University of Toronto T-Space - Mapping Changes in Urban ......of southern Ontario, with particular...
Transcript of University of Toronto T-Space - Mapping Changes in Urban ......of southern Ontario, with particular...
Mapping Changes in Urban Canopy Cover Following an Ice Storm Event: A Case Study of the December 2013 Ice Storm in
Toronto and Mississauga
by
Angela Robb
A thesis submitted in conformity with the requirements for the degree of Masters of Science
Department of Geography University of Toronto
© Copyright by Angela Robb 2016
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Mapping Changes in Urban Canopy Cover Following and Ice Storm
Event: A Case Study of the December 2013 Ice Storm in Toronto and
Mississauga
Angela Robb
Master of Science
Department of Geography University of Toronto
2016
Abstract
Urban forests provide ecosystem services and functions, but are vulnerable to stressful
environments and disruptive weather. One type of extreme weather, ice storms, can result in
damage to trees. In December 2013, an ice storm hit southern Ontario with significant social and
ecological impacts experienced in the Greater Toronto Area; where many cities are initiating
management plans to increase canopy coverage. The objective of this project is to explore the
changes in urban canopy cover before and after the ice storm through object-based image
analysis. The results of this analysis successfully show broad level canopy distributions, patterns
of canopy growth and loss, and 3-5% of canopy loss can be attributed to the ice storm on
residential land uses. A better understanding of the impacts of the 2013 ice storm addresses a gap
in our knowledge of how urban forests respond to extreme weather.
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Acknowledgments
I would like to express my gratitude to my supervisor, Dr. Tenley Conway, for her guidance,
support, and assistance throughout the duration of my research project. Many thanks to Dr.
Yuhong He and Dr. William Gough for being a part of my defense committee and for providing
their insight.
Additionally, I would like to extend my thanks to my peers responsible for facilitating and
geocoding the survey data used in this project.
Special thanks to my family, friends, and peers at UTM for their encouragement during my
studies.
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Table of Contents
Acknowledgments .................................................................................................................. iii
Table of Contents ................................................................................................................... ivList of Tables ......................................................................................................................... vii
List of Figures ....................................................................................................................... viii
Chapter 1 Introduction/ Overview ......................................................................................... 1
Chapter 2 Literature Review & Research Objective ............................................................ 3 Introduction ........................................................................................................................... 3
The Urban Forest .................................................................................................................. 32.1 Defining Urban Forests .................................................................................................. 3
2.2 Urban Forest Structure and Value .................................................................................. 42.3 Managing the Urban Forest ........................................................................................... 9
Impact of Ice Storms on Urban Forests .............................................................................. 10 Measuring the Urban Forest ................................................................................................ 14
4.1 Tools to Measure the Urban Forest .............................................................................. 144.2 Object Based Image Analysis ...................................................................................... 15
4.3 Change Detection ......................................................................................................... 19 Research Objectives ............................................................................................................ 21
Chapter 3 Study Area, The Ice Storm, & Data Used ......................................................... 23
Introduction ......................................................................................................................... 23 Study Area ........................................................................................................................... 23
2.1 City of Toronto ............................................................................................................ 232.2 City of Mississauga ...................................................................................................... 25
The December 2013 Ice Storm ........................................................................................... 28 Data & Geospatial Software ............................................................................................... 30
4.1 Satellite Imagery .......................................................................................................... 304.2 GIS Data ....................................................................................................................... 33
4.3 Survey .......................................................................................................................... 334.4 Software ....................................................................................................................... 34
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Chapter 4 Creating Urban Tree Canopy Maps .................................................................. 35 Introduction ......................................................................................................................... 35
Methods ............................................................................................................................... 352.1 Preprocessing ............................................................................................................... 35
2.1.1 Pan Sharpening the Imagery ............................................................................ 352.1.2 Georeferencing the Imagery ............................................................................ 35
2.1.3 Defining the Processing Area .......................................................................... 362.1.4 Compute NDVI layers ..................................................................................... 36
2.1.5 Layer Mixing ................................................................................................... 362.2 Processing .................................................................................................................... 37
2.2.1 Subset Selection ............................................................................................... 372.2.2 Segmentation .................................................................................................... 37
