In recent years, rapid urban expansion and increasing ecological sensitivity in arid zones have led to extreme imbalances in ecosystem development. Therefore, there is an urgent need to balance the dual goals of synergistic development of ecosystem services (ESs) and increased urbanization. Previous studies have analyzed the impacts of urbanization on ESs but have selected a limited number of indicators and have not focused on the impacts of urbanization on ES pair interactions. In this study, six key ESs (water yield, habitat quality, soil conservation, carbon storage, carbon sequestration and oxygen production, and food production) and total ecosystem services (TESs) were selected, and trends in the temporal and spatial relationship between trade-offs and synergies were analyzed over 20 years. This study refined the living standards urbanization indicator and evaluated the impact of urbanization and multiple drivers on ESs and ES pair interrelationships based on geo-detectors and segmented linear regression. The results show that there is heterogeneity in the overall and regional ES trade-offs and synergistic relationships, and water yield (WY)-related ES pairs generally exhibit synergistic relationships at the overall level. Spatially, however, the trade-off ratio exceeds the synergy ratio. Segmented linear regression results show that the relationship between all the urbanization indicators and TESs demonstrates an upward trend followed by a downward trend. Measures such as the increase in man-made oases in the early stages of urbanization did have some positive effects on TESs. However, as urbanization increased, these positive effects were quickly offset by the negative effects of overdevelopment and environmental degradation, leading to an overall decline in TESs. Urbanization of construction land (CL) had the most direct impact on ecosystem services. In summary, due to special climatic constraints, arid zones are more sensitive than other ecosystems, and urban development is strictly limited by oasis capacity. As cities expand, attention needs to be focused on protecting ecological land and limiting the expansion of CL to promote the synergistic development of urbanization and ecosystem services in arid zones.

Understanding the dynamic changes in the quality of the ecological environment and its potential driving forces is essential for protecting regional ecosystems and promoting sustainable development. In this study, we developed an improved remote sensing ecological index (IRSEI) by integrating the kernel normalized difference vegetation index (kNDVI) with an abundance index (AI) and conducted a comprehensive analysis of the spatiotemporal evolution of the quality of the ecological environment in the North China Plain (NCP) from 2000 to 2020. A multistep driving analysis framework was established to identify key climatic factors via the XGBoost algorithm and to quantify the effects of climate change and human activities through partial correlation analysis and a multiple regression residual model. The results indicate the following: (1) From 2000 to 2020, the ecological quality of the NCP significantly improved, with the average IRSEI increasing from 0.41 to 0.45. The proportion of areas with “good” or “excellent” ecological quality increased, revealing a south–north gradient, with higher values in the southern part and lower values in the northern part of the NCP. (2) Among the key climatic variables, surface temperature was significantly negatively correlated with the IRSEI, whereas atmospheric pressure and evapotranspiration were significantly positively correlated. (3) Approximately 51.97% of the ecological quality changes were jointly driven by climate change and human activities, with the contribution of human activities (28.80%) exceeding that of climate change (19.23%). These findings provide a scientific basis for understanding the driving mechanisms behind ecological environment changes and support ecological restoration and coordinated human–environment development in the context of climate change.

In recent years, convolutional neural networks (CNNs) have become a potent tool for hyperspectral image classification (HSIC), where classification accuracy, computational cost, and generalization ability are the main focuses. In this study, a novel approach to hyperspectral image classification is proposed. A tandem spectral–spatial attention module (TAM) was designed to select significant spectral and spatial features automatically. At the same time, a dense pyramidal residual module (DPRM) with three residual units (RUs) was constructed, where feature maps exhibit linear growth; a dense connection structure was employed between each RU, and a TAM was embedded in each RU. Dilated convolution structures were used in the last two layers of the pyramid network, which can enhance the network’s perception of fine textures and features, improving information transfer efficiency. Tests on four public datasets, namely, the Pavia University, Salinas, TeaFarm, and WHU-Hi-HongHu datasets, were carried out, and the classification accuracies of our method were 99.60%, 99.95%, 99.81%, and 99.84%, respectively. Moreover, the method enhanced the processing speed, especially for large datasets such as WHU-Hi-HongHu. The training time and testing time of one epoch were 53 s and 1.28 s, respectively. Comparative experiments with five methods showed the correctness and high efficiency of our method.