Search Results (282)

Urban green space (UGS) service mismatch is a key public space issue that needs to be addressed in the renewal of built-up urban areas. Existing studies have mostly evaluated UGS service levels from the perspective of green space supply or accessibility, but have paid insufficient attention to the mismatch among population demand, UGS supply, and UGS accessibility, as well as the built environment contexts associated with different mismatch types. Taking the urban core of Shenyang as a case study, this study constructs a UGS service mismatch typology and combines non-UGS built environment indicators, XGBoost, and SHAP to identify the spatial characteristics and nonlinear model-based associations of different mismatch types. The results indicate the following: (1) UGS service problems in the urban core of Shenyang can be divided into five types. Priority Improvement Area, Potential Activation Area, and Resource-rich Area together account for 56.05% of the study area, indicating that the mismatch among demand, resources, and accessibility has a high spatial coverage. (2) Different mismatch types correspond to differentiated built environment profiles. Priority Improvement Area co-occurs with service shortage, weak functional concentration, low development intensity, and weak transit support. Potential Activation Area has relatively good functional and transport foundations, but its UGS service transformation remains insufficient. (3) SHAP results show that public transit proximity, functional concentration, and building form are key indicators associated with model-based type differentiation. The contributions of mean building floor and POI density to Priority Improvement Area decline after exceeding their breakpoints, indicating that, within the model, this type is more likely to occur in areas with insufficient development intensity and functional concentration. By integrating the demand–supply–accessibility relationship, UGS service mismatch typology, and built environment characterization, this study provides a more spatially targeted analytical framework for UGS service diagnosis in built-up urban areas, and supports the shift from uniform green space provision to type-specific service optimization. Full article

High-resolution remote sensing imagery provides valuable data support for accurate binary urban green space extraction. However, due to complex urban backgrounds, existing methods still face challenges in accurately delineating green space boundaries and preserving fine-scale spatial details. To address these issues, this study proposes a novel network, termed Hierarchical Gated Residual Fusion and High-Frequency Guided Deformable Upsampler Network (HGRHDNet), for boundary-enhanced urban green space segmentation. The proposed framework adopts ConvNeXt-L as the encoder backbone and incorporates a Hierarchical Gated Residual Fusion Decoder (HGRFD) to adaptively fuse multi-scale features through dynamic weighting and residual feature propagation. In addition, a High-Frequency Guided Deformable Upsampler (HFGDU) is introduced to enhance high-frequency detail reconstruction and cross-resolution feature alignment, thereby improving boundary localization accuracy. The proposed method was evaluated on three public datasets with different spatial resolutions and spectral characteristics, including WHDLD, UGS-1m, and UBGG. Experimental results show that HGRHDNet achieves Boundary Intersection over Union (BIoU) values of 43.82%, 18.22%, and 64.55% on the three datasets, respectively, consistently outperforming state-of-the-art methods. Both quantitative and qualitative analyses demonstrate that HGRHDNet effectively preserves narrow gaps between adjacent green spaces, elongated vegetation structures, and fragmented green space patches while reducing boundary ambiguity in complex urban environments. These results indicate that HGRHDNet provides a robust and effective solution for high-resolution urban green space extraction and has considerable potential for applications in urban ecological assessment, green space inventory, and sustainable urban planning. Full article

The assimilation capacity of the biosphere and the sustainability of the living resources are enhanced by the efficient and continued contribution of the vegetation from all ecoregions of the Earth. The urban greenery fulfills many regulatory ecosystem services (RES) as well. In this regard, the importance of urban green spaces (UGS) in helping to reduce the adverse impacts of overcrowding and changing climate is of pertinence. Lack of quantitative information from urban settings in different climatic regions seriously constrains the recognition of the important role UGS play in carbon storage and sequestration. To assess how the UGS is aiding the retention of carbon, which is photosynthetically assimilated into biomass and/or sequestered, relevant field parameters were collected from 4010 trees belonging to 34 different species, different hedge plants, and groundcover grasses spread in 24,991 m² area in three parks of Panaji city, India. Standard methods were followed to derive carbon stock and sequestration rates by trees, hedge plants, and groundcover. Notwithstanding wide differences between tree species, the weighted mean of CO₂ sequestered per tree averaged 55 kg y⁻¹ (ca. 78.82 tons ha⁻¹) in Panaji city. Accordingly, the CO₂ sequestration potential of trees, in the UGS of Panaji (by 76,751 trees) and Tumkur (with an estimated 38,152 trees) cities, respectively, was 4221.31 tons y⁻¹ ha⁻¹ and 2098 tons ha⁻¹ y⁻¹ @ 55 kg tree⁻¹ y⁻¹. It is apparent from this first-time study that calculated tree carbon biomass and species-wise yearly carbon sequestration rates (CSRs) of 78.82 tons ha⁻¹ y⁻¹ and that of carbon production rates of 31.77 tons ha⁻¹ y⁻¹ are far higher than the previously reported CSR estimates variously from 1 to 8 tons ha⁻¹ y⁻¹ and carbon production rates 3.23 to 6.55 tons ha⁻¹ y⁻¹. The hedge row carbon biomass averaged 13.18 tons ha⁻¹ and sequestration of 48.38 tons ha⁻¹ y⁻¹ CO₂. Similarly, occupying over 42% of the UGS, the groundcover carbon biomass averaged 14.69 tons ha⁻¹ with sequestration of 53.92 tons CO₂ ha⁻¹ y⁻¹. Combined CSP of existing trees, groundcover, and hedge plants in Panaji and Tumkur city UGS apparently neutralize carbon footprint of over 4550 and 2200 Indians at an annual per capita emission of 1.94-ton. It is thus undeniable that in our global fight against climate change, the addition of inputs and data from studies like these can aid in planning mitigation measure as well as in fulfilling local/regional sustainability plans and needs. Full article

