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SENSEABLE CITY LAB

Tools and methods for monitoring the health of
the urban greenery
Akshit Gupta1,2† , Simone Mora1,3*† , Yakir Preisler4 ,Fàbio Duarte1 , Venkatesha Prasad2 , Carlo Ratti1,5
1* Massachusetts Institute of Technology, USA.
2 Technische Universiteit Delft, The Netherlands.
3 Norwegian University of Science and Technology, Norway.
4 Harvard University, USA.
5 Politecnico di Milano, Italy.

*Corresponding author(s). E-mail(s): moras@mit.edu;† These authors contributed equally to this work.
Keywords: urban greenery, greenery health, SDGs, remote sensing, embedded
sensors, thermal imaging, hypespectral imaging

Preface Urban greenery supports cities in achieving Sustainable Development Goals(SDGs), but it is increasingly affected by multiple stressors impacting its health. Dueto the high costs of greenery inspection and monitoring, local governments often lackadequate data to effectively manage their urban greenery and prevent damage. In
this Review, we present an overview of technology-supported methods and tools tomeasure the health of urban greenery and discuss the space-time resolution tradeoffs associated with the various methods presented. To inform researchers and policy
makers in global cities, we highlight how high-resolution urban greenery health data
can support achieving SDGs at scale.

Introduction
Urban greenery provide a wide range of ecosystem services such as air filtering [1],carbon sequestration [2], stormwater runoff [3], and lower local temperatures [4–6].
Urban greenery also act as a barrier for traffic noise [7], may encourage physical activity[8], and act as spaces for physical and mental restitution [9]. It also helps achieve the1

Sustainable Development Goals (SDGs) outlined by the United Nations Agenda 2030,
including climate action (SDG 13), sustainable cities and communities (SDG 11), life
on land (SDG 15), and good health and well-being for all at all ages (SDG 3).
Urban greenery encompasses greenery (trees, plants, flowers, shrubs, grass) on the
ground and on buildings [10]. In this paper we mainly focus on greenery on ground,in particular trees [11], as they are the most widespread form of urban vegetation.
Trees are characterized by a high heterogeneity across the urban landscape. They
can be evergreen or deciduous, belong to different species and have different sizes.
They can be found along streets, in parks, in wetlands, in unused land or sites underconstruction. They could be tightly nested with gray (human-constructed) structures
such as fences, light poles, façades in a green-to-gray continuum [12].
Our definition of greenery is limited compared to the broader concept of Urban Ecological Infrastructure (UEI) [13], which encompasses ecological structures (ecosystemof species, soils, waterways, etc.) and ecological functions (e.g. lifecycles and pollination). In addition, our definition only covers the green part of UEI and only greenerythat is, to some extent, managed. It also omits bare soil and aquatic vegetation. Yet,
we don’t exclude that the methods in this Review can be applied to forms of greenery other than urban managed trees, or provide information that is aggregated to an
ecology level. We are aware that geographical and domain shifts play a key role inevaluating the methods reviewed. For instance, methods designed for American cities
might not be applied to European cities due to different species distribution. Similarly, methods designed for monitoring deciduous trees might not apply to evergreen
trees. The aim of this review is to give an overall picture of all methods available.
Urban greenery is often affected by an ample amount of abiotic stressors, such as
the urban heat island (UHI) effect and soil salinity; and biotic stressors, such as insects
and bacteria attacks. The negative effect of these conditions is currently exacerbated
due to climate change [14, 15]. As a result, the functionality, productivity, and survivalof urban greenery is of increasing concern. Trees with poor health cannot provide mostof the beneficial ecosystem services [16] and thus, they are less effective in achieving
the SDGs. For instance, trees with low transpiration rates do not cool the environment
effectively and trees with low growth rates have a reduced shading effect.
Although frequent inspections can identify and rectify these stressors, inspection
costs can make urban greenery a high-maintenance asset. Globally, the total cost of
inspection, maintenance and settlement of tree damages is estimated to exceed $2
trillion USD [17, 18]. Maintaining large trees is particularly costly, yet large trees canprovide up to 8 times more ecosystem benefits compared to smaller ones [19].
The practice of measuring and monitoring urban trees began over a century ago[20]. Currently, a tree’s health can be inspected by arborists with good quality results,
but usually at high costs [21]. This leads to an assessment that has a low spatial andtemporal resolution, with cities conducting tree assessment rarely (e.g. every 3-5 years)[22]. In recent years, technology-assisted screening methods have been developed tocomplement manual methods, with trade-offs involved. Satellite-based imaging canprovide data over large areas, with the data quality susceptible to external parameterssuch as availability of clear skies, depending on the type of sensor [23]. Yet, highspatial-temporal resolutions (<10m) is achievable only through targeted acquisitions,2thus limiting the size of area covered [24]. Airborne sensing using Unmanned Aerial
Vehicles (UAVs) or airplanes leads to an increased spatial granularity [23], yet it
involves high operational costs and may not be suitable in urban environments due toaviation authority regulations. Furthermore, depending on the canopy density, both
airborne sensing and satellite imagery can only capture the overhead view of theurban greenery. As a result, lower vegetation elements such as green walls, short trees,or shrubs are often missed or misinterpreted in the gathered data [25]. In addition
to traditional approaches, a number of research projects have investigated the useof low-cost alternatives to survey the presence and species of urban greenery. For
instance, using Google Street View (GSV) images to detect the presence of trees[25], or to calculate changes in tree species diversity in cities [26]. These projects
are set within the field of opportunistic and low-cost sensing, aimed at developing
environmental platforms that can be deployed and operated without the need for
expensive infrastructures.
As the examples above demonstrated, the importance of urban greenery has been
fostering the use of different methods and tools to quantity urban greenery and,more particularly, to assess tree health. They range from highly specialized and costly
unmanned aerial vehicles to engaging residents in citizen-science approaches. Scientific
literature often presents detailed descriptions and discussions about one particularapproach.
In this paper, we review research methods and tools to map the health of urban
greenery on the ground. We highlight the type of attributes and information thatcan be mapped, and how technology-supported methods can complement traditional,
labor-intensive approaches. We propose use cases for the methods reviewed and we
discuss how scholars and policy makers can utilize greenery health data to support
achieving SDGs at scale. We highlight existing research gaps with the aim at informing
the development of new approaches.

