Urban Forestry & Urban Greening最新文献

{"title":"Socioeconomic inequities in visible, functional, and accessible green space exposure: A cross-sectional study in Flanders, Belgium","authors":"Melissa K. Lee ,&nbsp;Eline Rega ,&nbsp;Maarten Loopmans ,&nbsp;Martina Otavova ,&nbsp;Jos Van Orshoven ,&nbsp;Raf Aerts ,&nbsp;Ben Somers","doi":"10.1016/j.ufug.2026.129301","DOIUrl":"10.1016/j.ufug.2026.129301","url":null,"abstract":"<div><div>Green space offers numerous benefits for health and well-being, yet socioeconomic disparities continue to shape who has access to these benefits. The 3 + 30 + 300 guideline aims to reduce this inequity by promoting visible, available, and accessible green for all residents. This study assesses these components of green exposure across Flanders, Belgium, one of the most densely populated and highly urbanized regions in Western Europe, and examines how they intersect with multiple dimensions of socioeconomic deprivation, namely sensitivity and adaptive capacity. Using 264,622 building-level sample points, we quantified tree visibility from street-view imagery, tree canopy cover, and network distance to accessible green space. Median values of 5.0 (urban) and 6.0 (rural) visible trees, 16.1 % (urban) and 12.4 % (rural) canopy cover, and distances of 367 (urban) and 548 (rural) meters to accessible green were observed. Deprived neighborhoods, particularly those characterized by housing and health deprivation, showed the highest sensitivity to lack of green spaces, while areas with more elderly and higher-income residents had consistently greater exposure. These results suggest that green exposure and socioeconomic susceptibility intersect and reinforce one another, producing compounded distributive injustice. The findings highlight the need for equity-oriented interpretation and implementation of the 3 + 30 + 300 guideline and call for targeted greening strategies that address structural environmental and health inequalities.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"118 ","pages":"Article 129301"},"PeriodicalIF":6.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal coupling model for tree species identification using derivative calculation and texture filtering","authors":"Huaipeng Liu","doi":"10.1016/j.ufug.2026.129296","DOIUrl":"10.1016/j.ufug.2026.129296","url":null,"abstract":"<div><div>Accurate tree-species identification is crucial for forest management. Hyperspectral remote sensing provides rich spectral information for this purpose; however, challenges remain in extracting optimal features to distinguish spectrally similar species. Traditional approaches mainly rely on spectral bands (SBs) or texture features derived from SBs, while the potential of integrating derivative transformation with texture filtering has not been fully explored. This study aimed to assess whether coupling derivative calculation with texture filtering of hyperspectral imagery could enhance tree-species identification. It also sought to determine which processing order—calculating texture features from derivatives or derivatives from textures—was more effective. Using GaiaSky-min2-VN airborne hyperspectral data, five types of feature sets were constructed: SBs, first-order derivatives of SBs (Deriv(SBs)), texture features of SBs (Tex(SBs)), first-order derivatives of texture features (Deriv(Tex(SBs))), and texture features of first-order derivatives (Tex(Deriv(SBs))). Extreme gradient boosting (XGBoost) was employed to rank feature importance and derive optimal feature sets (OFSs) for classification. The XGBoost, random forest (RF), and support vector machine (SVM) classifiers were subsequently used to identify 22 urban-greening tree species based on each OFS. Results showed that for XGBoost and RF, Deriv(SBs) (67.48 %, 64.06 %) achieved higher overall accuracy than SBs (59.86 %, 56.74 %). Furthermore, Deriv(Tex(SBs)) (78.40 %, 73.90 %) outperformed both Tex(SBs) (75.32 %, 71.01 %) and Deriv(SBs). However, for SVM, Deriv(SBs) (75.39 %) had a lower accuracy than SBs (77.08 %), and similarly, Deriv(Tex(SBs)) (86.31 %) was lower than Tex(SBs) (88.23 %). A consistent and key finding across all classifiers was that Tex(Deriv(SBs)) achieved the highest overall accuracies (87.22 %, 84.68 %, and 92.41 %). These results confirm that coupling derivative computation with texture filtering markedly enhances tree-species recognition accuracy. Moreover, extracting texture features from the derivative data proved to be an optimal feature transformation mode, which successfully achieved the study’s objective of identifying the best feature organization strategy. The proposed method offers a robust and generalizable feature-engineering framework for precise tree-species identification, which holds significant implications for forest management and biodiversity conservation.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"117 ","pages":"Article 129296"},"PeriodicalIF":6.