2.2.3 Nearest Neighbour Supervised Classification ................................................. 392.2.4 Manual Edits .................................................................................................... 42
2.2.5 Export to Shapefile .......................................................................................... 442.3 Accuracy Assessment .................................................................................................. 44
Results ................................................................................................................................. 45 Discussion ........................................................................................................................... 50
Conclusion .......................................................................................................................... 51
Chapter 5 Change in Urban Canopy Cover ........................................................................ 53 Introduction ......................................................................................................................... 53
Methods ............................................................................................................................... 532.1 Identifying Changes in Urban Canopy Cover .............................................................. 53
2.2 Change in NDVI Values .............................................................................................. 542.3 Attributing Canopy Change Resulting from the Ice Storm ......................................... 54
2.3.1 Geocoding Survey Responses .......................................................................... 552.3.2 Selecting Survey Responses to Identify Canopy Change ................................ 55
2.3.3 Identifying Canopy Loss and NDVI Change from the Ice Storm ................... 55 Results and Discussion ........................................................................................................ 56
3.1 Total Canopy Change .................................................................................................. 563.1.1 Change in Canopy Cover ................................................................................. 56
3.1.2 Change in NDVI Values .................................................................................. 62
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3.2 Neighbourhood Canopy Change .................................................................................. 643.2.1 Change in Canopy Cover ................................................................................. 64
3.2.2 Change in Canopy Cover Resulting from the Ice Storm ................................. 673.2.3 Change in NDVI .............................................................................................. 74
Conclusion .......................................................................................................................... 76
Chapter 6 Conclusions & Recommendations for Future Research .................................. 77 Conclusions ......................................................................................................................... 77
Recommendations for Future Research .............................................................................. 79
References ............................................................................................................................... 83
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List of Tables
Table 1: Satellite Imagery Specifications ..................................................................................... 33
Table 2: Error Matrix in percentages for 2011 and 2014 classification accuracy ........................ 45
Table 3: Distribution of Canopy Cover ........................................................................................ 49
Table 4: Proportion of Land Use & Canopy Cover ...................................................................... 49
Table 5: Changes in Canopy Cover .............................................................................................. 59
Table 6: Change in NDVI Values from 2011-2014 ...................................................................... 63
Table 7: Survey Results of Damage to Trees on Private Properties ............................................. 67
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List of Figures
Figure 1: Extent of Satellite Imagery ............................................................................................ 31
Figure 2: Location of surveyed neighbourhoods .......................................................................... 34
Figure 3: Scale parameter selection (2014 image) ........................................................................ 38
Figure 4: Methods used in eCognition .......................................................................................... 43
Figure 5: 2007 Tree Canopy Distribution ..................................................................................... 46
Figure 6: 2011 Tree Canopy Distribution ..................................................................................... 47
Figure 7: 2014 Tree Canopy Distribution ..................................................................................... 48
Figure 8: 2007-2011 Canopy Cover Change ................................................................................ 57
Figure 9: 2011-2014 Canopy Cover Change ................................................................................ 58
Figure 10: 2007-2014 Canopy Cover Change .............................................................................. 59
Figure 11: Examples of Canopy Cover Change, 2007-2014 ........................................................ 61
Figure 12: Change in NDVI from 2011-2014 ............................................................................... 62
Figure 13: Canopy Cover Change 2007-2014 (Toronto) .............................................................. 65
Figure 14: Canopy Cover Change 2007-2014 (Mississauga) ....................................................... 66
Figure 15: Ice Storm Damage to Trees Reported by Toronto Residents ...................................... 68
Figure 16: Ice Storm Damage to Trees Reported by Mississauga Residents ............................... 69
Figure 17: Toronto Canopy Loss Attributed to the Ice Storm ...................................................... 71
Figure 18: Mississauga Canopy Loss Attributed to the Ice Storm ............................................... 72
Figure 19: Change in NDVI in the Mississauga Neighbourhood ................................................. 75
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Chapter 1 Introduction/ Overview
Trees in urban forests perform essential ecosystem functions that provide net benefits to
urban populations (McPherson et al., 1997). As the population of Canada becomes increasingly
urban, it is essential to maximize ecosystem services through effective urban forest management
to maintain healthy environments (Alberti, 2005). However, urban forests are susceptible to
stressful growing conditions, environmental threats, and disruptive weather (McPherson et al.,
1997; Dwyer, McPherson, Schroeder & Rowntree, 1992). North American climate change
predictions suggest an increasing frequency of severe weather events, which will impact the
integrity of urban forests (Gauthier et al., 2014). One type of extreme weather event, ice storms,
will be explored as it relates to changes in urban canopy cover.
