Dublin Tree Canopy Study
March, 2017.
Michael Brennan, Gerald Mills
and Tine Ningal
Du li T ee Ca op “tud
Final Report
Michael Brennan, Gerald Mills and Tine Ningal
28/03/2017
University College Dublin
School of Geography,
Newman Building, Belfield
Dublin 4
Contents
1. Introduction _________________________________________________________________ 2
2. Methods and results __________________________________________________________ 2
2.1 Deliverable 1: Assessment of total canopy cover and its geographical distribution ________________ 2
Urban canopy cover ___________________________________________________________________ 7
Rural canopy cover ___________________________________________________________________ 9
Tree canopy cover on OPW sites ________________________________________________________ 10
2.2 Deliverable 2: Estimation of the proportion of canopy cover in public ownership ________________ 12
Results ____________________________________________________________________________ 12
2.3 Deliverable 3: Comparison of Dublin canopy coverage with other major European cities: _________ 14
Results ____________________________________________________________________________ 14
2.4 Deliverable 4: Estimation of environmental services provided _______________________________ 19
2.4.1 Air quality _____________________________________________________________________ 19
2.4.2 Runoff ________________________________________________________________________ 23
3. Conclusions & Recommendations _______________________________________________ 28
4. References _________________________________________________________________ 30
Appendix 1.___________________________________________________________________ 33
Estimating canopy cover: Comparison with other methods ____________________________ 33
Appendix 2.___________________________________________________________________ 40
The databases generated for the DTCS _____________________________________________ 40
1
1. Introduction
This report describes the work carried out as part of the Dublin Tree Canopy Study (DTCS). The DTCS
undertook a complete aerial survey of the tree canopy cover for the Greater Dublin Region within
the lands managed by the four Dublin local authorities (Fingal (FCC), Dublin City (DCC), Dun
Laoghaire-Rathdown (DLR) and South Dublin (SDCC)), and the Office of Public Works (OPW). The
DTCS has five deliverables as described in the project methodology (Brennan et al., 2015), these
being:
1.
2.
3.
4.
5.
Assessment of the total canopy coverage and its geographical distribution
Estimation of the proportion of cover that is in public ownership
Comparison of Dublin canopy coverage with other major European cities
Estimation of the environmental services provided by the current cover
Recommendations for tree canopy cover
This report proceeds with an overview of the methods used to achieve Deliverables 1-4, with
Deliverable 5 being addressed in the conclusion.
2. Methods and results
2.1 Deliverable 1: Assessment of total canopy cover and its geographical distribution
To achieve Deliverable 1 several methodologies were employed. Firstly, satellite imagery for the
entire study area was downloaded using the SAS Planet software. This imagery was geo-rectified
(Pollefeys et al., 1999) and organised into larger image tiles for processing within the ArcGIS
platform. Supervised classification tools within ArcGIS were used in an attempt to classify the
imagery. However, despite initially promising results, these tools were unable to routinely
distinguish tree canopy from other vegetated land cover classes (e.g. grass, shrubs, etc.), and
significant misclassification of non-vegetated land covers (e.g. roads) occurred in shadowed areas.
In light of the difficulties encountered with image classification, another remote sensing approach
was implemented based on spatial sampling. This is a two-step process: first, a large number of
sample points are generated and superimposed upon imagery of the study area, and; second, the
land-cover below each point is visually classified into as one of 13 categories. This is a well-tested
methodology that is similar to the approach adopted by the i-Tree software1 (Nowak et al., 1996).
Table 1 describes the land cover classes used in the DTCS.
Sampling methodologies have been routinely employed in other jurisdictions to establish baseline
tree canopy cover and monitor changes over time (Heynen et al., 2006, Berland, 2012, Greater
London Authority, 2015). Though the number of points used varies between studies, guidance
provided by the London Tree Officers Association2 suggests at least 3,000 points be used per London
borough (15-150 km2), resulting in a sampling density of 0.2-2 points per hectare (ha). Given that the
DTCS study area consists of ~92,600 ha (926km2), this suggests that 18,521- 185,210 points be used.
1
2
https://canopy.itreetools.org/
http://www.ltoa.org.uk/resources/open-source-canopy-cover-audit-oscca
2
Table 1. Land cover classifications used in the DTCS
Land cover label
Description
1. Tree cover - deciduous
Deciduous trees, both single trees and woodland
2. Tree cover - coniferous
Coniferous trees, both single trees and woodland
3. Shrub cover
Ambiguous woody plants/Unmanaged and overgrown land
4. Herbaceous vegetation /
All types of grass covered land, including grazing land,
Grassland
amenity and domestic lawns, etc.
5. Cultivated and managed /
Includes cereals, vegetables, fruits and lands under
Cropland
glass/polythene
6. Regularly flooded / wetland
Includes marshes, bogland with surface water, flooded
quarries, plant choked ponds, etc.
7. Urban / built up - buildings
All built structures
8. Urban / built up - roads
All road surfaces, both asphalt and concrete
9. Urban / built up - other
Includes paved areas, footpaths, (non-grass) sports pitches,
impervious surfaces
driveways
10. Snow and ice
Snow and ice
11. Barren
Bare rock and soil, can include dirt tracks
12. Open water
All bodies of open water.
13. Not Sure
Uncertain land cover
However, preliminary results from a range of test locations established that using points within this
this density range, even at maximum, were not appropriate in all areas of the study area. In urban
areas, considerably more points were found to be necessary to capture an accurate estimate of tree
canopy due to the highly heterogeneous nature of the urban land cover. In contrast to rural areas,
having a generally more homogeneous land cover, required a much lower level of sampling to
estimate tree canopy cover accurately. The study area was divided into urban and rural areas using
the 2012 CORINE land cover dataset (Corine Land Cover, 2000, EPA, 2012). In the CORINE scheme,
u a o espo ds losel to the artificial landcover category.3
To ensure geographical cover a systematic random sampling method was employed. In the urban
areas of the study area 440,397 points were sampled at a rate of once each 30 m (resulting in
approximately 12 points per hectare). In the rural areas, a sampling rate of 100 m was employed,
resulting in 25,000 sample points (0.44 points per hectare). Altogether, 464,386 sample points were
categorised using the scheme shown in Table 1. Once classification of all points in both rural and
urban areas was completed the points were aggregated into grid cells, with the proportion of canopy
cover in each cell calculated as:
�
�
� �
=�
�
�
Owing to the different sampling densities employed in rural and urban landscapes, urban cells have
a cell size of 250m, and rural areas have a cell size of 1km – in other words the spatial detail for
urban areas is 16 times more precise. The area of canopy cover in each cell was then calculated as:
�
�
×
=
�
The canopy area values for each cell were then summed for the entire study area, urban areas, rural
areas and each of the four Dublin local authority areas.
3
http://image2000.jrc.ec.europa.eu/reports/technical_guide.pdf
3
The canopy assessment was completed at the end of January 2016. The full results expand on
findings presented at the Urban Forest event held in Wood Quay4, with tree canopy unevenly
distributed throughout the study area (Figure 1). Total canopy is estimated to be 9284 ha, covering
approximately 10% of the entire study area.
Figure 1. Total tree canopy cover assessment within the study area. A cell size of 250m is used in
urban areas and 1000m in rural areas.
4
https://www.youtube.com/watch?v=rAC-YJK39X0&index=3&list=PLHKVjBSDqMB7ZJCrB1i6qjRLQQ5qrGm23
4
Table 2. Canopy cover in each of the local authorities and for the study area.
Local
Authority
Area
(ha)
DCC
11,772
1,197
10.2
97.1
DLR
12,660
2,398
18.9
59.8
Fingal
45,806
2,996
6.5
25.7
SDCC
22,350
3872
17.3
42.2
Total
92,588
9,284
10.0
43.4
Canopy
(ha)
Percent
canopy
Percent
urban
When total canopy cover is examined by local authority area (Table 2) there are significant
differences. Of the four LAs, Fingal has the lowest cover and DLR the highest. These numbers reflect
the very different landuses within the LAs, especially the division between urban and rural uses (as
defined by the Corine landcover database). Figures 2-5 shows the tree cover in each of the LAs.
Figure 2. Estimated tree canopy cover across Fingal, which has predominantly rural landcover (74%)
and the estimated overall tree canopy cover is estimated at 6.5%.
5
Figure 3. Estimated tree canopy cover across DCC, which has predominantly urban landcover (97%
and the estimated overall tree canopy cover is estimated at 10.2%.
Figure 4. Estimated tree canopy cover across SDCC, which is 42% urbanised. The estimated overall
total canopy cover is 17%
6
Figure 5. Estimated tree canopy cover across DLR, which is nearly 60% urbanised. The estimated
overall tree canopy cover is 18.9%.
Urban canopy cover
The mean canopy cover of urban cells is 10.71% however the distribution is positively skewed and
most cells have cover fractions of 10%. The total urban canopy area is estimated at 4,503.19 ha.
Differences are evident between the four Dublin local authority areas. By comparison with the study
area as a whole and the other partner jurisdictions (Tables 3 & 4 and Figure 6) DLR has a significantly
higher canopy cover (10-20% and above). An axis of low canopy cover stretches from the docklands,
through the inner city and extending through Crumlin to Tallaght. Other areas of low canopy cover
include the North Inner City, the various business parks proximate to the M50 motorway and the
Fingal towns of Rush, Lusk, Skerries and Balbriggan. Areas of high canopy cover (excluding DLR)
include a g ee a is eginning at the National Botanic Garden, stretching into Glasnevin, along
Griffith Avenue, into Clontarf a d “t. A e s Pa k. Othe a eas of high a op o e i lude Howth,
Swords and Malahide in Fingal and Clondalkin in South Dublin.
7
Table 3. Proportion of urban grid cells in each canopy cover percentage class for each local authority
area.
Urban
areas
DCC
DLR
Fingal
SDCC
Total
0%-10%
0.63
0.36
0.64
0.66
0.59
10%-20%
0.23
0.32
0.22
0.21
0.23
20%-30%
0.09
0.19
0.08
0.07
0.10
30%-40%
0.03
0.09
0.04
0.04
0.04
40%-50%
0.01
0.03
0.01
0.02
0.02
≥50%
0.00
0.02
0.02
0.00
0.00
Table 4. Total urban tree canopy area, urban canopy cover as a percentage of urban area, mean
canopy cover per urban cell for each local authority area.
Urban
Area (ha)
Urban
Canopy (ha)
% canopy
cover
Mean canopy
per cell (%)
Mean canopy
per cell (ha)
DCC
11,425.27
1,185.11
10.37
10.02
0.58
DLR
7,565.79
1,238.90
16.38
15.83
0.81
Fingal
11,754.11
1,151.41
9.80
9.04
0.39
SDCC
9,426.95
927.77
9.84
9.07
0.42
Total
40,172.12
4,503.19
11.21
4.30
0.24
Figure 6. Proportion of urban grid cells (y-axis) in each canopy cover percentage (x-axis) class for
each local authority area.
8
Table 4a provides a breakdown of land-cover within the urban parts of the four LAs. As part of the
point sampling process the underlying cover was classified into one of nine categories (Table 1). The
distinction between tree and shrub is based on height and shape of the canopy. This table is a useful
assessment of the total green cover (the last column) in the urbanised part of Dublin county, most of
which is grass.
Table 4a. Percent land-cover in the urban parts of the four local authority areas.
Local
Tree
Authority
Shrub
Grass
Impervious
Road
Buildings
Green
DCC
10.37
6.18
25.69
26.50
9.23
20.28
42.24
DLR
16.38
9.98
28.12
19.27
8.54
15.04
54.48
Fingal
9.80
9.18
37.80
18.10
8.23
12.20
56.78
SDCC
9.84
7.68
33.87
21.76
9.12
14.37
51.39
Rural canopy cover
The majority of rural areas in the study area have relatively low tree canopy cover (0-10%), with the
mean canopy cover being lower than that for urban area (8.35%). Total canopy area in rural canopy
area is estimated at 4780.79 ha for the entire rural area. DLR, and SDCC are significantly different
compared to both the study area as a whole and the other partner jurisdictions (Table 5 & 6 and
Figure 7) as both include forested stands the Dublin Mountains, which have complete canopy cover.
Table 5. Proportion of rural grid cells in each canopy cover percentage class for each local authority
area.
Rural areas
DCC
DLR
Fingal
SDCC
Total
0%-10%
0.83
0.40
0.83
0.61
10%-20%
0.11
0.20
0.10
0.17
20%-30%
0.06
0.15
0.03
0.08
30%-40%
0.00
0.12
0.02
0.03
40%-50%
0.00
0.10
0.01
0.04
≥50%
0.00
0.04
0.01
0.06
0.74
0.13
0.05
0.03
0.02
0.01
Table 6. Total rural tree canopy area, rural canopy cover as a percentage of rural area, mean canopy
cover per rural cell for each local authority area.
Rural
Rural % canopy Mean canopy Mean canopy
Area (ha) Canopy (ha)
cover
per cell (%)
per cell (ha)
2.77
DCC
346.78
11.99
3.46
0.26
16.15
DLR
5,094.25
1,158.95
22.75
10.08
5.91
Fingal 34,052.25
1,844.47
5.42
3.43
2,944.46
22.78
13.56
SDCC 12,923.06
8.02
8.35
Total 52,416.35
4,780.79
9.12
0.24
9
Figure 7. Proportion of rural grid cells (y-axis) in each canopy cover percentage (x-axis) class for each
local authority area
Tree canopy cover on OPW sites
The sites managed by the OPW represent a special case (Table 7). The small number of sites which
are of interest to the OPW allowed the direct digitisation of the tree canopies within to be carried
out in ArcGIS. The OPW sites include: Arbour Hill Cemetery, Custom House, Dublin Castle, Farmleigh,
the Garden of Remembrance, Grangegorman Military Cemetery, the Iveagh Gardens, Leinster
House, the National Botanic Gardens, the National Gallery of Ireland, the National War Memorial
Gardens, St. Enda's Park, St. Stephen's Green and the Phoenix park.
Table 7. Digitisation status of each OPW site of interest
Site
Arbour Hill
Custom House
Dublin Castle
Canopy
Perimeter (m)
Canopy
Area (ha)
Site
Area (ha)
Canopy
Cover (%)
1191.34
0.43
1.41
30.88
576.13
0.14
1.78
8.11
643.47
0.13
6.21
2.09
19724.67
9.90
32.19
30.75
401.52
0.23
0.54
42.71
Grangegorman
1765.27
0.56
2.12
26.58
Iveagh Gardens
3723.53
2.03
3.32
61.30
Leinster House
734.70
0.27
1.93
13.90
14997.55
8.07
18.38
43.90
0.00
0.00
1.13
0.00
Phoenix Park
10987.8
182.33
708.07
25.75
St. Enda’s Park
8237.38
7.60
16.69
45.55
Stephens Green
8230.44
5.22
9.33
55.90
14415.71
9.14
22.67
40.32
Farmleigh
Garden of Remembrance
National Botanic Gardens
National Gallery of Ireland
War Memorial Gardens
10
Tree canopy has been digitised for all OPW sites (Figure 8). Despite a large range in canopy cover,
OPW sites generally contained higher canopy cover than the average urban cell, with a mean canopy
cover of 30.92%.
Figure 8. The digitised canopy within the OPW sites.