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing nonlinear impact disparities of vegetation-building configurations on carbon sequestration efficiency through representation learning","authors":"Mengqing Yu ,&nbsp;Yanting Shen ,&nbsp;Jiahong Ye ,&nbsp;Chenfeng Hou ,&nbsp;Mingyi He ,&nbsp;Chenyu Huang ,&nbsp;Jiawei Yao","doi":"10.1016/j.ufug.2026.129292","DOIUrl":"10.1016/j.ufug.2026.129292","url":null,"abstract":"<div><div>The carbon sequestration efficiency (CSE) of urban vegetation is influenced by both intrinsic attributes (e.g., species, morphology, age) and the surrounding environment. However, the effect of vegetation-building configurations on CSE remains largely unclear. In response, this study investigates how a range of urban spatial indicators (plane features, height features, and three-dimensional spatial features) can affect carbon sink capacity under different spatial configurations. We proposed a representation learning framework to analyse spatial disparities of vegetation and building configurations (VBC). First, a variational Autoencoder (VAE) model was employed to extract morphological vectors of clusters within a given study area. Then, machine learning and explainability analysis methods were applied to quantify the effect of indicators on CSE for these clusters. The study results indicate that trees exceeding 11 m serve as a robust positive driver of promoting CSE across diverse urban contexts, and three-dimensional spatial metrics like average vegetation volume suggest that optimal spatial dispersion is often more critical than sheer volume. From the results of the inter-cluster analysis, we can conclude that the sensitivity of the spatial response varies in different clusters. Smaller clusters with more aggregated vegetation exhibit the highest sensitivity to configurational changes. By applying explainable artificial intelligence algorithms, this study offers quantitative evidence to improve the CSE of urban green spaces (UGS) and thereby inform the development of climate resilience planning.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"117 ","pages":"Article 129292"},"PeriodicalIF":6.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic underestimation of urban green space by global land cover products","authors":"Shirui Yuan ,&nbsp;Xiaodan Liu ,&nbsp;Yan Li ,&nbsp;Yajun Hu ,&nbsp;Conghong Huang","doi":"10.1016/j.ufug.2026.129299","DOIUrl":"10.1016/j.ufug.2026.129299","url":null,"abstract":"<div><div>Accurate mapping of urban land cover is vital for sustainable urban development, yet the reliability of global land cover products in complex urban environments remains unclear. This study provides a comprehensive, independent evaluation of six fine-resolution global land cover products (ESRI2023, FROMGLC10_2017, FROMGLCplus_2023, GLC_FCS30_2022, GlobeLand30_2020, and WorldCover2021). Using a globally distributed sample of 10,000 reference points across 20 cities, we assessed overall accuracy, thematic accuracy, and, specifically, the estimation of urban green space coverage (UGSC). Our results reveal a significant and consistent underestimation of UGSC by all products, with median underestimation biases ranging from 5.1 to 24.0 %. The WorldCover2021 product consistently demonstrated the highest overall accuracy (median OA = 71.1 %), yet even this product encountered difficulties in delineating spectrally similar classes and fragmented vegetation, highlighting the inherent challenges of urban heterogeneity. The study provides crucial insights for researchers and policymakers on the strengths and limitations of current products, emphasizing the critical need for specific validation frameworks for urban land cover mapping to support effective urban planning and ecological management.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"117 ","pages":"Article 129299"},"PeriodicalIF":6.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring tree diameter at breast height in urban green spaces using ForestScanner: Implications for citizen science","authors":"Yamato Tsuzuki ,&nbsp;Akio Inoue","doi":"10.1016/j.ufug.2026.129293","DOIUrl":"10.1016/j.ufug.2026.129293","url":null,"abstract":"<div><div>A free mobile application called ForestScanner (FS) was recently developed to enable LiDAR-based forest inventories using LiDAR-equipped iPhones or iPads. Although FS has been shown to reduce the cost, labor, and time required to measure the diameter at breast height (<em>D</em>) of trees within forests, its applicability to trees in urban green spaces (UGS) has not been evaluated. Here, we conducted two experiments to evaluate the performance of FS in the UGS. The first experiment involved the measurement of 70 trees at various distances and directions. The results revealed that FS tended to underestimate <em>D</em> compared to the traditional diameter tape (DT), with greater bias and variability at longer distances. These results suggest that measurements should be taken as close to the tree as possible to improve the accuracy and precision. In the second experiment, 39 trees were measured repeatedly by multiple observers. The results demonstrated that FS produced slightly greater variability between and within observers than did DT, although the overall agreement remained high. The underestimation was more pronounced for larger trees across both experiments, and for broad-leaved trees than for conifers in the second experiment. Overall, these findings highlight the potential of FS for citizen science-based assessments of carbon stocks in UGS, while also emphasizing the need for careful application when measuring larger and broad-leaved trees.