In December 2013, a major ice storm event impacted many cities in the Great Lakes area
of southern Ontario, with particular intensity experienced in the Greater Toronto Area (GTA)
(Armenakis & Nirupama, 2014). This resulted in extensive damage to urban trees and
infrastructure, raising concerns about public safety as a result of downed branches and trees
across the GTA. Since urban forests experience unique environmental stressors and high
population densities, it is essential to examine how extreme climate events affect urban trees.
Moreover, given that many cities have goals to increase city-wide canopy cover, understanding
the potential impact of extreme weather events is essential for achieving this goal (City of
Toronto, 2013b; City of Mississauga, 2014b).
Spatial analysis using geographic information systems (GIS) and remote sensing imagery
is a cost-effective and non-invasive approach to investigate forest dynamics (Dwyer & Miller,
1999). Specifically, object based image analysis (OBIA) using high resolution satellite imagery
is a powerful and effective approach for analysing the distribution of urban forests (Mathieu,
Aryal & Chong, 2007; Myint, Gober, Brazel, Grossman-Clarke & Weng, 2011). An OBIA of
Toronto and Mississauga’s urban forests before and after the December 2013 ice storm provides
a better understanding of the impacts of the storm on forest distribution. This will address a gap
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in our knowledge of how urban forests respond to extreme weather events, thus providing
valuable information for enhancing urban forest management.
The primary purpose of this thesis is to determine the nature, extent, and distribution of
tree canopy loss as a result of the 2013 ice storm, and to more generally map canopy cover
change dynamics. The research focuses on the municipalities of Toronto and Mississauga, which
were hard hit by the ice storm as well as recent pest invasions, but where significant tree planting
initiatives are also occurring.
This is addressed through the following objectives.
1. Process and classify multispectral imagery using an OBIA approach to delineate tree
canopy distribution
2. Compare classified imagery to characterize canopy change. This provides baseline data
on change in the canopy without a disturbance event using the 2007 and 2011 imagery. A
comparison of the 2007 and 2011 images to the 2014 image quantifies the change in
canopy following the storm event.
3. Determine ice storm related canopy change on residential land uses from supplemental
survey data for two neighbourhoods to identify canopy losses.
A better understanding of the vulnerability of urban forests to extreme weather and other
threats is crucial for more effectively managing urban forests. By determining the impact of the
ice storm on canopy change, this study can be used to evaluate current management strategies
and to help achieve future forestry goals by being able to anticipate the effect of extreme weather
events.
The next chapter provides a review of urban forests and OBIA methodologies. Chapter 3
describes the study area and data used in the analysis. Chapter 4 presents the methods and results
of the image classification process, and Chapter 5 describes the methods and results associated
with the change detection and survey data analysis. The final chapter includes conclusions and
recommendations for future research.
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Chapter 2 Literature Review & Research Objective
Introduction
This chapter begins by establishing the context of urban forestry research as it relates to
the structure, functions, and values of the urban forest. The threats to the urban forest are then
discussed in relation to increased development pressure and climate change related stressors. The
impacts of ice storms, a particular type of extreme weather event, will also be reviewed and
discussed. This is followed by a discussion of geographic information systems (GIS) and remote
sensing approaches used to measure and evaluate urban forest structure, health and distribution.
The applications of object based image analysis (OBIA) as a way to evaluate the distributions of
urban forests are also described.
The Urban Forest
2.1 Defining Urban Forests
With 54% of the world’s population living in urban areas, and a projected 66% urbanized
population by 2050, urban areas face many social and environmental pressures (United Nations,
2014). In North America, 82% of the population lives in urban centres, placing significant
pressure on the local and regional ecosystems that sustain human and environmental well-being
(United Nations, 2014; Alberti, 2005). Urbanized areas are characterized by high population
densities and are dominated by heavily modified urban landscapes and structures (Escobedo,
Kroeger & Wagner, 2011). While urban populations may be disconnected from natural
ecosystems, urban forests can provide local benefits and services that are essential for thriving,
healthy, and vibrant cities.
