Camellia oleifera is an important economic tree species in China. Accurate estimation of canopy structural parameters of C. oleifera is essential for yield prediction and plantation management. However, this remains challenging in mountainous plantations due to canopy occlusion and background interference. This study developed a multi-level object-oriented segmentation method integrating UAV-based LiDAR and visible-light data to address this issue. The proposed approach progressively eliminates background objects (bare soil, weeds, and forest gaps) through hierarchical segmentation and classification in eCognition, ultimately enabling precise canopy delineation. The method was validated in a high-canopy-closure plantation characterized by a mountainous area. The results demonstrated exceptional performance; canopy area extraction and individual plant extraction achieved average F-scores of 97.54% and 91.69%, respectively. The estimated tree height and mean crown diameter were strongly correlated with field measurements (both R² = 0.75). This study provides a method for extracting the parameters of C. oleifera canopies that is suitable for mountainous regions with high canopy closure, demonstrating significant potential for supporting digital management and precision forestry optimization in such wooded areas.

Vegetation phenology is a key indicator of ecosystem responses to climate change. This study investigates the spatial-temporal dynamics of vegetation phenology in the Changbai Mountain Nature Reserve from 2001 to 2025 and projects future changes under CMIP6 scenarios using a particle swarm optimization–least squares support vector machine (PSO-LSSVM) model. The results show that SOS exhibits an advancing trend, while EOS is delayed, leading to an overall extension of LOS. Spatially, phenological patterns are strongly controlled by elevation, with higher elevations characterized by later SOS and shorter LOS. Correlation analysis indicates that SOS is primarily driven by spring temperature, whereas EOS is influenced by both temperature and precipitation, showing more complex responses. Notably, a negative relationship between autumn temperature and EOS suggests that factors other than temperature may play an important role. Future projections reveal that phenological changes intensify with increasing emission scenarios. By the end of the 21st century, SOS is projected to advance by 0.8–3.6 days, EOS to be delayed by 0.8–7.4 days, and LOS to extend by 1.6–11.8 days. Vegetation-type-based analysis further demonstrates significant heterogeneity in phenological responses. These findings improve the understanding of vegetation phenology in mountain ecosystems and provide a useful reference for assessing ecosystem responses under future climate change.