Metropolitan areas are the main spatial units sustaining development. Investigating internal factor changes in metropolitan areas are of great significance for improving the quality of development in these areas. As an emerging national central city of China, Zhengzhou has experienced rapid urban expansion and urbanization. In this study, principal component analysis and the model and Geodetector model were used to comprehensively analyze the influencing factors of land use change in Zhengzhou from 1980 to 2015. Based on the CA–Markov model, we improved the accuracy of multi-criteria evaluation of suitability factors and simulated land use change in 2015. The results show that land use conversions in the study area between 1980 and 2015 were frequent, with the areas of farmland, woodland, grassland, water, and unused land decreasing by 5.00%, 17.12%, 21.59%, 18.31%, and 94.48%, respectively, while construction land increased by 53.61%. The key influences on land use change are the urbanization and growth of residential or non-agricultural populations. In 2035, the area of farmland in the study area will decrease by 11.09% compared with that in 2015 and construction land will increase by 38.94%, while the area of other land use types will not significantly change. Zhengzhou, as the center city, forms a diamond-shaped core development area of Zhengzhou–Kaifeng–Xinxiang–Jiaozuo, while Xuchang is considered an independent sub-center uniting the surrounding cities for expansion. With its radiation power of unipolar core development for many years and the developmental momentum of Zhengzhou–Kaifeng integration, Zhengzhou city jointly drives the economic development of the surrounding cities. The protection of farmland and control of the expansion of construction land are the major challenges for the Zhengzhou metropolitan area to achieve sustainable development.

The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹ (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg⁻¹·H₂O) > Cropland WUE (0.972 gC·kg⁻¹·H₂O) > Grassland WUE (0.805 gC·kg⁻¹·H₂O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg⁻¹·H₂O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation.

How to optimize the spatial layout of the logistics industry is a challenge faced by every city under the rapid development of e-commerce nowadays. Enterprises and nodes as the core of logistics and their locational formation mechanisms are the key content of the study. Based on the API platform of Gaode Map, the POI data of Zhengzhou City with representative years during 2012–2021 were collected. Using the methods of industry concentration evaluation and nuclear density analysis, the spatial pattern changes of the logistics industry in Zhengzhou in recent years are demonstrated, the differences in location selection of the logistics industry are analyzed, and the analysis of its formation mechanism is made. The results of the study show that: (1) the logistics industry in Zhengzhou has developed rapidly in recent years, forming a “double core” spatial pattern of agglomeration near the four urban areas in the center of the metropolitan area and Xinzheng Airport, with a high degree of coordination in the agglomeration of industries and practitioners; and (2) the layout of logistics nodes is influenced by government planning, and logistics enterprises have strong autonomy in the layout. In the process of industrial agglomeration, the government guides the industry to gradually achieve agglomeration by laying out the transportation environment and providing low-rent land for industrial parks, while logistics enterprises provide services for different types of groups through segmenting specialized markets for different types of groups to provide services.

The Yellow River Basin (YRB) is a critical ecological zone in China now confronting growing tensions between land conservation and development. This study combines land use, climate, and socio-economic data with spatial–statistical models (GeoDetector [GD]–Patch-generating Land Use Simulation [PLUS]–Integrated Valuation of Ecosystem Services and Trade-Offs [InVEST]) to analyze land use changes (2000–2020), evaluate habitat quality, and simulate scenarios to 2040. Key results include the following: (1) Farmland was decreased by the conversion to forests (+3475 km²) and grasslands (+4522 km²), while construction land expanded rapidly (+11,166 km²); (2) the population and Gross Domestic Product (GDP) pressures drove the farmland loss (q = 0.148 for population, q = 0.129 for GDP), while synergies between evapotranspiration (ET) and the Normalized Difference Vegetation Index (NDVI) promoted forest/grassland recovery (q = 0.155); and (3) ecological protection scenarios increased the grassland area by 12.94% but restricted the construction land growth (−13.84%), with persistent unused land (>3.61% in Inner Mongolia) indicating arid-zone risks. The Habitat Quality-Autocorrelated Coupling Index (HQACI) declined from 0.373 (2020) to 0.345–0.349 (2040), which was linked to drought, groundwater loss, and urban expansion. Proposed strategies including riparian corridor protection, adaptive urban zoning, and gradient-based restoration aim to balance ecological and developmental needs, supporting spatial planning and enhancing the basin-wide habitat quality.

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies.

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning.