Search Results (442)

Urban forests support wildlife populations across North America and the world. Yet, challenges remain for research and practice to integrate wildlife habitat as a core component of the myriad objectives that urban foresters manage. Ecosystem services have been adopted as a dominant paradigm in urban forestry for both advocacy and management, yet accounting for contributions to wildlife habitat does not fit squarely within typical ecosystem service frameworks. The i-Tree program, a suite of urban forest ecosystem service models and tools developed by the US Forest Service, presented an opportunity to link widely used urban forest assessment field protocols with indicators of suitable habitat. In this reflection piece, we demonstrate how the i-Tree Wildlife project assessed whether urban forest structural assessment methods could be applied to assess wildlife habitat provision, operationalizing the fundamental question “How do urban forests support wildlife?” We describe the development process for integrating bird habitat suitability models for 12 species present in the northeastern US, ten native and two non-native birds, into the flagship i-Tree Eco tool. We offer reflections, challenges, and opportunities from this process. Ultimately, the improvement of ecosystem assessment tools like i-Tree can assist practitioners who aim to manage healthy and productive urban forests that benefit people and wildlife. Full article

Urban trees constitute a key component of sustainable urban green infrastructure, providing ecosystem services related to climate regulation, biodiversity conservation, and human well-being. At the same time, mature and veteran trees in public spaces are frequently perceived as a safety risk due to visible structural defects, often resulting in precautionary removal decisions based solely on visual assessment. This study evaluates the applicability of acoustic tomography as a non-invasive diagnostic tool supporting sustainable urban tree management using the city of Jarocin (western Poland) as a case study. Following preliminary Visual Tree Assessment (VTA), 20 mature urban trees were identified, of which six representative specimens were subjected to detailed analysis using the PiCUS Sonic Tomograph 3. The internal condition of tree trunks, sound wave propagation velocity, residual wall thickness (t/R ratio), and structural stability were analysed in relation to species characteristics and site conditions. The results demonstrated considerable variation in the internal condition of the analysed trees and revealed that visible external defects did not necessarily correspond to a critical reduction in mechanical stability. Five out of six examined trees met or approached the accepted safety threshold (t/R ≥ 0.30), supporting their retention rather than removal. In several cases, acoustic tomography identified substantially larger zones of structurally sound wood than suggested by visual inspection alone. The findings confirm that integrating acoustic tomography into urban tree risk assessment can improve decision-making accuracy, reduce unnecessary tree removal, and support biodiversity-oriented and climate-adaptive urban green space management. The proposed approach may serve as a transferable framework for sustainable management of mature urban trees in medium-sized cities. Full article

Urban trees are increasingly exposed to persistent anthropogenic drivers that extend beyond climatic forcing and fundamentally alter the conditions of secondary growth. While climatic controls of cambial phenology and xylogenesis are well established, the mechanisms by which non-climatic drivers regulate cambial activity and wood formation remain fragmented and are often inferred only indirectly. Here, we develop a cambium-centred framework to synthesise current evidence on how anthropogenic drivers shape wood formation in urban and peri-urban trees. To our knowledge, this is among the first syntheses explicitly linking anthropogenic drivers to distinct stages of xylogenesis. Anthropogenic drivers are typically chronic, spatially heterogeneous, and temporally decoupled from seasonal climatic rhythms, and may alter cambial kinetics and generate anatomical signatures not captured by ring width alone. We evaluate major driver domains, including root-zone constraints, altered hydrology, urban microclimate, pollution, salinity, and mechanical disturbance, while also considering emerging drivers such as artificial light at night and microplastics. Evidence is stratified into three levels: direct observations, indirect physiological evidence, and mechanistic plausibility. Across driver classes, three recurrent anatomical patterns emerge: reduced conduit size under hydraulic or osmotic stress; anomalies in wall deposition under carbon limitation or oxidative stress; and pronounced circumferential heterogeneity under spatially localised forcing. Integrative approaches combining xylogenesis monitoring, quantitative wood anatomy, dendrometer observations and spatially explicit sampling are essential to disentangle anthropogenic from climatic effects and improve assessment of tree resilience. Full article

