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Treglia, M. L., T. McPhearson, E. W. Sanderson, G. Yetman, and E. Nobel Maxwell. 2022. Examining the distribution of green roofs
in New York City through a lens of social, ecological, and technological filters. Ecology and Society 27(3):20. https://doi.org/10.5751/
ES-13303-270320
Research, part of a Special Feature on Holistic Solutions Based on Nature: Unlocking the Potential of Green and Blue
Infrastructure
Examining the distribution of green roofs in New York City through a lens
of social, ecological, and technological filters
Michael L. Treglia 1 , Timon McPhearson 2,3,4 , Eric W. Sanderson 5 , Greg Yetman 6 and Emily Nobel Maxwell 1
ABSTRACT. Green roofs provide multiple benefits including reducing the urban heat island effect, absorbing stormwater and air
pollution, and serving as habitat for wildlife. However, many cities have not taken advantage of green roofs as a nature-based solution.
In New York City (NYC), approximately 20% of the landscape is covered by buildings, thus rooftops present a substantial opportunity
for expanding green infrastructure. Spatial data on green roofs are critical for understanding their abundance and distribution, what
filters may drive spatial patterns, and who benefits from them. We describe the development of a green roof dataset for NYC based on
publicly available data and classification of aerial imagery from 2016. Of the over one million buildings in NYC, we found only 736
with green roofs (<0.1%), although there may have been others we did not detect. These green roofs are not evenly distributed in NYC
- they are most common in midtown and downtown Manhattan, while most other areas have few to none. Green roofs tend to be more
prevalent in parts of NYC with combined sewer systems, but some such areas, and those with the most heat-vulnerable communities,
have few if any despite their potential to help ameliorate stormwater and urban heat challenges. Though green roofs are providing
some benefits within NYC, we anticipate they are filtered based on dynamics of infrastructure, institutions, and perceptions, rather
than targeted to address climate and weather-related challenges. There is substantial opportunity in NYC to increase green roofs, and
equity of them. The dataset we developed is publicly available and can serve as a baseline for tracking these assets through time, while
supporting further research, conversations, and policies related to the benefits and distribution of green roofs. The underlying methods
can also be applied to help fill similar data gaps in other cities.
Key Words: cities, green infrastructure; green roofs; mapping; social-ecological-technical filters; urban remote sensing; urban systems
INTRODUCTION
In the urban century (Elmqvist et al. 2018, 2019), urban areas are
rapidly expanding with dense development that can force out
many species and limit opportunities for incorporating nature for
both human and non-human benefit (Parnell et al. 2018, United
Nations 2018). Cities offer opportunities for sustainability
(McPhearson et al. 2021) and can even present opportunities to
benefit certain biological taxa such as insect pollinators (Hall et
al. 2016), but in areas facing dense development, innovation is
needed to increase urban nature and to provide additional nature-
based solutions to address a range of urban environmental ills
(Kabisch et al. 2017, Keeler et al. 2019, Frantzeskaki et al. 2019).
For example, cities can be hot, flood-prone, polluted, and lack
accessible and safe spaces for people and biodiversity. Changing
the form of cities can ultimately help incorporate nature into them
and improve human well-being (McDonald 2015). We examined
green roofs as an emerging form of green infrastructure
(Grabowski et al. 2017) with potential to provide space for nature
in places (rooftops) not regularly considered in urban green space
planning.
Green roofs are roofs with vegetation planted in growing media,
on top of a waterproof membrane, root barrier, and drainage
layers. They can be extensive, with shallow substrates (2–20 cm)
and generally with plants selected for stress tolerance (e.g., Sedum
spp. or Sempervivum spp.), or intensive, with deeper substrates
(>20 cm) and generally more comparable to a garden at ground
level (Oberndorfer et al. 2007). Green roofs provide a myriad of
benefits, and as a form of green infrastructure, are sometimes
actively planned in cities to help alleviate local challenges (Mell
2011, Keeler et al. 2019, Frantzeskaki et al. 2019, Andersson et
al. 2019). As with other forms of green infrastructure, where they
can exist and their ultimate benefits depend on various constraints
and filters (Andersson et al. 2019). Some cities globally have
incentives and policies aimed at increasing the number of and
area covered by green roofs, and such efforts can be guided by
local needs (McPhearson et al. 2013, Meerow and Newell 2016,
2017, Kremer et al. 2016, Langemeyer et al. 2020), but
understanding where green roofs are located is critical for
informed decision-making. Data on existing green roofs can also
allow for an understanding of how various filters have resulted
in the present landscape of this asset and its respective benefits.
Green roofs can provide multiple benefits. Growing media and
vegetation components retain stormwater (Mentens et al. 2006).
Many cities have combined sewer systems that normally treat both
stormwater and sanitary sewage; in heavy precipitation events
excess stormwater causes these systems to discharge sewage into
local waterways, thus green roofs, among other green
infrastructure, can help address this challenge (De Sousa et al.
2012). Green roofs also serve as habitat for biodiversity (Kadas
2006, Parkins 2015, Partridge and Clark 2018), and
evapotranspiration by the plants and the low albedo of green
roofs help reduce the urban heat island effect (Susca et al. 2011).
