Urbanization is increasing on a global scale, with more than half of the world′s population presently living in cities, with expectations for cities to reach two-thirds of the world population by 2050 [1
]. This shift is driven by positive factors like economic opportunities and higher levels of innovation and technology [2
]. That said, in most cases, urban development and population concentration do not follow sustainability criteria, resulting in growing health risks [3
]. Additionally, energy-intensive systems significantly contribute to global carbon emissions, ecosystem degradation, and biodiversity loss on a global scale [5
]. It is well-known that urban ecosystems are heterotrophic ecosystems depending on natural capital and provisions from the ecosystem services (ES) of extra-urban areas [7
]. Conversely, the role of ecosystem services within the boundaries of the urban areas is still open to investigation to clarify many aspects of uncertainty and to identify the most representative ecological functions that describe meaningful ecosystem services for different purposes [10
]. Among the different ES, regulatory and cultural services are the most important for scientific and policy initiatives related to green infrastructure in urban areas [15
Urban green infrastructure (UGI), and trees in particular, play an essential role in the sustainable functioning of urban ecosystems and provide regulating and habitat ES such as carbon sequestration, microclimate formation, pollution and dust reduction in atmospheric air, water balance control, wildlife habitat, and wind and noise reduction [11
]. The magnitude of the ES provided depends on the characteristics of UGI, such as vegetation type, age, structure, and management practices, which are important for comparison with natural ecosystems [10
]. For the ES assessment the importance of developing appropriate indicators has been recognized [16
] and many ES indicators have been developed, applied, tested, and reviewed [11
]. ES indicators need to be relevant to a specific purpose (e.g., to reflect difference in land management—[19
]) or components (e.g., soils—[23
]) or spatial–temporal scale [25
] to avoid uncertainties from that side, but at the same time ES indicators should inform decision-making [20
]. It is clear for decision-makers that you cannot manage what you do not measure, thus these indicators should be linked to measurable policy targets and should help to monitor policy progress.
Although the concept of “ecosystem service” has made it much easier for citizens, policymakers, and urban planners to understand the advantages offered by green urban infrastructure, several limits to the operational definition and quantification of such entities still remain [24
]. This issue is particularly true for regulatory services for which it is necessary to identify and quantify a functional relationship between specific features of green infrastructure and environmental variables increasing human well-being and sustainability. This requires optimal technical solutions which are “user friendly,” meaning that they can be offered to non-scientific operators and municipal decision-makers, in the form of continuous and real-time data which can be collected and managed with minimal effort by the users. An additional relevant aspect of the economic feasibility of large-scale monitoring systems is the wide distribution of measuring stations that might be necessary to cover the complex spatial variability of the city [14
The fast-growing fields of information and communication technologies (ICTs) and IoT tools provide new ways to wire nature into “smart” monitoring systems. Smart technologies have been used in many environmental management efforts, such as mapping changes in vegetation composition and structure [30
], managing forest regeneration with precision farming [31
], running and regulating at-distance greenhouse systems with wireless sensor networks [32
], and monitoring urban noise pollution with acoustic sensors [34
This work presents the results of a pilot study which was conducted in a green area situated in the center of Moscow using a network of wireless, low cost, and multiparameter monitoring devices TreeTalkers (TT+) [36
], to monitor single-tree ecophysiological parameters. This study aimed at testing the feasibility of the proposed technological solution to provide real-time monitoring of regulatory ecosystem services, reported in the form of meaningful indicators for potential end-users and relevant for both human health and environmental policy targets. This is particularly relevant since the New Moscow Development Project, adopted in 2012, is continuing to expand UGI as result of active urbanization on an area of more than 1500 km2
, the impact of which on soils and ecosystems services is already visible [37
]. As such, its monitoring and operational management is greatly required.
According to our findings we can summarize ecosystem-services estimation for an individual tree as averaging 8.61 ± 1.25 kg (± standard error) of carbon stored, 137 ± 49 mm of water transpired, 2167 ± 181 kWh spent for microclimate regulation, and 5309 ± 808 g of PM10
adsorbed per the investigated period (July–November 2019). These numbers could be easily transformed to monetary values with the use of local prices for each of these services [110
]. For Moscow, it would amount to about $
150 per tree during the study period, mostly due to energy and particulate adsorption.
There are several approaches to provide ES information for the green infrastructure in urban areas. However, most of the inventory approaches, even when based on high resolution imaging, are limited by the temporal resolution which sometimes is important for detecting an early onset of ES decline. Our results show that an IoT tree-level network, using individual tree physiology sensing devices, such as TreeTalker+, or other similar devices, can be used in principle for monitoring urban green infrastructure ES in real-time. Furthermore, for some of the ES indicators, (e.g., water and cooling effects), they are most often based on models with indirectly-derived parameters [111
]. Having real-time and individual tree data can improve our predictions and urban green infrastructure planning. There are several advantages for increasing the granularity of ES monitoring, particularly in that individual trees can be managed with a greater accuracy. The cost of monitoring is therefore critical for IoT expansion in green infrastructure monitoring. In recent years technological development and low cost microprocessors, traditionally used in automation and industry processes (Industry 4.0), are creating new opportunities for their expansion in environmental monitoring, which we could define as a Nature 4.0 transformation [36
]. Typically, the total cost of a cluster of 20 trees is about 6000 euros, including the gateway, which corresponds to about 300 euros per tree. A single gateway can host up to 48 trees and the cost could decline with increasing monitored trees.
However, there are limitations and improvements to be considered in future work. First of all, the power consumption of the TreeTalker+ devices, used in the current work, is still a big limitation. Batteries need to be replaced every 1–1.5 months which requires a considerable human resource input. New batteries are being developed with much larger capacity that in principle could extend the battery life duration. In terms of improvement, a new IR sensor for distance sensing of canopy temperature could be very useful for improving the energy balance estimation and cooling effects. In particular, the installation of an anemometer will provide additional data on wind speed in the canopy, which influences the delivery of several ES. In addition, simple PM 2.5–10 optical devices can be included in the processor platform to get useful data on air quality using trees as monitoring stations. Further studies need to be conducted, but a noise sensor and microphone could also be included with the aim to provide useful information on the noise pollution and “soundscape” quality generated by trees in parks [35
] and also to evaluate associated biodiversity with the help of recorded bird songs [34
]. Nevertheless, the technical development of sensors along with people engagement to citizen science will be inevitable [32
], thus it will be important to adapt them to the task of monitoring those parameters that are important for urban planning decisions [116
Among the indicators presented in the article, perhaps not all of them can be directly used for practical purposes. For example, wind speed as well as air temperature and humidity under the canopy of city trees can be presented “as is” for the general public. However, in order to provide clear information on the quality of the urban environment associated with green infrastructure for public consumption, specific scales of air quality, microclimate comfort, and noise pollution levels should be developed. On the other hand, for spatial planning tasks, it will be useful to create an urban tree database on annual or seasonal indicators of ecosystem services provided by tree species at their specific age, height, and condition. This could be very useful for operational management of urban green infrastructures. [117
]. In addition, it is also necessary to take into account disservices associated with urban trees such weakened and diseased trees falling on cars, infrastructure and buildings, and the allergic reaction of people to tree pollen [118
]. These parameters should also be continuously monitored and reported in real-time for rapid response or timely prevention.