Urban green spaces play a key role in supporting biodiversity, climate regulation, and carbon storage in rapidly expanding cities. Urban and peri-urban parks can differ markedly in tree-community structure, floristic diversity, and carbon-storage capacity. The aim of the study was to compare these attributes between an urban and a peri-urban park. The study compared these attributes between an urban park and a peri-urban park in western Mexico using data collected in 500 m² circular plots. Tree structure was assessed through diameter at breast height, height, crown diameter, basal area, and crown projection area, while floristic composition and diversity were evaluated using richness, Shannon, Simpson, Pielou, and Menhinick indices. Aboveground biomass, belowground biomass, and carbon stocks were estimated using generalized allometric equations. A total of 1675 trees belonging to 19 families, 33 genera, and 49 species were recorded. The peri-urban park showed greater structural development, with significantly higher DBH, height, crown diameter, basal area, biomass, and carbon stocks, whereas the urban park supported greater species richness and higher Shannon diversity. Species composition also differed strongly between parks, and carbon storage was concentrated in a reduced number of dominant taxa in each site. DBH was the structural variable most strongly associated with total carbon per tree. These findings show that floristic diversity and carbon-storage capacity do not necessarily increase in parallel and that urban and peri-urban parks can provide contrasting but complementary ecological functions. Full article

Large trees are keystone ecological structures that sustain biodiversity and ecosystem services, particularly in human-altered landscapes. However, their persistence is increasingly threatened by land-use change, urban expansion, and inadequate monitoring. This study develops and validates a scalable, automated framework for monitoring the loss of large individual trees using satellite image time series and breakpoint detection. We compared four spectral indices (SIs): Enhanced Vegetation Index 2–EVI2; Normalized Burn Ratio–NBR; Normalized Difference Red Edge–NDRE, and the Normalized Difference Vegetation Index–NDVI derived from Sentinel-2 imagery (2015–2025) for 691 georeferenced trees in Lousada, northern Portugal. Data were accessed and processed in Google Earth Engine and analyzed using a custom R-based workflow, including cloud masking, gap-filling, temporal interpolation, upper-envelope smoothing, deseasonalization, and break detection. Five breakpoint detection algorithms were compared: BFAST, energy-divisive, linear regression of structural changes, wild-binary segmentation, and change point models. Detected breakpoints were subsequently post-validated to determine whether they were associated with declines in SIs, using three pre-/post-breakpoint methods: comparisons of short- and long-term medians and a randomized trend analysis. As a baseline, these algorithms/post-validation logic were compared against the Continuous Change Detection and Classification (CCDC) approach. The results indicate moderate but consistent break detection performance, with a maximum balanced accuracy of 73% (for EVI2 or NDVI and using the energy-divisive algorithm coupled with the long-term median post-validator) under conservative validation criteria and high specificity for surviving trees. CCDC ranked comparatively lower at 62%. Algorithm performance varied substantially, with the energy-divisive providing the most conservative detection and the wild-binary segmentation yielding higher sensitivity. Performance was further influenced by tree structural attributes and species identity, with larger, taller and isolated trees, as well as particular genera, showing higher detection accuracy, with genus Eucalyptus, Tilia and Celtis yielding top performance results (79–65%) and Quercus, Castanea and Platanus the lowest (62–60%). By integrating satellite observations with large-tree inventory data from the Green Giants citizen science project, this study demonstrates the potential of decentralized, Earth observation-based monitoring to support tree-level loss assessments in fragmented landscapes. The proposed framework provides a transferable foundation for wide-scale monitoring of large trees in peri-urban and mixed-use environments. Full article

Urban forestry research increasingly promotes proximity-based benchmarks, such as the 3–30–300 rule, to expand tree canopy, enhance access to nature, and support healthier and more climate-resilient cities. However, a growing body of evidence links green proximity to rising property values and residential displacement, raising concerns regarding green gentrification. These tensions suggest that proximity-based greening cannot be understood solely as an environmental or accessibility intervention; rather, its social outcomes are mediated by the broader housing system. This Perspective argues that the 3–30–300 rule operates as a value-generating urban forestry intervention whose distributive effects are conditioned by housing governance, tenure structures, and the presence of affordability protections. We advance a governance-conditional framework that reconceptualises the rule as a housing-conditioned greening strategy, illustrating how environmental improvements may translate into escalating housing costs and displacement pressures in contexts where housing regulation is weak or fragmented. The analysis highlights the institutional mechanisms through which environmental value is captured, retained, or redistributed across scales, without positing a deterministic relationship between greening and displacement. Aligning urban forestry initiatives with affordability measures and tenant protections is therefore essential if proximity-based greening is to contribute not only to greener and healthier cities, but also to more equitable ones. Full article

