Search Results (88)

The Xiong’an New Area, a newly established national-level zone in China, faces the threat of land subsidence and ground fissure due to groundwater overexploitation and geothermal extraction, threatening urban safety. This study integrates time-series InSAR and GNSS monitoring to analyze spatiotemporal deformation patterns from 2017/05 to 2025/03. The key results show: (1) Three subsidence hotspots, namely northern Xiongxian (max. cumulative subsidence: 591 mm; 70 mm/yr), Luzhuang, and Liulizhuang, strongly correlate with geothermal wells and F4/F5 fault zones; (2) GNSS baseline analysis (e.g., XA01-XA02) reveals fissure-induced differential deformation (max. horizontal/vertical rates: 40.04 mm/yr and 19.8 mm/yr); and (3) InSAR–GNSS cross-validation confirms the high consistency of the results (Pearson’s correlation coefficient = 0.86). Subsidence in Xiongxian is driven by geothermal/industrial groundwater use, without any seasonal variations, while Anxin exhibits agricultural pumping-linked seasonal fluctuations. The use of rooftop GNSS stations reduces multipath effects and improves urban monitoring accuracy. The spatiotemporal heterogeneity stems from coupled resource exploitation and tectonic activity. We propose prioritizing rooftop GNSS deployments to enhance east–west deformation monitoring. This framework balances regional and local-scale precision, offering a replicable solution for geological risk assessments in emerging cities. Full article

China’s accelerated urbanization has instigated construction land expansion and ecological land attrition, aggravating the carbon emission disequilibrium. Notably, the “land carbon emission elasticity coefficient” in urban agglomerations far exceeds international benchmarks, underscoring the contradiction between spatial expansion and low-carbon goals. Existing research predominantly centers on single-spatial-type or static-model analyses, lacking cross-scale mechanism exploration, policy heterogeneity consideration, and differentiated carbon metabolism assessment across functional spaces. This study takes Xiong’an New Area as a case, delineating the spatiotemporal evolution of land use and carbon emissions during 2017–2023. Construction land expanded by 26.8%, propelling an 11-fold escalation in carbon emissions, while emission intensity decreased by 11.4% due to energy efficiency improvements and renewable energy adoption. Cultivated land reduction (31.8%) caused a 73.4% decline in agricultural emissions, and ecological land network restructuring (65.3% forest expansion and wetland restoration) significantly enhanced carbon sequestration. This research validates a governance paradigm prioritizing “structural optimization” over “scale expansion”—synergizing construction land intensification with ecological restoration to decelerate emission growth and strengthen carbon sink systems. Full article

Urban thermal environment is directly linked to the health and comfort of local residents, as well as energy consumption. Drone-based thermal infrared image acquirement provides an efficient and flexible way of assessing urban heat distribution, thereby supporting climate-resilient and sustainable urban development. Here, we present an advanced approach that utilizes the thermal infrared camera mounted on the drone for high-resolution building wall temperature measurement and achieves centimeter accuracy. According to the binocular vision theory, the three-dimensional (3D) reconstruction of thermal infrared images is first conducted, and then the two-dimensional building wall temperature is extracted. Real-world validation shows that our approach can measure the wall temperature within a 5 °C error, which confirms the reliability of this approach. The field measurement of Yuquanting in Xiong’an New Area China during three time periods, i.e., morning (7:00–8:00), noon (13:00–14:00) and evening (18:00–19:00), was used as a case study to demonstrate our approach. The results show that during the heating season, the building wall temperature was the highest at noon time and the lowest in evening time, which were mostly caused by solar radiation. The highest wall temperature at noon time was 55 °C, which was under direct sun radiation. The maximum wall temperature differences were 39 °C, 55 °C, and 20 °C during morning, noon and evening time, respectively. The lighter wall coating color tended to have a lower temperature than the darker wall coating color. Beyond this application, this approach has potential in future autonomous thermal environment measuring systems as a foundational element. Full article

As China’s third national-level new area, Xiong’an New Area plays a pivotal strategic role in relocating non-capital functions from Beijing while serving as a model for sustainable urban development. This study investigates the spatiotemporal evolution of ecosystem service value (ESV) and landscape patterns in Xiong’an before (2014–2016) and after (2017–2022) its establishment, assessing the policy-driven impacts of green development initiatives. Using remote sensing data, random forest classification, and landscape pattern analysis, we quantified land use dynamics, landscape index, and ESV variations. Key findings reveal significant land use transformations, with cultivated land declining by 7.51% and coniferous forest expanding by 189.84%, driven by urbanization and afforestation efforts. The comprehensive land use dynamic degree reached 4.96% (2014–2022), while the land use intensity index decreased by 20.95%. Concurrently, the fragmentation index increased significantly (Diversity Index (SHDI) +45%; Edge Density (ED) +66.23%). Despite these changes, ESV surged by 57.51% (CNY 334.63 billion), primarily due to wetland and forest expansion. Statistical analysis revealed positive correlations between ESV and the fragmentation index (ED, NP, and SHDI), whereas the aggregated index (CONTAG and AI) exhibited negative correlations. The findings substantiate the policy effectiveness of Xiong’an’s ecological initiatives, revealing how strategic landscape planning can balance urban development with ecosystem protection, offering valuable guidance for sustainable urbanization in Xiong’an and comparable regions. Full article

