Measuring urban tree loss dynamics across residential landscapes

Multi-Scalar Drivers of Residential Vegetation Changes in Metropolitan Phoenix, Arizona

In the arid southwestern U.S., urban greening strategies have been promoted to alleviate ecosystem disservices associated with lawns, including the adoption of xeric yards with desert-adapted floras and gravel groundcover and wildlife-friendly yards with complex vegetation structure and composition. Scant studies have investigated the extent of different vegetation changes in urban greening practices and the complexity of associated human drivers. We addressed this gap by analyzing survey data from two survey periods (2017 and 2021) to answer the following questions: to what extent have residents from metropolitan Phoenix made different vegetation changes in their yards over the last decade, and how do multi-scalar human drivers affect different vegetation changes? We found a sustainable trajectory for residential vegetation changes in Phoenix since mid-2010s, with declining additions of grass and increases in trees and desert plants across residential neighborhoods. Esthetics was an influential driver of both tree planting and native gardening. Additionally, tree planting was associated with anthropocentric values (i.e., low-maintenance needs), while desert plant additions reflected the appreciation of nature (i.e., attitudes towards the desert) and environmental concerns (i.e., supporting wildlife). Institutions such as local government programs might shape residents' vegetation choices, as tree planting differed among municipalities. We also found counterintuitive influences of residential tenure controls on landscaping decisions. Specifically, renters were more likely to add yard trees compared to homeowners. Our results inform landscape sustainability by identifying potential pathways to residential yard changes that offer a multitude of services while being appreciated and maintained by residents.

Integration of Aerobiological Information for Construction Engineering Based on LiDAR and BIM

In green urban areas, the allergenic factor is important when selecting trees to improve the quality of life of the population. An application of laser imaging detection and ranging (LiDAR) in building information modelling (BIM) is the capture of geo-referenced geometric information of the environment. This study presents the process of digitalisation of a green infrastructure inventory based on the geolocation and bioparameters of the cypress species. The aerobiological index (IUGZA) was estimated by developing green infrastructure BIM models at different detail levels and with a new BIM dimension (6D) for the urban environment. The novelty of the study is the modelling of urban information for evaluating the potential environmental impact related to the allergenicity of the urban green infrastructure using LiDAR through BIM. The measurements of cypress trees based on bioparameters and distances were applied to the IUGZA. This innovation for describing the current 3D environments and designing new scenarios in 6D may prevent future problems in urban areas during construction projects.

Positive relationships among aboveground biomass, tree species diversity, and urban greening management in tropical coastal city of Haikou

Within urban green spaces, tree species diversity is believed to correlate with aboveground biomass, though there is some disagreement within the literature on the strength and directionality of the relationship. Therefore, we assessed the relationship between the biodiversity of woody species and the aboveground biomass of woody plant species in the tropical, coastal city of Haikou in southern China. To accomplish this, we obtained comprehensive tree and site data through field sampling of 190 urban functional units (UFUs, or work units) corresponding to six types of land uses governmental-institutional, industrial-commercial, park-recreational, residential, transport infrastructure, and undeveloped area. Based on our field data, we investigated the relationship between tree species diversity and aboveground biomass using multiple regression, which revealed significant relationships across all five types of land uses. Aboveground biomass in green spaces was also correlated with anthropogenic factors, especially time since urban development, or site age, annual maintenance frequency by human caretakers, and human population density. Among these factors, maintenance is the strongest predictor of aboveground biomass in urban green space. Therefore, this study highlights the critical role of maintenance of urban green space in promoting both aboveground biomass and woody biodiversity in urban ecosystems and, consequently, on urban ecosystem services. Our findings contribute to a deeper understanding of the ecosystem services provided by communities of woody plant species in urban areas.

The Benefits and Limits of Urban Tree Planting for Environmental and Human Health

Many of the world’s major cities have implemented tree planting programs based on assumed environmental and social benefits of urban forests. Recent studies have increasingly tested these assumptions and provide empirical evidence for the contributions of tree planting programs, as well as their feasibility and limits, for solving or mitigating urban environmental and social issues. We propose that current evidence supports local cooling, stormwater absorption, and health benefits of urban trees for local residents. However, the potential for urban trees to appreciably mitigate greenhouse gas emissions and air pollution over a wide array of sites and environmental conditions is limited. Consequently, urban trees appear to be more promising for climate and pollution adaptation strategies than mitigation strategies. In large part, this is due to space constraints limiting the extent of urban tree canopies relative to the current magnitude of emissions. The most promising environmental and health impacts of urban trees are those that can be realized with well-stewarded tree planting and localized design interventions at site to municipal scales. Tree planting at these scales has documented benefits on local climate and health, which can be maximized through targeted site design followed by monitoring, adaptive management, and studies of long-term eco-evolutionary dynamics.

