The development of urbanization is still expanding on the earth, and the rapid expansion of cities has changed the regional landscape pattern and significantly affected the value of regional ecosystem services in developing countries such as China. Zhongmu County, as the core area of Zhengzhou-Kaifeng integration, studying the temporal and spatial transformations of its landscape pattern and ecosystem service value (ESV) is of great significance to the region’s sustainable development. Based on remote sensing images and socioeconomic data, this study aims to explore the landscape pattern of Zhongmu County from 2005 to 2018 and its impact on ESV. Research methods include an ESV equivalent factor method, landscape pattern index, spatial autocorrelation, and other methods. The results showed that: (1) During the study period, the patch density and shape complexity, landscape diversity, and fragmentation of the overall landscape in the study area continued to increase while landscape connectivity decreased. (2) The total amount of ESV increased by 10.05 million USD; ESV had certain differences in spatial distribution: high-value clusters were mainly located at the boundary of the Yellow River in the north, while low-value clusters had a significant eastward expansion trend. (3) ESV increased significantly in areas where cultivated land was transferred to waters and forests, and ESV in areas where waters transferred to construction land and cultivated land was significantly reduced. (4) ESV had a significant positive spatial correlation with patch density, edge density, mean patch fractal dimension, mean patch size, and the landscape shape index, and a spatially negative correlation with the contagion index and cohesion index. The spatial and temporal changes in landscape patterns and ESV were all mainly driven by the regional development “Zhengzhou-Kaifeng integration” policy. Therefore, the ESV can be improved, and the ecological security of the urban integration area can be guaranteed through policy measures such as optimizing the layout of construction land and adjusting the uniform distribution of green areas by the land-use policy.

Quantifying the dynamics of green infrastructure (GI) in agricultural peri-urban areas is of great significance to the regional ecological security, food security, and the sustainable development of urban integration. Based on remote sensing images, this study aims to provide a spatiotemporal dynamic assessment of the GI in Baisha District from 2007 to 2018 to improve the layout of GI and planning policies from the perspective of ecological security and food security. Research methods include landscape pattern indices, spatial autocorrelations, and grid analyses in this case study. The results suggest that ensuring the dominant position of farmland is critical to maintaining the composition and connectivity of the overall GI. The recreation, inheritance of farming culture, and ecosystem service functions of farmland should be improved to meet the growing needs of urban residents. GI includes the farmland, greenspace, and wetland on both sides of the Jialu River that should be retained and restored as much as possible to protect natural ecological processes. Simultaneously, construction of important urban facilities and residential areas in flooded areas should be banned. A part of the evenly distributed large greenspace patches should be moved to both sides of the Jialu River to increase the agglomeration effect of GI. Optimization measures in this case study also offer a perspective for other agricultural peri-urban areas that have experienced similar urbanization.

With the rapid development of urbanization, the habitat quality (HQ) in urban areas has been eroded. This phenomenon is destroying the balance of ecosystems, triggering the reduction of biodiversity and the decay of ecosystem service functions. The study of the relationship between urbanization and HQ in Zhengzhou City is beneficial for the reference of sustainable urban ecological planning and management. Based on landscape classification data and socioeconomic data for three years, this study analyzes the spatial correlations between socioeconomic and landscape pattern factors and HQ, compares the dynamic changes in the explanatory power of different factors, and explores the joint effects between multiple factors. The results show that: (1) The overall value of HQ index in Zhengzhou City decreased by .10 during 2000–2020, mainly occurring in suburban areas, with a small amount of HQ improvement occurring in the core areas of ecological protection, such as mountains and river channels. (2) The spatial autocorrelation of all influencing factors with HQ increased during this period, while the negative impact from socio-economic sources was stronger than the positive impact from landscape patterns. (3) Intensive human activities lead to a single habitat type, which reduces HQ; rich landscape types and complex landscape composition can enhance HQ. Improving the connectivity of blue-green landscapes helps to attenuate the negative effects of urbanization on HQ. (4) Changes of HQ in the study area and the development of multi-factor effects on HQ are driven by the Zhengzhou Metropolitan Area Plan. Urban development policies and management can build idyllic complexes at the edge of urban development, preserving pristine blue-green patches to avoid their homogenized distribution and thus slowing the decline of HQ. The above results provide new ideas for the development of sustainable urban ecology.