The multiple layers and moisture of green roofs provide insulation
for buildings they are installed on, increasing efficiency for heating
and cooling (Gaffin et al. 2010), and the vegetation and growing
media can sequester carbon (Getter et al. 2009). Furthermore,
green roofs can be made accessible to people, serving as places of
respite, recreation, and education. These benefits can ultimately
lead to broader public good. For example, cooling and insulation
provided by green roofs can potentially improve public health
1The Nature Conservancy, New York State Cities Program, New York, NY, USA, 2Urban Systems Lab, The New School, New York, NY, USA,
3Cary Institute of Ecosystem Studies, Millbrook, USA, 4Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden, 5Wildlife
Conservation Society, Bronx, NY, USA, 6CIESIN, Columbia University, Palisades, NY, USA
Ecology and Society 27(3): 20
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outcomes in heatwaves, and improved access to green space can
offer mental health benefits (Nutsford et al. 2013). Green roofs
may be designed to provide one or more specific benefits while
also providing co-benefits. Benefits offered are influenced by
specific attributes of individual green roofs (e.g., growing media
depth and vegetation type). Who will benefit from green roofs
ultimately depends on the services they were designed to provide,
along with characteristics of the building and general location
(Berardi et al. 2014).
Benefits of green infrastructure are filtered through social,
ecological, and technological dimensions of urban systems
(Andersson et al. 2015, 2019, 2021, McPhearson et al. 2016, 2022).
For example, green roof benefits may be perceived as benefits by
some and not others; green roofs may not provide the same quality
or quantity of benefits based on underlying infrastructure, and
benefits may be influenced by the availability of institutions or
local stewards who can provide adequate management to
maximize benefits (Andersson et al. 2019). For example, in areas
with combined sewer systems, users of impacted waterways may
benefit, as will cities required to manage water quality for
compliance with environmental regulations. However, actual
provisioning of these benefits depends on infrastructure filters:
whether green roofs can be installed in geographic areas with
combined sewer systems given building constraints such as load
bearing capacity (Cascone et al. 2018), and whether they can be
built to specifications that contribute substantially as a solution
based on factors such as slope (Getter et al. 2007). Institutions
also play a role, as centralized goals can drive policies to increase
green roofs in areas where they are most needed based on
individual criteria or multi-criteria analyses (Langemeyer et al.
2020). Perceptions of residents, property owners, installers, and
engineers also influence where green roofs are installed and in
what form (e.g., plant palette; Butler et al. 2012). The general
cooling benefits of green roofs, with primary beneficiaries being
local residents, can similarly be influenced by these filters, and
institutional efforts to bring green roofs to scale can likely result
in larger, more regional effects (Sun et al. 2016). Increased access
to green space provided by green roofs may particularly be
influenced by institutional dynamics in terms of who has access
to these spaces; perceptions or norms can also play a role in terms
of identifying and leveraging opportunities to make green roofs
multifunctional. For example, property owners and installers can
actively work toward green roofs that absorb stormwater and
serve as accessible green spaces. The beneficiaries will vary but
could include children attending schools with green roofs,
residents of apartment buildings, or the broader community if
access is granted, respectively.
Many municipalities have institutional programs for improving
environmental quality that can leverage the benefits of green
roofs, setting up enabling conditions for broader implementation.
For example, Giannoppoulou et al. (2019) reported Basel,
Switzerland, has the largest green roof area per capita, largely
attributable to incentive programs and construction laws. These
programs can be supported by research that has estimated benefits
of green roofs and even suggested optimal siting for key benefits.
In Xanthi, Greece, for example, simulations have indicated green
roofs can provide substantial cooling benefits (Giannopoulou et
al. 2019). In both Barcelona (Langemeyer et al. 2020) and Madrid
(Velázquez et al. 2019), Spain, recent work has also been
developed to support prioritization of new green roofs based on
potential benefits. More exhaustive cost-benefit studies have also
been conducted, such as work in Atlanta, Georgia, USA (Lamsal
2012). Studies such as these have ultimately helped justify
incentives and policies for, and spatial prioritization of, green
roofs.
New York City (NYC) has multiple environmental sustainability
and human health initiatives including ones developed as part of
mayoral initiatives such as PlaNYC (City of New York 2007) and
OneNYC (City of New York 2015), as well as programs to
minimize stormwater entering combined sewer systems (e.g., a
green infrastructure grant program), and to manage the urban
heat island effect and the associated morbidity and mortality in
the most heat-vulnerable communities (City of New York 2017).
Green roofs can be part of the solution, and there are broader
efforts to expand them in NYC. As of 2019, there are local laws
(NYC Local Laws 92 and 94 of 2019) requiring green roofs or
solar panels on nearly all roofs of newly constructed buildings,
roofs that are replaced, and buildings undergoing substantial
expansion. Furthermore, in 2019 a tax abatement providing
financial incentives was renewed and amended (NY Senate Bill
S5554B), offering higher abatement rates in priority areas based
on a combination of heat vulnerability and potential to reduce
stormwater challenges the City faces. Such prioritization can be
invaluable, as both environmental challenges and assets are often
inequitably distributed within cities (Namin et al. 2020, Locke et
al. 2021). Green roofs are expensive to install, and both feasibility
and cost effectiveness are influenced by various factors such as
structural characteristics of buildings, roof slope, and roof size
(Ackerman et al. 2012, Shafique et al. 2018). Thus, we suggest
where green roofs exist, and who benefits, has ultimately been
filtered by urban development and historical structural factors
related to wealth inequality and building characteristics, which
result in spatial disparities in this important and growing resource.