Valuing the Role of Time in Urban Ecology

Cities and towns are complex ecosystems with features that can vary dramatically in space and time. Our knowledge of the spatial structure of urban land and ecological systems is expanding. These systems have been investigated across spatial scales, urban to rural gradients, networks of urban macrosystems, and global megalopolises. However, the temporal dimensions of urban ecosystems – such as those related to ecological cycles and historical legacies – are far less understood and investigated. Here, we outline the main dimensions of time that can shape how events in urban ecosystems unfold, which we categorize as: (i) time flows and duration, (ii) synchrony, lags, and delays, (iii) trends and transitions, (iv) cycles and hysteresis, (v) legacies and priming, (vi) temporal hotspots and hot moments, and (vii) stochastic vs. deterministic processes affecting our ability to forecast the future of cities and the species that live in them. First, we demonstrate the roles of these understudied dimensions by discussing exemplary studies. We then propose key future research directions for investigating how processes over time may regulate the structure and functioning of urban land and biodiversity, as well as its effects on and implications for urban ecology. Our analysis and conceptual framework highlights that several temporal dimensions of urban ecosystems – like those related to temporal hotspots/moments and stochastic vs. deterministic processes – are understudied. This offers important research opportunities to further urban ecology and a comprehensive research agenda valuing the “Urban Chronos” – the change of urban ecosystems through time.

Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

Developing a composite indicator to prioritize tree planting and protection locations

Trees provide numerous ecosystem services to benefit human health, and many cities have launched tree planting and management programs to increase tree populations and optimize tree locations through diverse tree priority schemes. Existing tree priority schemes are typically local-specific, expert-driven, and tree-planting-focused. In this study, a framework that captures interactions among the environment, tree and human demographic information is built. This framework provides a composite indicator, namely a tree priority planting or priority protection index (PPI), that can be integrated within a decision support system such as i-Tree Landscape to provide nationally consistent and locally relevant ways to strategically optimize tree planting and management locations across the entire United States. Three scenarios with the human health concerns are tested in a case study of New York City. The analyses are conducted at the census block group scale that is the finest-level scale available at i-Tree Landscape. The resulting PPI maps are analyzed using spatial statistics and compared against each other to investigate the impacts of alternative investments of limited public resources. The results show that: (1) tree priority patterns change greatly with alternative objectives; (2) adding more indicators to build PPIs lead to more diverse tree priority patterns as high (or low) values of different indicators are often not geographically coincident; (3) incorporating more indicators may not necessarily provide more useful information because the influences of individual indicators may be reduced and diluted by a higher level of aggregation; and (4) disaggregating PPIs may reveal corresponding contributions of individual indicators. Applying the proposed framework to build PPIs has important implications for tree priority effort, scientific exploration, education, and public engagement.

Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway

The predicted extreme temperatures of global warming are magnified in cities due to the urban heat island effect. Even if the target for average temperature increase in the Paris Climate Agreement is met, temperatures during the hottest month in a northern city like Oslo are predicted to rise by over 5 °C by 2050. We hypothesised that heat-related diagnoses for heat-sensitive citizens (75+) in Oslo are correlated to monthly air temperatures, and that green infrastructure such as tree canopy cover reduces extreme land surface temperatures and thus reduces health risk from heat exposure. Monthly air temperatures were significantly correlated to the number of skin-related diagnoses at the city level, but were unrelated to diagnoses under circulatory, nervous system, or general categories. Satellite-derived spatially-explicit measures revealed that on one of the hottest days during the summer of 2018, landscape units composed of paved, midrise or lowrise buildings gave off the most heat (39 °C), whereas units composed of complete tree canopy cover, or mixed (i.e. tree and grass) vegetation maintained temperatures of between 29 and 32 °C. Land surface temperatures were negatively correlated to tree canopy cover (R² = 0.45) and vegetation greenness (R² = 0.41). In a scenario in which each city tree was replaced by the most common non-tree cover in its neighbourhood, the area of Oslo exceeding a 30 °C health risk threshold during the summer would increase from 23 to 29%. Combining modelling results with population at risk at census tract level, we estimated that each tree in the city currently mitigates additional heat exposure of one heat-sensitive person by one day. Our results indicate that maintaining and restoring tree cover provides an ecosystem service of urban heat reduction. Our findings have particular relevance for health benefit estimation in urban ecosystem accounting and municipal policy decisions regarding ecosystem-based climate adaptation.