11
2.2 Deliverable 2: Estimation of the proportion of canopy cover in public ownership
To identify which trees are located in public spaces it was necessary to build a GIS dataset of likely
public spaces. This dataset is being created from three primary geographic datasets:
1. the road network,
2. the boundaries for the parks and other open spaces and,
3. other public spaces, such as those identified in DCC s Habitat Mapping Initiative O ‘iai et
al., 2006).
The creation of the public space dataset followed a series of steps. First, the public space around the
road network was estimated by creating a 5m buffer around the centreline of the road network.
Second, these spaces were merged with the park and open space coverages (boundaries were added
to the public space dataset using the Merge function of ArcGIS. Finally, public spaces recorded in
additional datasets (e.g. the Dublin City Habitat Mapping Initiative) were extracted and merged with
the others. In subsequent analysis, the area covered by this dataset (hereafter referred to as Public
Space dataset) was regarded as representing the publicly owned areas within the study area.
Calculation of the proportion of tree canopy in public ownership then proceeded on two parallel
tracks. Firstly, the Public Space dataset was used to locate all sample points (from Deliverable 1)
located in public space. This was achieved using the simple Select by Location5 function in ArcGIS.
Secondly, the Public Space dataset was used to calculate the proportion of public space in each
250m urban grid cell used previously in the canopy analysis. Thus, each grid cell then contained data
which detailed the proportion of that cell which was covered by tree canopy and the proportion of
that cell which consisted of public space. By combining these pieces of information, it was possible
to estimate the proportion of the cell that was covered by tree canopy in public ownership.
Results
The land in public ownership is estimated to be 22,351ha or 24% of the study area (Table 8). Stark
differences were apparent between urban and rural areas in terms of the proportion of land in
public ownership; over 40% of urban land was estimated to be in public ownership versus slightly
under 11% in rural areas. This is to be expected, but highlights that the policy options available to
the project partners should they seek to increase canopy cover will by necessity be very different in
urban versus rural areas.
5
http://desktop.arcgis.com/en/arcmap/10.3/map/working-with-layers/using-select-by-location.htm
12
Table 8. Proportion of the study area estimated to be in public ownership, for the entire study area
and broken down into urban and rural components.
Area breakdown
Area (ha)
% of total
Total Study area
92,588.47
100
Total Public Space
22,351.20
24.14
Total Private Space
70,237.27
75.86
52,416.35
100
Rural Public Space
5,724.90
10.92
Rural Private Space
46,691.45
89.08
Urban area
40,172.12
100
Urban Public Space
16,626.30
41.39
Urban Private Space
23,545.82
58.61
Rural
Rural area
Urban
The proportion of tree canopy in public ownership varied greatly between urban and rural areas and
between Dublin counties. Due to the relatively small amount of rural public land, in-depth analysis of
rural public tree canopy (which would have accounted for an even smaller land area) is not feasible
without undertaking more data gathering and analysis. Of the estimated 4,503 ha of tree canopy
cover in urban areas, less than half (1,969 ha) was calculated as being in public ownership (Table 9).
DCC differs in character compared to the other Dublin authorities with the majority (60%) of its
canopy located on publicly held lands.
Table 9. The amount of total urban tree canopy cover, and the amount of canopy on publicly and
privately owned land disaggregated to county level.
Urban
Area (ha)
Urban
Canopy (ha)
Public
Canopy (ha)
Private
Canopy (ha)
Public
Canopy (%)
Private
Canopy (%)
DCC
11,425.27
1,185.11
721.36
463.75
60.87
39.13
DLR
7,565.79
1,238.90
409.09
829.81
33.02
66.98
Fingal
11,754.11
1,151.41
479.94
671.47
41.68
58.32
SDCC
9,426.95
927.77
358.16
569.61
38.60
61.40
Total
40,172.12
4,503.19
1,968.55
2,534.64
43.71
56.29
13
2.3 Deliverable 3: Comparison of Dublin canopy coverage with other major European
cities:
As part of this deliverable, published estimates of canopy cover in a wide range of cities were
compiled through literature review. Additionally, queries were directed to the COST Action FP12046
European research group, which has a focus on urban forestry. The literature review highlighted that
the calculation of urban canopy cover has been conducted for many cities globally, using a variety of
methods. This variety of methods can lead to a range of estimates for total canopy cover the same
city (Heynen et al., 2006), and can be extremely time consuming for large study sites.
Given the difficulties encountered with image classification, as described above, a single coherent
methodology was required to compare the canopy cover of Dublin to a selection of cities including
other Irish urban centres, UK and European cities. With this in mind, the i-Tree canopy7 online
canopy classification tool was chosen as it was freely available and would be implemented in a
standardised manner across all chosen cities. i-Tree canopy operates in a similar way to the method
used in Deliverable 1, i.e. a large number of random points are placed over an area of interest and
visually categorised by the operator. The base image used for this system is GoogleEarth. The cities
chosen for comparison were European capitals, a selection of UK cities and the major Irish urban
centres. The comparison area for each city was defined by a 1km circular buffer around the city
centre point. Within each comparison area 6,000 randomly selected points were generated and
these were classified into the land-cover categories shown in Table 1. The canopy cover was
calculated as the percent of points identified as canopy within the city centre buffer.
Results
There are a great number of publications on urban greening; a simple search for potentially relevant
publications in Google Scholar yields over 20,000 results. Our focus on places where we could get
comparable canopy statistics is shown in Table 10. As expected, there is a wide range of values in the
published literature for urban canopy cover in different cities. This variation reflects both varying
canopy cover and definitions of what constitutes the urban area so that simple comparison between
cities is not very useful. For North American cities the median cover is 26.3%; for major cities the
highest cover (>50%) reported was for Atlanta and the lowest (12.4%) was for Miami. The canopy
cover values for European cities were generally lower: the median value was 15.25% with the
highest reported value (57.3%) for Stockholm and lowest (0.9%) for Athens.
In this context, the overall value for the Dublin study area (10%) would place it in the lower half of
European city values; DLR and SDCC would be placed in the upper half, Fingal in the lower half and
Dublin city close to the average.
6
7
http://www.cost.eu/COST_Actions/fps/Actions/FP1204
http://www.itreetools.org/canopy/
14
Table 10. Estimated tree canopy cover in cities around the world based on published results. Sources
are listed in the references.
Source
City
Canopy
cover (%)
Source
City
North America
(Donovan and Butry,
2011)
Portland, Oregon
(Szantoi et al., 2012)
Canopy
cover (%)
Europe
26
(Fryer, 2014)
Cardiff, UK
15.6
Miami-Dade County,
Florida
12.4
(Fryer, 2014)
Urban areas in
Wales
16.8
(Locke et al., 2013)
Baltimore, Maryland
27.4
(McPhearson et al.,
2013)
New York, New York
21
(Nowak et al., 2013)
Toronto, Ontario
26.6
(Merry et al., 2014)
Detroit, Michigan
19.6 – 30.8
i-Tree Survey
2014
Red Rose Forest
Survey 2007
(Davies and Doick,
2015)
(Davies and Doick,
2015)
(Merry et al., 2014)
Atlanta, Georgia
50 – 53
Washington, D.C.
31.8
St. Louis, Missouri
26
(Casalegno, 2011)
(Halter, 2013)
Austin, Texas
33
(Casalegno, 2011)
Athens, Greece
0.9
(Lee et al., 2016)
Sacramento,
California
18.2
(Casalegno, 2011)
Berlin, Germany
42
(Potyondy, 2011).
