“Two ecological barriers and three shelters” (TEBTS), which has the effect of relieving ecological pressure, is the national ecological security pattern in China. Calculating the value of TEBTS ecosystem services, clarifying the synergy/trade-off relationships between ecosystem services, and maximizing the value of regional ecosystem services are of great significance for maintaining the security of the ecological civilization. At present, the research on ecosystem service synergy/trade-off has become the frontier field of ecology and related disciplines at home and abroad, and many research results have been obtained. However, there is still room and significance for continuing research to think about the synergy/trade-off relationship of ecosystems from the perspective of temporal and spatial heterogeneity: clarifying the spatial scope and spatial transmission characteristics of ecosystem service synergy/trade-off; exploring the trend of ecosystem service synergy/trade-off, and simulating the dynamic characteristics of natural factors affecting ecosystem services; and analyzing the characteristics of different spatial attributes that lead to the synergy/trade-off of ecosystem services. In this study, the Songhua River Basin (SRB), where the NFB is located, is used as the research area, the ecosystem services are simulated through the ecosystem assessment model, ecological unit (EU) is constructed as a research carrier, which is used to define the spatial scope of ecosystem services, and the influence of spatial characteristics and attribute characteristics on the change trend of the ecosystem service synergy/trade-off relationship is analyzed. The research found that water retention, soil conservation, and biodiversity did not change much from 2000 to 2015, and these ecosystem services have a greater value in the NFZ. The amount of carbon sequestration increased rapidly from 2010 to 2015. Crop production showed an increasing trend year by year. As the main grain production area, the Songnen Plain provides the main crop production function, which is greatly affected by humans. In the spatial characteristic, water retention, soil sequestration, and biodiversity present a very significant synergistic relationship, which is manifested in the obvious high-value aggregation characteristics in the NFZ, and crop production and the other four types of ecosystem services are in a trade-off relationship. At the time scale, the four types of ecosystem services, including water retention, soil conservation, biodiversity, and carbon sequestration, are synergistic, and crop production and water retention are synergistic. The vegetation types exhibiting a synergy/trade-off relationship are mainly broad-leaved forests, and the soil types are mainly luvisols and phaeozems. These EUs are mainly distributed in the NFZ and have spatial topological characteristics: the area and circumference of these EUs are smaller, the radius of gyration is also significantly smaller than that of other EUs, and the shape is more regular. By focusing on the spatial aggregation characteristics and changing trends of the ecosystem service synergy/trade-off and clarifying the influencing factors of the ecosystem service synergy/trade-off, the ecosystem services can be integrated, and the ecosystem can be optimized. Thus, the value of regional ecosystem services can be maximized, and a certain data foundation and theoretical support can be provided for major projects, such as ecological restoration and ecological environment governance, which is of great significance for improving the pattern of ecological security.

Urban forests affect the filtration and absorption of airborne particulate matter, which can minimize the harmful effects to human health caused by airborne particulate pollution. Evergreen plants in urban forests play a major role in absorbing dust and purifying the air, especially in winter. Studying the spatial distribution of leaf dust and exploring the dust retention effect of evergreen shrubs are important for scientifically guiding urban forest construction and improving the living environment of cities in winter. The purpose of this study was to establish a dust inversion model by correlation analysis of spectral reflectance and the amount of dust absorption (ADA) of vegetation, using Sentinel-2 satellite remote-sensing images to obtain the dust distribution of the evergreen vegetation (mainly Euonymus japonicus Thunb.) in the Beijing urban area, and to determine the effect of the spatial pattern of E. japonicus woodland on ADA intensity. The result showed that the red band and near-infrared band are most sensitive to dust. The normalized difference phenology index (NDPI) is more suitable for building an inversion model, where the determination coefficient (R²) of the inversion model constructed by the ratio of the NDPI (R_NDPI) was 0.879. The inversion results show that the mean ADA in the enclosed area is smaller than that in semi-enclosed and open areas, and the regional distribution of high ADA in the urban area of Beijing was higher in the south with a tendency of the ADA to decrease from city center to the surrounding area. The size, shape, and percentage of landscape (PLAND) of E. japonicus woodland have a significant effect on ADA intensity. We found that, in the study area, when the PLAND of E. japonicus woodland is higher than 40%, its ADA intensity remains basically unchanged. When the vegetation coverage is fixed, the landscape shape index is negatively correlated with ADA intensity, and reduction of the overall shape complexity of forestland can enhance its dust retention effect and improve the air environment of the surrounding areas. The results of this study can be used as a reference for urban planners and landscape architects when building urban forests, providing a scientific basis for controlling and reducing air particulate pollution in Beijing’s winter and improving the living environment.

The water cycle in the key agricultural and pastoral zones (KAPZs) is an important factor for maintaining the stability of the ecosystem. Groundwater collection and lateral seepage are indispensable parts of the water cycle, and it is difficult to monitor the groundwater situation in each area. The strength of the alternate circulation of groundwater is directly related to the utilization value and development prospects of groundwater; therefore, creating an effective method for the detection of groundwater burial depth has become an issue of increasing concern. In this paper, we attempt to create a method for the detection of groundwater burial depth that combines cokriging interpolation, spatial autocorrelation, geographically weighted regression, and other methods to construct a quantitative relationship between different land cover types and groundwater depth. By calculating the band index of the land cover type, the groundwater depthinformation of the unknown area can be obtained more accurately. Through collaborative kriging interpolation, normalized difference vegetation index (NDVI), precipitation, and hydrogeological conditions were used as covariates. The groundwater burial depth of Wengniute Banner in 2005, 2009, 2013, and 2017 was the response variable, and the groundwater burial depth in the study area was calculated. The groundwater burial depth data after the cokriging interpolation was used to transform the raster data into vector data in space using the improved hydrological response unit (HRU) model to make it more suitable for the actual groundwater confluence. Subsequently, 551 minimum response units (MHRUs) were obtained by division, and the spatial autocorrelation analysis was performed accordingly. The groundwater burial depth in the study area is spatially distinct from east to west, and the groundwater level shows a trend of being high in the west and low in the east, gradually increasing due to precipitation and rivers. The average change of groundwater depth in the time series is not significant, but it does gradually show a trend of accumulation. According to the aggregation characteristics of spatial autocorrelation analysis, a geographically weighted regression model of groundwater depth and NDVI, normalized difference drought index (NDDI), and net relatedness index (NRI) was established. The NDVI representing the forest land and the Adjusted R² of the groundwater depth is 0.67. The NRI representing the cultivated land and the Adjusted R² of the groundwater depth is 0.8675. The NDDI representing the bare land and the Adjusted R² of the groundwater depth is 0.7875. It shows that the band index representing the ground type has a good fitting effect with the groundwater burial depth.