Species diversity is a crucial index used to evaluate the stability and complexity of forest ecosystems. Studying the relationship between stand structure and understory herbaceous plants species diversity is useful for managers to formulate the best forest structure optimization method with the goal of improving herbaceous species diversity. In this research, Platycladus orientalis plantations in Beijing were taken as the research object. Pearson’s correlation analysis was used to explore the single-factor correlation between stand structure and understory herbaceous plants species diversity; furthermore, a typical correlation analysis and multiple linear regression were used to explore the multi-factor correlation and analyze the dominant stand structure parameters affecting understory herbaceous plants species diversity. In the range of stand structures studied, the results showed that canopy density was negatively correlated with the Shannon–Wiener index and Simpson index (p < 0.01), and tree density was negatively correlated with the Shannon–Wiener index (p < 0.05). In terms of stand spatial structure, the mingling degree was positively correlated with the Shannon–Wiener index, Simpson index, Margalef richness index and Pielou evenness index (p < 0.05), while the uniform angle was negatively correlated with the Pielou evenness index (p < 0.05). The correlation coefficient of the first group of typical variables in the typical correlation analysis was 0.90 (p < 0.05); from this group of typical variables, it can be concluded that canopy density is the most influential indicator affecting the comprehensive index of understory herbaceous plants species diversity, with a load of −0.690, and the Shannon–Wiener index and Simpson index are the most responsive indicators of changes in the comprehensive index of stand structure, with loads of 0.871 and 0.801, respectively. In the process of the management of Platycladus orientalis plantations under a low altitude, south slope, thin soil layer and hard soil parent material, in order to improve the herbaceous species diversity, the canopy density of the overstory and tree density should be appropriately reduced. Additionally, it is necessary to regulate the horizontal spatial structure of stands. When the trees are randomly distributed and the mingling degree is high, the species diversity of herbs can be increased.

The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be helpful for ecosystem management. Based on the NDVI dataset, the study analyzed the trend of NDVI change from 2000 to 2019 using the Theil–Sen trend analysis and the Mann–Kendal significance test, detected the driving forces of the spatial heterogeneity of NDVI by the means of the geographical detector, and analyzed the relative contribution of climate change and human activities to interannual NDVI changes using residual analysis model. The results showed that, in terms of the spatial distribution, the pattern of NDVI showed that it is higher in the southeast and lower in the northwest region of the SACA. Elevation was the dominant factor influencing the spatial heterogeneity of NDVI, with the explanatory power of 64%, much larger than other factors, and vegetation type, temperature, precipitation, land use type, and soil type were the main factors. In addition, the explanatory power of the dual factor interaction was higher than that of the single factor effect, which showed two kinds of interaction relationships: bivariate enhancement and nonlinear enhancement. In terms of the temporal variation, 85.59% of the study area showed an increasing trend, and only 14.41% of the area showed a decreasing trend. The main factor affecting NDVI changes was human activities, and climate change was the secondary factor, with relative contributions of 71.35% and 28.65%, respectively. The study will promote a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention of vegetation degradation and vegetation restoration in the SACA.

Soil erosion (SE) is a critical threat to the sustainable development of ecosystem stability, agricultural productivity, and human society in the context of global environmental and climate change. Particularly in tropical island regions, due to the expansion of human activities and land use/cover changes (LUCCs), the risk of SE has been exacerbated. Combining the RUSLE with machine learning methods, SE spatial patterns, their driving forces and the mechanisms of how LUCCs affect SE, were illustrated. Additionally, the potential impacts of future LUCCs on SE were simulated by using the PLUS model. The main results are as follows: (1) Due to LUCCs, the average soil erosion modulus (SEM) decreased significantly from 108.09 t/(km²·a) in 2000 to 106.75 t/(km²·a) in 2020, a reduction of 1.34 t/(km²·a), mainly due to the transformation of cropland to forest and urban land. (2) The dominant factor affecting the spatial pattern of SE is the LS factor (with relative contributions of 43.9% and 45.17%), followed by land use/cover (LUC) (the relative contribution is 28.46% and 34.89%) in 2000 and 2020, respectively. (3) Three kinds of future scenarios simulation results indicate that the average SEM will decrease by 2.40 t/(km²·a) under the natural development scenario and by 1.86 t/(km²·a) under the ecological protection scenario by 2060. However, under the cropland protection scenario, there is a slight increase in SEM, with an increase of 0.08 t/(km²·a). Sloping cropland erosion control remains a primary issue for Hainan Island in the future.

Rainfall erosivity is one of the important factors leading to soil and water erosion, affecting the intensity of soil erosion and the variation of river sediment load. This work aimed to explore the distribution and variation characteristics of rainfall erosivity in the Nandu River Basin in tropical China and to clarify the effect of rainfall erosivity on sediment load variation. The daily rainfall data of 13 meteorological stations in the Nandu River Basin during 1971–2020 were used to calculate the rainfall erosivity of the river basin by using the rainfall erosivity model based on the daily rainfall. The analyses were conducted using the Mann-Kendall non-parametric trend/abrupt test, wavelet analysis, inverse distance weighted interpolation and other research methods to determine the temporal and spatial fluctuation patterns of rainfall erosivity and sediment load. The annual mean rainfall erosivity in the Nandu River Basin during 1971–2020 was 16,497.67 MJ mm·ha⁻¹·h⁻¹, which showed an upward trend. The distribution of rainfall erosivity was concentrated from July to September. Except spring, the variation of rainfall erosivity in summer, autumn and winter showed an upward trend. Spatially, rainfall erosivity showed a decreasing trend from south to north. The variation coefficient of each meteorological station ranged from 0.24 to 0.43, showing high regional variability. The correlation between rainfall erosivity and erosive rainfall in the basin was the highest, followed by altitude and latitude. From 1980 to 2020, the sediment load in the Nandu River Basin showed an overall upward trend, with obvious periodic changes. The main change period was 25 years, and the sediment load had a large inter-annual variation. Taking the period before the inflection point of the double accumulation curve of sediment load and rainfall erosivity as the base period, since 2009, the river sediment load increased by 46.36% compared with the base period. The influence of the variation of rainfall erosivity on sediment load is −22.23%, and that of human activities on sediment load is 122.23%. These findings can provide a scientific reference for soil erosion model prediction, soil and water conservation and environmental management in tropical basins of China.