With rapid urbanization, urban underground space (UUS) development has become crucial for sustainable urban growth. This paper systematically reviews geological suitability evaluation (GSE) methods for UUS, integrating theoretical frameworks, indicator systems, and assessment techniques. We establish a comprehensive evaluation framework based on environmental strategic assessment (ESA) principles, analyzing key geological factors, including rock/soil properties, hydrogeological conditions, geological hazards, and existing underground structures. The study compares weighting methods (AHP, EWM, CRITIC) and comprehensive evaluation models (FCE, TOPSIS, BNM), highlighting their advantages and application scenarios. A case study of Xiong’an New Area demonstrates how multi-layer UUS planning integrates geological constraints with sustainable development goals. The results show that combining 3D geological modeling with hybrid evaluation methods significantly improves decision-making accuracy. The review provides practical guidance for optimizing UUS utilization while addressing current challenges in indicator selection, weight rationalization, and heterogeneity management. Full article

The accuracy of deep temperature predictions is critical to the precision of geothermal resource exploration, assessment, and the effectiveness of their development and utilization. However, the existing methods encounter significant challenges in predicting the distribution characteristics of deep temperature fields with both efficiency and accuracy. Many of these methods rely on empirical formulas to approximate the relationship between geophysical parameters and temperature. Unfortunately, such approximations often introduce substantial errors, undermining the reliability and precision of the predictions. We present an advanced prediction methodology for deep temperature fields based on the rock resistivity–temperature coupling model (RRTCM). By converting the fixed parameters in the empirical formulas to variables dependent on the formation depth, we establish a dynamic model that correlates rock resistivity with temperature on the basis of limited constrained borehole data. We then input the 2D magnetotelluric inversion results into the model, and the subsurface temperature distribution can be predicted indirectly with high precision on the basis of the resistivity–temperature coupling relationship. We validated this method in the Xiong’an New Area, China, and the determination coefficient ($R^2$) of maximum temperature prediction reached 98.88%. The sensitivity analysis indicates that the prediction accuracy is positively correlated with the number and depth of the constrained boreholes and negatively correlated with the sampling interval of the well logging data. This method robustly supports geothermal resource development and enhances the understanding of geothermal field formation mechanisms. Full article

Heavy metal pollution of farmland is worsened by the excessive introduction of heavy metal elements into soil systems, posing a substantial threat for global food security and human health. The traditional laboratory-based methods for monitoring soil heavy metals are limited for large-scale applications, while hyperspectral imagery data-based methods still face accuracy challenges. Therefore, a fusion XGBoost model based on the superposition of ensemble learning and packaging methods is proposed for large-scale monitoring with high accuracy of soil heavy metal using hyperspectral imagery. We took Xiong’an New Area, Hebei Province, as the study area, and acquired heavy metal content using chemical analysis. The XGB-Boruta-PCC algorithm was used for precise feature selection to obtain the final modeled spectral response features. On this basis, the performance indicators of the Optuna-optimized XGBoost model were compared with traditional linear and nonlinear models. The optimal model was extended to the entire region for drawing the spatial distribution map of soil heavy metal content. The results suggested that the XGB-Boruta-PCC method effectively achieved double dimensionality reduction of high-dimensional hyperspectral data, extracting spectral response features with a high contribution, which, combined with the XGBoost model, exhibited greater general estimation accuracies for heavy metal (Pb) in farmland (i.e., Pb: R² = 0.82, RMSE = 11.58, MAE = 9.89). The results of the mapping indicated that there were exceedances for the southwest and parts of the west over the research region. Factories and human activities were the potential causes of heavy metal Pb contamination in farmland. In conclusion, this innovative method can quickly and accurately achieve monitoring large-scale soil heavy metal contamination in farmland, with ZY-1-02E spaceborne hyperspectral imagery proving to be a reliable tool for mapping soil heavy metal in farmland. Full article