Regular physical activity (PA) is essential for healthy aging. For older adults residing in urban areas, access to urban green space (UGS) can help increase PA levels. This systematic review examined the association between availability, accessibility, and attractiveness of the UGS on PA among older adults. PubMed, CINAHL, and Web of Science were searched for articles published 2016–2024 that used quantitative methods to explore associations between the qualities of UGS and measures of PA in this demographic. Findings from nine studies from Europe, China, Hong Kong, and Australia were synthesized by availability, accessibility, and attractiveness domains. For availability, the presence and size of UGS were positively associated with older adults’ PA in several studies, whereas UGS proximity was inconsistently associated with PA. For accessibility, infrastructure-related features such as trail length were positively associated with PA, but findings for park features and safety were mixed. Results were also inconsistent for attractiveness; some land-cover and amenity features showed positive associations, while others showed no clear relationship with PA. In addition to variability in associations, there was great heterogeneity across studies in age inclusion criteria (e.g., 45+ vs. 65+), participant inclusion criteria, setting, operationalization of UGS quality, PA measures, and study rigor, reducing the ability to draw conclusions about the association between UGS qualities and PA. Although findings can begin to inform urban planners, public health professionals, and policymakers in designing UGS to promote PA in older adults, the field would benefit from more consistent definitions and measures of UGS qualities and PA. Full article

Plateau valley cities are increasingly exposed to flood risk because topographic constraints, ecological fragility, and rapid urbanization jointly intensify runoff sensitivity. Although dynamic and process-based assessments of flood regulation service (FRS) have advanced, limited attention has been given to how changes in urban green space (UGS) components are associated with marginal runoff-retention responses in a plateau valley setting. Taking central Lhasa as a case, this study combined Sentinel-2-based UGS mapping, intPLUS land-use scenario simulation, Soil and Water Assessment Tool (SWAT) runoff modeling, XGBoost, SHapley Additive exPlanations (SHAP), and accumulated local effects (ALE) to evaluate the FRS responses under multiple scenarios. A pressure–response overlay was further used to diagnose spatial mismatches between runoff pressure and modeled regulation response. Results showed that UGS expanded by 5212.66 ha from 2017 to 2025, indicating a marked greening trend, but the natural development scenario projected a 1403.80 ha decline by 2033. In contrast, the ecological protection scenario projected a 915.98 ha increase and produced a stronger modeled runoff-retention response, although the improvement remained spatially uneven across subbasins. Model-based interpretation identified forest land as the most influential predictor of runoff-depth-based FRS response, while grassland, park green space, and waterfront green space were generally associated with runoff-depth reduction and showed distinct nonlinear response patterns. Specifically, forest land and grassland showed diminishing marginal effects after higher increment ranges, park green space exhibited delayed effectiveness before clearer regulation benefits emerged, and waterfront green space reached effective response ranges at relatively low proportional increments. Spatial diagnosis further showed that 19 subbasins, accounting for 18.88% of the study area, remained response-lagged, mainly along urban expansion fringes where runoff pressure was not matched by sufficient modeled regulation response. The integrated framework provides a transferable, spatially explainable basis for plateau valley FRS assessment, whereas the identified thresholds and zoning implications remain case-specific and require local validation. Full article