Current and future biomass carbon uptake in Boston's urban forest

Ecosystem services provided by urban forests are increasingly included in municipal-level responses to climate change. However, the ecosystem functions that generate these services, such as biomass carbon (C) uptake, can differ substantially from nearby rural forest. In particular, the scaled effect of canopy spatial configuration on tree growth in cities is uncertain, as is the scope for medium-term policy intervention. This study integrates high spatial resolution data on tree canopy and biomass in the city of Boston, Massachusetts, with local measurements of tree growth rates to estimate the magnitude and distribution of annual biomass C uptake. We further project C uptake, biomass, and canopy cover change to 2040 under alternative policy scenarios affecting the planting and preservation of urban trees. Our analysis shows that 85% of tree canopy area was within 10 m of an edge, indicating essentially open growing conditions. Using growth models accounting for canopy edge effects and growth context, Boston's current biomass C uptake may be approximately double (median 10.9 GgC yr^-1, 0.5 MgC ha^-1 yr^-1) the estimates based on rural forest growth, much of it occurring in high-density residential areas. Total annual C uptake to long-term biomass storage was equivalent to <1% of estimated annual fossil CO₂ emissions for the city. In built-up areas, reducing mortality in larger trees resulted in the highest predicted increase in canopy cover (+25%) and biomass C stocks (236 GgC) by 2040, while planting trees in available road margins resulted in the greatest predicted annual C uptake (7.1 GgC yr^-1). This study highlights the importance of accounting for the altered ecosystem structure and function in urban areas in evaluating ecosystem services. Effective municipal climate responses should consider the substantial fraction of total services performed by trees in developed areas, which may produce strong but localized atmospheric C sinks.

Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions

The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integrating remote sensing into HEI research have been and discuss emerging themes, challenges, and opportunities. Despite advances in applying remote sensing to understanding ever more complex HEI fields such as land use/land cover change and landscape degradation, agricultural dynamics, urban geography and ecology, natural hazards, water resources, epidemiology, or paleo HEIs, challenges remain in acquiring and leveraging accurately georeferenced social data and establishing transferable protocols for data integration. However, recent advances in micro-satellite, unmanned aerial systems (UASs), and sensor technology are opening new avenues of integration of remotely sensed data into HEI research at scales relevant for decision-making purposes that simultaneously catalyze developments in HDGC research. Emerging or underutilized methodologies and technologies such as thermal sensing, digital soil mapping, citizen science, UASs, cloud computing, mobile mapping, or the use of “humans as sensors” will continue to enhance the relevance of HEI research in achieving sustainable development goals and driving the science of HDGC further.

Substantial declines in urban tree habitat predicted under climate change

Globally, local governments are increasing investment in urban greening projects. However, there is little consideration of whether the species being planted will be resilient to climate change. We assessed the distribution of climatically suitable habitat, now and in the future, for 176 tree species native to Australia, commonly planted across Australia's Significant Urban Areas (SUAs) and currently grown by commercial nurseries. Species' occurrence records were obtained from inventories and herbaria, globally and across Australia, and combined with baseline climate data (WorldClim, 1960-1990) and six climate scenarios for 2030 and 2070 using climatic suitability models (CSMs). CSMs for each species were calibrated and projected onto baseline and future scenarios. We calculated changes in the size of climatically suitable habitat for each species across each SUA, and identified urban areas that are likely to have suitable climate for either fewer or more of our study species under future climate. By 2070, climatically suitable habitat in SUAs is predicted to decline for 73% of species assessed. For 18% of these species, climatically suitable area is predicted to be more than halved, relative to their baseline extent. Generally, for urban areas in cooler regions, climatically suitable habitat is predicted to increase. By contrast, for urban areas in warmer regions, a greater proportion of tree species may lose climatically suitable habitat. Our results highlight changing patterns of urban climatic space for commonly planted species, suggesting that local governments and the horticultural industry should take a proactive approach to identify new climate-ready species for urban plantings.