Sustainable land use policy planning requires integrated approaches that account for environmental and socio-economic trade-offs of greenhouse gas (GHG) mitigation measures. This study presents a spatial decision-support tool developed to support the evaluation of policy scenarios in non-urban land-use sectors, with application to the land use, land-use change, and forestry (LULUCF) sector in Latvia. The tool enables users to select predefined mitigation measures, apply spatial selection criteria, and generate quantitative and spatially explicit outputs. In addition to estimating GHG mitigation potential, it evaluates impacts on profitability, employment, and habitat quality, allowing the assessment of trade-offs and synergies across multiple dimensions. Scenario results are reported as both absolute and relative impacts, improving transparency and comparability. Developed in Python 3.10 and supported by a PostgreSQL 17/PostGIS 3.5 database, the tool operates through a web-based interface and supports efficient scenario construction and evaluation. While results depend on underlying data and assumptions, the tool provides a transparent framework for exploring policy options and supports evidence-based decision-making in land-use and climate policy planning. Full article

This paper is based on data from 121 cities in China’s ecologically fragile regions from 2008 to 2022; it constructs an indicator system for the efficiency of pollution control and carbon reduction in agricultural practices. This system includes expenditures on agriculture, forestry, and water affairs, arable land area, agricultural laborers, total agricultural output value, agricultural carbon emissions, and agricultural non-point source pollution. It uses a super-efficiency SBM model that incorporates non-desirable outputs to measure the synergistic efficiency and analyzes its dynamic evolution using the Malmquist–Luenberger index to reveal the spatiotemporal characteristics of the synergistic efficiency. A Tobit model identifies the influence of factors, such as the level of rural economic development, crop planting structure, the strength of fiscal support for agriculture, rural education level, urbanization rate, and mechanization level on the synergistic efficiency. The results show that, from a temporal perspective, the average synergistic efficiency was only 0.58, significantly below the effective value of 1, indicating substantial room for overall improvement. Only 10 cities met the benchmark, with distinctly different reasons for compliance, while the remaining 111 cities remained inefficient. Regarding influencing factors, crop planting structure, the strength of fiscal support for agriculture, and urbanization rate significantly and positively drive efficiency; the level of rural economic development and mechanization level significantly inhibit efficiency, and rural education level shows no significant impact. These findings provide targeted policy recommendations for the synergy effect in ecologically fragile areas, as well as for low-carbon agricultural development. Full article

Achieving sustainable development requires moving beyond pollution metrics to holistic measures, such as the load capacity factor (LCF), which balances ecological demand and supply. While recent studies have provided important insights into the determinants of LCF in OECD countries, further research is needed to incorporate additional determinants and updated estimation approaches. This study addresses this gap by examining the impacts of financial innovation, forestry, urbanization, population, and economic growth on the LCF in Organization for Economic Cooperation and Development (OECD) economies from 1990 to 2023. Using second-generation panel econometric methods, including tests for cross-sectional dependence, slope heterogeneity, second-generation unit roots, and cointegration techniques, this paper confirms a stable long-run relationship among the variables. The core analysis applies the method of moments quantile regression to uncover the heterogeneous effects across the LCF distribution. The results indicate that financial innovation consistently enhances the ratio of biocapacity to ecological footprint. In contrast, economic growth and urbanization exert significant negative pressure on the LCF, whereas population size shows a uniformly detrimental effect. Forestry has a positive but less pronounced influence. Robustness checks using fully modified ordinary least squares, dynamic ordinary least squares, and panel-corrected standard errors confirm these results. The present study concludes that targeted financial innovation and stringent urban demographic policies support OECD nations in improving ecological balance and reducing ecological deficits. Full article