Hibbing, Minnesota
18.03
(Casalegno, 2011)
(Boeur, 2010)
District of Sechelt,
British Columbia
59
(Casalegno, 2011)
(Jorgensen, 2011)
Saint Paul, Minnesota
32.5
(Casalegno, 2011)
(2011) City of
Bellingham
Bellingham,
Washington
40
(Casalegno, 2011)
Helsinki, Finland
49.1
(Shields, 2007)
Kirkland, Washington
40
(Casalegno, 2011)
Lisbon, Portugal
8.7
(Walton et al., 2008)
Syracuse, New York
21.4
(Casalegno, 2011)
Madrid, Spain
6.8
(Richardson and
Moskal, 2014)
Seattle, Washington
28.5
(Casalegno, 2011)
Paris, France
10.8
(McGee et al., 2012)
Winchester, Virginia
27
(Casalegno, 2011)
Rome, Italy
3.5
(Clayton, 2016)
Seattle, Washington
23
(Casalegno, 2011)
Stockholm,
Sweden
57.3
(Crowley, 2011)
Redlands, California
25
(Casalegno, 2011)
Warsaw, Poland
36.5
(Wilson and Lindsey,
2009)
Indianapolis (CPD),
Indiana
Sacramento,
California
Los Angeles,
California
17
(Casalegno, 2011)
Vienna, Austria
4.9
(Locke and Grove,
2016)
(Coble and Walsh,
2012)
(Schwarz et al., 2015)
(Schwarz et al., 2015)
54.64
Urban areas in
Australia
(Jim and Chen, 2008)
Guangzhou, China
(Rahman and Agarwal)
Bhopal, Madhya
Pradesh, India
Manchester, UK
15
15.5
Edinburgh, UK
17
Torbay, UK
12
(Tallis et al., 2011)
London, UK
20
(Strohbach and
Haase, 2012)
Leipzig,
Germany
Amsterdam,
Netherlands
Bruxelles,
Belgium
Budapest,
Hungary
Copenhagen,
Denmark
19
4.1
12
2.5
28.5
Dublin Canopy Study
Dublin
10.0
DCC
10.2
27 – 56
DLR
18.9
7
Fingal
6.5
10.29
SDCC
17.3
17.61
Australia-Asia
(Jacobs et al., 2014)
Glasgow, UK
15
For the direct comparison cities, i-Tree canopy assessments were completed. In addition, i-Tree
canopy assessment of Dublin city centre has also been completed. Compared to the city centres of
the other completed European and UK cities, Dublin city centre ranks lowest in terms of tree canopy
cover (Table 11). Similarly, Dublin has a lower level of canopy cover compared to all assessed Irish
urban centres with the exception of Belfast.
.
Table 11. Percentage tree canopy cover in comparison cities.
City
Dublin
# Points Mean % tree cover
135
4.50
177
Belfast
3.54
456
Cork
9.12
810
Derry
16.2
332
Galway
6.64
530
Limerick
10.6
Cardiff
467
15.6
Amsterdam
232
7.74
Berlin
205
6.84
Brussels
189
6.31
Copenhagen
342
11.4
Helsinki
455
15.2
Lisbon
381
12.7
Madrid
198
6.6
Oslo
278
9.27
The green infrastructure (GI) present in Irish urban centres was compared by Mills et al. (2015), see
Table 11 and Figures 9 & 10. This study highlighted the variability of GI and tree canopy cover among
Irish urban centres. Much of the differences can be explained by the size of the city and the land-use
geography. The city centres of Dublin and Belfast are dominated by commercial land-uses and have
the lowest green cover and the lowest tree canopy cover; what GI is present is in the form of street
trees and public parks. In other cities, the study area includes low-density residential areas, where
there are often large private gardens. This is clearest for Derry and Waterford, the smallest cities
included in this study but it can also be seen in Limerick, where commercial development is
concentrated to the south of the Shannon extending westward. However, it is important to point out
that there are a large number of residents living in city centres; in 2011 there were approximately
40,000 living in the Dublin study area and 22,000 in the Cork study area. If the GI and tree canopy
cover are expressed in terms of population, then the differences between cities becomes stark.
Dublin has the lowest ratio of GI and canopy per capita, significantly lower than the values for
Belfast (Table 12).
The results show that Dublin city centre has the lowest cover of all the European city centres
compared. Some of this variation can be explained by local landuse but it is consistent. This indicates
that there is a need for a policy on GI i Du li s it e t e that oth conserves the existing stock
and expands tree planting to improve the urban environment for visitors and the growing resident
population.
16
Table 12. A classification of the land-cover in Irish city centres based on 5000 randomly selected
points within a circular area (1 km radius) using the i-Tree Canopy software. The values in each cell
represent the percentage of points (and standard error) that correspond to that land-cover. The final
three rows are the land area (that is the study area excluding water), the percentage of land area
that is Green (that is, Tree or Shrub or Grass) and Impervious (Roof, Road and Other impervious).
Source: Mills et al., 2015.
Land-cover
Belfast
Cork
Derry
Dublin
Galway
Limerick
Tree
3.54±0.26
9.12±0.41
16.2±0.52
4.52±0.29
6.64±0.35
10.6±0.44
6.14±0.34
Shrub
0.86±0.13
0.98±0.14
4.74±0.30
1.24±0.16
4.78±0.30
8.10±0.39
7.10±0.36
Grass
4.20±0.28
21.1±0.58
14.8±0.50
3.68±0.27
14.6±0.50
11.6±0.45
14.7±0.50
Road
36.4±0.68
26.4±0.62
9.92±0.42
18.4±0.55
13.2±0.48
13.0±0.48
14.3±0.50
Roof
32.0±0.66
31.1±0.65
24.7±0.61
39.6±0.69
22.1±0.59
24.1±0.60
28.3±0.64
Other Impervious
17.7±0.54
6.06±0.34
11.9±0.46
28.5±0.64
18.0±0.54
18.9±0.55
13.7±0.49
Water
4.44±0.29
5.10±0.31
14.2±0.49
3.88±0.27
11.3±0.45
12.3±0.46
13.8±0.49
Other
0.78±0.12
0.10±0.04
3.52±0.26
0.28±0.07
9.46±0.41
1.42±0.17
1.88±0.19
300
300
269
302
279
275
271
8.67
86.78
31.23
63.62
37.04
48.22
9.47
86.74
28.74
58.87
30.74
56.81
28.48
57.38
Land area (ha)
Green (%)
Impervious (%)
Waterford
Figure 9. The Dublin city centre area study area as defined in i-Tree Canopy; 5000 randomly selected
points within the study area were classified into land-cover types. The red symbols represent those
sample points that fall on a tree canopy. The green areas represent the estimated green cover based
on the sampled points that represent trees, shrubs and grass. Source: Mills et al., 2015.
17
Figure 10. The study area of each of the Irish cities; the legend is the same as that for Figure 9.
Source: Mills et al., 2015.
18
2.4 Deliverable 4: Estimation of environmental services provided
Trees provide a number of environmental services (such as biodiversity, air quality, and water
management) but evaluating the value of these is not a simple task as it depends on species, age,
health, etc. of the tree stock.
2.4.1 Air quality
The tools for assessing air quality (AQ) services are best developed and are based on canopy plan
area, which is an indicator of the total leaf area exposed to the atmosphere. Trees remove pollutants
actively (through exchanges via stomata) and passively (by slowing air movement and providing
settling surfaces), hence the greater the leaf area exposed, the larger the potential removal. The
actual amount removed however depends on the ambient air quality to which the trees are
exposed, which is a function of the concentration of pollution (such as micrograms per cubic metre,
g m-3). The concentration itself is a product of both rate of pollutant emission and the volume of
atmosphere into which it is mixed. Naturally, the concentration of pollutants varies greatly in space
and time, especially in cities where there is a multitude of emission sources and mixing volumes. The
estimates shown here are based on typical removal functions associated with a climate and emission
properties similar to Dublin and are employed in i-Tree software (Morani et al., 2014).
Air quality is generally evaluated in terms of common urban pollutants that affect human health.
These include:
•
•
•
•
•
Nitrogen dioxide (NO2) and Nitrogen oxides (NOx) which are emitted by internal combustion
engines.
Sulphur Dioxide (SO2), which is linked to coal burning and is also emitted by diesel engines.