Tree’s attributes and health
The health, survival and functionality of trees depends on three main aspects: (i) theability of the root system to transport nutrients and dispose pollutants, (ii) functioning
water conduits (xylem) transferring water and healthy sieve-tube elements (phloem) to
all live organs; (iii) healthy leaves, the main site for gas exchange with the atmospherethrough photosynthesis processes.
Early detection of conditions affecting those three components, such as cavities
and diseases, can guide preemptive actions to maintain a tree’s optimal functionality[27]. Unlike external physical damage, physiological stress and internal damage areoften undetectable to the human eye, and the severe damage can be reached long
before symptoms become visible [28–30]. Furthermore, trees under stress reduce theirtranspiration rate to prevent excessive water loss, store less CO2 and decrease their
growth rate. Such trees have a weak defensive mechanism and their general healthstate is damaged, making them more vulnerable to the attack of parasites and diseases,
thus increasing their chances of mortality.

3Stressors affecting trees can be categorized as abiotic - caused by non-living factors,
and biotic - provoked by living agents.
Abiotic stressors are often related to soil and sunlight factors. Soil health is a primary determinant of a tree health. Urban soils can have highly variable attributes
such as different densities and different contents of organic matter due to a patchy distribution of natural or human-made materials, such as gravel or construction waste.
Furthermore, limited soil leads to restricted space for roots to develop, preventing
proper tree growth and eventually reducing a tree’s lifespan significantly [16]. Due tothe presence of pollutants, urban soils often have an increased salinity, which reduces
the ability of the roots to extract water and nutrients. Many urban environments provide limited or irregular sunlight due to the shadows projected by buildings which
can prevent the tree to reach its optimum photosynthesis machinery [31]. In addition,
the above-average air temperatures and heatwaves, which are increasingly occurringin urban environments [32] leads to trees losing an excessive amount of water. To
counteract water loss, trees close their stomata - small pores in the leaves, reducing
their beneficial ambient cooling effect. Another outcome of the “life-saving” stomatal regulation is a reduction in the photosynthesis process; thus resulting in reduced
growth.
Biotic stressors are often related to the physiological response of trees to the attackof agents such as insects, fungi, viruses, or bacteria [33]. Such response is usually
expressed in a decrease in functionality and productivity that can eventually result in
the death of the affected tree [34]. A tree’s defense mechanism against biotic agentsrequires a functional and healthy internal state. If resources are limited, e.g. due to apreexisting abiotic stress, the tree might succumb to the attack [19]. The development
and fast-spreading nature of biotic agents, as well as species homogeneity of manyurban environments, also contribute to the worsening of these biotic stressors [35].
If the health of a tree is poor, its contribution to the urban ecosystem is impaired.
Unlike trees in natural or planted forests, rectification measures for urban trees areneeded at a faster rate, due to the rapid rate of changes in local conditions (e.g.infrastructure, construction work, pruning) and due to immediate implications suchas potential physical damages to pedestrians and properties.

Inspection strategies for greenery healthWe survey inspection strategies and tools using two lenses.
First, we scope popular sensing principles and technologies. The choice of thesemethods directly affects the type of health information that can be sensed, as well as
the quality of the assessment. For example, hyperspectral and multispectral imaging
sensors are useful to estimate NDVI (Normalized Difference Vegetation Index) values,
while thermal imaging sensors can be used to compute CWSI (Crop Water StressIndex) values. These strategies lie within three clusters: manual techniques, physicaland chemical sensors, and imaging-based sensors.
Second, we discuss sensor deployment strategies to collect data. Different strategies
affect the time and space resolutions that can be achieved. For example, althoughsensors embedded in a tree can provide data at a high time resolution (more than once4

an hour), achieving high space resolution leads to high deployment costs (one sensor
per tree). On the other hand, remote sensing techniques may lead to a higher spacecoverage more cost-effectively, yet with constraints on the time resolution (dependingon the revisit rate of the sensor) and influence of environmental parameters such assky conditions.
We also highlight the level of automation required to collect data. For example,
embedded sensors can work with little (periodic calibration) to no supervision foryears, while airborne sensors require manual intervention as well as supervision toconfigure and deploy airplanes or UAVs over vast areas. A detailed list of the reviewed
works is provided in Supplementary Information (SI).

Manual Techniques
As a first step, arborists measure the health of trees by visual inspection and utilizing non-invasive tools for screening, diagnostic, or evaluation purposes [36, 37]. Water
limitations can be quantified by sensing air dryness (relative humidity and temperature), and by measuring the tree water consumption [38–40]. Insect-induced physicaldamage to the leaves and other elements can be detected visually [41–43]. However,external symptoms of decay may be absent even in the presence of internal decay [36].
In turn, this may lead to delayed actions taking place [44].
To provide more complete information, visual inspection might be supplemented
by non-invasive methods such as electronic nose to detect fungal decay. However, these
simple methods have low resolution and may miss small wooden decays or diseases
at very early stages. Hence, for improved resolutions, arborists may also use invasive
methods like electrical resistivity measurements (attaching electrodes and passing anelectric current through the trunk) or destructive instruments like increment borers(tools for extraction of a wooden core sample from the trunk of the tree). Although
effective, such invasive methods which require penetration in the living wood may
create an entry path for pathogens or may alter the structural integrity of a tree.
Finally, for the highest accuracy, high-cost methods such as electromagnetic or multipath stress wave tomography may be used. Overall, arborists usually start with themethod that causes the least damage, and successively apply more aggressive andcostly techniques to get more accurate information.[45].
Various manual inspection techniques exist and they are summarised in Supplementary Table 1 with their working principle, detection resolution, cost andinvasiveness. The cost for each method has been estimated based on the scale in[36]. Further details on the in-depth principle of manual methods can be found in[36, 37, 46].
Both invasive and non-invasive manual inspection techniques require intensive
human labor as deployment medium, leading to low automation prospects. Thus, manual techniques usually lead to poor scalability, as highlighted in Figure 2. In contrast,
methods discussed in the upcoming sections, which are summarised in Supplementary
Table 2 have different automation prospects and deployment media.

