Land change is a key topic in research on global environmental change, and the restoration of degraded land is the core component of the global Land Degradation Neutrality target under the UN 2030 Agenda for Sustainable Development. In this study, remote-sensing-derived land-use data were used to characterize the land-change processes in China’s Loess Plateau, which is experiencing large-scale ecological restoration. Geographically Weighted Regression was applied to capture the spatiotemporal variations in land change and driving-force relationships. First, we explored land-use change in the Loess Plateau for the period 1990–2015. Grassland, cropland and forestland were dominant land cover in the region, with a total percentage area of 88%. The region experienced dramatic land-use transitions during the study period: degraded grassland and wetland, expansion of cropland and built-up land and weak restoration of forestland during 1990–2000; and increases in grassland, built-up land, forestland and wetland, concurrent with shrinking cropland during 2000–2015. A Geographically Weighted Regression (GWR) analysis revealed altitude to be the common dominant factor associated with the four major land-use types (forestland, grassland, cropland and built-up land). Altitude and slope were found to be positively associated with forestland, while being negatively associated with cropland in the high, steep central region. For both forestland and grassland, temperature and precipitation behaved in a similar manner, with a positive hotspot in the northwest. Altitude, slope and distance to road were all negatively associated with built-up land across the region. The GWR captured the spatial non-stationarity on different socioeconomic driving forces. Spatial heterogeneity and temporal variation of the impact of socioeconomic drivers indicate that the ecological restoration projects positively affected the region’s greening trend with hotspots in the center and west, and also improved farmer well-being. Notably, urban population showed undesired effects, expressed in accelerating grassland degradation in central and western regions for 1990–2000, hindering forestland and grassland restoration in the south during 2000–2015, and highlighting the long-term sustainability of the vegetation restoration progress. Such local results have the potential to provide a methodological contribution (e.g., nesting local-level approaches, i.e., GWR, within land system research) and spatially explicit evidence for context-related and proactive land management (e.g., balancing urbanization and ecological restoration processes and advancing agricultural development and rural welfare improvement).

Large-scale vegetation restoration has caused complex changes in ecosystem service (i.e., ES) interactions. However, current analysis on the spatial interactions of ESs and their driving mechanisms remains deficient, limiting the adaptive management in vegetation restoration areas. This study focused on a representative restoration area (Yan’an) to analyze the relationships among carbon sequestration, water yield, baseflow regulation, and soil conservation from 1990 to 2020. Employing the bivariate boxplot and spatial autocorrelation methods, we identified the overall changes and spatial patterns of ES interactions. The geographically and temporally weighted regression (i.e., GTWR) model was applied to elucidate the driving factors of these spatial ES interactions. The results indicated the following: (1) Over the past three decades, synergies between carbon sequestration and water yield emerged as the joint results of spatial ‘low–low’ interactions and ‘high–high’ interactions between the two ESs, while other ES pairs generally exhibited comparatively weaker synergies, due to their spatial ‘low–high’ interactions in southern semi-humid areas. (2) In the northern semi-arid areas, both fractional vegetation cover (i.e., FVC) and climatic factors consistently exerted negative influences on all ‘low–low’ ES interactions, which caused a reduced area in synergies, while in the southern semi-humid areas, FVC suppressed the ‘low–high’ trade-offs between ESs, indicating the adaptability of grassland restoration efforts. (3) The impact of human activities on ES interactions has increased in the last 10 years, and exhibited positive effects on the ‘low–low’ ES interactions in northern semi-arid areas. However, the expansion of trade-off between soil conservation and carbon sequestration warrants attention. This study offers important insights into understanding the spatial interactions among carbon, water, and soil-related ESs in drylands.

The Kherlen River Basin is a typical basin in the eastern Mongolian Plateau and is dominated by grassland. This study estimated the grassland yield in the Kherlen River Basin using the Carnegie–Ames–Stanford approach (CASA) model, combined with Theil–Sen median trend analysis and the Geodetector, to explore its spatiotemporal changes and driving factors. This integrated framework links temporal trend detection with spatial interaction analysis to better reveal ecological responses to climatic and anthropogenic influences. The results showed the following: (1) The root mean square error (RMSE) between the estimated grassland yield and the laboratory measurements was 37.88 g/m², with an estimation accuracy (EA) of 73.52%. (2) From 2000 to 2024, the grassland yield increased significantly at a rate of 1.98 g/(m²·a) (p < 0.05), with the fastest growth in the middle reaches. (3) Spatially, 79.78% of the basin exhibited significant increases, mainly in the central and western regions. The proportion of significant increase was highest in the upper reaches (40.36%), followed by the middle (32.89%) and lower reaches (6.53%). (4) Due to limited temporal resolution of socioeconomic data, the driving factor analysis covered the period 2000–2020, during which the overall grassland yield was primarily influenced by the interaction between precipitation and elevation (q = 0.6371). Specifically, the upper, middle, and lower reaches were mainly influenced by the interactions between temperature and precipitation (q = 0.6772), precipitation and elevation (q = 0.6377), and temperature and elevation (q = 0.4255), respectively. The study indicates that grassland yield in the Kherlen River Basin exhibited an overall increasing trend during 2000–2024, with climatic factors (precipitation and temperature) and the geographic factor (elevation) identified as the dominant drivers. The influence of human activities was not significant, although this result may be affected by uncertainties associated with data resolution limitations. Future work should incorporate higher-resolution remote sensing and socioeconomic datasets to better assess the impacts of human activities.