Zhejiang Province, a pivotal economically developed region within China’s Yangtze River Delta, requires systematic investigation of spatiotemporal vegetation dynamics and their drivers to formulate targeted ecological protection policies and optimize vegetation restoration strategies. Utilizing the Google Earth Engine (GEE) platform, this study applied the Kernel Normalized Difference Vegetation Index (kNDVI) to assess vegetation responses to climate variability and human activities in Zhejiang Province from 2000 to 2022. Analytical methods included simple linear regression, Theil Sen trend analysis (Sen), Mann Kendall test (MK), Hurst index, partial correlation analysis, and correlation analysis. The results show: (1) The kNDVI exhibited a significant upward trend (0.001/year), covering 61.5% of the province. The Hurst index analysis revealed that 69.1% of vegetation changes exhibited anti-sustainability characteristics, with future vegetation degradation areas (56.4%) projected to exceed improvement areas (28.1%). (2) Human activities (57.11%) contributed more to kNDVI changes than climate change (42.89%). (3) Against the backdrop of climate change, kNDVI demonstrated a positive partial correlation with temperature (coefficient: 0.44) but exhibited a negative correlation with precipitation (coefficient: −0.056), confirming temperature as the dominant climatic driver. Overall, vegetation dynamics in Zhejiang Province from 2000 to 2022 were jointly driven by climate change and human activities.

Bamboo, characterized by its high growth speed and short maturation period, occupies 0.875% of the global forest area and significantly contributes to terrestrial carbon cycling. The state of shoot growth can essentially indicate a bamboo forests’ health and productivity. This study explored the potential of backpack laser scanning (BLS) for monitoring the growth of Moso bamboo shoots (Phyllostachys edulis), a key economic species in subtropical China. Initially, the accuracy of BLS in extracting attributes of bamboo and shoots (including diameter at breast height (DBH), height, and real-world coordinates) was validated. An optimized method was developed to address the lower precision of BLS in extracting the DBH for thinner species. Subsequently, this research analyzed the impact of spatial structure and other indicators on shoot emergence stage and growth rate using a random forest model. The results indicate that BLS can accurately extract Moso bamboo and shoot height (RMSE = 0.748 m) even in dense bamboo forests. After optimization, the error in DBH extraction significantly decreased (RMSE = 0.835 cm), with the average planar and elevation errors for Moso bamboo being 0.227 m and 0.132 m, respectively. The main indicators affecting the coordinate error of Moso bamboo were the distance to the start (DS) and the distance to the trajectory (DT). The emergence time of shoots was mainly influenced by the surrounding Moso bamboo quantity, with the leaf area index (LAI) and competition index (CI) positively related to the growth rate of shoots. The importance ranking of spatial structure for the carbon storage of shoots was similar to that of the growth rate of shoots, with both identifying LAI as the most significant indicator. This study has validated the value of BLS in monitoring the growth of shoots, providing a theoretical support for the sustainable management and conservation of bamboo forests.