5Physical, chemical and electrical sensors
These sensors are usually embedded into the bark (the protective outer sheath of
the wooden parts of a tree), or in the soil. The physical property under scrutiny canvary, from the detection of sudden vibrations induced by the presence of parasites tothe measurement of water uptake and transpiration. The data is generated at hightemporal resolution with little or no human supervision required.
Accelerometer-based sensors can detect the presence of insects and larvae by monitoring sudden minimal vibrations provoked by the insects’ activities such as feeding
and locomotion [47]. Accelerometers can also be used in tandem with other sensors
that measure moisture, light, temperature, humidity, and air quality.
Electrical Impedance Spectroscopy (EIS) is used to assess trees’ physiological status by leveraging electrical impedance, a measure of the opposition of a material to
the flow of alternating currents. Using a pair of electrodes placed in the trunk at diametrically opposite positions to measure impedance values, it has been demonstrated
that it is possible to disambiguate between multiple health states and identify water
stress, and diseases [44].
Dendrometers are tools employed to measure trunk growth and shrinkage happening during long-term seasonal growth patterns, daily cycles of water uptake, andtransient conditions like swelling after heavy rainfall. They could either be fully analog tools, as simple as a metal strap that is affixed around a tree stem using a spring
fastener, or fully digital tools based on contact or non-contact technologies. Dendrometers have been used for different purposes, including community-based monitoring
using DIY techniques [48], for irrigation scheduling [49] and to assess climate change
effect on Pinus sylvestris [50].
Internet of Things (IoT) approaches are often adopted to monitor multiple healthrelated parameters at a high frequency and to provide real-time alerts [51]. Data frommultiple embedded sensors can be used as features to train neural networks models forhealth classification and early warnings generation [52, 53]; and to develop aggregatedhealth indexes combining dynamic ambient features (e.g. light and wind exposure)with static or predictable features (e.g. species, age) [17].
Besides being used in research, several sensing approaches have recently paved theirway into commercial products, including non-invasive microneedle-based electrical
impedance sensors, multi-sensory platforms for stability monitoring application; andsensors embedded in the soil to monitor moisture and acidity. A review of additional
physical, chemical, and electrical sensors is provided in [54].
Similarly to manual techniques, physical, chemical and electrical sensors requirephysical access to trees.

Imaging-based sensors
Measuring Infrared (IR) radiation emitted from biological materials, a technique called
Infrared Thermography (IRT), is one of the emerging approaches for tree healthmonitoring.
The analysis of thermal images (see Figure 1-left) allows for early detection ofvarious health conditions including cavities, bark necrosis, and decay [55]. Thermal

