Human societies will have to face three big challenges during the 21st century. The first is an increasing share of population living in cities [1
]; second, the loss of biodiversity due to land-use changes and urban sprawl [2
]; and third, climate change, which will lead to an increase of thermal stress for the urban population with heatwaves occurring more frequently and with longer duration. The higher the temperatures, the more the ozone concentrations are rising, which in turn is detrimental to air quality in urban areas [4
]. These threats to societies are interconnected, as the heat island effect is becoming stronger as more people live in urban areas. Increasing energy consumption, soil sealing and the high competition for space between green and grey infrastructure in densifying urban areas may lead locally to a lack of urban greening [5
] and the loss of biodiversity. Climate change will potentially lead to a decline of quality of life in cities.
Urban planners have to work on solutions that counter the upcoming problems. Urban green infrastructure such as parks, urban woodlands and street trees play a key role in mitigating those impacts. The ecosystem services concept offers an analytical lens to components of urban green, underlying ecological processes and functions, and their relevance for a sustainable relationship between nature and society. Outcomes and functions of ecosystems that maintain air and soil quality and flood, storm water and disease control are categorized as regulating ecosystem services [6
]. In particular, urban trees provide several ecosystem services:
Reducing thermal stress due to shadow casting and transpiration [7
The leaves in the canopy act as a filter for gaseous pollutants (NO2
, CO) and particulate matter (PM10
The canopy can intercept huge amounts of water during rainfall [13
] which leads to decreased storm water runoff;
sequestration through photosynthesis helps to reduce greenhouse gases from the atmosphere [15
Trees provide a habitat for different animal species groups like insects, birds and mammals [17
] and enhance biodiversity in cities.
Even though research findings about these regulating ecosystem services are already at hand, they are not yet routinely integrated in planning processes. In many cases, the preservation of urban trees for enhancing ecosystem services has relatively low priority in urban planning. Citizens and the communal administration may be more concerned about the cultivation and maintenance costs of urban trees rather than recognizing their value for the urban ecosystem and human wellbeing [19
]. Approaches for supporting planners and practitioners in planning the urban tree stock under the paradigms of multifunctionality and climate change are emerging [20
]. Exact quantifications of ecosystem services may require complex, time-consuming and expensive field surveys. At the same time, the financial and personnel resources of city administrations are limited. In most cases the costs will exceed the usage of ecosystem service assessments. Because of that, practitioners look for more economically feasible solutions to assess ecosystem services with less effort.
An opportunity to assess regulating ecosystem services with given data lies in analyzing urban tree stock data. In European countries, tree ownership implies liability for damages; hence, civil law gives incentives for safety precautions and their legally watertight documentation. Many cities keep tree cadastres to monitor tree vitality and damages to comply with road safety regulations as an administrative task, often in a Geographic Information System (GIS)-compatible format. Those cadastres are extensive databases, including information on tree species, location, height, crown width, diameter at breast height (DBH), vitality and many more variables. We argue that regulating ecosystem services can be evaluated with relatively little cost and effort by analyzing those cadastres. Tapping into this data source for urban ecosystem service analysis has therefore great practical and planning potential.
Hence, the paper has the following objectives. Firstly, we discuss and demonstrate the assessment of regulating ecosystem services of urban trees by using tree cadastre data. As modelling software, we use i-Tree Eco (v. 5) which was developed by the US Forest Service [21
]. Not all required data for an i-Tree Eco analysis is provided by the cadastre; therefore, secondly, we developed statistical and geostatistical methods for filling those data gaps. Thirdly, we present a novel approach to quantify cooling effects through the canopy layer by working with the results from i-Tree Eco analysis to calculate energy reduction, which is missing from i-Tree Eco’s output. In addition to the above, our results for the city of Duisburg in Germany add to the number of European i-Tree Eco-projects that are published so far. Examples of European i-Tree Eco-projects are Strasbourg [22
], Bozen [23
], London [24
] and Barcelona [25
]; in contrast to our study, these have not included tree cadastre data.
The results of the provided ecosystem services are similar to previous findings in comparable studies. Baró et al. [25
] reported that the urban trees in Barcelona remove less than 1% of the urban NO2
-emissions but 22% of the particulate matter emissions. In Strasbourg, 7% of the PM10
-emissions are potentially removed by urban trees, while the removal of gaseous pollutants is small [22
]. The empirical study from Langner [42
] shows that 11% of the particulate matter emissions at a roadside can be removed by a single Acer platanoides
. Dochinger [43
] measured a reduction of particulate matter pollution in a deciduous forest of around 30%. The calculated annual CO2
-sequestration is comparable with the i-Tree Eco study from Barcelona and makes a modest contribution to climate change mitigation [25
]. It can be concluded that the Duisburg street trees have a remarkable effect on particulate matter removal and in reducing energy from radiation due to shadow casting. However, for the CO2
-sequestration, the hydrological benefits and the removal of gaseous pollutants are less important.