Urbanization presents significant challenges to biodiversity but also offers opportunities for its protection and development. While uncontrolled urban expansion has a destructive impact on biodiversity, effective urban planning can play a positive role in protecting and maintaining urban biodiversity. The positive role of human factors, such as urban planning, can protect and maintain the healthy development of urban biodiversity. This study conducted an in-depth analysis of the evolution of various wildlife migration corridors throughout the full-cycle construction of Xiong’an New Area (Xiong’an) in China, revealing the impact of urbanization on these networks. Habitats for species like Sus scrofa, Bufo gargarizans, and Parus minor have notably increased. Between 2016 and 2023, Sus scrofa habitats grew from 35 to 44, large-toed frog habitats from 24 to 35, and Chinese tit habitats remained stable. By the planning phase, Sus scrofa habitats expanded to 87, large-toed frog habitats to 97, and Chinese tit habitats to 58. Habitat areas also grew significantly, especially for Sus scrofa, which increased from 2873.84 hectares in 2016 to 7527.97 hectares in the planning phase. Large-toed frog habitats grew from 2136.86 hectares to 6982.78 hectares, while Chinese tit habitats expanded from 1894.25 hectares to 3679.71 hectares. These changes suggest that urban parks and green spaces have provided more extensive habitats for these species. In terms of migration networks, the number of dispersal routes increased considerably. In 2016, Sus scrofa had 77 routes, large-toed frogs had 16, and Chinese tits had 77. By 2023, Sus scrofa and large-toed frog routes increased to 91 and 49, respectively, while Chinese tit routes remained stable. In the planning phase, Sus scrofa routes surged to 232, large-toed frogs to 249, and Chinese tits to 152, indicating a denser migration network. The distribution of ecological pinchpoints also changed significantly. By 2023 and in the planning phase, pinchpoints were concentrated in densely built areas, reflecting urbanization’s impact on the ecological network. The ecological resilience, assessed through network performance, showed a gradual recovery. The ecological connectivity index decreased from 8.25 in 2016 to 7.29 in 2023, then rebounded to 11.37 in the planning phase, indicating that the ecosystem had adapted after initial urbanization pressures. Full article

The widespread application of terminal sensors in power pipe galleries (PPGs) has significantly improved our ability to monitor power equipment status. However, the difficulties in battery replacement caused by confined space and energy loss caused by communication conflicts between sensors due to existing low-power communication strategies results in a lack of reliable energy supply for terminal sensors. In this context, a low-power communication strategy for terminal sensors is proposed. Firstly, a demand analysis is conducted on the status monitoring of PPGs, and a technical framework for low-power communication of terminal sensors is proposed. Afterward, a system model for the low-power communication of terminal sensors is established based on cognitive backscatter technology. Subsequently, key technologies, such as RF energy harvesting of terminal sensors and distance–energy level coupling analysis, are proposed to achieve continuous energy supply and avoid communication conflicts in the system model. Finally, a wireless communication simulation environment for PPGs is established to simulate the status monitoring process, based on terminal sensors, in order to verify the effectiveness of the proposed method. Full article

With the increasing frequency of extreme rainfall events, enhancing urban drainage systems’ regulation capacity is crucial for mitigating urban flooding. Existing studies primarily analyze infrastructure impacts on peak flow delay but often lack a systematic exploration of time-lag mechanisms. This study introduces the time-lag parameter, using the hysteresis curve of the water level–flow rate relationship to quantify drainage system dynamics. An SWMM-based drainage model was developed for the Rongdong area of Xiong’an New District to evaluate the independent roles of green, gray, and blue infrastructures in peak flow reduction and time-lag modulation. The results indicate that green infrastructure extends the horizontal width and reduces the vertical height of the hysteresis curve, prolonging time lag and making it effective for small-to-medium rainfall. Gray infrastructure enhances drainage efficiency by compressing the hysteresis curve horizontally and increasing its vertical height, facilitating rapid drainage but offering limited peak reduction. Blue infrastructure, by lowering outlet water levels, improves drainage capacity and reduces time lag, demonstrating adaptability across various rainfall scenarios. This study systematically quantifies the role of each infrastructure type in time-lag regulation and proposes a collaborative optimization strategy for urban drainage system design. Full article

The long-term overextraction of groundwater in the Beijing–Tianjin–Hebei region has led to the formation of the world’s largest groundwater depression cone and the most extensive land subsidence zone, posing a potential threat to the operational safety of high-speed railways in the region. As a critical transportation hub connecting Beijing and the Xiong’an New Area, the Beijing–Xiong’an Intercity Railway traverses geologically complex areas with significant ground subsidence issues. Monitoring and analyzing the causes of land subsidence along the railway are essential for ensuring its safe operation. Using Sentinel-1A radar imagery, this study applies PS-InSAR technology to extract the spatiotemporal evolution characteristics of ground subsidence along the railway from 2016 to 2022. By employing a buffer zone analysis and profile analysis, the subsidence patterns at different stages (pre-construction, construction, and operation) are revealed, identifying the major subsidence cones along the Yongding River, Yongqing, Daying, and Shengfang regions, and their impacts on the railway. Furthermore, the XGBoost model and SHAP method are used to quantify the primary influencing factors of land subsidence. The results show that changes in confined water levels are the most significant factor, contributing 34.5%, with strong interactions observed between the compressible layer thickness and confined water levels. The subsidence gradient analysis indicates that the overall subsidence gradient along the Beijing–Xiong’an Intercity Railway currently meets safety standards. This study provides scientific evidence for risk prevention and the control of land subsidence along the railway and holds significant implications for ensuring the safety of high-speed rail operations. Full article

