Understanding spatiotemporal shifts in vegetation and their climatic and anthropogenic regulatory factors can offer a crucial theoretical basis for environmental conservation and restoration. In this article, the normalized difference vegetation index (NDVI) of the Miaoling area from 2000 to 2020 is studied using a trend analysis and the Mann–Kendall mutation test (MK test) to review the vegetation’s dynamic changes. Our study uses the Hurst index, a partial correlation analysis, and a geographic detector to investigate the contributions of climate change and human activities to regional vegetation changes and their drivers. We found that Miaoling’s annual average NDVI was between 0.66 and 0.83 in 2000–2020, with a mean of 0.766. The overall trend was slow upward (0.0009/year), and 53.82% of the region continued to grow and gradually increased from west to east in the spatial domain, among which the karst regional NDVI distribution area and its growth rate were higher than those of non-karst sites. Based on correlations between climatic factors and NDVI, precipitation seasonality (coefficient of variation, CV) had the strongest correlation (positive correlation) with NDVI, while vapor pressure deficit (VPD) had a negative correlation with NDVI. In the interaction, human activities played a dominant role in the influence of NDVI on the vegetation of Miaoling. The night light index had the most explanatory power on the NDVI (q = 0.422), and the interaction between anthropogenic factors and other factors dominated its explanatory power. This study has academic and practical importance for the management, protection, and sustainable development of karst basins.

The biomass of a secondary evergreen and deciduous broad-leaved mixed forest was comprehensively inventoried in a permanent 2 ha plot in southwestern China. Biomass models, sub-sampling, soil pit method, and published data were utilized to determine the biomass of all components. Results showed that the total biomass of the forest was 158.1 Mg ha; the total biomass included the major aboveground (137.7 Mg ha) and belowground (20.3 Mg ha) biomass components of vascular plants as well as the minor biomass components of bryophytes (0.078 Mg ha) and lichens (0.043 Mg ha). The necromass was 17.6 Mg ha and included woody debris (9.0 Mg ha) and litter (8.6 Mg ha). The spatial pattern of the aboveground biomass was determined by the spatial distribution of dominant trees with large diameter, tall height, and dense wood. The belowground biomass differed in terms of root diameter and decreased with increasing soil depth. The belowground biomass in each soil pit in local habitats was not related to the spatial distribution of woody plants and soil pit depth. The karst forest presented lower biomass compared than the nonkarst forests in the subtropical zone. Biomass carbon in the karst terrains would increase substantially if degraded karst vegetation could be successfully restored to the forest. Comprehensive site-based biomass inventory of karst vegetation will contribute not only to provide data for benchmarking global and regional vegetation and carbon models but also for regional carbon inventory and vegetation restoration.−1 −1 −1 −1 −1 −1 −1 −1

The fragile karst habitat is extremely sensitive to human activities such as large-scale engineering construction. To explore the influence of the construction and operation of the GH (Guiyang-Huangguoshu) highway on the vegetation within a certain range and the response of NDVI to climate factors, Landsat data were used to synthesize annual NDVI maps using the maximum value compositing method. Trend, correlation, and coefficient of variation analyses were performed. The results demonstrate that: (1) During the construction and operation periods, NDVI showed an overall upward trend, and the NDVI value and growth rate in the contrast area were greater than those in the core area; (2) the correlation between temperature and vegetation cover along the GH highway was stronger than that between precipitation and vegetation; (3) construction of the GH highway has had a significant impact on the surrounding vegetation, with the impact on vegetation ecology along the road mainly concentrated within the 2 km range. The increase of artificial surfaces along the road has had a great impact on the NDVI, and the vegetation cover change in the core area is more significant than that in the contrast area; and (4) the overall disturbance of the GH highway project to the surrounding ecology was mainly observed in the form of low and medium fluctuations. This study aims to provide a reference for environmental assessment and management in karst areas.

Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient of variation, Hurst index, and land-use and land-cover change (LUCC) transition matrix, to conduct a thorough study of NPP variations in the Shandong Hilly Plain (SDHP) region. Furthermore, the geographic detector method was used to investigate the synergistic effects of meteorological changes and human activities on NPP in this region. Between 2000 and 2020, the vegetation NPP in the SDHP exhibited an average increase rate of 0.537 g C·m⁻²·a⁻¹. However, the fluctuation in mean annual NPP, ranging from 203 to 230 g C·m⁻²·a⁻¹, underscores an uneven growth pattern. Significant regional disparities are evident in vegetation NPP, gradually ascending from the southeast to the northwest and from the coastal areas to inland regions. The average Hurst index for the entire study area stands at 0.556, indicating an overall sustained growth trend in the time series of SDHP vegetation NPP. The vegetation NPP changes in SDHP can be well explained by climate variables (mean annual temperature, mean annual precipitation) and human activities (LUCC, night light index); of these, LUCC (q = 0.684) has the highest explanatory power on the impact of NPP and is a major influencing factor. This study deepens the understanding of the driving factors and patterns of vegetation’s dynamic response to climate change and human activities in the SDHP region. At the same time, it provides valuable scientific insights for improving ecosystem quality and promoting the carbon peaking and carbon neutrality goals.