This work shows that it is possible to assess ecosystem services with tree cadastre data. The presented method is cost-efficient, time-saving and can be reproduced with every tree cadastre which has the same or a better database. The administration can generate a report of the status of the ecosystem services provided by urban trees on a species and on a spatial level. Furthermore, ecosystem services can be observed over time periods. With that information, urban areas deficient in the ecosystem services can be identified, as shown in Figure 4
. Planning scenarios for improving the welfare function of urban green can be created and the value of trees for the citizens can be articulated. Those findings can be used as arguments that counter deforestation or sacrificing trees for preferred and competing urban land uses.
However, there are still some constraints in the method which need to be taken into account as discussed in the following paragraphs.
The ecosystem services were calculated with a model for which the fundamental research occurs mostly in the USA. Models are constructs based on specific conditions, but they cannot completely reflect the full ecological complexity. Aguaron & McPherson [44
] calculated C-storage and CO2
-sequestration for the urban trees of Sacramento with different models (among others, i-Tree Eco) and found that i-Tree Eco in comparison mostly underestimated these ecological functions. The overall variability was about 29% (38–49 t ha−1
) in C-storage and about 55% (1.8–2.8 t ha−1
) in CO2
-sequestration. Considering these results, it can be assumed that field surveys will provide results with less bias in most cases and that i-Tree Eco provides conservative estimates of ecosystem services. We caution that the results presented here may not precisely represent the real scenarios of regulating ecosystem service provision by urban trees due to a number of assumptions and simplifications that are inherent to the approach presented here and inherent to the application of the i-Tree Eco model. However, it should be kept in mind that the basis of models such as the i-Tree Eco is the use of allometric equations, which are applied by the model to the sampled urban trees to estimate biomass, leaf area and subsequent C storage. This is an internationally accepted approach that avoids destructive sampling, i.e., felling, weighing of trees on site, and determining the leaf area with a planimeter [23
]. This would allow for measuring C-storage, CO2-
sequestration, and pollutant removal. For interception assessment, bulk precipitation in open areas and throughfall could be measured with fixed rainfall collectors. Leaf area density measurements would have to be combined with thermal imagery or other surface temperature measurements to quantify the effect of shading for a larger sample of trees and sites than demonstrated in the study of Gillner et al. [7
]. This discussion underscores that inaccuracies in the estimates and error propagation in the i-Tree Eco model are to be taken into account when interpreting the results. Inaccuracies in the estimates are inevitable as some parameters are based on assumptions and simplifications rather than measurements of the circa 50,000 street trees in our sample (see Figure 1
In conclusion, the justification for using the i-Tree Eco model is the practicability: empirical surveys to measure ecosystem services for a whole city are very time-consuming and expensive and the costs will exceed the usefulness of the results in most cases for city administrations. A typical regional field survey for an i-Tree Eco assessment will cost around $
80,000 for 300 plots [44
]. The costs for measuring ecosystem services for 50,000 trees are not numerable. Therefore, it is useful to take available data, like tree cadastres, which already give a lot of information for the assessment of ecosystem services. However, it should be kept in mind that tree cadastre data typically contain only trees that must be monitored due to road-safety. The cadastre of Duisburg, for example, contains only trees at roadsides and in public places. Based on estimates in the literature, street trees comprise about 2% of the urban tree stock including urban forests. In public and private urban green areas such as parks, gardens and streets, the proportion of public trees can be as high as 60% [45
]. This shows the scope of urban climate change adaptation and mitigation which can be addressed by public tree management.
The tree cadastre data can have gaps in some necessary parameters which in this study were filled by statistical and geostatistical methods. The methods for estimating tree height and CLE have limitations. In urban areas, growth conditions can be very heterogeneous due to the substrate in the tree pit limiting space for the rhizome, the effect of drought, stress because of salt used for de-icing and many more factors which influence the growth of trees significantly. Because the growth equations were generated from urban trees in the same growth region, we assume that most impacts are considered in the equations, but they may not give the exact tree height under all urban conditions. Alternatively, tree height could also be measured by using a surface model derived from remote sensing or airborne laser scanning datasets [46
]. In that case, the user should be sure that there is no time gap between the aerial survey for the surface model and the tree inspection. Otherwise, newly planted trees cannot be found in the surface model or some trees may be cut or pruned. For practitioners, it should be noted that the growth equations for predicting tree height are calculated for urban trees in a specific climate region. Applying those equations to trees under different growth conditions, in dense tree stands or forests for example would lead to inaccuracies. For example, [46
] did not find a power relationship, but a linear relationship between the DBH and the tree height in a forest of Japanese cypress (Chamaecyparis obtusa
). Despite the high correlation coefficients and the validation that the equations give realistic results for the tree height (Table 2
), we caution that for some species the sample size is rather low (e.g., Ailanthus altissima
with 15 trees). More research is needed to increase sample size and to subsequently improve the equations for urban trees under specific growth conditions.
