Landscape 05

Urban Ecosystem

The Urban Ecosystem category includes both natural and human-made spaces such as urban forests, parks and publicly accessible vegetated spaces that provide both social and environmental benefits as well as supporting biodiversity. 

The Urban Ecosystem category primarily emphasizes the availability and maintenance of vegetation within a city,  with a particular focus on tree cover as a more precise and relevant metric compared to a broader measure of greenspace. It includes two indicators: Tree Cover Per Capita and Tree Canopy Cover Loss.

Tree Cover Per Capita evaluates a population’s access to its urban forest by measuring the extent of tree cover available per person within the defined area of analysis. A variation of this indicator, Tree Cover per Capita Deficit, is also calculated, defined as the additional square meters of tree cover needed to reach the UN-Habitat’s suggested 15 square meters (m2) per capita.

The Tree Canopy Cover Loss indicator measures the total area (in km2) of urban tree loss from 2001 to 2023, using the tree cover baseline extent in 2000 as a benchmark. Tree cover loss refers to a stand-replacement disturbance, which involves a change from a forested to non-forested state, such as the removal or death of trees, regardless of the cause and inclusive of all types of tree cover, including deforestation of primary forests and harvesting of tree plantations. 1

Description

Green spaces and parks have been integral to cities since ancient times. Starting in the late 19th century, various urban planning schools began to explore and define the role and significance of these spaces within urban environments. The Garden City Movement5, founded in 1898 by Ebenezer Howard in the United Kingdom, proposed the development of limited-size cities surrounded by rural green belts, with proportionate areas for housing, industry, and agriculture. The United States’ City Beautiful Movement, developed in 1893, promotes the beautification of cities using, among other features, parks and green spaces6. The New Urbanism movement, which gained prominence in the 1990s, advocated for establishing public spaces– especially public green spaces7. The Sustainable Communities Movement, founded in the mid-1990’s, aims to bring sustainability to urban contexts and highlights the use of green spaces to address urban challenges8.

All of these movements, which aim to improve citizens’ quality of life in response to poor conditions in urban centers, include green spaces as important tools. More recently, the adoption of the SDG11 and the New Urban Agenda (NUA), which includes the explicit objective of achieving equitable access to green spaces for all citizens, demonstrate the increasing recognition of urban green spaces’ importance in making urban centers more sustainable.

Green spaces and tree cover are critical components of thriving urban environments. They provide spaces for economic, social, and environmental benefits and improve the quality of life for the city’s residents. These benefits are extensively documented in academic literature and include:

  • Regulation and reduction of air pollution.9 10.
  • Improvement of stormwater infiltration11, and reduction of surface water runoff12.
  • Regulation of the urban heat island effect13, resulting in a decrease in energy used for cooling purposes14. 
  • Conservation of wildlife refuge and habitat15.
  • Increased physical and mental health16.
  • Potential economic development17, and increased property values18in areas with improved streetscapes and proximity to urban forests
  • Community empowerment and social cohesion, both actively, through the process of greening the community, and passively, from the use of the green spaces19.

Many cities have included green spaces as a component for evaluating and managing sustainable urban centers because of an increased awareness of the benefits of urban green spaces. These initiatives use methods ranging from analyzing remote sensing imagery to administrative data of parks and tree inventories to allow decision makers to evaluate trends over time to inform citywide and district, or even communitywide strategies.

Access to green space is a key aspect of urban sustainability. A disproportionate lack of access to green spaces in relation to social factors, such as income and ethnicity, has also been highlighted as a particularly relevant aspect of environmental equity20 21. Providing public services and access to infrastructure can reduce income inequality, and has a stronger redistributive effect among specific groups at higher risk of poverty (OECD 2011)22, because low-income communities have less access to private alternatives for these services (Calderon and Server, 2014)23. Given the links between environmental health risks and poverty (UNEP, UNDP 2011)24, and the economic and social benefits associated with green space, access to inclusive and safe green spaces is one way to reduce inequalities within urban areas. Target 11.7 of Sustainable Development Goal (SDG) 11 aims to provide universal access to safe, inclusive, and accessible green and open spaces, measured through the proportion of open space–including green space–for public use in cities’ developed areas.