The urban forest is “a dynamic system that includes all trees, shrubs and understory
plants, as well as the soils that sustain them, located on public and private property” (TRCA,
2011). While the composition of forests in both natural and urban settings include a variety of
trees and other vegetation, urban forests are distinct in the extent to which humans facilitate the
species composition, location, and distribution of trees (Escobedo et al., 2011).
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The urban forest also differs from conventional, continuous forest systems as they have
modified growth processes and intra-ecosystem functions (Alberti, 2005; Konijnendijk, Ricard,
Kenney & Randrup, 2006). Cities are highly heterogeneous, made up of clusters of various land
uses, with urban forests dispersed throughout (Landry & Pu, 2010). Urban landscapes change
forest energy demands, frequency and intensity of disturbances, while management often
restricts native species and successional regimes (Alberti, 2005). With the global increase in
urbanized populations, it is essential to understand how to maximize the structure and
performance of urban forests in order to sustain their benefits (Alberti, 2005).
2.2 Urban Forest Structure and Value
The structure of the urban forest is based on measurable physical characteristics,
including the number of trees, spatial distribution of canopy, age distribution, and species
composition (McPherson, et al., 1997). Urbanized areas are generally characterized by multi-
functional forests that provide a diversity of ecosystem services based on contextual
requirements (McPherson et al., 1997). Urban forest extents in Canadian cities are shaped by
historical land use trends and natural disturbance legacies, including colonization, intensive
agriculture, and the expansion of transportation routes or other infrastructure networks (Sanders,
1984). Past disturbance events, including wildfires, timber harvesting, and invasive pests have
also impacted urban forests by altering species composition and age distribution (Pan et al.,
2011). In terms of the ecological factors that shape urban forest structure, the most evident are
temperature regimes, moisture availability, soil characteristics, and seed sources (Sanders, 1984).
Planting space is often a limiting factor due to urban morphology, as low soil volumes limit the
ability to support tree development (Sanders, 1984). Thus, urban forests typically have spatial
distributions, age distributions, and species compositions that are unique to each urban setting.
First, urban forests are unevenly spatially distributed throughout cities, a result of
ecological processes, patterns of development, and direct management. (Lowry Jr, Baker &
Ramsey, 2012). Studies examining the spatial distribution of urban forests often focus on canopy
cover, a two-dimensional measurement of the proportion of ground area covered by the tree
crown (Nowak et al, 1996). The proportion of canopy cover within a boundary area (such as
municipal boundaries) as viewed from above is the percentage of canopy cover for an area
(Walton, Nowak & Greenfield, 2008). The use of aerial and satellite imagery allows for the
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distribution of canopy cover to be spatially represented and for measurements to be taken from
private or inaccessible areas (Walton et al, 2008; Nowak et al., 1996, Dwyer & Miller, 1999).
The prevalence of built land cover often results in fragmented and discrete canopy
patches (Sanders, 1984). These patches vary in size, shape, and interconnectivity, which have
implications for species habitats and resource availability (Alberti, 2005). Larger patches are
typically found in parks, open greenspaces, and along river or ravine networks, which may be
remnant native forests or carefully managed stands (Ordóñez, Duinker & Steenberg, 2010).
Urban trees can also be rows of street trees, and trees located on residential properties as single
trees or in clusters (Ordóñez et al., 2010).
Generally, the majority of canopy cover is found on private and residential lands (TRCA,
2011; City of Toronto, 2013a). Residential lands also have the most available ground area for
future tree planting and are the most opportune land use for increasing the urban forest (Pelletier
& O’Neill-Dunne, 2011a). Dense city cores and industrial areas are the least suitable land uses
for tree canopy due to harsh growing conditions, limited space, environmental contamination,
and poor access to resources for tree maintenance (Goddard, Dougill & Benton, 2010).
The urban forest canopy distribution also varies according to different socio-demographic
characteristics. Generally higher household income is positively correlated with higher canopy
cover (Conway & Hackworth, 2007). Conversely, low-income areas of a city often do not
experience the benefits of urban trees as there is a reduced tree density and lower species
richness (Pham, Apparicio, Séguin, Landry & Gagnon, 2012).
