The Loess Plateau plays a significant role in the implementation of China's Grain for Green Project due to severe ecological damage in the region. In order to monitor and evaluate the effects of Grain for Green Project, a study was conducted in Wuqi County, which is representative of the Loess Plateau. The study utilized remote sensing (RS) and geographic information system (GIS) technologies to analyze the spatial and temporal patterns of Grain for Green Project and assess its effects. The findings indicate that the Grain for Green Project resulted in notable improvements in Wuqi County from 2000 to 2018. Firstly, there was a significant increase in vegetation coverage, accompanied by a reduction in soil erosion intensity. Secondly, approximately 64 % of cropland was converted, leading to an expansion of forest and grassland areas. Thirdly, the focus of vegetation restoration was primarily on converting cropland to grassland, indicating its suitability for the county compared to forestation. Lastly, the conversion of steep cropland (>25(degrees)) was influenced by the density of less steep cropland (<25(degrees)). This study emphasizes the importance of guiding farmers in selecting appropriate vegetation restoration strategies and finding a balance between erosion control and agricultural production within the Grain for Green Project. Furthermore, the study recognizes that the project's significant effects are not solely attributed to land use conversion but also to the self-restoration of vegetation. This shift towards a self-restoration perspective is crucial for the future high-quality development of the Grain for Green Project.

Vegetation is a natural link between the atmosphere, soil, and water, and it significantly influences hydrological processes in the context of climate change. Under global warming, vegetation greening significantly aggravates the water conflicts between vegetation water use and water resources in water bodies in arid and semiarid regions. This study established an improved eco-hydrological coupled model with related accurately remotely sensed hydrological data (precipitation and soil moisture levels taken every 3 j with multiply verification) on a large spatio-temporal scale to determine the optimal vegetation coverage (M*), which explored the trade-off relationship between the water supply, based on hydrological balance processes, and the water demand, based on vegetation transpiration under the impact of climate change, in a semiarid basin. Results showed that the average annual actual vegetation coverage (M) in the Hailar River Basin from 1982 to 2012 was 0.62, and that the average optimal vegetation coverage (M*) was 0.56. In 67.23% of the region, M* was lower than M, which aggravated the water stress problem in the Hailar River Basin. By identifying the sensitivity of M* to vegetation characteristics and meteorological parameters, relevant suggestions for vegetation-type planting were proposed. Additionally, we also analyzed the dynamic threshold of vegetation under different climatic conditions, and we found that M was lower than M* under only four of the twenty-eight climatic conditions considered (rainfall increase by 10%, 20%, and 30% with no change in temperature, and rainfall increase by 20% with a temperature increase of 1 degrees C), thereby meeting the system equilibrium state under the condition of sustainable development. This study revealed the dynamic relationship between vegetation and hydrological processes under the effects of climate change and provided reliable recommendations to support vegetation management and ecological restoration in river basins. The remote sensing data help us to extend the model in a semiarid basin due to its accuracy.

Coal mining in arid western regions is damaging the fragile ecology, causing problems such as surface damage, vegetation destruction, and soil erosion. These issues are obstacles to the development of green coal, as mining activities can disrupt the distribution of surface vegetation, leading to its spread outside the mining area and affecting surrounding areas. Based on Landsat data, the binary pixel model was used to calculate the vegetation coverage (FVC) in mining area from 2005 to 2021. Through vegetation coverage classification and regression trend analysis, the temporal and spatial changes and evolution trends of vegetation disturbance caused by coal mining and climate were analyzed. Correlation analysis revealed the range of ecological disturbance caused by coal mining at the coal mine scale and mining area scale. The results show that the vegetation coverage of the mining area showed a decreasing trend from 2005 to 2021. Winter and spring precipitation was the primary factor affecting vegetation growth in the area, while coal mining had indirect and secondary effects on vegetation. Human activities played a significant role in improving vegetation, and between 2015 and 2018, the area of vegetation improvement increased by 133.41% compared to that of 2009-2014. Compared to the reference area, the impact range of vegetation disturbance in the mining area is 2.5-5 km, while the impact range of vegetation disturbance in the coal mine is less than 500 m. Therefore, this study provides a theoretical basis for studying the impact of mining activities on vegetation and boundary identification.

Soil hydrological properties not only directly influenced soil water content, evapotranspiration, infiltration, and runoff, but also made these factors of concern in arid and semi-arid regions. Prior research has focused on the temporal and spatial variation in soil hydrological properties and the impacts of climate change, ecosystem changes over time or human activities on soil hydrological properties. However, studies conducted on the differences in soil hydrological properties between shady and sunny slopes have seldom been conducted, especially in the permafrost region of the Qinghai-Tibetan Plateau. To investigate the variation in soil hydrological properties on shady and sunny slopes, we chose the Zuomaokongqu watershed of Fenghuo Mountain, which is located on the Qinghai-Tibetan Plateau, as the study area. Three experimental sites were selected in the study area, and the distance between experimental sites was 100m. Based on the differences in altitude and vegetation coverage, five sunny slope sample points and three shady slope sample points were selected in each experimental site. At each of these sample points, the soil water content, soil-saturated conductivity, soil water-retention curve, soil physico-chemical properties, aboveground biomass, and underground biomass were examined in the top 0-50cm of the active layer. The results showed that the soil hydrological properties of shady slopes differed significantly from those of sunny slopes. The soil water content of sunny slopes was 20.9% less than that of shady slopes. The soil-saturated water content of sunny slopes was 12.2% less than that of shady slopes. The soil water content of sunny slopes at -0.3Mpa and -0.7Mpa matric potential was 23.5% and 21.4% less than that of shady slopes, respectively. It was indicated that the soil water-retention capacity of sunny slopes was lower than that of shady slopes. However, the soil-saturated conductivity of sunny slopes was 84.5% larger than that of shady slopes and exceeded the range of soil-saturated conductivity, which was useful for plant growth. Meanwhile, the vegetation coverage on sunny slopes was lower than that on shady slopes, but the soil sand content showed the opposite relationship. Pearson's coefficient analysis results showed that vegetation coverage and soil desertification, which are affected by permafrost degradation, were the main factors influencing soil hydrological properties on shady and sunny slopes. These results will help determine appropriate hydraulic parameters for hydrological models in mountain areas.