Forest ecosystems, as the primary component of terrestrial ecosystems, provide essential ecosystem services (ESs) critical for sustainable human development. However, changes in climate and vegetation can alter these forest ESs. Understanding the complex relationships between regional climate, vegetation, and ESs is key to ensuring the sustainable management of forest ESs. Therefore, this study, using Baishanzu National Park as a case example, analyzed the impacts of regional climate and vegetation dynamics (vegetation coverage, forest type, and forest structure) on forest ESs, specifically water yield (WY), soil conservation (SC), net primary productivity (NPP), and habitat quality (HQ). The results indicate that from 2000 to 2020, the forest Composite Index of Ecosystem Services (CIES) in Baishanzu National Park increased. Climate and vegetation dynamics have significant effects on forest ESs. Specifically, changes in WY and SC are primarily influenced by climate change, while changes in NPP and HQ are mainly affected by changes in forest type and structure. Complex trade-offs and synergies exist among different ESs, and the driving mechanisms of climate and vegetation changes on ES variations are also complex, involving both direct and indirect effects, with significant spatial heterogeneity. This study provides important references for the sustainable management and appropriate restoration of regional forest ESs.

Vegetation cover plays a fundamental role in maintaining ecosystem structure and function. Understanding its spatial and temporal variability, along with its driving factors, is critical for advancing environmental studies. This research targets the subtropical Zhejiang region in southeastern China, utilizing MODIS-derived NDVI data covering 2001 to 2020. By integrating Sen’s slope estimator, Mann–Kendall trend analysis, spatial autocorrelation (Moran’s I), and the Geodetector framework, we assessed trends, patterns, and primary influencing factors of vegetation change. Our findings include: (1) a statistically significant upward trend in NDVI across 59.4% of the study area (Sen’s slope = 0.0025, p < 0.01), corresponding to an approximate annual increase of 0.44%; (2) notable spatial clustering of NDVI values, with high NDVI zones located in southwestern forested areas and low NDVI zones in expanding urban regions, indicating a clear spatial differentiation between natural and human-dominated landscapes; (3) elevation (Q = 0.64), nighttime lights (Q = 0.63), and slope (Q = 0.57) showed relatively higher explanatory power, and the interaction between nighttime lights and land use (NTL × LULC) exhibited the strongest explanatory power (Q = 0.72); (4) high-risk zones, associated with dense populations and intense urban development, coincided with lower NDVI values. These results deepen our understanding of vegetation dynamics in subtropical zones and provide insights for sustainable ecosystem and land management.

In forest resource plot surveys, tree relative positioning is a crucial task with profound silvicultural and ecological significance. However, traditional methods such as compasses and total stations suffer from low efficiency, high costs, or poor environmental adaptability, while single-sensor technologies (e.g., UWB or IMU) struggle to balance accuracy and stability in complex forest environments. To address these challenges, this study designed a multi-sensor fusion-based tree positioning device. By integrating the high-precision ranging capability of Ultra-Wideband (UWB) with the dynamic motion perception advantages of an Inertial Measurement Unit (IMU), a dynamic weight fusion algorithm was proposed, effectively mitigating UWB static errors and IMU cumulative errors. Experimental results demonstrate that the device achieves system biases of −1.54 cm (X-axis) and 1.27 cm (Y-axis), with root mean square errors (RMSE) of 21.34 cm and 23.93 cm, respectively, across eight test plots. The average linear distance error was 26.23 cm. Furthermore, in single-operator mode, the average measurement time per tree was only 20.89 s, approximately three times faster than traditional tape measurements. This study confirms that the proposed device offers high positioning accuracy and practical utility in complex forest environments, providing efficient and reliable technical support for forest resource surveys.