Given the benefits it provides, the identification and monitoring of urban vegetation has been explored through different techniques, primarily through remote sensing imagery or survey-based data25. Remote-sensed imagery has been used extensively in studies that aim to relate land use patterns with other spatial features, such as land temperature or air pollution. Survey-derived data, such as administrative inventories of green spaces, have been used in studies exploring issues around access to green spaces and the equitable distribution of green spaces in a city. Box 1: Complementary measures of greenness in urban spaces, highlights the relationship between satellite-derived measures of urban tree canopy and additional sources of information, such as administrative data about parks and street trees within a city.

While not a part of the UESI analysis, it is worth mentioning that different forms of urban green space impact the city environment and urban citizens in different ways. For example studies have shown different associations between greenspace and residents’ well-being26  27 or temperature reduction depending on the green space configuration and the type of vegetation used28 29.

As with any indicator or dataset, there are certain caveats and limitations to consider. While we have incorporated high-resolution ESA Landcover data to address some limitations of the Global Forest Watch data, the definition of city boundaries significantly impacts tree cover estimates. In some districts, areas with dense tree cover and very low population density are included, which can result in unusually high Tree Cover Per Capita values for those districts.

Box 1: Complementary measures of greenness in urban spaces

The most common approaches to classifying urban vegetation involve remote sensing or field surveys. These very different techniques have their own strengths and limitations, which may result in different but complementary pictures of a city’s greenness. In the Urban Ecosystem category, we employ remote sensing techniques. Yet, even purely from a remote sensing standpoint, there exists a range of methods that could be used to measure a city’s greenness. Different remote sensing products, as well as varying techniques for mapping tree cover, can produce a range of classification results providing different levels of accuracy and information30. 

To explore these complementarities, we compare the results of three different indicators of green space, each derived from a different method, in San Francisco, USA and Sao Paulo, Brazil. We look at park areas as designated by each city’s municipal government, tree cover data derived from the ESA WorldCover land cover map 31, and the Global Forest Change v1.11 forest extent map (Hansen)32. City park designation is  an example  of data obtained through field surveys, while the latter two are remote sensing products. The following map shows a snippet of San Francisco, with green spaces identified by ESA data (left plot) and Hansen data (right plot) and park data from the city of San Francisco (both plots) overlaid.

Figure B. Green spaces and park areas identified in Sao Paulo (left plot), against satellite imagery from Google Maps (right plot)

Figure A. Map of tree cover in San Francisco, as identified by ESA (left ) and Hansen (right), and park areas (both plots).

An initial glance highlights the differences between the three data sources. Due to their resolution, the Global Forest Watch data has missed out smaller pockets of green spaces embedded within the urban spaces of the city. On the other hand, while ESA provides data at a higher resolution, it lacks a quantitative measure of greenness such as the percentage of tree coverage in an area, which Global Forest Watch provides. Such data can be essential for assessing the quality of green spaces -  for instance, a higher percentage of canopy cover tends to be associated with greater shading benefits. Of particular importance, the official designated parks data also differs greatly from the two- there are large plots of green spaces that do not fall within parks, which can be seen in the top left and bottom right corners.

Although officially designated park inventories may overlook a significant portion of green space coverage, they offer valuable insights into the characteristics of these green spaces that remote sensing data might miss. Green spaces officially designated as a park by the city may have privileges that other green spaces do not have. This could include regular maintenance, public facilities, and most importantly, public access to these green spaces. These characteristics are crucial to assess the social benefits that these spaces provide for the general population. For example, for the city of Sao Paulo below, the ESA data shows a large area of green space, but park data from the city only encompasses a small portion of it (left plot). However, when examining the area using Google Maps satellite imagery, it's evident that the green spaces outside of designated park areas are located on private properties and are likely inaccessible to the general public (right plot).  Remote sensing green space classification methods tend to miss out such nuances of the green spaces they identify, which can lead to crucial information being left out about the characteristics and quality of such spaces.

Figure A. Map of tree cover in San Francisco, as identified by ESA (left ) and Hansen (right), and park areas (both plots).

Figure B. Green spaces and park areas identified in Sao Paulo (left plot), against satellite imagery from Google Maps (right plot)

As a result, more specific and localized inequality analyses should incorporate multiple datasets, including official park inventories from local governments whenever possible. Although the UESI integrates two distinct and complementary remote sensing products to provide a more accurate estimate of tree cover access, limitations remain, particularly in developing countries, where access to official park inventories is often restricted. This constraint hinders the further integration of such administrative records from a global perspective. 