Policies and incentives designed to increase green roofs to improve
environmental quality should be informed by spatial data, both
on environmental quality and green roofs themselves. New York
City has some data related to environmental issues including heat
vulnerability (Madrigano et al. 2015), combined sewer overflows
(e.g., NYC Department of Environmental Protection 2019), and
accessibility of parkland (e.g., walking distance to parks, City of
New York 2015). The City also tracks some environmental assets
including ground level vegetation and tree canopy, captured in
high resolution land cover data (available for 2010 and 2017;
MacFaden et al. 2012) and individual street trees, inventoried in
decennial censuses.
Unlike ground level vegetation, green roofs have not been
thoroughly documented across NYC. No single entity has
consistently monitored and tracked installations. Prior efforts to
capture them have been piecemeal, such as City agencies tracking
projects they are involved in and installers sometimes logging
them in either their own or industry websites. Aggregating existing
data is challenging, given different types of information in
different formats. Thus, there have not been sufficient data to
enable an understanding of the current and potential roles of
green roofs in NYC. Further, cities vary in their tracking of green
roofs - a generalizable, more automated approach for mapping
them in NYC can also potentially improve broader understanding
of this asset.
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We specifically fill a data gap in NYC, while providing a workflow
that can enable filling of similar data gaps elsewhere. We then
illustrate the value of these data by using them to highlight
implications of the uneven distribution of green roofs in NYC
and discuss potential filters underlying the distribution.
METHODS
Mapping green roofs
Training Data
To identify green roofs in New York City we used a supervised
image classification, with subsequent post-processing based on
rule-sets and visual inspection. We developed a training dataset
for classification primarily based on locations of green roofs for
NYC, available from City agencies (NYC Dept. of Environmental
Protection and NYC Dept. of Parks and Recreation) and two
websites, http://www.greenroofs.com/ and https://greenhomenyc.
org/. Datasets were compiled by early 2017 and any without
geographic coordinates were geocoded using the Esri World
Geocoder. Vegetated surfaces of green roofs were digitized based
on 15.2 cm resolution orthoimagery for NYC from 2016,
distributed by New York State and the City of New York (available
at http://gis.ny.gov/gateway/mg/2016/new_york_city/). Any green
roofs noted in the aforementioned sources that we could not
visually detect in the orthoimagery were not considered further.
Those that we could visually identify, and others that we
incidentally encountered, were digitized. We omitted surfaces that
were clearly potted plants or other features not integrated into
the roof surface, sometimes leveraging supplemental information
from web-searches about buildings, and 3-D views in Google
Earth and Google Maps. The resulting dataset comprised over
1000 polygons across 155 buildings, covering 11.2 ha of green
roofs, used as training data for our classification.
In classifying single land cover types (green roof surfaces in this
case), most classification algorithms require training data for non-
target classes as well as the main class of interest. Thus, we
digitized polygons of non–green roof rooftops, ranging in color,
geography, and size to encompass a variety of surfaces with
different spectral characteristics (Campbell and Wynne 2011).
Particular attention was given to roof types likely to be confused
for green roofs given their spectral characteristics, based on
exploratory data visualization. These included non-vegetated
roofs that were red, green, or very dark in color, as well as artificial
turf (e.g., playgrounds) and rooftops covered by other vegetation
such as overhanging trees. Strong shadows, for which underlying
roof type could not be determined, were also captured. We
compiled 272 polygons representing various non–green roof
surfaces, covering 1.3 ha. Though balanced training datasets can
benefit performance of some classification algorithms and reduce
bias (e.g., for random forests; Chawla et al. 2003), we worked with
this training sample due to resource constraints.
Imagery and image processing
The imagery we used for classification was the aforementioned
15.2 cm resolution orthoimagery for NYC from 2016, collected
from 26 March to 5 April as part of a routine update. The imagery
undergoes true orthorectification to remove the appearance of
buildings leaning and is comparable to that used for development
of a robust planimetric dataset, thus it is of high spatial accuracy.
The imagery includes four spectral bands: red; blue; green; and
near-infrared. Given the considerable size of the dataset (the
extent of the imagery covered 104 billion pixels), we uploaded
imagery and other required datasets into Google Earth Engine
(GEE; Gorelick et al. 2017) for processing and classification. We
also uploaded the building footprint data provided by the City of
New York from 30 August 2017 (available at https://data.
cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-
w8eh) and tree canopy based on a 0.91 m 2010 land cover dataset
for NYC (available at https://data.cityofnewyork.us/widgets/9auy-76zt).
The building footprint data enabled us to restrict classification to
areas occupied by buildings, avoiding potential for confusion with
ground-level vegetation, and the land cover data allowed us to
mask out tree canopy that overlapped with buildings, which could
otherwise be confused for vegetation that is part of green roofs.
Scripts used for processing and classification of the imagery in
GEE, and training data, are available in a GitHub release at
https://github.com/tnc-ny-science/NYC_GreenRoofMapping/releases/
tag/1.0.0 and are also provided in Appendices 1–2 of this paper.
From the imagery, we derived the normalized-difference
vegetation index (NDVI; Campbell and Wynne 2011) as a new
layer to better identify actively photosynthesizing vegetation. We
also performed a principal component transformation (Jolliffe
2002) on the image bands to derive new layers; visual inspection
indicated the first two principal components helped further
separate vegetation in general and were included in classification.
Due to computational constraints in GEE, data were analyzed at
0.5 m resolution.
