Exploring the dynamic variation in ecosystem services and clarifying the driving mechanism will help with the formulation of effective ecological environment protection policies. Accordingly, this study sought to reveal the complex variability in ecosystem services in the Yellow River Delta (YRD) at a higher temporal resolution and the transition between the main driving factors in different periods. To this end, we used the economic equivalent factor valuation method to quantify the ecosystem service value from 2000 to 2019 at 5-year intervals. Furthermore, the Geo-detector model was used to identify the main driving factors and interaction between the driving factors of ecosystem service value variations. Then, we analyzed the temporal and spatial dynamic variations in the ecosystem service value and the transitions between the main driving factors in different periods. The main results are as follows: (1) From 2000 to 2019, the ecosystem service value of the YRD showed an increasing trend followed by a decline, whereby water and construction land increased and the other classes of land decreased. Overall, the inland and coastal distribution patterns exhibited low and high values, respectively. (2) The main driving factors of ecosystem service value variations were the NDVI and topographical factors (aspect, slope, elevation), which had q values that were stable and greater than those of the other factors. Although human activity, tourist resource concentration and traffic convenience factors had a comparatively minor effect on ecosystem services, we noted a trend where their effects increased from 2000 to 2019. (3) The detection of interactions revealed complex mechanisms affecting the variation in the YRD. Interactions between variables had a stronger influence than individual effects. The interactions between the Normalized Difference Vegetation Index (NDVI) and other factors consistently had the most significant impact. These interactions primarily shaped the spatial and temporal distribution of ecosystem services. The NDVI and human activities exhibited nonlinear enhancement. These results contribute to improving our cognition of the factors and mechanisms influencing ecosystem services, offering theoretical support for the improvement of ecosystem services in the YRD.

Although research relating to the urban heat island (UHI) phenomenon has been significantly increasing in recent years, there is still a lack of a continuous and clear recognition of the potential gradient effect on the UHI—landscape relationship within large urbanized regions. In this study, we chose the Beijing-Tianjin-Hebei (BTH) region, which is a large scaled urban agglomeration in China, as the case study area. We examined the causal relationship between the LST variation and underlying surface characteristics using multi-temporal land cover and summer average land surface temperature (LST) data as the analyzed variables. This study then further discussed the modeling performance when quantifying their relationship from a spatial gradient perspective (the grid size ranged from 6 to 24 km), by comparing the ordinary least squares (OLS) and geographically weighted regression (GWR) methods. The results indicate that: (1) both the OLS and GWR analysis confirmed that the composition of built-up land contributes as an essential factor that is responsible for the UHI phenomenon in a large urban agglomeration region; (2) for the OLS, the modeled relationship between the LST and its drive factor showed a significant spatial gradient effect, changing with different spatial analysis grids; and, (3) in contrast, using the GWR model revealed a considerably robust and better performance for accommodating the spatial non-stationarity with a lower scale dependence than that of the OLS model. This study highlights the significant spatial heterogeneity that is related to the UHI effect in large-extent urban agglomeration areas, and it suggests that the potential gradient effect and uncertainty induced by different spatial scale and methodology usage should be considered when modeling the UHI effect with urbanization. This would supplement current UHI study and be beneficial for deepening the cognition and enlightenment of landscape planning for UHI regulation.