Second, a varied age distribution is necessary for healthy urban forest performance, as
young and moderately aged trees are much more resilient than seedlings and mature trees, and
urban areas tend to have relatively few mature trees (Ordóñez et al., 2010). In urban parks, large,
mature trees are desirable, as they increase habitat resources for wildlife, species richness, stand
complexity, and community engagement (Stagoll, Lindenmayer, Knight, Fischer & Manning,
2012). Locke and Baine (2015) determined that a community’s recent historical socioeconomic
status, such as income and proportion of renters, impacts the age structure of trees on residential
properties.
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Third, greater species richness allows for greater stand complexity, higher productivity,
increased resilience, as well as improved citizen participation in urban forest conservation
(Goddard et al., 2010). Species richness, the proportion of species within defined areas, is
typically high in urban areas, primarily due to the presence of exotic species (Godefroid &
Koedam, 2007; Bourne & Conway, 2014). Moreover, richness is strongly related to land use type
in urban areas, as variation in land use type results in differences with respect to site availability,
site quality, intended purpose for tree planting, and tree maintenance authority (Bourne &
Conway, 2014; Godefroid & Koedam, 2007). Bourne and Conway (2014) found that residential
areas had the highest species diversity, as compared to other urban land uses, likely due to
homeowners selecting trees for aesthetic purposes. Like age structure, sociodemographic
conditions may also play a role in urban forest species compositions (Godefroid & Koedam,
2007)
The value of urban forests is derived from both perceived and quantifiable benefits. There
are numerous ecosystem functions and services provided by the urban forest that benefit the
urban landscape and provide for the broader ecological community (Konijnendijk et al., 2006).
Ecosystem services (that which benefits humans), and ecosystem functions (naturally occurring
processes that occur regardless of the benefit to humans) are both discussed in urban forest
literature (Escobedo, et al., 2011). Literature published on the topic of urban tree benefits
document diverse social, economic, and ecological benefits that result from a complex series of
urban forest services and functions (Roy, Bryne, Pickering, 2012).
Urban forests with large intact forest patches, strong internal interconnectivity, and high
tree species diversity often corresponds with relatively high wildlife biodiversity (Goddard et al.,
2010). Not only do trees contribute to overall urban biodiversity, but they host many other
organisms which increase the biodiversity of urban flora and fauna (Duinker, Ordóñez &
Steenberg, 2015). The interaction between the built form and vegetation communities has
resulted in unique hybrid habitats for urban wildlife (Dwyer et al., 1992; City of Toronto,
2013b). Species living here have adapted to new breeding patterns, migratory paths, foraging
routines, and territorial boundaries in response to the novel conditions presented by the form of
the urban forest.
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Trees in urban forests also contribute to mitigating local climate change impacts. Urban
areas typically experience higher temperatures than surrounding rural areas, known as the urban
heat island effect (UHI) (Loughner et al., 2012). UHI results from the large proportion of
impervious surfaces which increase rates of runoff, reduce evapotranspiration, and produce a net
increase in the absorption and retention of solar heat energy (Loughner et al., 2012; Wang &
Akbari, 2016). This can result in warmer air temperatures, increased air pollution, and greater
electricity demands for heating and cooling (Loughner et al., 2012; Rahman, Armson & Ennos,
2015). Urban trees can mitigate these impacts by increasing shade provision and
evapotranspiration, which help lower surface temperatures and reduce cooling loads (Rahman,
2015; Wang & Akbari, 2016). While there are many factors that impact a trees’ ability to
evapotranspire, such as tree species, leaf physiology, and soil moisture conditions, a higher leaf
area index (larger tree canopy volume) will generally result in more evapotranspiration and UHI
mitigation capabilities (Rahman et al., 2015).
Additionally, urban trees also facilitate local hydrological functions. Due to the
prevalence of impermeable ground cover, increased runoff during large precipitation events can
overwhelm urban storm water infrastructure (Alberti, 2005; Duinker, et al., 2015). Trees can
function as a network of green infrastructure within the urban environment, facilitating efficient
uptake of storm water runoff (McPherson, Simpson, Peper & Xiao, 1999; Zhu & Zhang, 2008).