Classification and results refinement
We used a minimum distance classifier based on Mahalanobis
distance (Mahalanobis 1936, Sekovski et al. 2014) to classify green
roofs and non–green roof surfaces. In preliminary work, we
explored performance of various algorithms, both based on visual
inspection of results and back-prediction accuracy. In exploratory
analysis, some algorithms had very high classification accuracy
(e.g., random forest and classification and regression trees had
>98% per-pixel classification accuracy based on back-prediction
to the original training data), although visual inspection of those
results indicated there was severe over-prediction of green roofs
across the landscape and the results were not reliable. The
Mahalanobis distance classifier, in contrast, had overall per-pixel
classification accuracy of 78% but did not exhibit such over-
prediction. This accuracy was reasonable given land use and land
cover classifications often range about 70–80% accuracy (e.g.,
Manandhar et al. 2009, Wickham et al. 2013). We also examined
performance of the Mahalanobis distance classifier more in-
depth to understand transferability of this work, withholding 20%
of the full training dataset as a test sample. User’s and producer’s
accuracy for green roof areas, specifically, were 74% and 64%,
respectively. Exploratory classifications were evaluated using
coarsened imagery (1 m resolution) due to computational
constraints.
Recognizing that all results would be visually inspected, we did
not use discrete subsets of data for training and testing for the
final product but included all data in the classification to maintain
the breadth of spectral characteristics represented by training
data. For pixels classified as green roofs, we removed clusters
smaller than 12.5 m² to eliminate small but numerous false
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positives of only a few pixels. In our training dataset representing
green roofs, smaller polygons did exist but were typically part
of larger green roof installations that were captured in the
classification. Data were exported from GEE as raster data, and
clusters of pixels classified green roof were vectorized as
polygons using ArcMap version 10.3.1.
This process yielded 9672 polygons classified as green roof
surfaces. To address remaining false positives, we visually
inspected all of these polygons by overlaying them with the
orthoimagery in QGIS and removed those that did not appear
to represent a green roof. For polygons we could not readily
discern as a green roof, we also considered earlier imagery (from
2014) as well as imagery in Google Maps and Google Earth. In
some cases, we also leveraged photographs from real estate
listings found in Google searches for specific addresses. In the
process of visual inspection, shapes were adjusted to better fit
the general area of green roof surfaces, and additional green
roofs incidentally observed based on the 2016 imagery were also
added to the dataset to yield as complete a product as possible.
All manual refinement was conducted by a single member of
our team (MLT), and co-authors inspected results. Lastly, we
merged refined results with our original green roof training data
to ensure any missed in the classification were in the finalized
dataset. Refinement of image classification is commonly used
to improve final data products (e.g., Manandhar et al. 2009) and
while the manual effort we used was time consuming, it
ultimately contributed to a hybrid approach of automated
classification and manual refinement that was more efficient
than digitization of imagery alone.
Instances of multiple green roof sections per building were
merged into MultiPolygon objects, and we added area in square
feet as an attribute to the data (in line with the coordinate
reference system used and relevant to local users). We included
the building footprint area based on the building footprint data,
and calculated the proportion of rooftop surface that was green
roof. To enable broader use of the dataset, we also added
Building ID Number (BIN) and Borough, Block, and Lot
Number (BBL) from the Buildings Dataset based on a spatial
overlay, and joined fields related to zoning and type of owner
from a generalized parcel dataset for NYC (PLUTO/
MapPLUTO version 18v1; available at https://www1.nyc.gov/
site/planning/data-maps/open-data/bytes-archive.page). For these
spatial overlays and all analyses presented, spatial data were
created or downloaded in, or reprojected to, a locally
appropriate coordinate reference system (New York State Plane,
Long Island Zone; EPSG 2263) such that datasets would align
and area calculations were locally appropriate.
Examining social, ecological, and technological-infrastructural
filters in NYC
The presence of buildings that can accommodate a green roof
is a fundamental built infrastructure filter likely to impact the
distribution of green roofs in NYC. We characterized green roofs
in NYC according to the number and proportion of buildings
with green roofs, as well as their area and the proportion of
rooftop area consisting of them, across the entire city and by
New York City Council District. City Council Districts are
relevant for policy and local decision-making, and have fairly
consistent population sizes across the 51 units. Though not all
buildings are suited to green roofs (e.g., limited roof area, too
sloped), analysis of building suitability for them was out of scope
for this study and, recognizing these limits, we sought to capture
high-level trends and recognize that these infrastructure filters are
at play and should be examined more deeply in future research.
Interest in green roofs by different types of owners (e.g., public
vs private) and suitability for them in different land use contexts
are additional filters that may drive variation in green roof
prevalence, size, and type. Further, desire or stated need for green
roofs in specific areas to achieve social and ecological benefits,
and to mitigate environmental hazards, can also filter where green
roofs have been installed and in what form. For example, formal
incentive programs like the aforementioned green infrastructure
grant program, as well as informal recognition or understanding
of their benefits may have influenced the landscape of green roofs
documented in our dataset.
We characterized the green roofs in NYC across public vs. private
properties and different land uses based on the parcel dataset for
NYC, MapPLUTO (version 18v1), informed by inspection of the
dataset itself and the metadata. Properties with ownership type
coded in MapPLUTO as City or Other Public were considered
public, while properties with all other ownership classes were
considered private. These data are imperfect, though this
breakdown generally captures public vs private ownership. We
identified general land uses for properties in which green roofs
were identified based on MapPLUTO (buildings on properties
with land use of “Park and Open Space” in MapPLUTO were
considered as Facilities and Institutions for this work). Given that
feasibility for green roofs may vary with building types, land uses,
and ownership, and that we do not have a robust understanding
of these dynamics, we did not conduct statistical tests in this realm
and presented general trends.
