Exploring land use change is crucial to planning land space scientifically in a region. Taking the ecological conservation area (ECA) in western Beijing as the study area, we employ ArcGIS 10.2, landscape pattern index and multiple mathematical statistics to explore the temporal and spatial variation of land use from 2000 to 2020. Patch-generating Land Use Simulation (PLUS), Future Land Use Simulation (FLUS) and Markov models were used to simulate and predict the current land use in 2020. The models were evaluated for accuracy, and the more accurate PLUS model was selected and used to simulate and predict the potential land use in the study area in 2030 under two management scenarios. The main findings of this research are: (1) From 2000 to 2020, the construction land increased constantly, and the area of cultivated land and grassland decreased significantly. (2) For predicting the spatial distribution of land use in the study area, the PLUS model was more accurate than the FLUS model. (3) The land-use prediction of the study area in 2030 shows that the area of grassland, forest and water is approximately equal to their corresponding value in 2020, but the construction land increased constantly by occupying the surrounding cultivated land. According to this research, the continuous decrease of cultivated land in favor of increasing construction land will cause losses to the ecological service function of the ECA, which is not beneficial to the sustainable development of the region. Relevant departments should take corresponding measures to reduce this practice and promote sustainable development, particularly in the southern and western areas of the ECA where there is less construction land.

As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (PDPs) are one of the most widely used methods to estimate the marginal effect of independent variables on the predicted outcome of a machine learning model, and it is regarded as the main basis for conclusions in relevant research. As more controversies regarding the reliability of the results of the PDPs emerge, the uncertainty of the PDPs remains unclear. In this paper, we experiment with real, remote sensing data to systematically analyze the uncertainty of partial dependence relationships between four climate variables (temperature, rainfall, radiation, and windspeed) and vegetation growth, with one conventional linear model and six machine learning models. We tested the uncertainty of the PDP curves across different machine learning models from three aspects: variation, whole linear trends, and the trait of change points. Results show that the PDP of the dominant climate factor (mean air temperature) and vegetation growth parameter (indicated by the normalized difference vegetation index, NDVI) has the smallest relative variation and the whole linear trend of the PDP was comparatively stable across the different models. The mean relative variation of change points across the partial dependence curves of the non-dominant climate factors (i.e., radiation, windspeed, and rainfall) and vegetation growth ranged from 8.96% to 23.8%, respectively, which was much higher than those of the dominant climate factor and vegetation growth. Lastly, the model used for creating the PDP, rather than the relative importance of these climate factors, determines the fluctuation of the PDP output of these climate variables and vegetation growth. These findings have significant implications for using remote sensing data and machine learning models to investigate the quantitative relationships between the climate and terrestrial vegetation.

The area of karst terrain in China covers 3.63×10⁶ km², with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30×10⁶ km² of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.

Forestation as part of the Returning Farmland to Forest Project was implemented to mitigate soil erosion in southwestern China. However, whether forestation has effectively reduced soil erosion in southwestern China remains unclear, mostly because of the lack of monitoring forest cover change and soil erosion at watershed scales. We interpreted forest cover change from satellite images and simulated soil erosion changes for the period of 1986–2018 in the Chong’an River Basin with the Water and Tillage Erosion Model and Sediment Delivery Model. Our results show that the change in forest cover has the highest correlation coefficient with the sediment yield in the watershed, with an obvious inverse phase relationship between them for all the simulated years. From 2002 to 2014, large-scale forestation and frequent droughts caused the forest cover to vary, resulting in significant changes in the annual soil erosion amount. Because crevices favoring tree growth are more developed in limestone than in dolomite, the forest cover reduction on dolomite is significantly higher than that on limestone under severe droughts in karst areas. Our study implied that the function of forestation in preventing soil erosion depends on lithology in karst areas.

The Guizhou karst area is one of the largest continuous areas of karst in the humid climate zone and is representative of karst landforms in China. Large portions of the karst system are characterized by extremely shallow soils underlain by weathered bedrock and water deficits are common. Although the distribution of ecosystem productivity is largely related to variations in the temperature and precipitation, the influence of the substrate in karst areas requires further exploration. We explored the relative importance of the bedrock geochemistry (characterized by the concentrations of Ca, Mg and Si) and climatic factors (temperature and precipitation) to explain the spatial variability in gross primary productivity (GPP) with various degrees of water deficit during the time period 2001–2015. Our results show that the impact of bedrock geochemistry is an important parameter in changing the original relationship between climate and the GPP. The bedrock geochemistry functioned as a “regulator” of the relation between climate and the GPP, which strengthened with decreasing climate favourability. The variations in GPP and surface water storage were significantly different when different elements (Ca, Mg or Si) were dominant. The Mg-rich regions showed the greatest annual variations in the GPP, whereas the Si-rich regions had the strongest surface water storage potential to support vegetation growth. The results of our study are important for systematically evaluating the effects of climate on vegetation productivity and provide a benchmark for global vegetation modelling predictions.