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.

Pinus armandii Franch., a key native conifer in China, faces severe Dendroctonus armandi infestation, threatening forest ecosystems. Reducing infestation probability and enhancing resistance are essential for transforming Pinus armandii forests. This study investigates the correlation between stand structure, topographic factors, and the probability of Pinus armandii infestation by Dendroctonus armandi. Based on these correlations, it selects suitable mixed-species combinations of native tree species with low infestation probabilities that are adapted to regional characteristics. A random survey was conducted in 58 plots (6,021 trees) in Shennongjia. Logistic regression and analysis of variance (ANOVA) revealed: (1) Infestation rate increased with elevation, peaking at 84.38% above 2,000 m; between 1,500–2,100 m, probability rose 4.3% per 100 m elevation gain; (2) Steeper slopes (> 25°) reduced infestation (46.03%), with risk decreasing 1.9% per 1° slope increase (0°–40°); (3) Larger DBH (> 30 cm) trees had higher infestation (82.93%), increasing 4.5% per 1 cm DBH; (4) Higher species mingling (four neighboring non-Pinus trees) lowered infestation to 63.39%, reducing risk by 54.3% per mingling unit; (5) Healthy Pinus armandii were frequently neighbored by Litsea pungens, Carpinus cordata, Phellodendron chinense, and Betula platyphylla. Prioritizing slopes > 25° and elevations < 2,000 m for afforestation, mixed with Litsea pungens, Carpinus cordata, or Betula platyphylla, can mitigate infestation. These findings provide actionable strategies to enhance Pinus armandii forest resilience against Dendroctonus armandi threats.