The geography of environmental hazards such as high heat
exposure can influence spatial prioritization for green roof
investments. For example, though green roofs may frequently be
installed as real estate amenities, expanding cooling benefits of
green roofs in areas with high heat vulnerability is an increasing
priority in NYC (e.g., per the priority areas of the aforementioned
tax abatement). We examined whether heat risk of communities
may have served as a filter influencing the distribution of green
roofs by analyzing whether green roofs were more prevalent in
areas of the City deemed more heat-vulnerable, based on an index
for NYC that characterizes community vulnerability to extreme
heat events based on susceptibility of residents to mortality and
morbidity in them (building on work described by Madrigano et
al. 2015). We used the most recent data available from the New
York City Department of Health and Mental Hygiene
(representing 2018) in the form of ranked quintiles (1 = least
vulnerable; 5 = most vulnerable) at the finest scale available for
these data, Neighborhood Tabulation Areas (NTAs; available at
http://a816-dohbesp.nyc.gov/IndicatorPublic/VisualizationData.
aspx?id=2411,719b87,107,Map,Score,2018). This dataset is not
available specifically for City Council Districts; NTAs are smaller
than City Council Districts, though generally nested within them.
NTAs are designed to have a fairly even number of residents, at
minimum 15,000, and they range in size from 51.86 ha to 3040.63
ha (larger areas correspond to lower population densities). Given
the finer scale, NTAs also represent more spatial heterogeneity
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Table 1. Number and area of green roofs and buildings by property type.
Property Type Number of Green
Roofs
Total Number of
Buildings
Green Roof Area (ha) Total Building Area
(ha)
Residential 257 952,393 3.98 9715.25
Mixed-Use (Commercial and Residential) 221 52,950 5.03 1162.10
Facilities and Institutions 127 28,407 10.12 2058.96
Commercial, Manufacturing, Industrial, and Parking 131 44,034 5.50 2830.65
than Council Districts. We identified the highest heat vulnerability
value within each City Council District to characterize whether
those districts with highly heat-vulnerable populations are being
served by green roofs. A 152.4 m inner buffer was applied to the
NTA boundaries for this overlay analysis to avoid capturing small
slivers of NTAs minimally within Council Districts. We tested for
a significant Kendall’s τ correlation (Kendall 1938) between the
highest HVI in City Council Districts and both proportion of
buildings with a green roof and proportion of total rooftop area
covered by green roofs using function cor.test() in R, version 3.5.4
(R Core Team 2018).
Green roof investments may also be prioritized in areas with
combined stormwater and sanitary sewer systems (combined
sewer areas), given the potential for green roofs to absorb
stormwater and ultimately reduce overflow events and associated
water pollution. Thus, the arrangement of combined sewer areas
may act as a filter on the spatial variation in green roofs. We
examined this dynamic by comparing the percentage of each City
Council District overlapping combined sewer areas from the NYC
Department of Environmental Protection with the proportion of
buildings and proportion of rooftop area consisting of green
roofs. Though ideally analyses would be based on more specific
metrics such as volume of combined sewer system discharge
attributable to individual areas, no official datasets are released
by the City. These sewershed boundaries were made available as
part of a summary dataset of 2010 tree canopy and land cover
(available at https://data.cityofnewyork.us/Environment/NYC-
Urban-Tree-Canopy-Assessment-Metrics-2010/hnxz-kkn5). As
with heat vulnerability, we evaluated Kendall’s τ. Data used in
these analyses, aggregated by City Council District and R code
are provided in Appendix 3.
RESULTS AND DISCUSSION
The resulting green roof dataset from this work reflects 736
buildings with green roofs as of 2016 (Fig. 1), covering 24.62 ha
of green roof surface. These represent 0.07% of the buildings and
0.15% of rooftop area in NYC. Our study substantially increased
the number of green roofs documented, as only 155 green roofs
covering 11.94 ha were reported in source data leveraged in this
effort. The image classification detected 119 of the 155 green roofs
from the training dataset and 569 additional ones we had not
previously documented. Of the green roofs in the training data,
13 were smaller than the 12.5 m² minimum size of areas classified
as green roof to be further evaluated, thus 84% of the green roofs
that could have been detected in the classification were; because
the classification was applied on a per-pixel basis, these smaller
green roofs still contributed to training the model. Nine more
green roofs were added during visual inspection and manual
refinement of the classification results. Median green roof size
was 109.1 m², and sizes ranged 0.94–25,763.66 m², with the
distribution highly right skewed (very few large green roofs). The
green roof dataset is available via the online data repository,
Zenodo, at https://zenodo.org/record/1469674 (Treglia et al.
2018), where updates can be deposited and versioned. Google
Earth Engine Code used and training data are available on
GitHub, at https://github.com/tnc-ny-science/NYC_GreenRoofMapping/
releases/tag/1.0.0, as well as in Appendices 1–2 of this paper.