Carbon monoxide (CO), which is a by-product of any fossil fuel combustion.
Particulate matter (PM) less than 10 mm (PM10) and less than 2.5 mm (PM2.5), which are
sourced from road dust, tyre fragments, fires and combustion engines.
Ozone (O3) which is a secondary pollutant created when other primary pollutants (such as
NOx) are present and atmospheric conditions are favourable.
The health impacts of these pollutants are measured in terms of threshold values that represent
either extreme values (daily or hourly) and mean background values. For some there are a set
number of days where the threshold values cannot be exceeded. Given the nature of the pollutants,
these values differ for each pollutant type (Table 13). Observations at a variety of stations around
Dublin (Table 14) are used to assess compliance. In general air quality in Dublin is described as good
and in its latest report the EPA indicated that no EU limit threshold values were exceeded. However,
potential challenges to improved air quality include switching from solid fuel to cleaner fuels for
home heating and the potential for increased NO2 pollution associated with transport.
Carbon dioxide (CO2) is not included in the urban air pollutants as it is not linked directly to human
health. However, the human contribution to global concentrations of CO2 is a major driver of climate
change and mitigating carbon emissions is a national objective. As trees play an important role in the
carbon cycle, the potential of the urban forest to offset CO2 emissions is examined.
19
Table 13. Summary of the Ai Qualit Di e ti e s li it alues for the protection of human health.
Abstracted from Table A1 in EPA (2015)
Pollutant
Averaging
period
SO2
Value
350 g m-3
125 g m-3
200 g m-3
40 g m-3
10 mg m-3
Hour
Day
Hour
Day
Max daily
8-hour
mean
Day
Year
year
Max daily
8-hour
mean
NO2
CO
PM10
PM2.5
O3
Num. of allowed
occurrences.
24
3
3
50 g m-3
20 g m-3
25 g m-3
120 g m-3
35
0
0
25
25
41
1
0.4
14
9
43
14
9
15
22
16
25
31
67
14
22
14
24
9
14
7
0.5
11
18
17
7
8
6
51
14
Clonskeagh
Tallaght
Balbriggan
Swords
St Anne’s Park
Blanchardstown
Ballyfermot
Dun Laoghaire
Rathmines
17
27
3
Marino
31
59
1
0
Finglas
NO2
NOx
SO2
CO
O3
PM10
PM25
Coleraine St.
Pollutant
Winetavern St.
Table 14. Annual means (g m-3) for air selected pollutants at stations in Dublin (EPA, 2015).
53
23
15
Table 15 shows the estimated pollutant removal based on the plan area of canopy cover. The
estimates are provided for each local authority area and their rural and urban parts. The removal
functions are listed in the table caption and represent the estimated potential contribution of the
u a fo est to Du li s ai ualit . The a tual alues ould depe d o the a ie t ai ualit a d
would require an analysis of the tree stock and the local climate. The estimated pollution removal by
the total urban forest is 11.8 tCO, 65.0 tNO2, 502 tO3, 25.6 tPM2.5 and 142 tPM10, which is about
equally divided between urban and rural contributions.
We can place these values into context by estimating the total amount of pollutants in the urban
atmosphere from Table 14. For example, a reasonable spatial average for urban NO2 is 20 g m-3 so
that 65 tNO2 represents 3250 km3 of cleaned air or a layer of air over the city 8 km high. Of course,
average NO2 values are an outcome of fresh emissions and losses due to chemical reaction and
advection; we might expect that the atmospheric boundary layer over Dublin refreshes itself about
once an hour (based on a 5 ms-1 windspeed) so that, at best, the removal by the canopy in the study
20
a ea a ou ts fo o e da s e issio s o e a ea . We could estimate the impact as a function of
vehicle emissions; assuming a vehicle emits 17 kg yr-1 of NOx and if 50-70% is converted to NO2 then
65 tNO2 is the equivalent of about 5000-7500 vehicles.
The carbon functions of trees can be assessed in terms of sequestration (CO2sq) and storage (CO2st),
which measures annual net carbon captured by leaves and stored in the woody skeleton,
respectively. The overall values are 103,702 tCO2sq and 1,687,549 tCO2st which are about evenly split
between urban and rural contributions. While the urban atmosphere is enriched with carbon, it is
not a local health issue. Instead the question: is to what extent the urban forest can offset CO 2
emissions. If we assume that a vehicle emits about 250 gCO2 km-1 or 3.75 tCO2 yr-1 then the urban
forest can offset annual emissions from just 30 vehicles; moreover, the total CO2 in storage is worth
450 vehicles. The estimated emission within the DCC area was 5 million tCO2 in 2010 (DCC 2012): it
would take the entire urban forest in County Dublin about 5 years to sequester this quantity and
about 400 years for the DCC forest to do the same. So, while the trees provide a valuable function,
their air quality value should not be overstated. Much of the Dublin tree stock is deciduous so that
du i g i te the apa it of the t ee sto k to lea the ai is g eatl edu ed. However increasing
tree canopy cover as part of an air pollution strategy that manages emissions can improve air
quality.
The contributions of the tree canopy in each local authority can be evaluated simply as the
proportion of the forest cover in each local authority. Of course, there are significant differences
between each local authority in terms of canopy cover in rural and urban areas; DCC has by far the
smallest area classified as rural and perhaps the smallest capacity to increasing canopy cover (Table
5). If we restrict ourselves to just the urbanised part of the study area, DLR is exceptional as it has
the largest canopy percent (16%) and has nearly 1.5 times the canopy cover per unit area compared
to the other local authorities. However, much of this canopy is on private land (67%) which means
that the management of much of the GI relies on the goodwill of the property owner. By
comparison, DCC has the largest canopy in public space both in actual area and percent (Table 9).
21
Table 15. Area of tree canopy cover (hectares) in the study area decomposed into Local Authority
(LA) and urban and rural landcover. The potential air quality benefits are shown as pollutant removal
based on i-Tree Canopy software. Benefits are estimated based on tree canopy area cover using the
following multipliers in g m2 yr-1: Carbon monoxide (CO) 0.127; Nitrogen Dioxide (NO2) 0.700; Ozone
(O3) 5.404; Sulphur Dioxide (SO2 .
; Pa ti ulate atte less tha . μ PM2.5) 0.276 and less
tha
μ
PM10) 1.534; Carbon dioxide sequestered (CO2sq) 1,117 and; Carbon dioxide stored
(CO2st) 18,177. The values in the table are in kg with the exception of CO2, which are in metric tonnes
(1000 kg).
LA
Area
CO
NO2
O3
SO2
PM2.5
PM10
CO2sq
CO2st
Urban
DCC
1,185
1,505
8,295
64,037
4,076
3,271
18,178
13,236
215,397
DLR
1,239
1,574
8,673
66,956
4,262
3,420
19,006
13,840
225,213
Fingal
1,151
1,462
8,060
62,222
3,961
3,178
17,663
12,861
209,292
SDCC
928
1,178
6,494
50,137
3,192
2,561
14,232
10,363
168,641
Total
4,503
5,719
31,522
243,352
15,491
12,429
69,079
50,301
818,545
DCC
12
15
84
648
33
184
134
2,179
DLR
1,159
1,472
8,113
62,630
3,987
3,199
17,778
12,945
210,662
Fingal
1,844
2,342
12,911
99,675
6,345
5,091
28,294
20,603
335,269
SDCC
1,765
2,242
12,358
95,401
6,073
4,872
27,081
19,719
320,893
Total
4,781
6,072
33,466
258,354
16,446
13,195
73,337
53,401
869,004
Total
9,284
11,791
64,988
Study area
501,706 31,937
25,624
142,416
103,702
1,687,549
Rural
41
22
2.4.2 Runoff
That urban forests have the ability to regulate storm water flows is well known (Xiao and
McPherson, 2002). Urban trees perform this function by facilitating the infiltration of water into the
soil and evapotranspiration (Figure 11), as well as regulating the amount of throughfall reaching the
ground via direct rainfall interception (Cappiella et al., 2005). These processes reduce the flow rate
of urban runoff and shift the runoff concentration time by temporarily storing rain water on the
canopy surfaces (Sanders, 1986).