Results 

Cities’ performance in this component show a particularly large range for both Tree Cover per capita and Tree Cover Loss, particularly at the district level, highlighting the great variability across cities and districts in the availability and maintenance of urban tree canopy. 

The Tree Canopy Cover Loss indicator across all districts reveals a mean district Tree Canopy Cover Loss of 5.29%, and a median loss of 0.53%. This result indicates a heavily skewed distribution, where most districts have a very small proportion of Tree Cover Loss, while a handful of other districts have very high values of Tree Cover Loss. Only 0.06% of the districts had lost 100% of their tree cover, while around 33% of the districts had not experienced any tree cover loss.  Figure 1 shows the neighborhood median and range for Tree Cover Loss of the top 10 and bottom 10 ranked cities in the entire Urban Ecosystem category.  

The results for the top 10 cities indicate that they all have virtually no tree cover loss, with a median value close to 0% and very narrow ranges, as seen in cities like Portland (USA) and Hannover (DEU). On the other hand, the lowest scoring cities have a wider range of tree cover loss across their neighborhoods. For example, the city of Monterrey (MEX) has a median value of 32% of tree cover loss, but most of its neighborhoods have a value between 0 and 80 % with some even close to 100%, compared to Paris (FRA), which has a median Tree Cover Loss of 10% with a very narrow range. The high variability in district Tree Cover Loss within each city can be attributed to several factors, such as the level of urbanization. In cities within developing countries, this urbanization is often increasing over time, particularly in areas surrounding the more densely populated urban cores. 

Similarly, Tree Cover per capita shows a similar distribution across all UESI districts with a mean tree cover per person of 1,173 m2 and a median of 45.89 m2, which also indicates a skewed distribution with significant outliers. In fact, about 68% of all districts within the UESI cities have amounts of tree cover per capita greater than the 15 m2 per capita target, though many cities have at least one neighborhood that has 0 m2 of Tree Cover per person. Figure 2 shows the neighborhood median and range for Tree Cover per Capita of the top 10 and bottom 10 ranked cities in the entire Urban Ecosystem category. 

The results of Tree Cover Per Capita for in the top 10 cities shows a very high range of values at the neighborhood level. For instance, Salt Lake City (USA) has a median value of 90.6 m2 per person with most districts having a value between 38 and 142 m2 per person but with a minimum of 4 m2 per person. Similarly, Vancouver (CAN) has a median value of 32.4 m2 per person with most districts having a value between 2 and 64 m2 per person with some districts having 0 m2 per person of tree cover. Conversely, the bottom 10 cities display very narrow ranges, indicating that insufficient access to tree cover is consistently experienced across most of the city, with only a few outliers.

Figure 1: Tree Cover Loss for Top 10 and Bottom 10 performing cities in the Urban Ecosystem Category. The number in the circle is median tree cover per capita for the city, while the error bars represent the median absolute deviation of Tree Cover Loss values across different neighborhoods of the city.

Figure 1: Tree Cover Per Capita for Top 10 and Bottom 10 performing cities in the Urban Ecosystem Category. The number in the circle is median tree cover per capita for the city, while the error bars represent the median absolute deviation of Tree Cover Per Capita values across different neighborhoods of the city.

Another way to look at the Tree Cover Per Capita indicator is through its inverse, Tree Cover Deficit, which is the amount of tree cover that citizens in each neighborhood are lacking to reach the 15 m2 per capita target. This analysis provides interesting insights and a different perspective of the distribution of tree cover for the before-mentioned cities. The Tree Cover Deficit calculated at the city level reveals that most cities have enough total tree cover to provide at least 15 m² of green space per resident, with only 38 cities experiencing an overall tree cover deficit. However, when examining the deficit at the district level, only 74 cities have districts where all residents have sufficient tree cover to meet the 15 m² standard. This disparity underscores the importance of spatially explicit analyses to highlight unequal neighborhood-level distributions within cities that may score well on average. 