This can reduce urban flooding as trees act as water retention sinks (Alberti, 2005; Dwyer et al.,
1992). Trees also reduce the amount of storm water runoff through interception during large
precipitation events (Dwyer et al., 1992).
Urban forests are integral for improving air quality as trees are active agents of air
pollutant removal and filtration. In particular, trees remove tonnes of ozone (O3), nitrogen
dioxide (NO2), small particulate matter (PM10), sulfur dioxide (SO2), and carbon monoxide (CO),
(TRCA, 2011; McPherson et al., 1999). Due to the increased temperature of urban areas and the
resulting air quality concerns associated with smog, the active filtration of air pollution from
abundant tree coverage has health benefits for urban residents, reduces health care costs, and
increases outdoor recreation (Escobedo et al., 2011; Dwyer et al., 1992). The number of healthy
trees, the relative size of tree biomass, tree species, and the spatial distribution all affect air
quality benefits (TRCA, 2011; Dwyer et al., 1992). By strategically planting trees in locations
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near schools or hospitals that benefit from active air pollution mitigation, increased quality of life
can be integrated into urban sustainability plans (Escobedo et al., 2011).
Similarly, urban trees actively sequester large quantities of carbon dioxide (CO2) through
active tissue growth, and store the carbon by holding onto accumulated CO2 in the tissue space as
they age (Rowntree & Nowak, 1991; Pan et al., 2011). The functionality of carbon sequestration
and storage again, depends on tree species, age, maturation, biomass and crown coverage
(Nowak et al., 1996; Dwyer et al., 1992; Pan et al., 2011). Higher rates of carbon sequestration
result in higher quality of life locally and regionally. Increased atmospheric carbon is a major
contributor to climate change, so any carbon sequestration has been argued to promote urban
sustainability (Duinker et al., 2015).
Urban trees have a number of economic and social benefits as well. They help to reduce
energy demands and costs associated with the heating and cooling of buildings, as buildings
surrounded by trees do not heat up as quickly, or as much, due to the direct path of the sun being
blocked (Dwyer et al., 1992). Trees also act to buffer high winds, potentially reducing heating
costs (McPherson et al., 1999).
Healthy vegetation cover is also often linked to high property values (Conway &
Hackworth, 2007). Property owners with trees not only benefit themselves, but adjacent
properties also increase in value when there is abundant canopy cover (Zhu & Zhang, 2008).
Residential neighbourhoods close to parks and greenspaces also have increased property values
(Zhu & Zhang, 2008).
Cities with accessible parks and greenspaces are generally more desirable environments
for people to live and work in. Urban trees promote recreation and spaces for leisure, and
generally increase quality of life (Dwyer et al., 1992). Urban trees can also increase the aesthetic
value of a city through seasonal blooms and changing leaf colours, which contribute visual
beauty (Duinker et al., 2015). Additionally, trees also serve a function of providing a noise
buffer, as trees reduce noises attributed with urban life (Dwyer et al., 1992). Trees can also
provide employment opportunities, as they require constant maintenance to promote smart tree
growth and to reduce any ecosystem disservices. Finally, highly biodiverse urban forests attract
new residents and businesses (McPherson et al., 1999).
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In addition to the benefits from urban forests, there are costs, or ecosystem disservices
that are associated with urban trees. Ecosystem disservices are the product of tree function that
have a negative impact or cost to society (Escobedo et al., 2011). Ecosystem disservices occur
alongside benefits, as a tree may provide aesthetic value and shade for some, yet may be a source
of litter, allergens, or obstructed views for others (Escobedo et al., 2011). Tree mortality, while
not unusual, may be an inconvenience and an unwanted task to manage. Tree mortality varies
across land use, with transportation networks, commercial/ industrial, and urban open land uses
experience highest morality rates (Nowak, Kuroda & Crane, 2004). Other economic costs
include budgets for pruning and tree maintenance, pest management, irrigation, and damage to
infrastructure (Escobedo et al., 2011). Disservices that are social nuisances are tree litter,
allergens, obstruction of views, and decreased aesthetics (Escobedo et al., 2011). Residents
experience disservices resulting from extreme weather events that damage trees on their
properties, such as ice storms, that result in negative experiences (Conway & Yip, 2016).
