheye lens (SFL) is proposed. The traditional Otsu method was enhanced to improve the accuracy and efficiency of foreground segmentation. The experimental samples were located in Gansu Ziwuling National Forest Park in Qingyang. In the accuracy parameter improvement experiment, the variance of the average LAI value obtained by using both zenith angle segmentation and azimuth angle segmentation methods was reduced by 50%. The results show that the segmentation of the front and back scenes of the new Otsu method is more accurate, and the obtained LAI values are more reliable. In the efficiency parameter improvement experiment, the time spent is reduced by 17.85% when the enhanced Otsu method is used to ensure that the data anomaly rate does not exceed 10%, which improves the integration of the algorithm into mobile devices and the efficiency of obtaining LAI. This study provides a fast and effective method for the near-ground measurement of forest vegetation productivity and provides help for the calculation of forest carbon sequestration efficiency, oxygen release rate, and forest water and soil conservation ability.

For extracting tree structural data from LiDAR point clouds, individual tree segmentation is of great significance. Most individual tree segmentation algorithms miss segmentation and misrecognition, requiring manual post-processing. This study utilized a hierarchical approach known as segmentation based on hierarchical strategy (SHS) to improve individual tree segmentation. The tree point cloud was divided into the trunk layer and the canopy layer to carry out trunk detection and canopy segmentation, respectively. The effectiveness of SHS was evaluated on three mixed broadleaf forest plots. The segmentation efficacy of SHS was evaluated on three mixed broadleaf forest plots and compared with the point cloud segmentation algorithm (PCS) and the comparative shortest-path algorithm (CSP). In the three plots, SHS correctly identified all the trunk portion, had a recall (r) of 1, 0.98, and 1, a precision (p) of 1, and an overall segmentation rate (F) of 1, 0.99, and 1. CSP and PCS are less accurate than SHS. In terms of overall plots, SHS had 10%–15% higher F-scores than PCS and CSP. SHS extracted crown diameters with R²s of 0.91, 0.93, and 0.89 and RMSEs of 0.24 m, 0.23 m, and 0.30 m, outperforming CSP and PCS. Afterwards, we evaluate the three algorithms’ findings, examine the SHS algorithm’s parameters and constraints, and discuss the future directions of this research. This work offers an enhanced SHS that improves upon earlier research, addressing missed segmentation and misrecognition issues. It improves segmentation accuracy, individual tree segmentation, and provides both theoretical and data support for the LiDAR application in forest detection.

The Liaodong oak (Quercus wutaishanica Mayr) is dominant in the composition scheme in Qingyang City, and its growth performance and management practices have long been central concerns of forest management. However, the long cycles and complex dynamics of forest development make accurate prediction difficult, thereby constraining the design of optimal silvicultural strategies. To remedy the slow growth and suboptimal timber quality of Q. wutaishanica plantations—while fostering large-diameter trees, increasing merchantable yield and the output of high-value timber, and enhancing forests’ carbon-sequestration and oxygen-release services—there is an urgent need for a rigorous predictive framework. Using data from the sixth, seventh, and eighth National Forest Resource Inventories, we developed a transition-matrix growth model comprising growth, ingrowth, and mortality sub-models. With this model, we selected representative plots and simulated 25-year trajectories of stand diameter-class structure and growing stock under three climate scenarios (RCP2.6, RCP4.5, RCP8.5). Results indicate divergent trends in growing stock among scenarios; under RCP2.6, stands attain higher growing stock, a more balanced diameter-class distribution, and a markedly larger number of large-diameter trees. Moreover, Q. wutaishanica exhibits relatively stable growth throughout the simulation horizon. Overall, the transition-matrix model effectively captures short-term stand dynamics, fills a regional research gap for Qingyang City, and provides a robust evidence base for subsequent science-based forest management.