Urban parks provide essential ecosystem services (ESs) that enhance human wellbeing. However, discrepancies often arise between objective assessments of these services and stakeholders’ subjective perceptions. This study addresses a research gap concerning the synergies and tradeoffs between objective evaluations and subjective perceptions of key ecosystem services across various spatial scales. We investigated six key ecosystem services in Century Park, Shanghai, across seven buffer radii (8–100 m). Objective data were obtained from park view images (PVIs) and spatial analysis, while subjective perceptions were gathered through a scoring survey of 33 stakeholders. The key finding is that a buffer radius of 35 m offers optimal synergy between objective and subjective assessments for most ESs, particularly in pollution mediation, temperature regulation, and cultural services. Professionals showed stronger alignment in regulatory services like pollution mediation and temperature regulation, while residents exhibited higher synergy in net primary production (NPP) beyond a 75 m radius. Notably, cultural services displayed nuanced differences, with professionals preferring simpler landscapes and residents demonstrating varied aesthetic preferences. These findings emphasize the importance of integrating objective data and human perceptions in urban green space planning and governance. By incorporating diverse stakeholders and identifying optimal buffer zones, planners and designers can effectively balance ESs with human experiences. This approach ultimately fosters more sustainable and wellbeing-centered urban environments.

With the growing awareness of public health, urban parks have increasingly become popular venues for physical activities due to their accessibility and pleasant landscapes, among which walking, jogging, and running dominate. This study innovatively integrates exercise trajectory data from the Strava platform and semantic segmentation technology to analyze the interaction mechanisms among park view elements, physical activities, and physiological responses, based on empirical data from three representative parks in Shanghai. This study includes the following: (1) acquiring hotspot exercise paths and physiological data (heart rate and speed) of walking, jogging, and running users through the open Strava platform; (2) conducting semantic segmentation on real-word photos of three case parks to extract 17 types of park elements; (3) applying Spearman’s correlation analysis to reveal the differential impacts of park elements on physiological responses under walking, jogging, and running behaviors, demonstrating that combinations of elements such as trees, water bodies, fences, and sky influence exercise performance; and (4) constructing scene modules for site attraction, training improvement, and restorative relaxation for walking, jogging, and running, and proposing phased landscape scene sequence strategies to provide quantitative guidance for health-oriented park planning and design. This study breaks through the limitations of traditional subjective evaluations by coupling objective physiological data with spatial elements, offering novel insights for optimizing the exercise functionality of urban green spaces.

Objective: To explore the correlation between park view elements and their combinations on the heart rate (HR) and speed of walkers, joggers, and runners in different groups of people’s profiles and walking types, provide suggestions for the planning and design of walking suitability of walking trails in parks, and guide people with different walking needs to scientifically choose walking trails in parks.Methods: Profile data and exercise data of users who recorded walking activities in Century Park are collected on Strava, and the park view images (PVIs) were taken and segmented semantically. Data are grouped according to gender, age, weight and exercise type, and the quantitative relationship between HR, speed and 17 park view elements is studied by Spearman correlation analysis.Results: (1) The influence of the same park view elements on the exercise physiological indicators of different genders is small; (2) Park view elements combination based on sky, grass-plant and tree can better stabilize the walking HR of the older adult; (3) Semi-enclosed trail dominated by tree can improve the walking HR and speed of people with larger body weight; (4) Natural routes dominated by sidewalk-path and supplemented by tree and sky elements are more suitable for walking, while the trails with larger sky area, no trees and wider trails are more suitable for running.

As urbanization accelerates globally, urban areas have become major sources of greenhouse gas emissions. In this context, urban parks are crucial as significant components of carbon sinks. Using Shanghai Century Park as a case study, this study aims to develop an applicable and reliable workflow to accurately assess the carbon sequestration capacity of urban parks from a spatial–temporal perspective. Firstly, the random forest model is employed for biotope classification and mapping in the park based on multi-source data, including raw spectral bands, vegetation indices, and texture features. Subsequently, the Net Primary Productivity and biomass of different biotope types are calculated, enabling dynamic monitoring of the park’s carbon sequestration capacity from 2018 to 2023. Moreover, the study explores the main factors influencing changes in carbon sequestration capacity from the management perspective. The findings reveal: (1) The application of multi-source imagery data enhances the accuracy of biotope mapping, with winter imagery proving more precise in classification. (2) From 2018 to 2023, Century Park’s carbon sequestration capacity showed a fluctuating upward trend, with significant variations in the carbon sequestration abilities of different biotope types within the park. (3) Renovation and construction work related to biotope types significantly impacted the park’s carbon sequestration capacity. Finally, the study proposes optimization strategies focused on species selection and layout, planting density, and park management.

Urban green spaces provide essential ecosystem services, yet mismatches between subjective perceptions and objective assessments may constrain effective planning. This study examines the correspondence between perceived and measured ecosystem services across two contrasting urban green spaces in Shanghai: Century Park, a managed urban park, and Sanlin Green Space, a naturalistic urban forest. Objective ecosystem services (regulating, supporting, and cultural) were quantified using UAV-based biotope mapping and indicators including biophysical metrics (Net Primary Production, Water Retention, PM10 removal, and Land Surface Temperature), structural diversity indices (Shannon Diversity of land cover, vegetation, and tree structure), and visual–spatial proxies (Green View Index, Sky View Index, Water View Index, color metrics, and spatial openness). Subjective perceptions were derived from panoramic image-based questionnaires, with perception scores predicted using XGBoost and aggregated via SHapley Additive exPlanations (SHAP). Correlation analyses, spatial regression models, and partial least squares structural equation modeling were applied to explore relationships and pathways. Results show weak but significant positive associations in the urban park, whereas no overall correspondence was observed in the urban forest. Spatial mismatches were concentrated in biotopes with distinctive visual–ecological features and in fragmented areas. Green View Index is associated with higher perceptions in both sites, while the Sky View Index reduced perception in the forest context. These findings highlight strong context dependence in perceived–measured ecosystem service relationships and underscore the importance of integrating ecological structure and visual legibility in the design and management of the studied urban green spaces in Shanghai.

As high-density cities face severe conflicts between urban expansion and limited ecological space, exploring the potential drivers of carbon sequestration within urban green spaces is crucial. The overarching aim of this study is to establish an interpretable, model-based analytical framework for assessing the Annual Carbon Fixation Rate (ACFR). To address the limitations of linear models and “black-box” machine learning algorithms, this study analyzed 149 urban parks in Nanjing by coupling Sentinel-2 high-resolution data with a Random Forest algorithm and the SHAP framework to model Annual Carbon Fixation Rate. The model achieved high predictive reliability (R² = 0.9690). This study quantitatively identified critical nonlinear thresholds: an impervious surface proportion exceeding 30% is associated with a structural decline in carbon sinks, while the optimal Leaf Area Index interval for maximum efficiency is 2.5–3.5. Moreover, the present research identified a potential model-derived ecological compensation effect; optimizing vegetation community structures under high anthropogenic disturbance showed a 23.9% higher modeled marginal ACFR response compared to low-disturbance habitats. This explainable framework attempts to translate complex model-derived biophysical associations into referenced quantitative thresholds. Importantly, the ACFR and derived thresholds represent remote sensing-based model estimates rather than field-measured ecological causality, serving as exploratory planning references. These findings challenge the assumption that greening is inefficient in degraded environments and provide concrete control indicators for targeted micro-renewal and sustainable urban regeneration.