Manual refinement of the classification improved our results,
eliminated false positives, and detected at least some false
negatives. We acknowledge there may have been false negatives
in our analysis (i.e., green roofs that are present but not detected)
that we are unable to fully quantify. As additional validation, we
reviewed our results with the Green Roof Researchers Alliance,
a group of individuals from various institutions that are
knowledgeable about green roofs in NYC, before release. No
substantial omissions or over-estimations were identified in this
process. If implemented, systematic tracking of green roof
installations (e.g., by a City agency) would enable additional
verification of analyses like ours. The dataset we generated
indicates the distribution of green roofs is uneven across New
York City (Figs. 1, 2a). Over one half numerically (414) and nearly
one half by area (12.21 ha) were concentrated in six contiguous
City Council Districts within Manhattan, from the southern
boundary of the borough through approximately the Upper East
and Upper West sides. Even in other boroughs, green roofs were
often concentrated closer to these parts of Manhattan. Overall,
eight districts out of 51 total had no green roofs, and more than
half (27) had only between 1 and 10.
Of the 14,478 publicly owned properties in NYC, 73 (0.5%) had
a green roof, and of the 843,090 privately owned properties, 663
had a green roof (0.07%). Thus, green roofs were proportionally
more common on public buildings. In terms of land use, most
green roofs occur on private, residential, and mixed commercial/
residential buildings, followed by commercial buildings (Table 1).
The largest cumulative area of green roofs by land use type was
on institutional properties. The green roofs in this category
included the largest ones in NYC, on the Jacob Javits Convention
Center (2.58 ha) and Barclays Center (1.19 ha), which is a large
venue for sports and other events, in addition to those on
universities, and City-owned buildings such as recreation centers
run by the NYC Department of Parks and Recreation.
We found no association, positive or negative, between the
distribution of green roofs and the maximum heat vulnerability
index observed within City Council Districts, (see maps, Fig. 2a,
b). There was no significant correlation with either the proportion
of buildings covered by green roofs or the proportion of rooftop
area composed of green roofs (proportion of green roofs: τ =
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Fig. 1. Estimated locations of green roofs in New York City based on aggregation of existing data, remote
sensing analysis, and manual refinement.
0.009, z = 0.09, p = 0.93; proportion of rooftop area consisting
of green roofs: τ = -0.08, z = -0.73, p = 0.46). Notably, areas with
populations identified as most heat-vulnerable are generally not
well served by green roofs. None of the aforementioned City
Council Districts in midtown and downtown Manhattan, which
in aggregate contained more than half of the green roofs in NYC,
overlap NTAs ranked as the most heat-vulnerable; 22 City
Council Districts overlap NTAs ranked as the most heat-
vulnerable, though over half of these had less than 10 green roofs,
and four had none. These numbers translate to a small portion of
buildings with green roofs, with the highest percentage of green
roofs among these Council Districts being 0.50%.
In contrast to heat vulnerability, the prevalence of green roofs
was positively correlated with the percentage of City Council
District area overlapping combined sewershed areas, based on
both the proportion of buildings with a green roof and the
proportion of rooftop area with green roofs (Fig. 2a, c),
(proportion of buildings with green roofs: τ = 0.25, z = 2.59, p =
0.01; proportion of rooftop area consisting of green roofs: τ =
0.20, z = 2.10, p = 0.04). The relationship, while significant, was
relatively weak, and both the number and area of green roofs was
small; the highest percentage of buildings with a green roof
among Council Districts that overlap with priority sewershed
areas was 1.9% (occupying 0.84% of rooftop area) in the southern
part of Manhattan. Furthermore, there were various exceptions
to the trend, for example, of 37 Council Districts with over 50%
overlap with priority sewershed areas, five had none and five had
only one.
Though not all buildings may be suitable for green roofs, we
anticipate there is substantial capacity to increase them in number,
area, and distribution, with potential to provide benefits to areas
that need them the most. The dataset we developed can serve as
a baseline to facilitate tracking change through time, and it can
be leveraged for additional research. These data can also inform
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Fig. 2. A) Percentage of buildings with a green roof by New York City Council District, B) the highest heat vulnerability at the scale
of Neighborhood Tabulation Areas within each City Council District (as of 2018), and C) the estimated percentage of overlap for
each City Council District with combined sewer system areas.
policy, management of environmental challenges, and advocacy
to increase green roofs and equity of them in NYC. While our
work was specific to NYC, the general methodology we employed
can be leveraged in other cities, and respective datasets can be
used to similarly compare the distribution of green roofs relative
to the need for their benefits. Our exploratory classifications
suggested that in future work, both in NYC and elsewhere, careful
attention should be paid to the training data. For example, in
subsetting data to evaluate performance on separate testing and
training datasets, we found per-pixel accuracy decreased both
overall and for the test dataset. Thus, the broader spectral
variability in the entire training dataset supported higher
accuracy.
As with most forms of green infrastructure, we anticipate the
distribution of green roofs, and their benefits, is filtered by a
combination of infrastructure, institutions, and perceptions
(Andersson et al. 2019). As has been shown in other cities (e.g.,
Grunwald et al. 2017, Giannopoulou et al. 2019), infrastructure
considerations such as size, age, height, and other technical factors
of buildings likely contribute to where green roofs have been and
can be installed in NYC. Environmental benefits are strongly
filtered by the type of roof (intensive or extensive), which is also
associated with soil depth, and thus this ecological factor is
affected by the underlying infrastructure and its ability to support
the weight of green roofs (Oberndorfer et al. 2007, Getter et al.