Figure 11. Processes provided by urban trees which regulate the runoff of precipitation (Levia and
Frost, 2006, Inkiläinen et al., 2013).
Decreasing the amount of storm runoff reduces the risk of flooding, reduces surface pollutant
washoff, and lowers the amount of pollutant in the runoff. Thus, by reducing urban runoff urban
trees ultimately reduce the cost of managing urban runoff and storm water treatment (Xiao et al.,
1998, Xiao and McPherson, 2002).
There have been a large number of studies exploring the mechanisms and extent to which urban
trees and forests regulate storm water flows (Sanders, 1986, Xiao et al., 1998, Xiao and McPherson,
2002, Link et al., 2004, Asadian and Weiler, 2009, Xiao and McPherson, 2011, Farrugia et al., 2013,
Inkiläinen et al., 2013) with a large range, from 10% to over 60% (Asadian and Weiler, 2009), in the
amount of rainfall being intercepted. How much rainfall is intercepted by urban trees depends on
the character of the rainfall event, the type of trees and their canopy extent, local climate and the
surrounding urban landscape. More intense and longer duration rainfall events have been
highlighted as important factors determining rainfall interception efficiency. Similarly, different tree
species can intercept different amounts of rainfall before becoming saturated, with conifers tending
to intercept a higher proportion of rainfall compared to broadleaves (Llorens and Domingo, 2007).
As with air quality, seasonal factors are important, with broadleaf trees losing much of their
interception capabilities in the winter. Finally, the height, composition and spacing of urban
buildings can enhance or reduce the delivery of rainfall to urban trees depending on their exact
characteristics.
23
The data required to quantify the amount of rainfall intercepted by an urban forest is extensive (Xiao
and McPherson, 2011), and beyond the scope of this study. It requires not only knowledge of canopy
area, but also leaf area index (LAI), tree height, tree diameter, stem-flow rate, canopy drip rate, and
climatic variable to estimate evaporation (Xiao and McPherson, 2002). Some of this data (e.g. tree
height, diameter and generalised LAI) could be gathered from existing sources, for example from
street tree inventories8. That being said, studies have identified canopy area as being one of the key
variables that explains the variations in rainwater between areas (Inkiläinen et al., 2013). However,
the potential for the urban forest to regulate storm water flows should not be overstated. For
example, Inkiläinen et al. (2013) report that each additional percentage point of canopy cover within
an area decreased the throughfall by 0.05mm. Given the above, the literature would suggest that
ethods to a i ise ai fall i te eptio
a it s u a fo est should fo us o i easi g a op
area bearing in mind these considerations;
1. Placement of additional trees,
2. Preferential siting of additional trees on impervious surfaces rather than green spaces,
3. Utilisation of wide canopy species,
4. Increasing the proportion of conifers with the urban forest,
5. Ensuring overlap between canopies is limited,
6. Inclusion of understory species
Results: Precipitation in Dublin originates mostly from large storms that produce frequent low
intensity events. Annual rainfall totals about 760 mm and this is distributed evenly throughout the
year (Figure 12). Daily rainfall totals are usually small; since 1942 just 3.5% of days recorded values
of 16 mm or greater. Flooding events then are usually the outcome of persistent events that
saturate the ground over a period of time so that additional rainfall causes water to pool on the
surface (pluvial flooding) and/or rivers to overflow their banks (fluvial flooding). The potential for
flooding is greater in the winter months when evaporation is supressed. Dublin also experiences
coastal flooding as a result of a combination of high tide, low pressure and easterly airflow. Here we
can offer only a crude evaluation of the contribution of trees to water management in Dublin.
Total
(mm)
0
0-2
2-4
4-8
8-16
16-32
32-64
64-128
Frequency
(days)
11634
8575
2737
2465
1403
479
65
5
Figure 12. Precipitation at Dublin Airport (1942-2015). The table lists the number of days according
to recorded daily precipitation amounts. The graph shows the distribution of monthly rainfall over a
year.
8
Fingal Tree database: http://www.arcgis.com/home/item.html?id=f6a5ffbb0d6d4054b2d83bfc911b73c3
24
Trees can affect pluvial and fluvial flooding by intercepting rainfall and delaying its delivery to
watercourses. Table 16 shows the volume of water intercepted by the tree canopy across Dublin
assuming a uniform annual receipt of 760 mm.
Table 16. The estimated annual water volume (in cubic metres) that precipitates over Dublin
(assuming 760 mm) and the amount intercepted by the tree canopy.
DCC
Total volume
m3
868,320
DLR
575,000
94,156
Fingal
893,312
87,507
Intercepted m3
90,068
SDCC
716,448
70,510
Total
3,053,081
342,242
There are a couple of caveats: first, as the tree stock is dominated by deciduous species their
capacity to intercept rainfall is greatest in summer time and will be smaller in winter and; second,
precipitation varies across the study area, being highest in the south, close to the Dublin-Wicklow
mountains. So, a value closer to 200,000 cubic metres might be more reasonable. The value of this
ecosystem service depends on how much of it will eventually make its way into the drainage system
for treatment, and the unit cost of water treatment. Currently, commercial entities pay
app o i atel € / 3 for the treatment of wastewater (Walker, 2015). If we take the above
estimated volume of annually intercepted rainwater as an example, and assume 50% of all
intercepted water would require treatment, this ould t a slate to €200, 000 of avoided costs.
These impacts on the city on local hydrology and flooding are strongly linked to the proportion of
impervious surface cover (Figure 13), which is strongly concentrated in Dublin city centre. Much of
the landscape in the case study area is not urbanised and is permeable; planting trees in this
landscape has the same delaying effect however the underlying soil also acts to retain water, much
of which is subsequently returned to the atmosphere via evaporation. In impervious landscapes,
where there is little soil storage potential, the impact of trees could be greater as it can act as
u
ella slo i g the deli e of ater to the underlying roads.
25
Figure 13. Proportion of impervious surface within each study area grid cell
26
However, these functions must be placed in the context of the character of flood risk. Figure 14
shows estimated pluvial and fluvial flooding patterns for the Dublin urbanised area; these coverages
are generated by the OPW and represent 1% Annual Exceedance Probability (AEP) or the likelihood
of re-occurrence once in a 100 years. The river patterns show the influence of natural hydrology (as
water accumulates along the water course) and of urbanisation, which has restricted the river
channel and in some cases has placed the river in a culvert. The pluvial patterns are summarised
from the patches of surface water that accumulate in low lying and saturated areas following rainfall
events. For comparison, the tree cover has been superimposed on this map. There is little spatial
correspondence between these coverages, though one site that is characterised by both high flood
risk and very low levels of tree canopy is in south-west Dublin. This area has been identified by SDCC
as highly vulnerable to flooding9 and significant expansion of the urban canopy in this area would be
an appropriate part of flood mitigation measures.
Figure 12. The distribution of trees and predicted flood hazard (medium probability) associated with
rivers and rainfall events. The hazard information is courtesy of the OPW.
9
http://www.southdublindevplan.ie/sites/default/files/documents/SDCC%20SFRA%20Feb%2023%202016.pdf
27
3. Conclusions & Recommendations
A tree canopy cover of 15% would make Dublin comparable to other European cities (Table 10);
currently cover across the county is 10% but there are large variations between local authorities.
Even still, the environmental services provided by the existing tree stock are considerable: the value
of intercepted run-off is estimated at €
,
a d that for CO2 sequestered at €622,212 per annum
10
(assuming a a o
edit alue of € /to ). The four local authorities are distinct in terms of size,
degree of urbanisation and public/private split (Table 9). Given this variation, no single policy
measure is appropriate for all authorities, or indeed for all areas within each authority. Policy
options for increasing tree canopy cover will be discussed generally, followed by each local authority
in turn.
