The Yellow River Basin (YRB), a pivotal ecoregion in China, has long been plagued by a range of ecological problems, including water loss, soil erosion, and ecological degradation. Despite previous reports on the ecological environment of YRB, systematic studies on the multi-factor driving mechanism and the coupling between the ecological and hydrological systems remain scarce. In this study, with multi-source remote-sensing imagery and measured hydrological data, the random forest (RF) model and the geographical detector (GD) technique were employed to quantify the dynamic spatiotemporal changes in the ecological environment of YRB in 2000–2024 and identify the driving factors. The variables analyzed in this study included gross primary productivity (GPP), fractional vegetation cover (FVC), land use and cover change (LUCC), meteorological statistics, as well as runoff and sediment data measured at hydrological stations in YRB. The main findings are as follows: first, the GPP and FVC increased significantly by 37.9% and 18.0%, respectively, in YRB in 2000–2024; second, LUCC was the strongest driver of spatiotemporal changes in the ecological environment of YRB; third, precipitation and runoff contributed positively to vegetation growth, whereas the sediment played a contrary role, and the response of ecological variables to the hydrological processes exhibited a time lag of 1–2 years. This study is expected to provide scientific insights into ecological conservation and water resources management in YRB, and offer a decision-making basis for the design of sustainability policies and eco-restoration initiatives.

The change detection of high-resolution remote sensing images has always been a research difficulty and hot spot, which has a great application demand in land use change and ecological environment monitoring. U-type neural network can train a better model with less samples, and has good experimental effect in medical image segmentation, but it is rarely used in remote sensing image change detection research. In this paper, the U-type neural network is used to detect the change spots in GF-1 image of Yuzhou City, Henan Province, and compared with FCN, SegNet and Siamese-CNN. Experimental results show that the F1 score of U-type neural network model are 0.699 and 0.66 respectively, which are better than the other three methods, and the omission is lower, which is closer to the label map. Therefore, it is feasible to use U-type neural network for change detection of high-resolution remote sensing images, and it can have high detection accuracy.