The Loess Plateau is one of the most fragile ecological regions in China due to its shortage of water resources and severe soil erosion. The rapid development of urbanization and the implementation of the project of returning farmland to forest (grass) have caused the ecological environment of the region to be strongly impacted by human activities. It is necessary to investigate the spatial and temporal evolution characteristics of ecosystem services and trade-off/synergy relationships on the Loess Plateau, to achieve scientific management of ecological services and sustainable development of the region. This study quantitatively assesses three ecosystem services of water yield (WY), carbon storage (CS), and soil conservation (SC) on the Loess Plateau under different scenarios from 2000 to 2030 by using the InVEST and PLUS models. Further, the trade-off and synergistic relationships among the ecosystem services have been investigated by Spearman correlation analysis. The results showed that the land-use differences are more obvious under different policy scenarios, with a sharp expansion of constructed land, a gradual increase of forest land, and a continuous decrease of arable land in the Loess Plateau from 2000 to 2020; the water yield and soil conservation increase from 2000 to 2020, and the carbon storage shows an opposite trend. The soil conservation and carbon storage scenarios are the best under the ecological conservation scenario in 2030, while the water yield service is the best under the economic development scenario. There is a synergistic relationship between CS and SC, while there is a trade-off relationship between CS and WY. In addition, there are significant trade-off effects between SC and WY. These results can support guiding land-use management and ecological restoration.

Assessing the spatio-temporal variability and driving forces of soil erosion on the Loess Plateau is critical for ecological and environmental management. In this paper, the Revised Universal Soil Loss Equation (RUSLE) model, the patch-generating land use simulation, and the Geographical Detector are used to investigate the spatio-temporal variations of the Loess Plateau’s soil erosion from 2000 to 2050. The results showed that: (1) The primary categories of soil erosion from 2000 to 2020 were moderate, mild, and slight, and the average level of soil erosion exhibited a decreasing and then an increasing tendency during the last 20 years. (2) Soil erosion was directly impacted by changes in land use, with cropland and forest being the primary land use and land cover changes in the study region. Cropland and construction land being turned into woodland between 2000 and 2020 resulted in a significant decrease in the severity of soil erosion. Projected soil erosion is expected to increase significantly between 2020 and 2050 due to arable land being converted into construction land. (3) The key variables impacting the spatial distribution of soil erosion were LUCC (Land-Use and Land-Cover Change), NDVI (Normalized Difference Vegetation Index), and slope, and the interplay of these variables may increase their ability to explain soil erosion. Grasslands with an NDVI ranging from 0.9 to 1, rain ranging from 0.805 to 0.854 m, a slope above 35°, and a terrain elevation ranging from 1595 to 2559 m were identified as having a high risk of soil erosion. Soil erosion prevention and management efforts should focus on the ecological restoration of upland areas in the future.

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy (${\mathrm{R}}^2$ = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine.

Habitat quality has great significance in terms of regional ecological conservation and human welfare. In this study, we evaluated the spatial and temporal characteristics of land use and habitat quality in the temperate desert sub-region of the Ordos Plateau using patch-generating land use simulation (PLUS) and integrated valuation of ecosystem services and trade-offs (InVEST) models. From 2000 to 2020, the areas of grassland, cropland, and unused land in the study area increased significantly; the areas of water bodies and woodland increased slightly; and the area of wasteland decreased significantly. Moreover, the habitat quality in the temperate desert subzone of the Ordos Plateau showed a trend of initial increase and then decrease between 2000 and 2020. The areas of lower and low habitat quality first decreased and then increased, and the overall area decreased over time. Conversely, the areas of high and higher habitat quality initially increased and then decreased, and the overall area increased over time. The area of medium habitat quality first decreased and then increased, although the overall change was minimal. Based on the PLUS model, the predicted habitat quality of the study area in 2025 under the natural development scenario was compared to that predicted under the ecological conservation scenario. The comparison of results showed higher habitat quality and lower habitat degradation under the ecological conservation development scenario. These results can be used to provide a scientific basis and decision reference for the sustainable use of land resources and encouragement of high-quality socio-economic development in the temperate desert sub-region of the Ordos Plateau.

The study area was the Hulunbuir Plateau in northeastern China, based on a natural resource element observation study. The assessment of the spatial and temporal variation of soil erosion is crucial for implementing environmental management in the fragile ecosystem of the Hulunbuir Plateau. The study provides an interesting basis for soil erosion control on the Hulunbuir Plateau and other areas with similar climatic conditions, with the aim of providing sound data to support environmental protection policies in the study area. In this study, the spatial and temporal variations in soil erosion in the region from 2000 to 2020 were quantitatively assessed using the Revised Universal Soil Loss Equation. Furthermore, the patch-generating land use simulation model predicted future soil erosion. Land use prediction data were examined using Kappa coefficients. The prediction of future land use types using CMIP6 data and natural social data in the PLUS model were used to predict soil erosion for different future scenarios. The results showed that the soil erosion rate on the Hulunbuir Plateau showed a significant increasing trend in time from 2000 to 2020. Spatially, soil erosion increases gradually from the west to the east. Soil erosion occurs mainly on grasslands, while cultivated lands show a significant increasing trend by 2020. Slope erosion occurs mainly in areas between 15° and 35°. From 2020 to 2050, soil erosion will increase significantly due to increased precipitation. The soil erosion in SSP2–4.5 is better than the other scenarios.

The increase in atmospheric CO₂ leads to global warming and ecological environment deterioration. Carbon storage modeling and assessment can promote the sustainable development of the ecological environment. This paper took Xinjiang as the study area, analyzed the spatial and temporal evolution of land use in four periods from 1990 to 2020, explored the spatial relationship of carbon stocks using the InVEST model, and coupled the GMOP model with the PLUS model to carry out multiple scenarios for the future simulation of land use in the study area. We found (1) Over time, the types with an increasing area were mainly impervious and cropland, and the types with a decreasing area were grassland, snow/ice, and barren; spatially, the types were predominantly barren and grassland, with the conversion of grassland to cropland being more evident in the south of Northern Xinjiang and north of Southern Xinjiang. (2) The evolutionary pattern of terrestrial carbon stocks is increasing and then decreasing in time, and the carbon sink areas are concentrated in the Tarim River Basin and the vicinity of the Ili River; spatially, there are differences in the aggregation between the northern, southern, and eastern borders. By analyzing the transfer in and out of various categories in Xinjiang over the past 30 years, it was obtained that the transfer out of grassland reduced the carbon stock by 5757.84 × 10⁴ t, and the transfer out of Barren increased the carbon stock by 8586.12 × 10⁴ t. (3) The land use layout of the sustainable development scenario is optimal under the conditions of satisfying economic and ecological development. The reduction in terrestrial carbon stocks under the 2020–2030 sustainable development scenario is 209.79 × 10⁴ t, which is smaller than the reduction of 830.79 × 10⁴ t in 2010–2020. Land optimization resulted in a lower loss of carbon stocks and a more rational land-use layout. Future planning in Xinjiang should be based on sustainable development scenarios, integrating land resources, and achieving sustainable economic and ecological development.