6Fig. 1

cameras are non-invasive and scalable tools, e.g. when the camera is mounted on amoving vehicle. Differences in thermal patterns on the tree surface can indicate deteriorated areas: sections with cavities or physical damages show local cooler temperatures[45, 56], sections affected by infections caused by spores or bacteria display local
warmer temperatures [57, 58]. However, this approach requires a substantial amountof manual work to review the images by experts and it is often paired with manual
inspections; the technique does not allow for the fully quantitative assessment that
could enable scalability [55]. In addition, to provide optimal results, the bark should beshielded from direct sunlight, dry, and free from moss — elements that could interferewith temperature readings, hiding potential damages. Although some of these factors
can be mitigated by comparing temperature patterns within different parts of the tree
expected to behave similarly [45], IRT performs well only to assess significant external
damages [59]. In addition, there are no generalized temperature patterns that can beused to detect damage across various species [37].
IRT can also be used to measure water stress [23]. Rather than bark temperature,
the focus is on leaf temperature — a physiological trait that can be used as a proxy
for tree irrigation [60, 61]. For this specific application, thermal images are fusedwith RGB images, in order to use image segmentation algorithms to automate theextraction of thermal data from leaves only (e.g. removing thermal data of the sky andthe soil). The usefulness of IRT as a plant water stress indicator has been evaluatedwith different species, including persimmon and citrus trees [62], apple orchards [63],and conifers [64].
In hyperspectral and multispectral imaging (HMI), various bands in the electromagnetic spectrum are captured. This data is used to calculate various vegetationindexes, including Normalized Difference Vegetation Index (NDVI). NDVI synthesizes
the ratio between the visible red radiation and the Near Infrared Radiation (NIR)
reflected by the vegetation. It leverages the property of chlorophyll in the leaves toabsorb red light, and the cell structure of leaves to reflect NIR light. High NDVI values are a proxy for healthy photosynthetic capacity. Low NDVI values can be linked
to overall poor health, the presence of stress or parasites, or the absence of greenery.
The calibration of HMI sensors is an important aspect that affects the overall qualityof results, especially for low-cost sensors [65, 66]. Once calibrated, HMI sensors arehighly reliable, at least when comparing NDVI values within the same species, as theranges of healthy NDVI values can vary between species. Low-cost HMI sensor alternatives have been recently presented, e.g. modifying regular RGB cameras to capture
light outside the visible spectrum [67]. HMI sensors are usually deployed on satellites
and drones [68, 69], although static sensors also exist. In the latter case, the sensors
are cheaper, but they need to be located in close proximity to the leaves. A sample
HMI image is depicted in Figure 1-center.
Likewise IRT sensors, HMI instruments can be deployed in tandem with othersensors, most commonly, with Light Detection and Ranging (LiDAR) instruments.
LiDAR uses the time-of-flight of pulsed laser light to determine the distance between
the sensor and an object or a surface. LiDAR is used for greenery health applications
to measure geometrical parameters such as the number of leaves surrounding a branch,
the crown diameter, and the Leaf Area Index (LAI). LAI is computed by measuring

7

the total area of leaves per unit of ground area and it’s directly related to the amountof light that can be intercepted by a tree. Although neither the geometrical propertiesof a tree nor the LAI is a direct measure of greenery health, they can be used toassess the development of a tree over time with respect to target growth goals foreach species. LiDAR combined with HMI sensors have also been useful for species
identification [70]. Similarly to other imaging-based methods, LiDAR and HMI sensors
have been deployed using airborne [71] and ground-based approaches [72]. A sample
LiDAR point cloud is depicted in Figure 1-right.
Street-view images captured for mapping and navigation purposes have been
recently used to quantify the extent and the location of urban greenery [73, 74], their
species [26], and shading effect [75]. Although the extraction of health parametersfrom street view images is still to be investigated, information about the location andcoverage can be combined with a city’s inventory of trees to provide an indirect healthassessment.
Finally, several methods can be combined in multi-sensory approaches provide ahigher-fidelity assessment of a single parameter; for example combining LiDAR andhyperspectral cameras [76]. This strategy also serves to map how a single stressor
impacts different aspects of a tree [64].
Imaging-based methods can allow for more flexibility compared to manual andchemical, physical and electrical methods. They can be used to map greenery in remoteareas, when deployed on drones or satellites, and in areas where frequent physicalaccess to greenery might be precluded due to safety reasons, e.g. along highways.
Further the output of these sensors, such as the NDVI index, can be used to assess
the conditions of greenery beyond street trees, including plants, grass and shrubs.

Relevance for the SDGs
Large-scale monitoring of urban greenery can deliver hyper-local data useful for a broad
range of applications, tailored on issues of specific geographical areas. Hyper-local
data has a fine-granular space-time resolution and is meaningful to address (greenery
health) issues that are relevant to a very specific and small geographical areas (forinstance, at the street level, or at individual trees). This Section highlights use cases
and their relevance for the SDGs. Each use case (UC) has a numerical identifier (e.g.
UC1), a title and a mention to the SDG(s) it relates to.
SDG 15 proposes ”Protect, restore and promote sustainable use of terrestrial
ecosystems, sustainably manage forests, combat desertification, and halt and reverse
land degradation and halt biodiversity loss”. We identify at least two advantages of
having hyper-local data on greenery that can help cities to achieve this goal.
UC1 Selective Watering (SDG 15) - Currently, urban trees irrigation is irregularand inconsistent or absent [77, 78], resulting in trees being over-irrigated (which maylead to anoxia) or under-irrigated (which may lead to soil-drought). This issue mainly
occurs due to high heterogeneity in water consumption by individual trees and soil
hydraulic properties. Data at high temporal and spatial resolution can help create
the feedback loops necessary to implement selective watering strategies, optimizing
irrigation as a function of each tree’s water use. In turn, data-driven urban irrigation8