To advance the Sustainable Development Goals, it is essential to correct imbalances in how the benefits of urban trees are distributed across different demographic and socioeconomic groups. Environmental justice studies have frequently overlooked assumptions regarding the data-generating process and have not considered spatial confounding. This oversight potentially misestimates patterns of inequity. This study evaluates the sensitivity of inequity to model assumptions using urban tree inventories from Málaga and Sevilla and Bayesian hierarchical models. City-level differences dominated the inequity patterns, and model specification influenced the magnitude, precision, and credibility of estimated effects, though directionality remained consistent. Patterns were highly consistent across the four ecosystem services, indicating that model assumptions affected all services equivalently. Málaga and Seville exhibited divergent inequity patterns, indicating that local urban context mediates these relationships. In Seville, inequity patterns were inconsistent with the luxury hypothesis and occurred primarily across age-based demographic strata, whereas in Málaga they manifested predominantly along ethnicity, with weaker evidence of income inequities. We advocate for explicitly modeling spatial data-generating processes and comparing conventional versus confounding-mitigated approaches. This city-specific rigor is essential for urban planners to prevent resource misallocation, ensuring that tree-planting strategies address genuine inequities rather than methodological biases. Full article

Accurate individual tree crown identification is essential for urban forestry, yet existing datasets often lack exhaustive annotations and multi-temporal diversity. To address this limitation, an exhaustively annotated dataset was curated for crown instance segmentation, comprising 47,754 labeled individual crowns from approximately 110 species across three temporal phases. Anchored in a “crown geometry” labeling criterion focusing on upper-canopy individuals visible in the imagery, and the high-resolution imagery captured seasonal variations in shape, color, and texture, providing an empirical basis for within-site robustness. Utilizing this dataset, this study (1) compared five instance segmentation models; (2) evaluated their generalization capabilities across different temporal phases; and (3) tested a multi-temporal joint training strategy and a non-maximum suppression (NMS)-based fusion. The experiments revealed significant overfitting in single-temporal models. While ConvNeXt-V2 achieved a high segmentation mean Average Precision (Segm_mAP) of 0.852 within the same temporal phase, its performance dropped sharply to 0.361 across phases. Bi-temporal joint training significantly mitigated this issue, improving cross-temporal performance to 0.665 and further increasing within-phase accuracy to 0.874. In contrast, tri-temporal training reduced accuracy (0.748), demonstrating that effective generalizability depends on the strategic selection of complementary temporal phases rather than the mere accumulation of data. The multi-temporal training framework provided in this study could serve as a practical reference and a foundational benchmark for further urban forest structural monitoring research. Full article

This manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration and transparent, rule-based decision support for tree risk management. The workflow integrates (i) Structure-from-Motion/Multi-View Stereo (SfM–MVS) reconstruction from multi-view imagery, (ii) independent referencing to ensure metric scaling and a consistent local frame, and (iii) point cloud analytics to derive branch-level geometric descriptors (e.g., base diameter, length, inclination, slenderness, and projected reach). A clear rule-based layer operationalizes Tree Risk Assessment Qualification (TRAQ)-style risk components and As Low As Reasonably Practicable (ALARP) principles to map geometry and exposure into auditable management recommendations (e.g., monitoring intervals, pruning/weight reduction, supplemental support, and exclusion-zone planning). To provide a real-data example, the demonstration uses the public Fuji-SfM apple orchard dataset, including three neighboring trees with partially overlapping crowns for tree instance extraction and subsequent TRAQ/ALARP scenarios on an outer tree. The proposed decision layer is intentionally based on external geometry and exposure; internal decay indicators and species-specific mechanical properties (e.g., Modulus of Elasticity (MOE), Modulus of Rupture (MOR)) are outside this demonstration and should be incorporated via complementary diagnostics in operational deployments. Full article

Aiming at diverse urban tree structures and difficulties in vegetation point cloud extraction and utilization, this study proposed single-tree-scale biological parameter estimation methods for urban scenarios to enhance point cloud’s application value in urban greening management. For single-tree segmentation, it constructed a method based on the constraints of the trees’ geometric features and combined the gravitational modeling characteristics, called the CGF-CG single-tree segmentation method. This method (i) combines clustering and principal direction analysis to extract trunk points, (ii) introduces canopy segmentation based on trunk positions, (iii) optimizes edge point attributes via a gravitational model. Based on CGF-CG’s accurate results, an improved random forest method for single-tree biological parameter (IRF-BP) estimation (aboveground biomass, carbon storage, leaf area index, living vegetation volume) was proposed: (i) correlation analysis with variable screening, (ii) adaptive feature selection and pigeon-inspired optimization to enhance model generalization, (iii) adopting Shapley Additive Explanations (SHAP) to improve interpretability. Based on these, a complete model for different tree species was constructed. Validation showed that CGF-CG exhibited negligible over-segmentation and under-segmentation in the selected study areas, with overall average precision, recall, and F1-score over 98.5%. Additionally, on the selected overall region, the overall mF1 score, mPTP, and mPTR of our method are 99.13%, 99.15%, and 99.12%, respectively, which are superior to Forestmetrics, lidR, PyCrown, and DBSCAN methods. IRF-BP performed well, with a highest R² of 0.81 and a lowest mean absolute percentage error of 7.5%, effectively surpassing the performance of traditional models such as RFR, GBR, KNN, and XGB. In summary, results provided theoretical and technical support for urban green resource management and evaluation. Full article