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"118 ","pages":"Article 129293"},"PeriodicalIF":6.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence (AI) models for detecting urban green spaces: A multi-city and multi-country contexts approach","authors":"Jiawei Zhao ,&nbsp;Matthew H.E.M. Browning ,&nbsp;Marco Helbich ,&nbsp;SM Labib","doi":"10.1016/j.ufug.2026.129295","DOIUrl":"10.1016/j.ufug.2026.129295","url":null,"abstract":"<div><div>Urban green space (UGS) maps are essential for identifying and assessing the multifunctional benefits of nature in cities. However, obtaining reasonable-quality UGS data across the Global North and South remains challenging due to methodological inconsistencies and the high costs of field-based data collection. We developed a scalable and replicable framework that leverages freely available, moderate-resolution satellite images, combined with artificial intelligence-based (AI) image segmentation, to detect and map UGSes. Sentinel-2 images were retrieved across 16 cities in North America, Europe, the Middle East, and South Asia. Using raw and processed Sentinel-2 spectral information, we trained and validated an AI hybrid model combining U-Net and ResNet-50 on varying combinations of data layers (i.e., normalized difference vegetation index [NDVI], normalized difference water index [NDWI], and normalized difference built index [NDBI]). The trained models achieved approximately 90% accuracy in identifying UGS and demonstrated a substantial overlap with the ground-truth data across diverse urban settings. However, consistent with known limitations of moderate-resolution imagery, the models underperformed in detecting relatively small UGS patches. To test the geographic transferability of the model, we applied the trained model to detect UGS in an African city (Kampala, Uganda), where ground-truth data were unavailable. We found that the UGS identified from the model partially overlapped with the UGS in Kampala, as derived from OpenStreetMap data, suggesting that combining AI-derived and volunteered geographic information can produce more comprehensive UGS inventories. Overall, this scalable framework for identifying UGS in places with limited existing data could enable cities to inventory their UGS and target the Sustainable Development Goals.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"118 ","pages":"Article 129295"},"PeriodicalIF":6.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban forest regulations and incentives for preserving trees on private land: A legal perspective","authors":"Yifat Holzman-Gazit ,&nbsp;Eran S. Kaplinsky","doi":"10.1016/j.ufug.2026.129281","DOIUrl":"10.1016/j.ufug.2026.129281","url":null,"abstract":"<div><div>Trees on private land constitute a foundational part of the urban forest, but their legal status as private property exposes them to persistent removal pressures. Municipal policies that prioritize the preservation of private trees over replacement or offsetting align with sustainability principles and, specifically, with the Mitigation Hierarchy. While research increasingly advocates combining regulatory (“sticks”) and incentive-based (“carrots”) approaches, the legal foundations supporting these tools remain underexplored. This study examines the legal dimensions of sticks and carrots in four common law jurisdictions: Australia, Canada, England, and the United States. Legal research methods were used to analyze tree ordinances, planning regulations, case law, and incentive programs. Four recurring themes influencing policy design and performance were identified: legal authority, property rights, enforcement capacity, and defensible legal standards. Findings show that unclear statutory mandates, rigid property rights, weak enforcement mechanisms, and inadequate legal standards can undermine both regulatory and incentive approaches aimed at protecting trees on private land. By illuminating law’s ability to enable or constrain effective private tree protection, this study underscores that urban forest governance cannot achieve resilience without legal robustness. Integrating legal expertise into urban forestry is not merely procedural but a strategic imperative for sustaining canopy cover on private land in the face of removal pressures.