Urban development of clusters situated along rivers significantly affects the health of the river ecosystems, the quality of urban environments, and the overall well-being of local communities. Ecosystem service supply value (ESSV) measures the delivery of ecosystem goods and services within a specific timeframe in a particular area. Using the lower Yellow River urban agglomeration (Shandong section) as a case, we comprehensively applied land use structure and intensity change analysis, quantitative calculation of ESS, and geographical probe methods to unveil ESS and its mechanism of response to the spatio-temporal evolution of the intensity of land use in urban agglomeration along the river. The key results were as follows: (1) Over the past two decades, farmland and construction land areas have continued to decrease and increase, respectively, with the intensity of land use change being highest from 2005 to 2010. (2) ESS has continued to rise over the past 20 years, with the income in 2020 being 11.142 billion yuan, an increase of 31.13%. The “low-value areas” are mainly concentrated in Liaocheng City, Dezhou City, and Tai’an City, which are characterized by predominantly flat terrains where farmland constitutes the principal land use type. Conversely, “high-value areas” are largely in the counties bordering the Yellow River, including the upper estuary in the north and the rugged, southeastern terrains. (3) Areas with concentrated ESSV were primarily localized in the northern estuary area and along the Yellow River in a scattered point-like pattern. The spatial distribution of hotspots has become increasingly concentrated, transitioning from points to planes. Conversely, cold spots initially increased in number before subsequently decreasing. Waterbody was the most sensitive ESSV-determining factor. (4) The spatial heterogeneity of ESSV emerges as a consequence of the interaction of multiple factors, and among these interactions, those involving NDVI and POP contain the greatest explanatory power. Our findings are expected to offer a scientific foundation for optimizing land spatial patterns and enhancing ecological management in the lower Yellow River region.

The gully morphology parameter is an important quantitative index for monitoring gully erosion development. Its extraction method and accuracy evaluation in the “soil-rock dual structure area” are of great significance to the evaluation of gully erosion in this type of area. In this study, unmanned aerial vehicle (UAV) tilt photography data were used to evaluate the accuracy of extracting gully morphology parameters from high-resolution remote sensing stereoscopic images. The images data (0.03 m) were taken as the reference in Zhangmazhuang and Jinzhongyu small river valleys in Yishui County, Shandong Province, China. The accuracy of gully morphology parameters were extracted from simultaneous high-resolution remote sensing stereo images data (0.5 m) was evaluated, and the parameter correction model was constructed. The results showed that (1) the average relative errors of circumference (P), area (A), linear length of bottom (L1), and curve length of bottom (L2) are mainly concentrated within 10%, and the average relative errors of top width (TW) are mainly within 20%. (2) The average relative error of three-dimensional (3D) parameters such as gully volume (V) and gully depth (D) is mainly less than 50%. (3) The larger the size of the gully, the smaller the 3D parameters extracted by visual interpreters, especially the absolute value of the mean relative error (Rmean) of V and D. (4) A relationship model was built between the V and D values obtained by the two methods. When V and D were extracted from high-resolution remote sensing stereo images, the relationship model was used to correct the measured parameter values. These findings showed that high-resolution remote sensing stereo images represents an efficient and convenient data source for monitoring gully erosion in a small watershed in a “soil-rock dual structure area”.

Accurately estimating latent heat flux (LE) across water-limited grassland ecosystems is critically hampered by strong land-surface heterogeneity and pronounced intra-annual variability. Here, we proposed a ConvTransformer framework by integrating MODIS remote sensing products, China Meteorological Forcing Dataset (CMFD) data, and eddy covariance observations from six grassland sites to estimate daily LE across the Inner Mongolia grasslands. The model was evaluated using a leave-one-site-out cross-validation strategy and compared with three widely used machine learning models, including random forest (RF), gradient boosting regression trees (GBRT), and support vector regression (SVR). Across the six validation sites, the ConvTransformer achieved an average R² of 0.69, an RMSE of 12.56 W m⁻², a Bias of 0.67 W m⁻², and an average KGE of 0.82. Although RF produced slightly higher R² values at several individual sites, the ConvTransformer exhibited the highest overall KGE and the most stable performance, indicating superior cross-site generalization. Based on the trained model, a 1 km daily LE dataset for the Inner Mongolia grasslands during 2003–2018 was generated. The estimated LE revealed a distinct decreasing gradient from southeast to northwest and marked seasonality, with summer dominating the annual latent heat exchange. These results suggest that the ConvTransformer constitutes an effective framework for regional LE estimation, while also offering a valuable alternative for ecohydrological studies and regional water-resource assessment in water-limited grassland ecosystems.

Climate change is exacerbating urban heatwave, posing critical public health risks that are amplified by accelerating urbanization trends. The multilevel spatiotemporal interactions among environmental factors, heatwave characteristics, and public sentiment re-main unclear, especially in urban agglomerations. This study introduces an innovative analytical framework combining Geographical Convergent Cross Mapping (GCCM) for bidirectional causal inference with Bayesian Generalized Linear Mixed Models (GLMMs) for multilevel effect quantification. By systematically integrating a decade (2014-2023) of multi-source remote sensing data, environmental indicators, and 3.2 million heat-perception social media posts across five major Chinese urban agglomerations, this study explore dynamic spatiotemporal relationships between environment, heatwaves, and emotions. Results reveal pronounced asymmetric causality, with environmental im-pacts on heatwaves significantly exceeding heatwave feedback to environments. Intensity indicators (HWA, HWM) show strongest responses to environmental factors, frequency indicators (HWF, HWday) demonstrate moderate-to-strong responses, while duration indicators (HWD) exhibit slightly lower causality. Standardized effect sizes rank environ-mental factors as: Albedo (0.87), FVC (0.79), NDTI (0.75), water bodies (0.57), NTL (0.62) and Elevation (0.35), confirming the dominant influence of surface properties over hydro-logical and topographic factors. Heatwaves and public emotional responses show significant nonlinear relationships with regional variations. As heatwave intensity in-creases, all five emotional dimensions rise with distinct patterns: heat perception responds most directly, health concerns accelerate during moderate-to-high intensity events, outdoor activity preferences decrease significantly during moderate events, and energy consumption concerns peak during high-intensity heatwaves. Each urban agglomeration exhibits unique emotional response patterns due to spatial and geographical differences. These findings provide critical insights into urban thermal environment mechanisms and their societal impacts, highlighting the necessity of region-specific strategies for urban planning and climate adaptation.