(a practice that is becoming common in agriculture [79]), can save water and improvetrees’ overall health along with soil fertility. Methods and tools capable to capture
CWSI (Crop Water Stress Index) information, including EIS [44], and IRT [23] canbe applied.
UC2 Early detection of diseases (SDG 15) - Diseases and parasites lead to a reduction in tree health and increased mortality [80]. Detecting diseases at an early stage
can prevent permanent damage, and mitigate the risk of mortality. For instance, someearly signals can be detected using parameters generated by multispectral/hyperspectral images [43], as well as continuous measurements via embedded sensors [28]. Thesemethods can also be used to measure the efficacy of pest treatments over time, via
repetitive measurements. Further, data from multiple parameters at high temporaland spatial resolutions can serve as training datasets to allow the development ofmachine-learning algorithms for the automatic identification of specific parasites.
SDG 11 proposes ”Make cities and human settlements inclusive, safe, resilient andsustainable”.
UC3 Continuous monitoring (SDG 11) Long-term monitoring data is essential tounderstand changes over time, including trends in tree growth, health, and mortality.
Without detailed data collected over time, it is not possible to implement effective
management solutions and measure their success rates, estimate urban forest value,or inform policy-making [81]. Yet,the frequency of monitoring campaigns is hamperedby resource limitation and specifically the lack of staff time [82]. Several recent survey methods have an elevated degree of automation (as reported in Supplementary
Table 2), which enables long-term monitoring campaigns. In addition, advances in AI
allow for training machine learning (ML) algorithms can perform tasks such as sensor calibration, calculation of geometric property of trees, and pattern identification
based on multi-sensory input [52, 53]. Although human judgment is still required atcertain stages of the process, AI has the potential to decrease human involvement inrepetitive and trivial tasks and reduce the number of site visits.
Additionally to SDG 11, we highlight how hyper-local, continuous monitoring
and can also benefit SDG 8: ”Promote sustained, inclusive and sustainable economic
growth, full and productive employment and decent work for all.”
UC4 Quantifying cost-gain balances (SDGs 8,11) The global cost for tree maintenance exceeds 2 trillion dollars worldwide [17, 18]. Yet a comprehensive quantificationof the economic benefits provided by urban greenery is tangled by the multifaced
nature of the ecosystem benefits as well as the lack of models (and data) for several
regions around the globe [83]. Data on urban trees’ location, species, health, and age
can help develop economic models to assess the value of urban trees in relation totheir environmental risk mitigation, public health, and energy-saving benefits; as wellas their aesthetic and cultural relevance.
Another application that can be improved with hyper-local data is the mitigationof urban heat island effects and carbon removal, which we argue is connected with
SDG 3 (”Ensure healthy lives and promote well-being for all at all ages”) and SDG
13 (”Take urgent action to combat climate change and its impacts”).
UC5 Mitigation of Urban Heat Island Effect (SDGs 3,13) - Urban surfaces, such as
façades and road pavements, play an important role in the Urban Heat Island (UHI)9effect [4]. UHI happens when dense urban environments show higher local temperatures than suburbs and rural areas [84, 85] — and this condition is being exacerbatedby climate change. Increased greenery is associated with a reduction in Land Surface
Temperature (LST) and reduced risk for pedestrian heat exposure [86, 87]. By quantifying the cooling effect for different species as a function of their health statuses,
such as canopy density, tree height, and leaf thickness, guidelines for planting andmaintenance strategies tailored to specific UHI risk areas can be defined.
One of the key applications that can be improved with hyper-local data is community engagement, which is present in several SDGs. We particularly highlight SDG
4 (”Ensure inclusive and equitable quality education and promote lifelong learning
opportunities for all”), SDG 10 (”Reduce inequality within and among countries”),
SDG 11, and SDG 17 (”Strengthen the means of implementation and revitalize the
Global Partnership for Sustainable Development”).
UC6 Community Engagement (SDGs 4,10,11,17) Citizen involvement in themonitoring of urban greenery can complement traditional approaches conducted bygovernment agencies, companies, and research institutes. This action can build on several methods and tools in the field of citizen science — the inclusion of non-experts
in research efforts [88–90]. In addition to providing data to public administrations, it
increases public awareness of the benefit provided by urban greenery, fostering a senseof stewardship and appreciation [91], and steering public debates around equity andenvironmental justice. Community engagement also fosters lifelong learning, as the volunteers need to be trained to collect data [81]. This approach has been demonstrated
by several campaigns such as the 2015 TreesCount! inventory in NYC and the i-Tree
Eco assessment in London, England. Novel low-cost and open-source greenery healthmapping tools [25, 48, 66, 67, 92] can facilitate the work of citizen scientists enablingthem to collect quantitative evidence in a cost-efficient way, which is of particular
relevance for low-income communities.
We believe that collecting hyper-local data, although an absolute optimum, could
be unnecessary for certain use cases. For example for SDGs 4, 10, 17 addressed in the“community engagement” use case, accessibility (e.g. open source code) and cost of
implementation are the main drivers for the selection of a method. For SDGs 8,11 thepriority might be the degree of automation of the methods. More in detail, we believethat hyper-local data is relevant for UC1 to understand water consumption at the treelevel, UC2 to have a big dataset to train ML algorithms for disease detection, UC3 toallow for evaluation of changes or interventions over time, and UC5 because UHI is a
hyperlocal phenomenon itself. On the other hand, we believe that hyper-local data isless relevant for UC4 whereas widespread coverage is a priority, as well as UC6 wherecost and open access to data and analysis tools could be more relevant.