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"117 ","pages":"Article 129281"},"PeriodicalIF":6.7,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"False equity: Demographic shifts and urban tree cover in Northeast us cities","authors":"Matthew Walter ,&nbsp;Idowu Ajibade ,&nbsp;Jing Gao ,&nbsp;Pinki Mondal","doi":"10.1016/j.ufug.2026.129288","DOIUrl":"10.1016/j.ufug.2026.129288","url":null,"abstract":"<div><div>Urban tree canopy is unevenly distributed across U.S. cities, often reflecting legacies of discriminatory planning practices such as redlining and disinvestment. While prior research has documented disparities in tree cover favoring wealthier neighborhoods, the drivers of urban tree equity remain complex and context-dependent. In this study, we employ machine learning models to analyze 2020 tree cover data across 3211 census tracts in five U.S. cities: Washington D.C., Baltimore, Philadelphia, New York City, and Boston. We assess how tree cover distribution correlates with socioeconomic indicators and historical redlining maps and evaluate whether municipal tree-planting goals are equitably met across income groups. Contrary to earlier studies, we find no consistent correlation between total tree canopy and income across the five cities. However, street tree planting goals are more likely to be met in higher-income neighborhoods, highlighting ongoing implementation disparities. We also confirm that redlined areas continue to have significantly lower tree cover, reinforcing historical inequities in access to green infrastructure. Furthermore, our longitudinal analysis of demographic change within formerly redlined areas reveals growing income gaps between majority White and non-White neighborhoods, an indicator of potential gentrification that can distort tree equity assessments through demographic turnover, a phenomenon we term \"false equity.\" Our findings underscore the need for urban greening initiatives to be paired with affordable housing protections and anti-displacement measures. Without such integration, tree-planting programs risk reinforcing inequality rather than addressing it. Ultimately, equitable greening must be economically, socially, and racially inclusive.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"117 ","pages":"Article 129288"},"PeriodicalIF":6.7,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Residents’ heterogeneous willingness-to-pay for green roofs revealed via demographic segmentation: A case study in a southern Chinese city","authors":"Jiahong Wu ,&nbsp;Pan Yang ,&nbsp;Guanhui Cheng ,&nbsp;Qian Tan","doi":"10.1016/j.ufug.2026.129282","DOIUrl":"10.1016/j.ufug.2026.129282","url":null,"abstract":"<div><div>Green roof (GR) represents a crucial nature-based solution for advancing urban sustainability across multiple Sustainable Development Goals (SDGs). Yet, its widespread adoption among urban residents was hindered by a lack of understanding regarding residents’ heterogeneous willingness-to-pay (WTP). This study addresses this issue by employing the latent profile analysis to segment 964 residents from Shenzhen into four groups based on their preferences over a range of GR-related aspects: “Residents with low support” (12.14 %), “Residents with high support” (9.44 %), “Residents with moderate support” (61.41 %), and “Cautious supporter” (17.01 %). The identified groups exhibit distinct socio-economic backgrounds, preferences regarding GR, and WTP. Group-specific multinomial logit regression models reveal significant differences in the drivers of WTP among groups, and offer greater predictive accuracy than a homogeneous model. The findings thus highlight the need for targeted policies to promote GR.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"117 ","pages":"Article 129282"},"PeriodicalIF":6.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does leisure satisfaction enhance visitors' mental health in peri-urban forest parks? Evidence from a mixed-methods study","authors":"Bin Zhou ,&nbsp;Tianyu Qu ,&nbsp;Minchen Huang ,&nbsp;Hu Yu ,&nbsp;Xueling Tan","doi":"10.1016/j.ufug.2026.129280","DOIUrl":"10.1016/j.ufug.2026.129280","url":null,"abstract":"<div><div>Against the backdrop of mental health becoming an increasingly interdisciplinary research focus, how leisure satisfaction influences individual mental health has not yet received sufficient attention. This study employed a mixed-methods approach in the context of peri-urban forest parks. First, semi-structured interviews were conducted to identify key psychological constructs and their structural relationships. Subsequently, a structural equation model was developed and empirically tested using survey data. The results reveal that leisure satisfaction does not directly predict the mental health of visitors to peri-urban forest parks. Instead, its influence was mediated by two distinct mediating pathways: nature connectedness and meaning in life. By integrating these four variables into a unified analytical framework, this research not only advanced the theoretical understanding of the mechanisms influencing mental health in nature-based leisure contexts but also offered practical implications for enhancing the mental restorative benefits of peri-urban forest parks.</div></div>","PeriodicalId":49394,"journal":{"name":"Urban Forestry & Urban Greening","volume":"117 ","pages":"Article 129280"},"PeriodicalIF":6.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}