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.

The accuracy of high slope deformation data is crucial for slope stability assessment and disaster warning. However, a large amount of noise often exists in the actual monitoring data, which affects the quality of the data and the reliability of the analysis results. In view of this, this paper proposes a noise cancellation method for high slope deformation data based on empirical modal decomposition and SG filtering. Firstly, the original deformation data are decomposed into a number of intrinsic modal functions (IMFs) using EMD, then the decomposed IMF components are processed by SG filtering respectively, and finally the filtered IMF components are reconstructed to obtain the deformation data after noise cancellation. The effectiveness and superiority of the method is verified by processing and analyzing the deformation data of actual high slopes.