OutlookFig. 2

Due to the high costs involved with manual approaches, cities survey their urban
greenery once every few years, with several cities that have never carried out a census.
The development of highly scalable and reliable approaches is necessary to acquirelarge data about the health of urban greenery, which fosters in achieving various SDGs.

10

Achieving scalability is complex. The health attributes monitored are dictated by
the type of sensing technology employed, and the deployment strategies affect thepotential for scalability. For instance, physical, chemical, and electrical sensors canonly be embedded into the trunk or in the near proximity of a tree. They generatecontinuous streams of data at high temporal resolutions; however, the spatial resolution might be limited — embedding sensors on every tree in the city will result in highcosts. On the contrary, imaging-based sensors can be used hand-held during manual
inspections, or deployed on satellites, UAVs, or even terrestrial vehicles. While satellite and airborne-based remote sensing approaches can cover large areas [23], the datais generated at a low temporal resolution mainly constrained by the revisit rate of thevessel and the availability of clear skies. Airborne sensing using Unmanned Aerial Vehicles (UAVs) or airplanes leads to an increased spatial granularity [23], yet it involves
high operational costs and may not be suitable in highly urbanized environments due
to aviation authority regulations. Furthermore, these approaches can only provide aneye-bird view of the greenery, ignoring information about the trunk as well as shorter
elements such as shrubs and green walls that might lie hidden under larger trees. On
the other side of the spectrum, street-view-based methods [74, 75, 93, 94] are highly
scalable, but they are only able to quantify the presence and species of urban greenery
rather than its health and are limited to mapping public spaces. Yet, ground-based
sensing can look at urban greenery in a more holistic manner. The deployment of sensors in a drive-by scheme has the potential for high scalability [95]. In this researchstrand, only a few works [23] have investigated ground-based monitoring approacheswith hyperspectral/multispectral and thermal imaging. Although scalability can beincreased by deploying sensors on vehicles, most research initiatives, except very few[23, 64] still requires manual judgment and processing by humans on the data collected.
Finally, imaging-based methods deployed as drive-by platforms or handheld devices
may raise privacy or ethical concerns [96], although several techniques are available totackle this issue; e.g. by pedestrians’ thermal fingerprints [97].
The high heterogeneity of the urban landscape also affects the choice of method
used for monitoring. For example, trees may be accessible via pedestrian-only routes,
public or private roads, or be confined in private backyards. Drive-by methods(Figure 2) can only be applied to trees close to private or public roads. Physical accessto trees for visual inspection, installation and maintenance of embedded sensors could
be limited by safety barriers; for example trees along busy roads, informal settlements
or in high-crime neighborhoods. Remote sensing is less suitable when green and graystructures [12] are tightly nested due to occlusion.
Another challenge concerns the validity of the studies available in the literature.
Most of the reviewed techniques, except for a few [17, 55, 56, 98] have been evaluatedon a few dozen of trees, in a controlled environment. It’s still unclear how factors likethe weather, climate, and interference from elements of the built environment might
affect the validity and performance of those techniques. Finally, the transferability ofmethods across species hasn’t been achieved. Most methods still require a substantial
amount of manual work, either for analyzing the data, e.g. in the case of thermal
images; or for sensor deployment and operations, as in the case of operating UAVs.