For rural areas, being largely in private ownership, the policy options are limited to incentivisation.
Currently existing incentivises include the Afforestation Grant and Premium Scheme 2014-202011,
Native Woodland Scheme12 and the FEPS Scheme13 are aimed at increasing forestry cover; the local
authorities could promote these incentives throughout their rural areas. In terms of direct action,
the authorities could ensure that existing canopy, for example present in hedgerows, is retained
through the strict enforcement of hedgerow cutting legislation.
Table 17. The size, proportion of urban land in public ownership and degree of urbanisation for each
local authority.
Total
Urban Public Proportion Proportion Urban : Rural
Area (ha)
Area (%)
Urban
Rural
Ratio
DCC
11,772.05
55.45
0.97
0.03
32.95
DLR
12,660.04
34.94
0.60
0.40
1.49
Fingal 45,806.36
40.04
0.26
0.74
0.35
SDCC
22,350.01
39.41
0.42
0.58
0.73
Total
92,588.47
41.39
0.43
0.57
0.77
In urban areas, which have a higher proportion of publicly owned land, more options are available
for direct action by the local authorities. Authorities can increase number of trees on their directly
administered land through a range of mechanisms, and indeed this is existing policy in several of the
authorities14. An important consideration for increasing tree canopy in all urban areas is the amount
and distribution of street trees. Streets make up the largest component of publicly administered
land within each of the authorities and, unlike other public lands (e.g. parks) which are unevenly
distributed within urban areas, streets are closely associated with residential populations.
10
https://euobserver.com/environment/132045
https://www.teagasc.ie/media/website/crops/forestry/AfforestationSchemeEd2190315.pdf
12
http://www.woodlandsofireland.com/native-woodland-scheme-nws
13
http://www.agriculture.gov.ie/press/pressreleases/2007/january/title,13210,en.html
14
See: Halpenny, K. J. (2010) The Forest of Fingal. A Tree Strategy for Fingal. Fingal County Council Parks
Division, Community, Recreation Senior Parks Superintendent & Amenities Department; DLR (2011) A Tree
Strategy for Dun Laoghaire-Rathdown 2011-2015. Dun Laoghaire-Rathdown County Council; SDCC (2015)
Li i g ith T ees. “outh Du li Cou t Cou il s D aft T ee Ma age e t Poli
- 2020. South Dublin
County Council. DCC (2016) Dublin Draft Tree Strategy 2016-2020. Dublin City Council.
11
28
The Dublin City Council (DCC) is dominated by urban land cover (97% urban), with less space for
new tree planting compared to other local authorities. This makes the realisation of large scale
canopy increases a difficult prospect, particularly if this canopy were to be contiguous, as this would
imply the removal of some built land (i.e. buildings, roads, etc.) to make space for trees. The need
for increased canopy cover is clearest for Dublin city centre which has a low cover compared with
other European city centres.
Dublin city council is likely to have an increased opportunity to directly implement such a policy
compared to the other authorities due to the high level of land in public ownership (~55%), due in
large part to the high road density with the authority. Increased street tree planting could and
should be integrated into all road maintenance/traffic management procedures. The total road
length within DCC is 1,550 km, if the average distance between street tree plantings is assumed to
be 20m, this suggests a theoretical maximum of 77,500 street trees could emplaced. In reality the
maximum number will be lower due to context specific factors (e.g. siting conflicts with signage,
utilities and infrastructure), but this theoretical maximum is useful to consider as an upper limit, and
is comparable to the current estimate of 60,000 street and roadside trees present within all of
Dublin county1516.
Dun Laoghaire-Rathdown (DLR) is the leafiest autho it , i that it has the highest p opo tio of
canopy cover. This is fortunate, as it has lowest proportion of public land on which it can take direct
action (34.94%) and (unsurprisingly) lowest amount of public canopy (33.02%). DLR has 821 km of
road, suggesting space for a maximum of 41,000 street trees. Ballinteer, Dun Laoghaire, Stillorgan,
Leopardstown and Stepaside are among the areas which should be prioritised for tree planting, as
many parts of these areas have less than 10% canopy cover.
Fingal is the least leaf i te s of p opo tio of a op o e , though due to its la ge size it has
the se o d la gest a ou t of a op i te s of a ea. Fi gal s u al a eas a e used fo la ge scale
and productive agriculture activities, which may make afforestation incentives less effective
o pa ed to “DCC o DL‘. “o e hat ou te i tuiti el , Fi gal s o ti ued a d fast pa ed
urbanisation (CSO, 2016) provides the authority with an opportunity to increase canopy by
mandating street tree planting into the design of any new developments. At present Fingal has 1,506
km of road suggesting space for a maximum of 75,328 street trees, which is approximately double
the estimated 33,000 street trees within the Fingal Street Tree database. In terms of areas to be
prioritised, approximately two thirds of urban Fingal has below 10% canopy cover meaning most
areas of Fingal would benefit from additional plantings. That being said, the more northerly towns in
the county, i.e. Lusk, Rush, Donabate, Ballbriggin and Skerries, could be said to be in particular need
of additional tree planting.
South Dublin County Council (SDCC) displays a strong urban/rural divide in terms of canopy cover.
Rural areas contain approximately double the canopy cover per unit area compared to its urban
areas, due in part to the presence of forested areas in the south Dublin Mountains and areas
adja e t to the i e Liffe . “DCC s u a a eas a e i po e ished i te s of t ee a op compared
15
2016. Dublin Draft Tree Strategy 2016-2020. Dublin City Council.
. Li i g ith T ees. “outh Du li Cou t Cou il s D aft T ee Ma age e t Poli
Dublin County Council
16
- 2020. South
29
to neighbouring DLR, with approximately two thirds of urban SDCC having less than 10% canopy
cover. As with Fingal, most urban areas of SDCC would benefit from increased plantings, though in
contrast to Fingal SDCC has a relative abundance of rural canopy. Areas of particular need of tree
planting are Adamstown, City West, Firhouse, Jobstown, Tallaght, Palmerstown, Perrystown and
Walkinstown. In terms of potential space for street trees, SDCC has 1,084Km of road suggesting
space for a maximum of 54,216 street trees.
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32
Appendix 1.
Estimating canopy cover: Comparison with other methods
In this section of the report we compare the canopy results of the systematic sample method with
that based on image analysis of a high-resolution IR image, which covers a small section of the study
area. The area was selected to include Phoenix Park and extend from areas outside Dublin into the
city centre.
Figure A1.1. The IR image and its location within the study area.
33
The image itself is taken from the P1 Pleiades17 satellite and provides orthorectified data in four
spectral bands: blue, green and red, which combine to represent visible radiation (VIS) and near
infra-red (NIR). The date of the image was 20/07/2013 and its resolution is (pan/colour) 50cm/2m.
Figure A1.2. The digitised canopy cover using the IR method.
Tree canopy were extracted using ENVI (Environment for Visualizing Images) software which
includes feature extraction in its toolset. In the first step, vegetative cover is extracted by obtaining
the normalised difference vegetative index (NDVI),
� �� =
�� − ��
�� + ��
In the second step, segmentation is used to identify shapes and texture. In this procedure, the user
identifies tree canopies and the software uses this information to train the software to identify
similar features across the entire image. The net effect is the automated generation of tree canopies
based on the training areas; the user can increase the number of training areas to improve the
result. Even so, the automated procedure will misclassify areas of grassland or shrub cover as tree
canopy cover and the result must be manually inspected to be improved. In this work, considerable
effort was expended on improving the tree canopy cover in Phoenix Park.