However, the functional benefits of trees generally override the negative costs, and trees are
often understood to be essential to the sustainability of urban areas (Roy et al., 2012; McPherson
et al., 1997).
2.3 Managing the Urban Forest
Urban forests face many challenges, stressors, and threats that can inhibit full ecosystem
potential (Konijnendijk et al., 2006). Urban areas are stressful growing environments due to
competition for limited resources and poor quality growing conditions (Gauthier et al., 2014;
Kenney & Idziak, 2000). Invasive species are particularly threatening to urban forests, due to the
uneven species assemblages, and clustering of similar tree species. Street trees are often
dominated by a few species in cities, making them vulnerable to pest outbreaks (such as the
Dutch Elm Disease, Emerald Ash Borer, and Asian Long-Horned Beetle) that can result in
significant tree loss (TRCA, 2011).
Urban forests also face many challenges associated with global climate change.
Increasing temperature in the mid to high latitudes will result in changes in growing seasons,
temperature and moisture conditions, as well as more frequent severe weather events (Gauthier et
al., 2014; Ordóñez et al., 2010). Climate change will likely impact tree species suitability and
urban forest age structure (Ordóñez et al., 2010).
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In order to maximize the benefits of urban trees, minimize the costs, and ensure the long
term survival of the urban forest, effective management policies must be established. Urban
Forest Management Plans (UFMP) are created by municipalities to outline best-management
strategies associated with maintaining ecological integrity and vitality through sustainable
practices, and help to provide an understanding of the complexity of urban forest ecosystems
(Kenney & Idziak, 2000). An adaptive management approach is often implemented, as it allows
for the evaluation of policy strengths and weaknesses, and takes into account the state of urban
forest, which is constantly in flux (Kenney, van Wassenaer & Satel, 2011).
Impact of Ice Storms on Urban Forests
With global climate change, it is anticipated that one impact will be an increase in the
frequency and intensity of extreme weather events, which may result in more wind and ice
storms in southern Ontario (Gautheir et al., 2014; Forests Ontario, 2014). Ice storms, a natural
weather phenomenon, occur when a cooler layer of surface-air crosses with a warm, moist air
front, resulting in freezing rain that accumulates ice on exposed tree branches and surfaces,
known as ice glaze (Hauer, Dawson & Werner, 2006; Rustad & Campell, 2012). Ice storms in
North America have a historic local return time of 20-100 years (Pasher & King, 2006). Climate
change models focusing on the expected frequency of ice storms in North America suggest that
southern areas may experience fall storm events, while areas with increasing latitude will
experience more frequent ice storms in mid to late winter (Kllma & Morgan, 2015). One of the
largest ice storms recorded impacted the North Eastern United States and Eastern Canada in
January 1998 (Pisaric, King, MacIntosh & Bemrose, 2008; Rustad & Campbell, 2012).
Ice storms impact urban populations through damage to properties, infrastructure,
transportation, and energy systems, resulting in concerns for public safety (Smith, 2015). High
economic costs include restoration of power, branch or tree removal, and replanting efforts
(Degelia et al., 2016). Ice accumulation on surfaces results in slick conditions for pedestrians and
drivers, and residents are encouraged to stay indoors until conditions are clear (Degelia et al.,
2016). Downed power lines results in loss of power and utilities, which is a significant concern
for residents and community services (Degelia et al., 2016; Hauer, Hauer, Hartel & Johnson,
2011). In some cases, the rapid melting of ice accumulation can result in flooding (Degelia et al.,
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2016). Increased stress resulting from these conditions may result in increased rates of injury and
illness (Rajram et al., 2016).
Ice storms impact urban forests by altering the age and species composition, as well as
changing resource availability and wildlife habitats (Pasher & King, 2006). For example, a study
by Zhang et al. (2016), found that avian species composition was altered following an ice storm
in China as a result of changing habitat conditions. Branch loss associated with ice storms also
impact ecosystem functions, such as shading capabilities and evapotranspiration rates (Rustad &
Campbell, 2012).