11

Scalable methods and tools for urban forest monitoring are necessary to support
SDGs for all. We hope that this review will spur interest and collaboration among different stakeholders, ranging from urban planners and policy advisors to environmental
engineers and computer scientists. For the first group, our work can be used to inform
planting strategies, measure the outcome of greenery maintenance practices, and foster community engagement. For the latter, it can be used as a foundation to overcome
the trade-offs and challenges related to the scalability, robustness, and transferabilityof the methods and tools currently available.
Acknowledgments. The authors thank all the members of the MIT Senseable CityLab Consortium for funding this research: Consiglio per la Ricerca in Agricoltura
e l’Analisi dell’Economia Agraria (CREA) with Carabinieri Forestali, Dubai Future
Foundation, Toyota Woven City, UnipolTech, Volkswagen Group America, FAE Technology, MipMap, GoAigua, Shell, ENEL Foundation, Kyoto University, Weizmann
Institute of Science, KTH Royal Institute of Technology, AMS Institute and the cities
of Helsingborg, Stockholm, Amsterdam. A.G. thank Renswoude Foundation, FAST
Delft and EFL Stichting for their financial support.
Authors Contributions. Conceptualization: A.G., S.M., Y.P.; data curation: A.G.;
methodology: S.M.; formal analysis: A.G., S.M., Y.P.; writing - original draft: A.G.,
S.M., Y.P., F.D.; writing - review & editing: A.G., S.M., Y.P., F.D., V.P., C.R.;
supervision: S.M.; project administration: S.M., F.D., V.P.; funding acquisition: C.R..
Competing Interests.

The authors declare no competing interests.

Correspondence and requests for materials.
adressed to Simone Mora at moras@mit.edu.

Correspondence should be

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21

Figure Captions

Fig. 1. Left - A thermal image captured using longwave-infrared (@8µ- 14µ) FLIR
Lepton 3.5 camera
Center - A multispectral image captured using a MAPIR Survey 3W camera(red@660nm, green@550nm, near-infrared@850nm)
Right - A LiDAR point cloud captured using an Apple iPhone 12 Pro and the Polycam
3D Scanner app

Fig. 2. Space/Time resolution of classes of methods reviewed with respect to SDGs
requirements

22

FiguresFig. 1: Left - A thermal image captured using longwave-infrared (@8µ- 14µ) FLIR
Lepton 3.5 camera
Center - A multispectral image captured using a MAPIR Survey 3W camera(red@660nm, green@550nm, near-infrared@850nm)
Right - A LiDAR point cloud captured using an Apple iPhone 12 Pro and the Polycam 3D Scanner app

23hourlyHigh

SDG 3,13,15 | Ecosystems, climate change

SDG 11 | Resiliency

Drive-byEmbeddedVia dedicated vehicles oropportunistic use ofexisting fleets (e.g. taxies)

Time Resolution

Via sensors embedded in thesoil or in the trunk of a tree

Scalability
Low

High

Airborne*

Via airplanes or UAVs
*space and time resolution affected by clear sky conditions

Remote*Via weather balloons orsatellites*space and time resolution affected by clear sky conditions

Low

less than yearly

Manual approaches

Via visual or instrumented

inspection
SDG 4,10,17 | EngagementLow

few trees per city

SDG 8 | GrowthSpace ResolutionHighmany trees per city

Fig. 2: Space/Time resolution of classes of methods reviewed with respect to SDGs
requirements24