Soil nutrients and water are often distributed heterogeneously in space, yet how plant roots forage in response to such heterogeneity and how their strategies relate to functional traits remain poorly understood. Here, we conducted an indoor pot experiment manipulating water and nutrient supply in both homogeneous and heterogeneous patch patterns using seedlings of four tree species, focusing on root functional traits and foraging strategies. The results indicate that root foraging behavior exhibits both resource specificity and species specificity: roots tend to proliferate toward nutrient-rich and low-water patches as an adaptive strategy. Although no strict dichotomy was observed between high foraging scale (low precision) and low foraging scale (high precision) strategies under heterogeneous conditions, fine-rooted species (Acer truncatum and Koelreuteria paniculata) exhibited traits leaning toward “precise foraging”, whereas coarse-rooted species (Prunus davidiana and Quercus variabilis) tended toward a conservative “random walk” pattern, with no trade-off between root foraging scale and precision. Root morphological traits exerted significant nonlinear regulation on foraging scale: root biomass foraging scale (FS_RB) correlated positively with root diameter (RD) but negatively with specific root length (SRL) and specific root area (SRA); root length foraging scale (FS_RL) correlated positively with root length (RL), root tip number (RTN), SRL, and SRA. In contrast, root morphological traits could not explain the variation in foraging precision, suggesting that foraging precision constitutes another distinct dimension in root-trait space. In summary, this study provides key insights into the foraging strategies of plant roots in heterogeneous environments, expanding our understanding of the multidimensionality of root functional traits. Full article

Carbon storage of terrestrial ecosystems is highly susceptible to land use/cover change (LUCC). In order to optimize land use patterns and advance the dual carbon goals (carbon peaking and carbon neutrality), it is imperative to clarify the role of LUCC in controlling regional terrestrial carbon storage. This study utilized a land use dataset spanning from 1990 to 2020 and incorporated 12 pivotal driving factors. Based on these data and factors, this study constructs four distinct future development scenarios: natural development scenario (ND), cropland protection scenario (CP), ecological protection scenario (EP), and urban development scenario (UD). By integrating the Integrated Valuation of Ecosystem Services and Trade-offs model (InVEST) with the Patch-Generating Land Use Simulation model (PLUS), this study simulated the dynamic changes in land use types and the spatiotemporal evolution of carbon storage in the Qinba Mountains (QBMs). The results revealed that between 1990 and 2020, built-up area and water area experienced substantial expansion with growth rates of 67.89% and 20.39%, respectively. In addition, cropland decreased by 3.09% and grassland decreased by 2.49%. Notably, cropland exhibited the most pronounced conversion intensity among all land use types during this period. Correspondingly, the total terrestrial carbon storage in the study area declined slightly from 7471.08 × 10⁶ t in 1990 to 7437.25 × 10⁶ t in 2020. Forestland dominated the regional carbon pool, accounting for an average of 47.67% of the total carbon storage over the three decades. Further analysis identified natural factors as the primary drivers of LUCC and associated carbon storage changes, with DEM exerting the greatest influence, followed by mean annual temperature and mean annual precipitation. Projection analyses for 2030 reveal divergent carbon storage outcomes across different land use scenarios relative to the 2020 baseline. Under the natural development (ND) and urban development (UD) scenarios, total carbon stocks are projected to decline by 37.63 × 10⁶ t and 19.99 × 10⁶ t, respectively. Conversely, implementation of conservation-oriented strategies yields substantial increases, with the cropland protection (CP) and ecological protection (EP) scenarios enhancing carbon storage by 16.87 × 10⁶ t and 13.07 × 10⁶ t, respectively. These findings underscore the critical role of protection-focused land use policies in strengthening ecosystem carbon sequestration capacity. The study provides a scientific foundation for formulating targeted forestry management policies and enhancing the terrestrial ecosystems’ capacity to act as carbon sinks in mountainous areas. Full article