The mapping and monitoring of urban green space (UGS) are of great significance for ecological assessment and sustainable development in urban settlements. However, for fine-grained classification tasks in high-resolution remote sensing imagery, existing methods suffer from significant challenges due to the high spectral similarity between low-growing and dense vegetation, as well as the complexity of spatial structures. To overcome these challenges, this paper proposes a Dual-Encoder Multi-Source Feature Fusion Network (DMSF-Net) for fine-grained urban green space segmentation. The proposed method constructs a parallel encoding structure for RGB and auxiliary features (NDVI and LBP), introduces an Adaptive Feature Fusion Module (AFFM) during the encoding phase to achieve dynamic weighted fusion of cross-source features, and designs a Boundary-Aware Up-Sampling Module (BAM) during the decoding phase to strengthen the representation of complex boundary regions through joint modeling of regional semantics and boundary information. Experimental results on a self-constructed UrbanGreen dataset and the publicly available Vaihingen dataset demonstrate the superior performance of DMSF-Net over existing mainstream methods across several evaluation metrics, achieving mIoU values of 82.27% and 74.73%, with improvements of 1.07% and 0.57% over the best baselines, respectively. The model demonstrates particularly strong discrimination capability for the fine-grained category of low vegetation. Ablation experiments further validate the usefulness of each structural module, with AFFM playing a key role in overall performance improvement, while the BAM improves boundary delineation as observed in visual comparisons. Through the synergistic integration of multi-source feature information and structural optimization, DMSF-Net effectively enhances fine-grained UGS segmentation in complex urban scenes, thereby providing an effective approach for high-resolution remote sensing-based urban ecological monitoring. Full article

Urban green space (UGS) with multidimensional attributes provides crucial ecosystem services and public health benefits. However, there is no systemic knowledge on how these diverse UGS attributes meet needs with different socio-economic and demographic characteristics. This study conducts a meta-analysis of the existing UGS case studies from the Web of Science database to reveal the knowledge breadth, depth and applicability for future research and practice in UGS. We evaluated how 75 socio-economic and demographic indicators shape 73 UGS indicators across 48 global case studies using a combination of relational analysis, meta-analytical effect and spatiotemporal distribution. The relation analysis reveals significant imbalances of UGS knowledge across attributes (Accessibility, security and amenities) and users’ characteristics (e.g., age), the meta-analytical effect size analysis indicates that many associations have not yet received consistent and conclusive empirical support for direct application to UGS planning and design, and the spatiotemporal analysis indicates that the concentration of the sampled studies in China and the United States further constrains the external applicability of the synthesized evidence. These findings recognize the multidimensional nature of UGS and the need for context-sensitive consideration and highlight the urgency of developing a unified assessment framework to improve knowledge discovery and applicability in UGS planning for urban sustainability. Full article

Urban Green Spaces (UGSs) monitoring usually focuses on the extraction of vegetation in the physical layer, while neglecting their functional attributes. This renders the monitoring results unable to objectively reflect the rationality of UGS planning. To address these issues, this study proposes a multi-level classification method integrating multi-source remote sensing and geospatial big data to bridge the semantic gap between the physical layer and the functional layer. In this method, a strategy of prior knowledge injection and semantic reconstruction was developed through the fine-tuning of a BERT model with cross-mapping rules. This strategy aims to classify the urban area into 24 functional categories, generating the social-functional basemap in a functional layer, based on Point of Interest (POI), OpenStreetMap (OSM), and Global Urban Boundary (GUB). Meanwhile, a novel deep learning architecture, namely the Multi-Shape and Spectral Aware Network (MSSANet), was designed for precise vegetation classification of UGSs in the physical layer. Finally, a “function-first, vegetation-second” coupling paradigm containing three functional attribute layers, referring to the Code for Classification of UGS in China (CJJ/T 85-2017), was established. This paradigm integrates the social-functional basemap with physical vegetation patches to build a multi-level UGS classification framework, i.e., the 5 major UGS categories, 11 intermediate UGS categories, and 24 fine-grained UGS sub-categories. Experiments conducted in Jinan and Qingdao, China, demonstrate the efficacy of the proposed method for refined multi-level UGS mapping. Full article