City-wide canopy cover decline due to residential property redevelopment in Christchurch, New Zealand

Urban redevelopment influences urban forests, with consequences for ecosystem service provision. Better understanding the effect of redevelopment on trees in cities can improve management and inform policy, thus having positive effects on ecosystem service provision and human wellbeing. This study quantified the effect of residential property redevelopment on canopy cover change in Christchurch, New Zealand. By applying an object-based image analysis (OBIA) technique to aerial imagery and LiDAR data, this study delineated tree canopy cover city-wide in 2011 and again in 2015 and then spatially quantified changes in city-wide canopy cover between 2011 and 2015. Changes in tree canopy cover were also determined at a finer scale, that of the meshblock, a geographic boundary used for census purposes. The results show a small absolute magnitude of city-wide tree canopy cover decline, from 10.84% to 10.28% between 2011 and 2015, but a statistically significant decrease in meshblock-scale mean tree canopy cover. Tree canopy cover losses were more likely to occur in meshblocks containing properties that underwent redevelopment, but the loss was insensitive to the density of redeveloped properties within meshblocks. These findings show that property redevelopment is an important influencer of urban forest dynamics.

Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea

Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal airborne light detection and ranging (LiDAR) datasets enable us to quantify the vertical and lateral growth of trees across a landscape scale. The goal of this study is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, we generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests.

Remote Sensing in Urban Forestry: Recent Applications and Future Directions

Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions.

Live fast, die young: Accelerated growth, mortality, and turnover in street trees

Municipalities are embracing greening initiatives as a key strategy for improving urban sustainability and combatting the environmental impacts of expansive urbanization. Many greening initiatives include goals to increase urban canopy cover through tree planting, however, our understanding of street tree ecosystem dynamics is limited and our understanding of vegetation structure and function based on intact, rural forests does not apply well to urban ecosystems. In this study, we estimate size-specific growth, mortality, and planting rates in trees under municipal control, use a box model to forecast short-term changes in street tree aboveground carbon pools under several planting and management scenarios, and compare our findings to rural, forested systems. We find accelerated rates of carbon cycling in street trees with mean diameter growth rates nearly four times faster in Boston, MA, USA (0.78 ± 0.02 cm yr-1) than in rural forest stands of MA (0.21 ± 0.02 cm yr-1) and mean mortality rates more than double rural forested rates (3.06 ± 0.25% yr-1 in street trees; 1.41 ± 0.04% yr-1 in rural trees). Despite the enhanced growth of urban trees, high mortality losses result in a net loss of street tree carbon storage over time (-0.15 ± 0.09 Mg C ha-1 yr-1). Planting initiatives alone may not be sufficient to maintain or enhance canopy cover and biomass due to the unique demographics of urban ecosystems. Initiatives to aid in the establishment and preservation of tree health are central for increasing street tree canopy cover and maintaining/increasing carbon storage in vegetation. Strategic combinations of planting and maintenance will maximize the viability of greening initiatives as an effective climate mitigation tool.

Can Field Crews Telecommute? Varied Data Quality from Citizen Science Tree Inventories Conducted Using Street-Level Imagery

Street tree inventories are a critical component of urban forest management. However, inventories conducted in the field by trained professionals are expensive and time-consuming. Inventories relying on citizen scientists or virtual surveys conducted remotely using street-level photographs may greatly reduce the costs of street tree inventories, but there are fundamental uncertainties regarding the level of data quality that can be expected from these emerging approaches to data collection. We asked 16 volunteers to inventory street trees in suburban Chicago using Google Street ViewTM imagery, and we assessed data quality by comparing their virtual survey data to field data from the same locations. We also compared virtual survey data quality according to self-rated expertise by measuring agreement within expert, intermediate, and novice analyst groups. Analyst agreement was very good for the number of trees on each street segment, and agreement was markedly lower for tree diameter class and tree identification at the genus and species levels, respectively. Interrater agreement varied by expertise, such that experts agreed with one another more often than novices for all four variables assessed. Compared to the field data, we observed substantial variability in analyst performance for diameter class estimation and tree identification, and some intermediate analysts performed as well as experts. Our findings suggest that virtual surveys may be useful for documenting the locations of street trees within a city more efficiently than field crews and with a high level of accuracy. However, tree diameter and species identification data were less reliable across all expertise groups, and especially novice analysts. Based on this analysis, virtual street tree inventories are best suited to collecting very basic information such as tree locations, or updating existing inventories to determine where trees have been planted or removed. We conclude with evidence-based recommendations for effective implementation of this type of approach.

