Lili Han

Improvements in vegetation coverage are driven by both resource endowment conditions and policy behaviors. To accurately reflect the vegetation restoration effect after ecological policies, this study used the potential realization model to calculate the potential realization degree of vegetation restoration on the Loess Plateau and to assess the vegetation restoration effect after the Grain for Green Program from 2000 to 2020. Then, the influencing factors were explored using the spatial Dubin model. The results reveal that (1) the EVI value of the Loess Plateau in northern Shaanxi increased from below 0.25 at the beginning of the study to approximately 0.35 by the end, indicating that the green territory of the Loess Plateau gradually expanded to the northwest over the study period, and that the east and west of the Loess Plateau are key areas of vegetation cover for further improvement; (2) compared to the traditional EVI indicator, the potential realization degree can more accurately evaluate the vegetation restoration effect driven by ecological policies; (3) policy intensity is positively correlated with the growth rate of the vegetation restoration potential realization degree by 0.183 and significant at 1% level, making it the primary factor influencing the effect of vegetation restoration. Additionally, annual average precipitation and annual sunshine percentage have significant spatial positive contributions to the improvement of vegetation restoration on the Loess Plateau. The study’s findings are expected to contribute to the development of a scientific basis for adjusting the vegetation restoration policy on the Loess Plateau and enhancing ecological restoration efforts.

Quantification of soil water-physical properties and their spatial variation is important to better predict soil structure and functioning, as well as associated spatial patterns in the vegetation. The provision of site-specific soil data further facilitates the implementation of enhanced land use and management practices. Using geostatistical methods, this study quantified the spatial distribution of soil bulk density (SBD), soil moisture (SM), capillary water-holding capacity (CWHC), capillary porosity (CP), non-capillary porosity (NCP), and total porosity (TP) in southern subtropical forests located at the Tropical Forest Research Center in Pingxiang City, China. A topographic map (scale = 1:10,000) was used to create a grid of l km squares across the study area. At the intersections of the grid squares, the described soil water-physical properties were measured. By calculating the coefficient of variation for each soil water-physical property, all measured soil water-physical properties were found to show moderate spatial heterogeneity. Exponential, gaussian, spherical, and linear models were used to fit the semivariograms of the measured soil water-physical properties. Across all soil water-physical properties, the range A₀ variable (i.e., the separation distance between the semivariance and the sill value) measured between 3419 m and 14,156 m. The nugget-to-sill ratio ranged from 9 to 41%, indicating variations in the level of spatial autocorrelation among the soil water-physical properties. Many of the soil water-physical properties were strongly correlated (as assessed using Pearson correlation coefficients). Spatial distribution maps of the soil water-physical properties created via ordinary kriging (OK) showed that most water-physical properties had clumped (aggregated) distributions. SBD showed the opposite spatial pattern to SM and CWHC. Meanwhile, CP and TP showed similar distributions.

Total nitrogen (TN), total phosphorus (TP), total potassium (TK), and soil organic matter (OM) can significantly affect forest growth. However, these soil properties are spatially heterogeneously distributed, complicating the prescription of forest management strategies. Thus, it is imperative to obtain an in-depth understanding of the spatial distribution of soil properties. In this study, soils were sampled at 181 locations in the Tropical Forest Research Center in the southwestern Guangxi Zhuang Autonomous Region in southern China. We investigated the spatial variability of soil OM, TN, TP, and TK using geostatistical analysis. The nugget to sill ratio indicated a strong spatial dependence of soil TN and a moderate spatial dependence of soil OM, TP, and TK, suggesting that TN was primarily controlled by intrinsic factors (e.g., soil texture, parent material, vegetation type, and topography), whereas soil OM, TP, and TK were controlled by intrinsic and extrinsic factors (e.g., cultivation practices, fertilization, and planting systems). Based on the spatial variability determined by the geostatistical analysis, we performed ordinary kriging to create thematic maps of soil TN, TP, TK, and OM. Model validation indicated that the thematic maps were reliable to inform forest management.