When using the predicting method to analyze CLE, it is crucial to document the exact stem coordinates as inaccurate coordinates will lead to errors; furthermore, the crown width must be measured correctly. To support ecosystem service assessment with tree cadastres and prevent the need for models to estimate missing data, practitioners should consider adding measurements of tree height and CLE to regular tree inspections.
So far, the assessment of cooling effects from trees due to shadow casting and transpiration cannot be calculated by i-Tree Eco. However, there are a few approaches to assess cooling effects for trees in urban sites (for example [8
]). In this study, the energy reduction in the shadow of the trees was calculated with different equations from the literature by using the cadastre and i-Tree Eco data. This approach considers both the reduction in thermal radiation due to the reduced surface temperature and the reduction in direct radiation through the canopy layer and can be a useful add-on for an i-Tree Eco analysis. For a more simple evaluation to predict energy effects through shading, the equation in Section 3.2
can be used, which shows that energy reduction increases by 0.53 kW per 1 m2
ground cover. Because the formula from [36
] is an iterative equation, energy reduction can be calculated hourly, depending on the solar angle, eccentricity and emissivity, which makes it suitable for an additional module in i-Tree Eco considering energy effects. The assessment shows that the overall energy reduction is mainly controlled by the reduction of the solar irradiance. This considers the fact that human thermal comfort is mainly achieved by the reduction of short-wave solar radiation by shading (a reduction about 850 W m−2
can occur [50
]), while the air temperature reduction due to evapotranspiration has only a little effect on the thermal load (0.5 K up to 3 K measured by [52
] on a small urban green wooded site). 80% of the cooling effect comes from shading, 20% from evapotranspiration [50
]. The cooling effect can significantly reduce the need for air-conditioning in houses [53
]. However, it should be kept in mind that the calculation of the thermal energy was based on a study carried out in Dresden (Germany) for a specific day and year during a heatwave. The thermal energy produced by the asphalt under trees may differ during the year but also with different materials and locations. Therefore, future measurements should include multiple days, locations and surface materials to improve the correlation between the LAD
and the surface temperature reduction. For the Monsi–Saeki Law, we assumed an extinction coefficient of 0.7 which is a typical value for deciduous forests. However, the coefficient may not be valid for urban trees. The measurement of the extinction coefficient under urban trees should be carried out in following studies regarding the energy reduction provided by street trees.
By using tree cadastre data, the results from ecosystem service assessments depend on the amount and accuracy of the available data. There can be spatial gaps when street trees are not monitored; results will underestimate ecosystem services if trees in parks or on private property are not included. Leaving out shrubberies and hedges means failure to account for their high pollution removal capacity. Another restriction is the time gap: as trees enlarge, the ecosystem service provision increases. A storm event or pests may damage the tree stock and it will take until the next inventory update to reflect such changes in the cadastre. However, this equally applies to remote sensing based assessments. Therefore, it is necessary to calculate these services again in different time periods. When the site and species specific growth conditions are known (annual gain in leaf area and DBH), a forecast can be made to show how the ecosystem service provision may change [8
]. To close the temporal and spatial gaps, it is possible to collect more data and develop species specific regression equations for the known trees in the cadastre and to use them for the missing trees.
Urban trees may not be the panacea to urban environmental problems, but they can provide microclimate regulation at a scale that is highly relevant for urban inhabitants: the local scale. Moreover, urban greening contributes to climate change adaptation and mitigation in urban areas. City administrations could pay more attention to these ecosystem services and need more guidance for planning and design of urban green infrastructure, including street trees. Our study points out the removal of particulate matter and the reduction of direct and thermal radiation by publicly owned and managed trees. Based on these results, further research could be directed at planning recommendations and urban climate modeling. Including the orientation of the streets into the estimation of cooling benefits could refine the results. Norton el al. showed that street trees would provide less benefit in narrow street canyons with a high degree of self-shading [54
]. Also, the exchange of air can be blocked by trees in narrow streets, leading to an accumulation of pollutants [55
]. Hence, broad streets with high solar exposure should be prioritized for tree planting. However, such recommendations for planting trees will vary with geographic location and climate of the city. We address the local scale, while urban climate modeling studies that go beyond that include the radiative heat exchange among trees and sky, building walls and the ground to simulate cooling and energy saving potentials of urban trees on the neighborhood to city scales [56
Empirical evidence on cooling effects of urban trees is usually based on observational studies of a small number of green sites or beneath trees and the results are not easily scalable to neighborhood and city scales or other geographic locations and climates [54
]. Our novel approach takes a different angle and uses a tree cadastre database on which methods are applied to fill data gaps on tree height and crown light exposure. The scale of the results is determined by the location and distribution of the modelled trees in the real urban setting. Ground validation of the results would encompass several additional observational studies and analyses that are subject to further research, yet beyond the scope of the present paper.