Examining the city level performance scores for both indicators, we can observe some insights from a regional perspective (Figure 2). Concerning Tree Cover Loss scores, Sub-Saharan Africa and South Asia demonstrate some of the best performance, characterized by high median scores and narrower interquartile ranges, though there are some medium-performance outliers The remaining regions exhibit very large ranges, with East Asia & Pacific containing some of the poorest performers, such as Vientiane (LAO) and Shenzhen (CHN). Although this region has the widest range, others, including Latin America and Europe & Central Asia, closely follow, with slightly higher median scores and narrower ranges

Figure 2: Tree Cover Per Capita and Tree Cover loss Scores by regions.

Regarding  Tree Cover Per Capita, we can clearly see cities in Europe and North America are some of the best performers, considering their high median score and relatively short range, followed by cities in East Asia & Pacific. Latin America &  Caribbean is a mixed region, having an overall high median score but also including some of the lowest performers on those indicators such as Lima (PE). Finally, South Asia and Sub-Saharan Africa are the lowest performers with a median score between 60 and 70 but with some of the worst scoring cities such as Nouakchott (MRT), Kabul (AFG), Kinshasa (COD) among others. 

Overall, the performance results of the Urban Ecosystem component do not indicate any particular region as the top performer. However, due to their unique characteristics, some cities in North America and Europe emerge as high scorers in the Urban Ecosystem component—not because of exceptional performance in both indicators, but rather due to a balanced, median performance across them. This outcome further emphasizes that no city is currently achieving both sufficient access to tree cover for its citizens and optimal maintenance of its urban forest. Instead, these two indicators often do not align, as exemplified by Vientiane (LAO), which has the highest tree cover per capita at over 300 m² but also the lowest tree cover loss score, having lost over 40% of its original tree cover in the past 20 years . 

Tree cover and equity 

Using the approach detailed in the Equity and Social Inclusion Chapter, we performed an analysis comparing distributional equity of both income and the Tree Cover per Capita indicator. The quadrant plot in Figure 3 presents the UESI’s proposed typology that categorizes the relationship between environmental inequality and income inequality, using the Environmental Concentration Index (ECI) and the Gini Coefficients, respectively. 

When assessing the equity of Tree Cover per Capita distribution across neighborhoods, the quadrant plot reveals that most UESI cities (164 cities) are positioned on the left side of the plot, indicating that access to tree cover in these cities disproportionately disadvantages less affluent citizens These cities are spread across all regions, indicating that the inequitable distribution of tree cover is pervasive, regardless of geographical location or level of economic development.  On the other hand, for the 104 cities located in the right side of the quadrant plot, wealthier citizens have the least access to greenspaces within their neighborhoods.

A more stark conditions is present in the 41 cities in the bottom left quadrant, located primarily in North America, and to a less degree Latin America & Caribbean and Europe, where there is both an inequitable distribution in tree cover as well as high income inequality, creating negative conditions where citizens with less economic resources have less access to vegetated spaces and the social and environmental benefits they provide, reducing their adaptive capacity to other negative environmental pressures like urban heat or air pollution. Cities in this group include Salt Lake City (USA), Portland (USA), Madrid (ESP) and Bogota (COL).

Finally, we can see that performance is not associated with equity, since both the top and bottom-performing 10 cities are distributed primarily across the left quadrants. Some notable cases include Monterrey (MEX), Salt Lake City (USA), Detroit (USA), Vancouver (CAN) and Paris (FRA), illustrating that even cities with exemplary performance in certain environmental outcomes must explicitly analyze equity in distribution. 

Figure 3. Tree Cover per Capita Equity typology plot. The plot defines four quadrants from the Income Gini and Tree Cover Per Capita ECI. The Income Gini Values represent the distribution of wealth across the population and range from 0 to 1. A Gini value of zero indicates a perfectly equal distribution of income across the population, while a high Gini value suggests a highly unequal distribution of wealth. The ECI measures the variation of Tree Cover per Capita in response to income. Negative ECI values indicate that the environmental benefit is allocated on the wealthier citizens, while a positive ECI indicates that the environmental benefit is allocated on the poorest citizens. See the Equity and Social Inclusion Profile for a more detailed description of this plot.