Climate change and rapid urbanization are two global processes that have significantly aggravated natural disasters, such as drought and flooding. Urbanization without resilient and sustainable planning and execution could lead to undesirable changes in landscapes and stormwater regulation capacity. These changes have exacerbated the effects of extreme climatic events with disastrous consequences in many cities worldwide. Unfortunately, the major storm in Zhengzhou, China on 20 July 2021 was one of these examples. This event provided a rare opportunity to study the key roles of green infrastructures (GI) in mitigating flooding risks in a major urban center after a devasting flood event. Using the data from high-resolution images collected via two satellites, a comprehensive study of the Jialu System in Greater Zhengzhou was conducted to systematically compare how far the river water had reached before and after the 20 July 2021 major storm in order to identify the main weak links in the city’s GI and stormwater management system. A flood inundation intensity index (FI) in the Upper (UJLR), Middle (MJLR), and Lower (LJLR) Regions of the Jialu River System was generated. Bivariate Moran’s I, a correlation coefficient between FI and landscape characteristics, was calculated and used to identify problem areas for future improvements. Our results showed that the MJLR had the severest flooding impacts. LJLR had the biggest change in how far the river water reached after flooding, ranging from 4.59 m to 706.28 m. In UJLR, the percentages of mine, crop land, and green space had the highest global bivariate Moran’s I correlation coefficients. In MJLR, the percentages of vacant land, impervious surfaces, and water body had the highest global bivariate Moran’s I correlation coefficients. In LJLR, the percentages of vacant land, water body, and crop land had the highest global bivariate Moran’s I correlation coefficients. The total percentages of both high landscape characteristics indices-high flood inundation intensity indices and low landscape characteristics indices-high flood inundation intensity indices areas are 12.96%, 13.47%, and 13.80% in UJLR, MJLR, and LJLR, respectively. These land cover composition types identified for each region can be treated as areas of primary focus. However, GeoDector Model (GDM) analyses showed that our eight variables of landscape characteristics were not independent. Hence, a more comprehensive approach integrating all eight variables is still necessary in future flood mitigation efforts.

Following severe anthropogenic pressure from rapid economic development, wetland biodiversity is now decreasing alarmingly, thus leading to adverse effects. Protected areas (PAs) can be crucial conservation tools to secure wetland biodiversity. However, whether these PAs exhibit high conservation efficiency in buffering wildlife and habitats from human pressures needs to be understood. Given their sensitivity to habitat quality and regional resource changes, threatened waterfowl could be suitable wetland ecosystem indicators. This study examined the conservation effectiveness of Henan Yellow River Wetland National Nature Reserve (HYRWNNR), which is a crucial region on the East Asia–Australia route for global bird migration. We performed Maximum Entropy (MaxEnt) modeling based on field survey data of the 19 threatened waterfowl species, and Human Impact Index (HII) was further mapped with waterfowls distribution to identify the conservation gap and priorities of the HYRWNNR. The results indicated that threatened waterfowl distribution were affected by both environmental factors and human pressure, and a conservation gap existed in the HYRWNNR. Two conservation scenarios were generated based on the spatial pattern of conservation priorities, and their corresponding management strategies were suggested. This study identifies conservation priorities from a novel perspective by synthesizing habitat suitability and human pressure, which can present basic information regarding the HYRWNNR management while supporting waterfowl conservation planning, ultimately promoting wetland habitats sustainability.

The ecological impacts of dam and reservoir construction necessitate systematic environmental quality evaluation (EEQ) to reconcile ecological protection with sustainable development. To address this need, we integrated the Remote Sensing Ecological Index (RSEI)—a comprehensive metric synthesizing greenness, humidity, heat, and dryness—with a Land Use Change Ecological Response (LUCER) model to quantify the long-term EEQ dynamics in reservoir-affected regions. This study utilized Landsat and Sentinel-2 remote sensing imagery with a 10 m resolution from the years 2000, 2005, 2010, 2015, and 2020 to compute the RSEI for the Qianping Reservoir area in Henan Province, investigating the spatiotemporal variations in EEQ. Key findings reveal: (1) Temporal trend: EEQ showed fluctuating improvement, with RSEI projected to rise gradually until 2030. (2) Spatial pattern: A lower ecological quality in central reservoir zones contrasts with higher quality in surrounding mountainous areas. (3) Mechanism: The Land Use Change Ecological Response (LUCER) model reveals that the conversion of cultivated land to forestland and grassland drives significant EEQ improvements, counterbalancing the negative impacts of hydrological fragmentation caused by reservoir construction and urbanization. This study advances RSEI applications in reservoir ecology by establishing a coupled monitoring–prediction framework, providing actionable insights for dam-related ecological restoration and governance.