Land use and land cover change is an important driving force for changes in ecosystem services. We defined several important human-induced land cover change processes such as Ecological Restoration Project, Cropland Expansion, Land Degradation, and Urbanization by the land use / land cover transition matrix method. We studied human-induced land cover changes in the Yellow River Basin from 1980 to 2015 and evaluated its impact on ecosystem service values by the benefit transfer method and elasticity coefficient. The results show that the cumulative area of human-induced land cover change reaches 65.71 million ha from 1980 to 2015, which is close to the total area of the Yellow River Basin. Before 2000, Ecological Restoration Project was the most important human-induced land cover change process. However, due to the large amount of cropland expansion and land degradation, the area of natural vegetation was reduced and the ecosystem value declined. Since 2000, due to the implementation of the "Grain for Green" program, the natural vegetation of upstream area and midstream area of Yellow River Basin has been significantly improved. This implies that under an appropriate policy framework, a small amount of human-induced land cover change can also improve ecosystem services significantly.

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.