2009). The benefits ultimately realized also depend on spatial
distribution of green roofs compared to the need for them (e.g.,
Velázquez et al. 2019, Langemeyer et al. 2020). For example, are
green roofs installed in areas that would benefit the most, given
stormwater or urban heat challenges, or where there is limited
access to green space? Institutional filters such as policies and
incentive programs can also play a key role - incentives for specific,
individual benefits (e.g., stormwater management) may bias
aspects of design and siting of green roofs at the expense of other
benefits. Furthermore, social goals and perceptions around green
roofs can influence what types of green roofs are built, what
functions they are designed for, and how multifunctional and
accessible they are (Jungels et al. 2013, Vanstockem et al. 2018,
Sarwar and Alsaggaf 2020).
Green roofs in NYC include urban farms for food production,
sedum roofs primarily designed for cooling and stormwater
absorption, recreational green roofs on residential buildings that
serve as spaces for socializing, as well as green roofs on top of
schools that serve numerous purposes (Fig. 3). These contexts
have influenced the siting and form of green roofs in NYC,
subsequently filtering the benefits. The green roof dataset
described herein can ultimately enable further research on how
factors such as age of buildings, slope of roofs, household income,
real estate value, and local interests are acting as filters on where
green roofs are installed, their form, and the benefits realized. It
may even be possible to infer causal relationships, which could
inform policy interventions, incentive programs, building or green
roof design alterations that can support installation of green roofs
more broadly, and realization of their benefits more where they
are most needed. Functionally, with better information on these
filters, it may be possible to change how they operate. This work
can also support spatial prioritization of green infrastructure that
can be conducted using tools like multi-criteria analysis (Meerow
and Newell 2017, Langemeyer et al. 2020), though the actual
future of green roofs and their benefits will depend on
infrastructure dynamics, institutional considerations, and
perceptions of stakeholders involved, which warrant further
research.
Our results suggest social, infrastructural, and economic filters
play out spatially in NYC, with most green roofs concentrated in
midtown and downtown Manhattan, which tend to have residents
with higher incomes (e.g., Lazar et al. 2016). Thus, where green
roofs exist may be driven by factors such as wealth (who can afford
a green roof) and building characteristics related to feasibility of
installing them. These dynamics functionally filter who benefits,
and whether environmental challenges are being addressed
through green roofs in equitable ways. Based on these data, green
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Fig. 3. Example green roofs in New York City showcasing a diversity of green roof applications, including, top row: Brooklyn
Grange rooftop farm in the Brooklyn Navy Yard used for production agriculture, and bottom row: diverse meadow and recreation
space on the green roof at Vice Media HQ in Williamsburg, Brooklyn (photo credits: Timon McPhearson).
roofs are filtered such that they have not been installed as
frequently in the most heat-vulnerable communities of NYC as
other areas, despite their ability to provide cooling benefits and
additional insulation on buildings. In contrast, green roofs have
been installed more frequently in areas dominated by combined
sewer systems, albeit they comprise a small portion of the rooftops
and rooftop area. Areas with the highest prevalence of green roofs
are densely built and consist largely of impermeable surfaces, thus
green roofs can provide benefits there. However, these areas may
not be the ones that stand to gain the most based on specific
considerations such as heat vulnerability. We anticipate some
areas of NYC, such as most of Staten Island, northern Bronx,
and eastern Queens, are generally less able to support green roofs,
given that they anecdotally consist of lower-density residential
neighborhoods with steep-sloped, peaked roofs. However, areas
such as the southern Bronx and eastern Brooklyn, both with low
prevalence of green roofs and higher heat vulnerability, consist
of higher density development with larger, flat-roofed buildings
that may be more suitable.
Green roofs also appear to be filtered by institutional dynamics
related to building ownership and type, although with so few green
roofs installed in NYC it is challenging to draw rigorous
conclusions. The trends we observe, with proportionately more
green roofs installed on public buildings, may partly be a
consequence of associated building type. For example, publicly
owned properties generally do not include 1–2 family houses,
often with peaked roofs, which comprise a large portion of the
private building stock and may not be feasible or cost-effective
for green roofs. Though most of the green roofs, numerically, are
installed on residential buildings, those buildings are generally
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apartmentment buildings. The trend of proportionally more
green roofs on public buildings may also be related to economies
of scale in that it can be more feasible or cost-effective for a single
entity with many buildings, like a City agency, to install green
roofs more broadly. For example, the NYC Department of Parks
and Recreation alone maintains about 1.8 ha of green roofs on
buildings they manage (https://www.nycgovparks.org/greening/
sustainable-parks/green-roofs). Institutional buildings in both
public and private sectors are also often large, as with recreation
centers, convention centers, and university campuses. Thus, while
few have green roofs in aggregate compared to residential
properties, they represent a large portion of the green roof area
in the city. Given the small number of green roofs in NYC, we
anticipate there is substantial opportunity to increase them on
various building types, both publicly- and privately-owned.
To fully understand how underutilized green roofs are across
buildings in NYC, future work will benefit from robust estimates
of potential for buildings to support them. Earlier work has
estimated that 5701 buildings with large rooftops (≥ 0.093 ha)
could support production-scale rooftop agriculture (Ackerman
et al. 2012). However, we anticipate many more buildings could
support green roofs in general, with no limit on minimum size
and potential for shallower growing media, thus less weight to
bear. This gap in information on realistic potential for green roofs
also limits some of our analyses herein. Ideally, our analyses of
the distribution of green roofs across the City, by City Council
District, and across public and private lands, would be based on
the proportion of buildings (or area) suitable for green roofs.