Rapid urbanization facilitates socioeconomic development but also exacerbates land use/cover change (LUCC), significantly impacting ecological environments. Timely, objective, and quantitative assessments of ecological quality changes resulting from LUCC are essential for safeguarding the natural environment and managing land resources. However, limited research has explored the potential interrelationships between the spatio-temporal heterogeneity of LUCC and ecological quality during urbanization. This study focuses on the Xiong’an New Area, a region experiencing rapid urbanization, utilizing the remote sensing-based ecological index (RSEI) to monitor ecological quality dynamics from 2017 to 2023. To address the computational challenges associated with large-scale regions, a streamlined RSEI construction method was developed using Landsat imagery and implemented via Google Earth Engine (GEE). A geographically weighted regression (GWR) analysis, integrated with Sentinel-2 land use data, was employed to examine the influence of LUCC on ecological quality. The findings reveal the following: (1) Ecological quality in the Xiong’an New Area has exhibited an overall positive trajectory, with improvements elevating the ecological status to above moderate levels. (2) Urban expansion resulted in a 17% reduction in farmland, primarily converted into construction land, which expanded by approximately 12%. (3) Ecological protection policies have facilitated the conversion of farmland into wetlands and urban green areas, which emerged as the principal contributors to ecological quality enhancement. (4) A positive correlation was observed between changes in ecological land and ecological quality, while a negative correlation was identified between shifts in the construction land and farmland and ecological quality. This research provides valuable scientific insights into ecological conservation and land use management, thereby establishing a foundation for the development of rational land resource planning and sustainable ecological development strategies in the Xiong’an New Area. Full article

With the rapid pace of urbanization, the integrity and connectivity of ecosystems are under serious threat, making biodiversity conservation a top priority. We use the Xiongan New Area in China as a case study to explore the significance and application of constructing urban ecological networks in the development of new cities. This study systematically applied the categorization of green space systems using remote sensing technology; MSPA was used to identify key landscape patches; InVEST was employed to assess habitat quality; and potential ecological corridors were established using the minimum cumulative resistance model (MCR). Moreover, targeted recommendations for optimizing ecological green spaces were put forward. The findings demonstrate that the Xiongan New Area has significant potential and needs for ecological network construction, and it faces the issue of ecological network fragmentation. This research highlights the significance of developing ecological networks within urban planning and proposes optimization strategies tailored to these networks. The objective is to offer scientific guidance for the design and development of emerging cities, such as the Xiongan New Area, to facilitate the alignment and integration of ecological preservation efforts with urban expansion, ultimately achieving the sustainable development goal of harmonious coexistence between the environment and urban areas. Full article

Lake ecosystems are impacted by anthropogenic disturbances and have become vulnerable worldwide. Highly disturbed lake ecosystems are not well understood due to the lack of data on changes in the structures and functions of ecosystems. In this paper, we focus on Lake Baiyangdian (BYDL), the largest shallow lake in North China. Following the establishment of the Xiong’an New Area (XNA) in 2017, concerted efforts to restore BYDL’s aquatic environment have been undertaken, which has led to significant changes in the structures and functions of the ecosystems. We evaluated the biomass dynamics of main biological communities and detected the regime shifts of environmental factors in BYDL from 2016 to 2023. Further, we constructed a food web model for the BYDL ecosystem in 2023 by using Ecopath with Ecosim (EwE) and made a comparison with the reported results in 2018. The results showed significant changes in the ecosystem structure of BYDL over the last 6 years. In 2023, the submerged macrophytes biomass in the system increased by 4.2 times compared to 2018, leading to an increase in total system throughput. We found that BYDL changed from an algal-type lake to a macrophyte-dominated lake. In addition, we found TN, NH₄⁺-N, and COD_Mn were significantly decreased in BYDL during the restoration. TN and NH₄⁺-N had a change point in approximately 2021, indicating that a regime shift had occurred during restoration. Overall, the BYDL ecosystem was in an immature but developing state, as indicated by ecological network analysis indicators. Nutrient-loading reduction, hydrological regulation, and rational biomanipulation may be the potential driving factors of change in the BYDL ecosystem. We strongly recommend the timely harvesting of submerged macrophytes, the proliferation and release of herbivorous fishes, and the assessment of the ecological capacity of carnivorous fishes in the future ecological restoration of BYDL. Full article

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model’s ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong’an New Area, China, using high-resolution imagery from China’s Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong’an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong’an New Area. Full article