The association between urban green space (UGS) and public health represents a core interdisciplinary issue in ecological city construction and the “Healthy China” strategy. However, the underlying mechanisms, contextual constraints, and regional heterogeneity of the impact of UGS on public health at the provincial macro-scale remain insufficiently understood. Existing research primarily examined the health effects of green space at the resident-level micro-scale, with samples often limited to single cities or local urban clusters. Furthermore, limited attention has been paid to the moderating mechanisms and nonlinear threshold characteristics through which green space affects public health. Using panel data from 30 provinces in China from 2012 to 2023, this study empirically investigates the effects, moderating mechanisms, threshold effects, and regional heterogeneity of UGS on public health through two-way fixed-effects ordinary least squares (OLS) regression, moderation models, and threshold regression models. UGS The results show the following: (1) From 2012 to 2023, the overall levels of provincial UGS and public health in China increased, exhibiting a spatial distribution pattern characterized by higher levels in the east and lower levels in the west. (2) The development level of UGSs is significantly and positively associated with provincial public health in China; (3) The health-promoting effects of UGSs exhibit both moderation and threshold characteristics. Environmental regulation, traffic accessibility, and population aging exert positive moderating effects, whereas the level of urbanization exhibits a nonlinear threshold relationship. (4) Regarding regional heterogeneity, the health benefits of green spaces are more pronounced in urban functional zones and coastal regions, whereas key ecological functional zones and major grain-producing areas demonstrate synergies between ecological protection and health improvement. Accordingly, China should strengthen the coordinated advancement of UGS planning and public health protection, optimize the spatial layout of age-friendly green space, improve the supporting environmental regulation systems, and thereby promote the coordinated development of residents’ health and well-being and ecological environmental quality. Full article

Amid tightening ecological constraints, accelerating urbanization transition, and increasingly complex spatial governance, the coordinated evolution of Urban Green Space (UGS) and New-Type Urbanization (NTU) has become central to green transition and high-quality development in major river basins. Drawing on city-level panel data for the Yangtze River Economic Belt (YREB) and the Yellow River Basin (YRB) from 2006 to 2022, this study integrates a Coupling Coordination Degree (CCD) model, spatial statistical analysis, and interpretable machine learning to investigate UGS-NTU coordination, with emphasis on spatiotemporal evolution, spatial differentiation, and nonlinear contribution pathways. The findings indicate that: (1) UGS and NTU levels rise in both basins, but their spatial trajectories differ substantially. The YREB exhibits river-oriented expansion and gradient diffusion, whereas the YRB features nodal agglomeration and discontinuous expansion. (2) The CCD improves overall in both basins, with downstream areas leading, the middle reaches following, and the upper reaches lagging behind; UGS lag is widespread in the middle and upper reaches. (3) The YRB shows stronger spatial agglomeration, more pronounced regional differentiation, and more persistent low-value clustering, while the YREB is characterized by stable high-value clustering in the Yangtze River Delta. (4) The YREB is mainly associated with green space system optimization, whereas the YRB is more closely associated with improvements in the foundational capacities of NTU. Both associations exhibit clear nonlinear characteristics. This study provides empirical support for differentiated green transition and high-quality development strategies in major river basins. Full article

Urban green spaces (UGSs) are considered crucial for enhancing older adults’ subjective well-being. However, limited studies have explored the synergistic effects of UGS quality and quantity on satisfaction across green spaces, residential environments, and life domains, making it challenging to uncover the multifaceted sustainable benefits of UGSs on older adults’ subjective well-being. This study drew on multi-source data and place attachment theory to depict neighborhood-accessible UGS quantity (provision, accessibility, and visibility) and quality (cognition, behavior, and affect). Through the geographical visualization of bivariate SHapley Additive exPlanations (SHAP) interaction values extracted from the trained eXtreme Gradient Boosting (XGBoost) model, and the comparison of bivariate SHAP maps with univariate SHAP maps, the study revealed the nonlinear geographic associations between UGS quantity and quality and three types of satisfaction. The results showed that when UGS quantity and quality coexisted, variations in the impact of quantity on older adults’ satisfaction were associated with quality differences. The gain effect of quality on quantity was more significant in areas with limited green space within a 500 m buffer zone. UGSs made a direct contribution to green space satisfaction, while their indirect association with life satisfaction surpassed that of residential satisfaction due to their provision of emotional qualities. This study calls for neighborhood green planning aimed at improving older adults’ subjective well-being, which should shift focus from quantity to quality and balance the relationship between quantity and quality based on regional characteristics. Full article