Without such a dataset, there is no perfect way to standardize
these data, and any approach has various underlying assumptions.
Some of the trends we observe are likely based, in part, on the
variability of building type across NYC and where there are
buildings that can support a green roof. Efforts to fill this gap may
build on similar work, such as in Xanthi, Greece (Giannopoulou
et al. 2019), and Braunschweig, Germany (Grunwald et al. 2017).
As with development of any new dataset, limits are important to
name for potential users. First, this dataset represents a specific
point in time, late March and early April 2016, when the imagery
underlying our analysis was collected. Since then, more green
roofs have been installed, and some have been removed, thus
updates of these data will be critical to maintaining a robust
understanding of the green roof landscape in NYC. While we are
generally confident in the dataset we produced and have had
validation of this from dataset users within City agencies and
other organizations, there were limits in our ability to accurately
discern green roofs from other roof types. We used the best
available data and multiple sources to verify green roofs that were
detected through image classification as possible, though it is
possible that some rooftops that were heavily vegetated with
potted plants were inadvertently included. We recognize
extremely small green roof patches (<12.5 m²) were generally not
captured based on parameters of the classification work, and
areas that were heavily shadowed were difficult to discern. Thus,
this is the most complete green roof dataset for NYC to date, and
it should be taken as an estimate rather than a complete census,
recognizing potential for additional green roofs we did not detect.
Further, our work does not capture specific details about green
roofs such as depth of growing media and vegetation type, which
relate to benefits. Ongoing and future efforts can help fill these
data gaps, which will allow more accurate estimates of the benefits
green roofs are providing.
CONCLUSION
In this study we mapped green roofs in NYC by classifying aerial
imagery and manually refining the results, resulting in the most
comprehensive dataset of this type for NYC. We detected only
736 green roofs covering about 25 ha, out of over one million
buildings with a total footprint of nearly 16,000 ha. Overall, less
than 0.01% of buildings, and 0.16% of rooftop area in NYC were
identified as having a green roof. While not all buildings are
suitable for green roofs, and there may have been green roofs we
did not detect in this study, we anticipate there is substantial
opportunity to increase the number and area of green roofs within
NYC. Our results indicate that green roofs are not evenly
distributed across the landscape, with the highest number and
overall prevalence in midtown and downtown Manhattan. This
uneven distribution is likely driven by various filters and
influences where benefits of green roofs are realized given
variability in local conditions. We found no relationship between
prevalence of green roofs and heat vulnerability of communities,
despite the potential for green roofs to help ameliorate threats of
extreme heat. However, there tends to be higher prevalence of
green roofs in City Council Districts overlapping larger portions
of combined-sewer sewersheds, indicating that the distribution is
conducive to helping address stormwater management challenges
that NYC faces.
This study fills a data gap for NYC, establishing a baseline
understanding of green roofs that can be leveraged and built on
in future work. For example, this dataset can be used to develop
spatially explicit estimates of benefits, support advocacy efforts
for more equitable green infrastructure, and it can be used to better
understand filters that affect the distribution of green roofs.
Though we focus specifically on NYC, many cities lack robust
data on green roofs, and our approach can be applied to fill similar
gaps elsewhere. Filling these data gaps can allow a more robust
understanding of what factors drive the siting of green roofs, who
has access to them, what benefits they provide, and how these
benefits are filtered by social, ecological, and technological
characteristics of the building, neighborhood, and broader
community. Thus, improved data on green roofs, as we provide
here, can support efforts in cities to improve the equity of green
infrastructure planning and implementation, and to support city
and community efforts to ensure that neighborhoods with the
greatest need for the benefits of cooling, stormwater absorption,
and access to green space, are prioritized for green roof and other
green infrastructure investments.
Responses to this article can be read online at:
https://www.ecologyandsociety.org/issues/responses.
php/13303
Ecology and Society 27(3): 20
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Acknowledgments:
This work was supported by a grant from the J.M. Kaplan Fund to
The Nature Conservancy, and the NYC Green Roof Researchers
Alliance, led by New York City Audubon with funding from New
York Community Trust. Use of Esri products (ArcMap 10.3.1) by
MLT at The Nature Conservancy was also supported by license
grants from Esri. TM was supported by U.S. National Science
Foundation grants (#1444755, #1927167, and #1934933). EWS
was supported by a Cultural Innovation Grant from the Rockefeller
Foundation for visionmaker.nyc. We thank the editors of this special
issue and anonymous reviewers for constructive feedback that helped
improve this manuscript.
Data Availability:
Data used in this study are generally publicly available at sources
referenced in the text. The main resultant dataset of this work,
representing estimated green roof footprints for New York City,
NY, USA, is available on Zenodo at https://zenodo.org/
record/1469674 . Code used to help develop this dataset within
Google Earth Engine, and training data are available within a release
in a GitHub Repository at https://github.com/tnc-ny-science/
NYC_GreenRoofMapping/releases/tag/1.0.0 and these materials
are also available as appendices with this manuscript. Summarized
data used for correlation analyses presented with associated R code
are also available as appendices.
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Appendix 3. Summarized data of green roofs, heat vulnerability, and estimated proportion of area covered by combined sewer area
for City Council Districts, and R code used to run correlation analyses presented in the manuscript.
Please click here to download file ‘appendix3.zip’.