Climate differentiates forest structure across a residential macrosystem

The extent of urban ecological homogenization depends on how humans build, inhabit, and manage cities. Morphological and socio-economic facets of neighborhoods can drive the homogenization of urban forest cover, thus affecting ecological and hydrological processes, and ecosystem services. Recent evidence, however, suggests that the same biophysical drivers differentiating composition and structure of natural forests can further counteract the homogenization of urban forests. We hypothesize that climate can differentiate forest structure across residential macrosystems at regional-to-continental spatial scales. To test this hypothesis, forest structure (tree and shrub cover and volume) was measured using LiDAR data and multispectral imagery across a residential macrosystem composed 1.4 million residential parcels contained in 9 cities and 1503 neighborhoods. Cities were selected along an evapotranspiration (ET) gradient in the conterminous United States, ranging from the colder continental climate of Fargo, North Dakota (ET = 464.43 mm) to the hotter subtropical climate of Tallahassee, Florida (ET = 1000.47 mm). The relative effects of climate, urban morphology, and socio-economic variables on residential forest structure were assessed by using generalized linear models. Climate differentiated forest structure of the residential macrosystem as hypothesized. Average forest cover doubled along the ET gradient (0.39-0.78 m² m^-2), whereas average forest volume had a threefold increase (2.50-8.12 m³ m^-2). Forest volume across neighborhoods increased exponentially with forest cover. Urban morphology had a greater effect in homogenizing forest structure on residential parcels compared to socio-economics. Climate and urban morphology variables best predicted residential forest structure, whereas socio-economic variables had the lowest predictive power. Results indicate that climate can differentiate forest structure across residential macrosystems and may counteract the homogenizing effects of urban morphology and socio-economic drivers at city-wide scales. This resonates with recent empirical work suggesting the existence of complex multi-scalar mechanisms that regulate ecological homogenization and ecosystem convergence among cities. The study initiates high-resolution assessments of forest structure across entire urban macrosystems and breaks new ground for research on the ecological and hydrological significance of urban vegetation at subcontinental scale.

Spatiotemporal variation characteristics of green space ecosystem service value at urban fringes: A case study on Ganjingzi District in Dalian, China

In this study, a green space classification system for urban fringes was established based on multisource land use data from Ganjingzi District, China (2000-2015). The purpose of this study was to explore the spatiotemporal variation of green space landscapes and ecosystem service values (ESV). During 2006-2015, as urbanization advanced rapidly, the green space area decreased significantly (359.57 to 213.46 km²), the ESV decreased from 397.42 to 124.93 million yuan, and the dynamic degrees of ESV variation were always <0. The green space large plaque index and class area both declined and the number of plaques and plaque density increased, indicating green space landscape fragmentation. The dynamic degrees of ESV variation in western and northern regions (with relatively intensive green space distributions) were higher than in the east. The ESV for closed forestland and sparse woodland had the highest functional values of ecological regulation and support, whereas dry land and irrigated cropland provided the highest functional values of production supply. The findings of this study are expected to provide support for better construction practices in Dalian and for the improvement of the ecological environment.

The Provision of Urban Ecosystem Services Throughout the Private-Social-Public Domain: A Conceptual Framework

As cities are largely private systems, recent investigations have assessed the provision of ecosystem services from the private realm. However, these assessments are largely based on the concept of ownership and fail to capture the complexity of service provision mediated by interactions between people and ecological structures. In fact, people interact with ecological structures in their role of land tenants and stewards, further modulating the provision of ecosystem services. We devise a theoretical framework based on the concepts of ownership, tenancy, and stewardship, in which people, as mediators of ecosystem services, regulate the provision of services throughout the private-social-public domain. We survey relevant literature describing these dimensions and propose a comprehensive framework focused on the private-social-public domain. Our framework can advance ecosystem service research and enhance the provision of ecosystems services. The inclusion of people's individual, social and public roles in the mediation of ecosystem services could improve how benefits are planned for, prioritized, and optimized across cities.