The Saudi Green Initiative (SGI) represents a major national effort to enhance environmental sustainability and urban livability in Saudi Arabia. Despite its ambitious targets, limited empirical research has evaluated its spatial performance and social impacts. This study assesses the progress of SGI implementation in Riyadh by examining the spatial distribution, accessibility, and equity of urban green spaces (UGS), alongside residents’ perceptions of their quality. A mixed-methods approach was adopted, integrating Geographic Information Systems (GIS)-based spatial analysis with a structured survey of 180 residents. Spatial indicators were evaluated against the World Health Organization (WHO) benchmark of 9 m² per capita and the SGI target of 28 m² per capita. The results reveal that although total green space has increased between 2018 and 2024, its distribution remains uneven, with high-density neighborhoods consistently falling below recommended standards. Survey findings indicate high satisfaction with recreational and environmental benefits, but lower satisfaction with facilities and public engagement. The study highlights that increasing total green space alone does not ensure equitable access and emphasizes the need for population-sensitive planning strategies. These findings provide practical insights for improving the spatial equity and effectiveness of urban greening initiatives and contribute to broader sustainable urban development goals. Full article

In an increasingly urbanized world, ensuring equitable access to urban green spaces (UGS) is essential for human well-being. Previous studies have largely focused on measuring proximity or availability of UGS, often neglecting the role of the walkable environment and the interaction between supply, demand, and movement flows. To address this gap, we develop a novel modeling framework that integrates the Detour Index (DI) and Local Significance (LS) to jointly capture physical barriers and recreational flows within urban street networks. Using openly available data from OpenStreetMap and Urban Atlas, we model the walkable environment in Leipzig, Germany, at a high spatial resolution. The approach enables the identification of inefficient routes, potential barriers, and areas of high use intensity, providing actionable insights for urban planning. By combining network-based accessibility with flow-based indicators, our method advances existing approaches that rely on static distance measures. The analyses of different planning alternatives further demonstrate how changes in urban structure affect accessibility and crowding patterns. The framework is transferable and based on open data, providing a foundation for future research to integrate behavioral factors and richer datasets to further refine accessibility modeling. Full article

Urbanization can act as a powerful ecological filter, restructuring biodiversity through species loss, replacement, and altered resource pathways. While urban green spaces (UGS) are recognized as potential biodiversity refuges, the effectiveness and mechanisms for conserving insect diversity across the urban-to-natural gradient remain poorly understood. Here, we combine full-season Malaise trapping (April–November) with MinION-based DNA barcoding to test two predictions about how urbanization reshapes Diptera communities across five sites in Haidian District, Beijing, ranging from residential areas and urban parks to a nearby shallow mountain reserve (BWM). Based on 5528 barcoded individuals, we identified 686 putative species from 39 families. As predicted, β-diversity between urban and mountain sites was overwhelmingly driven by species turnover rather than nestedness, demonstrating that cities do not simply receive subsets of the surrounding fauna but actively reassemble communities. This filtering effect was, however, trophic-guild specific. Detritivores showed the highest replacement, consistent with a shift from natural to anthropogenic resource subsidies, while predators/parasitoids exhibited significant nested loss, aligning with their hypothesized sensitivity at higher trophic levels. Vegetation structure further clarified these patterns: vegetation density, not plant species richness, was the primary bottom-up driver for herbivore and predator/parasitoid diversity, whereas detritivores were decoupled from living plant biomass. These findings demonstrate that urban and near-natural habitats maintain distinct species pools via guild-specific assembly pathways, highlighting the need for guild-specific conservation strategies for urban biodiversity conservation. Extending beyond compositional analysis, we propose a temporal-abundance framework, classifying species by persistence and abundance, as a diagnostic tool for assessing ecological integrity and guiding conservation in urbanizing landscapes. Full article

This study developed a comprehensive framework integrating ecosystem services (ESs), ecological adaptability, and ornamental value to guide tree species selection in historic cities constrained by soil salinization and subsurface heritage conservation. Taking Kaifeng, Henan Province, as a case study, we employed field surveys, i-Tree Eco, the Analytic Hierarchy Process, and K-means clustering to evaluate trees across protective, park, attached, and square green spaces. Results showed that carbon-related services dominated Kaifeng’s urban ES profile, with carbon storage (CS) and sequestration (CSE) value densities of 9.09 ¥·m⁻² and 0.84 ¥·m⁻²·y⁻¹, respectively. Air pollutant removal (AR) (0.21 ¥·m⁻²·y⁻¹) and P (0.009 ¥·m⁻²·y⁻¹) values remained comparatively low. Camphora officinarum Nees ex Wall delivered the highest annual ES value per tree (33.24 ¥·y⁻¹). Plaza greenery outperformed other space types in overall service provision, and deciduous broadleaf species generated greater ES value than evergreen conifers. Cluster analysis identified four functional groups: stress-tolerant pioneers, balanced adapters, high-efficiency carbon sinks, and ornamental specialists—each suited to specific green space contexts. This integrated framework offers a transferable approach for evidence-based tree selection in saline historic cities, supporting nature-based solutions in urban green space (UGS) planning. Full article