Figure 4 presents a more detailed view of the distribution of Tree Cover Per Capita across a selection of cities, based on the construction of Environmental Concentration and Income Lorenz curves. The position of the curves relative to a 45 degree line, which represent a scenario of perfect equity, provides information about the segments of the population where the environmental outcome is unequally allocated. For example, the position of the Concentration curve below the 45 degree line for cities such as Johannesburg and Singapore indicates that there is more tree cover per capita allocated to those with more income. On the contrary, the position of the curve above the 45 degree line in cities like Sao Paulo indicates that there is more tree cover per capita available for those with lower income. 

Although the Concentration Index provides an overall summary of the distribution, examining the curve can reveal specific population segments that may have disproportionately less access to Tree Cover, even if the overall index suggests low inequity. Thus, an analysis of the Environmental Concentration curves and the data used for their construction can allow the decision makers to have a more comprehensive picture of the specific distribution of their cities, both in terms of the allocation and the intensity of the environmental outcomes, as well as its relation with income to develop useful interventions to address these issues.

Figure 4: Tree Cover per Capita and income distribution curves for selected UESI cities. These plots show the distributions of Tree Cover per Capita (the concentration curve in blue) and income (the Lorenz curve in red) throughout city neighborhoods. Deviations from the dotted line (the line of perfect equity) illustrate cities that are less equitable in their distribution of Tree Cover per Capita. Concentration curves above the line of equity indicate the environmental benefit is more heavily allocated to those with less income; concentration curves below the line of equity indicate that the environmental benefit is more heavily allocated to those with greater income. See the Equity and Social Inclusion Profile for a more detailed description of this plot and the Cities Page for a full exploration of all cities’ environmental and income distribution curves.

Box 2. Biophilic Urbanism in Singapore

Singapore is a highly urbanized, population dense and land-scarce city state. Yet, driven by targeted state policies and biophilic urbanism, a planning paradigm that seeks to bring nature into the city, Singapore has successfully embedded its urban cityscape with natural systems and greenery to create a highly successful verdant city33 34.

Today, Singapore’s urban greening is driven by the Singapore Green Plan, which aims to e transform Singapore into a “City in Nature.” This plan is driven by key strategies to improve greenery in urban spaces and mitigate increased human-wildlife conflicts that come with more natural spaces. Strategies include increasing nature park space on the island, intensifying nature in existing natural parks, and enhancing animal management in the city35.

One notable strategy aims to restore nature in Singapore’s urban areas, which includes tree planting efforts and the encouragement of skyrise greenery. The latter is driven by programs such as Urban Redevelopment Authority’s Landscaping for Urban Spaces and High-Rises (LUSH) program, which launched in 2009 to encourage the integration of greenery into building construction projects36. Now in its third iteration, LUSH provides incentives in the form of subsidies for developers to integrate features such as rooftop urban farming, green walls, landscape decks and others in their building developments37. Additionally, certain areas of Singapore are subject to a mandated Landscape Replacement Policy, which requires developers replace greenery lost from their developments by constructing high rise greenery in these developments38. Singapore aims to have developed 200ha of high-rise greenery by 203039.

Figure C: Parkroyal collection Pickering, one of Singapore's most famous examples of biophilia

Figure C: Parkroyal collection Pickering, one of Singapore's most famous examples of biophilia

Another notable strategy seeks to connect green spaces in Singapore by creating a network of ecological corridors within the city to facilitate the movement of wildlife. Running along Singapore’s roads, these ecological corridors are green spaces planted with specific trees and shrubs aiming to replicate the natural structure of forests, which allows animals like birds or butterflies to move along them41. These strategies have allowed the integration of greenery into every facet of the city’s urban landscape, allowing Singapore to overcome land use limitations to create a greener city.

Footnotes

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  2. Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020 v100. (doi:10.5281/zenodo.5571936)

  3. Global Forest Change 2000–2016 v1.4. Retrieved from: https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.4.html

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  31. Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P., Ramoino, F., Arino, O., 2022. ESA WorldCover 10 m 2021 v200. (doi:10.5281/zenodo.7254221)

  32. Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. "High-Resolution Global Maps of 21st-Century Forest Cover Change." Science 342 (15 November): 850-53. [10.1126/science.1244693](https://doi.org/10.1126/science.1244693) Data available on-line at: [https://glad.earthengine.app/view/global-forest-change](https://glad.earthengine.app/view/global-forest-change).

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