17
http://www.satimagingcorp.com/satellite-sensors/pleiades-1/
34
Figure A1:3. Examples of digitised canopy cover using the IR method for the Phoenix Park and Dublin
city centre areas.
35
Comparison of canopy cover: The sample method used in this study was compared to the IR method
for the areas where there was sufficient information. The approach taken is to superimpose the
250m grid cell network used in the main body of the study over the canopy cover derived using the
IR method.
1. Sample method: Cell canopy cover was calculated as the percent of sample points that
represented trees and shrubs within each cell. This operation was restricted to those parts
of the image where sampling was done at high density (30 m) and the number of sample
points was 20.
2. IR method: Cell canopy cover was calculated by dividing the canopy area in each cell by the
area of that cell.
Figure A1:4. The comparison study area and the cells considered for comparison purposes.
Table A1:1. Canopy cover (%) statistics for the cells depicted in Figure*.
Statistic
Mean
Median
Standard Deviation
Skewness
Range
Minimum
Maximum
Count
Sample
method
11.91
8.00
13.84
1.96
96.00
0
96
2315
IR
method
18.56
11.56
20.07
1.40
98.64
0
98.64
2315
Difference
-0.64
0
9.36
-0.80
94.41
-63.65
30.77
2315
The comparative analysis is based on 2315 cells. Overall, the canopy cover for the study area is
higher using the IR method when compared with the Sample method. The distribution of the errors
36
is positively skewed; in other words for any given cell, the IR method returns a higher value than the
point sample method. The differences or errors are well distributed and there are very few cells
where the difference are large >20%
Figure A1.5. The distribution of the error, defined as the difference between the canopy cover (%)
calculated by the Sample and IR methods.
Figure A2.6 shows a scattergram of the canopy calculations in which each point represents a cell and
the position represents its value using the Sample (x) and IR (y) methods. The agreement overall is
very good, with a coefficient of agreement (R2) of 78.5% - in other words most of the variation in the
IR estimates can be explained statistically by variation in the Sample estimates.
Figure A1.6. Scattergram of comparison cells based on estimated canopy cover.
37
These differences can also be mapped to see if there are consistent patterns of over(under)
estimation. Overall there is little discernible pattern, which would be indicative of systematic bias.
The greatest discrepancies are found close to the Phoenix park (Sample underestimates) and close
to Ringsend (Sample overestimates).
Figure A1.7. A map of the difference between canopy cover as calculated by the Sample and the IR
method. Each cell is 250 m on a side and the comparison is restricted to cells where there are 20
Sample points.
Figure A1.8. Detail of a part Dublin where the Sample and IR estimates differ by up to 30%: A) shows
the image; B) the Sample point distribution – yellow dots represent trees/shrubs; C) the IR derived
canopy and; D) the percent differences.
38
Discussion & Conclusion: There are several potential errors that explain the discrepancies between
the two estimation methods. First, the images used by both approaches are different so that trees
may be visible on one but not on another; this is likely a small error as the Sample points generally
coincide with the location of trees on the IR image. Second, the Sample points are incorrectly
classified. Again, this is likely a small error although shrubs and trees can be mixed. In the
comparison, we have added these together but not so in the main report. Third, the IR approach
relies on software to identify features, based on guidance provided by the operator. This is an
iterative process that seems to be effective in identifying vegetated areas (grass, trees, shrubs) using
the NIR band. However, significant errors still remain as the area of tree canopy is generally
overestimated (Figure A1.8) and often includes grass areas and shrubs. This means that intensive
subsequent work is required to lea the a op ge e ated. I this o k, the Phoe i Pa k a op
was extracted.
Each method has its own advantages and disadvantages. The Sample method:
1. Is time consuming but is easy to operate and is robust as the operator classifies the scene
into clear types; so, it can provide information on building density, paving etc. in addition to
vegetative cover.
2. It provides accurate aerial estimates but the accuracy is dependent on the density of
sampling – greater sampling provides more spatial detail. In this case, we used a systematic
sampling strategy to capture spatial patterns across Dublin.
The IR method:
1. Requires less time to operate but the imagery is expensive to obtain. Image analysis, rather
than generic GIS, software is needed to extract out canopy features but the approach is
effective at extracting vegetated areas.
2. It can provide great spatial detail but considerable work is needed to refine the canopy and
ensure its accuracy.
39
Appendix 2.
The databases generated for the DTCS
1. Gridded_Landcover
FID Count Tree Shrub Grass Imp. Road Build. Water Barren Area
0
6
0
4
0
0
0
0
0
2 159612
1
32
0
28
0
0
0
0
0
4 719710
2
34
0
31
0
0
0
0
0
3 797927
3
0
0
0
0
0
0
0
0
0 24429.5
4
14
0
14
0
0
0
0
0
0 267827
5
41
0
35
0
0
0
0
0
6 980853
6
49
0
48
0
0
0
0
0
1 1000170
7
49
0
49
0
0
0
0
0
0 1000170
8
21
0
20
0
0
0
0
0
1 518675
9
12
0
11
0
0
0
0
0
1 265944
This Arc shapefile file contains the sample counts for grid cells in Dublin county. The columns refer to
the FID (location), total sample (count) and the classification of points into tree, shrub, grass,
impervious, road, building, water and barren (bare soil/sand) and area of cell.
40
2. OPW_Sites
This shapefile consists of the digitised tree canopies in the OPW parks. Digitisation was completed
using Google Earth and Pleiades_RS_Image.
41
3. Pleiades_RS_Image.
This image is a sample of Dublin from the Pleiades satellite. The image resolution is 3m and it
records in the three visible wavelengths and near IR. See appendix 1.
4. Tree canopies
This is a shapefile generated from the Pleiades image which identifies green canopy cover using the
near IR band.
42
5. Google_Image_GDA
This image file was generated from Google Earth and shows the entire study area. Unfortuantely, it
was not possible to extract the canopy cover from the three visible bands in this image.
43
GIS Data
Layers
Gridded Land
Cover
OPW Sites
Pleiades
Image
Canopy
Google Image
Description
This layer contains grids of 1000 m for rural and 250
m for urban with different cover classes and
statistics. The attributes include counts of land cover
objects in each grid and their proportions in trees,
shrubs, grasses, impervious, roads, buildings, water,
barren, canopy, green space, impervious and grass.
This data is on OPW Heritage sites which contains
their names and areas. The sites mostly fall in Co.
Dublin and a small portion in Counties Fingal and
South Dublin.
Pleiades RS satellite image with NIR band for
vegetation delineation. The image acquisition date is
December 2, 2012 and has a 4m resolution. This
image was used to extract tree canopies.
This file contains the canopies extracted from the
Pleiades satellite image using remote sensing
software. The Pleiades image has near infrared band
and this band was used along with shape and
texture to extract the canopies.
This is mosaiced Google image covering GDA at 10
m resolution
Support Data
Source
Type
UCD
Geography
ESRI polygon
UCD
Geography
ESRI polygon
Centre
National
d’Etudes
Spatiales
(CNES)
TIF
UCD
Geography
ESRI polygon
UCD
Geography
TIF
Buildings
Building footprints described by types and mames
OSM
ESRI polygon
Land Use
Natural
Features
Land use descriptions by names and types
OSM
ESRI polygon
Natural land cover by types and names
OSM
ESRI polygon
Places
Places by types and names
OSM
ESRI point
Points
Points by types and name (Eg. trafic signals,
crossing, road junctions, roundabouts, stop signs,
gas stations, parking, bus stop, give way signs etc)
OSM
ESRI point
Railways
Roads
Waterways
Railway type and name
Road types, names, speed limit)
Rivers by types, names and widths
OSM
OSM
OSM
ESRI point
ESRI Polyline
ESRI Polyline
The datasets used and created in this project (top layer). The support data are generic Open Street
Map files used to help in mapping. All files use the WGS 84 coordinate system apart from
GriddedLandCover, which uses the Irish Grid.
44