Trees vary in their susceptibility to ice storm damage, based on their species, age, size,
location, position, and soil conditions. Trees are more fragile in the winter (the typical time for
ice storms to occur) as they are dormant (Armenakis & Nirupama, 2014). Trees may be more
vulnerable to damage due to weak branch junctures, pre-existing dead branches, and poor root
systems or tree crown conditions (Hauer et al., 2006; Smith, 2015). Trees may also be more
vulnerable if they have been previously exposed to tree pathogens or invasive insects (Weeks,
Hamburg & Vadeboncoeur, 2009). Local topography (changes in elevation) also results in varied
distribution of ice storm damage (Shi et al., 2013). The amount of damage is also dependent on
the severity of the ice storm event, as measured by the amount of ice accumulation, intensity of
winds, and the duration of the storm (Hauer et al., 2006; Irland, 2000).
Damage to trees can include broken branches, bending of the stem, split trunks, or
complete uprooting of a tree (Forests Ontario, 2014). In many cases, ice accumulation increases
the branch weight by a factor of 10 to 100 (Hauer et al., 2006). Branch loss occurs from this
increased stress on branch junctures. Mid-sized branches are usually more resilient than small or
large branches, as they have the most strength proportional to the branch juncture (Degelia et al.,
2016). The intensity and duration of high winds increase the potential for branch loss during ice
storms (Hauer et al., 2006; Irland, 2000). Trees with large tree crowns have a larger surface area,
with an increased potential to experience damage to branches, while trees with unbalanced
crowns end up with an unequal distribution of ice accumulation, and are more vulnerable to
uprooting. Trees with smaller diameters experience less damage than larger trees of the same
species (Hopkin, Williams, Sajan, Pedlar & Nielsen, 2003). As discovered by Pasher and King
(2006), patches of forest that were more isolated experienced more damage. Since urban forests
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are made up of fragmented, isolated patches unevenly distributed, this suggests that there is
increased opportunity for damage.
Trees that are more resilient to the impacts of ice storms include species that have conical
branching patterns and low surface areas (such as many conifers), strong branch attachments, and
flexible branches (Hauer et al., 2006). As documented by Hopkin et al. (2003), conifers
experienced less damage than deciduous trees impacted by the same storm event. Young to
moderately ages trees also have more flexible branches that may reduce ice storm branch loss.
Trees located in the understory are less vulnerable to direct ice storm impacts, although they may
experience damage as a result of branches falling onto them. Finally, seed sources influence ice
storm resistance, as seeds may be sourced from trees that have withstood ice storm damage and
therefore, are more resilient (Hauer et al., 2006).
Following an ice storm event, the recovery process varies depending on the type of
damage experienced. Debris removal and hazardous tree assessments are challenging, yet
necessary tasks to be done (Hauer et al., 2011). Tree damage must be dealt with quickly and
properly in order to reduce the risk of increased property damage and public safety concerns.
Branch loss, or branch breaking, is the most common type of ice storm damage, and is the easiest
to manage (Hauer et al., 2006). Trees that experienced a bent stem will often recover, and the
lack of splitting or uprooting suggest high structural integrity (Hauer et al., 2006; Forests
Ontario, 2014). The damage of ice storms may also not be immediately evident, and issues of
broken branches may become problematic a few years following the storm (Hauer et al., 2006).
Older trees generally have reduced ability to recover from storm damage than younger trees of
the same species (Hauer et al., 2006). Trees that are not maintained following an ice storm may
also be more susceptible to insects, disease, and increased damage during future storms (Forests
Ontario, 2014).
An experimental ice storm was simulated under controlled conditions by Rustad and
Campbell (2012), in order to evaluate the nature and type of damage from this controlled weather
event. They found that based on the collected litter (composed of small branches), the following
growing season had experienced decreased primary production and ecosystem function resulting
from increased canopy openness. In a study conducted on maple (Acer) trees damaged by the
1998 ice storm by Pisaric et al. (2008), analysis of the crown indicated positive recovery
13
following the ice storm, however dendrochronological analyses found that stem growth did not
recover in the 6 years following the storm event. A study in New York state explored the impact
of the 1998 ice storm on Norway (Acer platanoides), Silver (Acer saccharinum), and Sugar
(Acer saccharum) Maples (Luley & Bond, 2006). They discovered that after six years following
the ice storm, tree species was a better determinant of tree recovery than age or size (Luley &
Bond, 2006).
Determining the distribution of ice storm damage is challenging, as it is difficult to
collect data on forest conditions just prior to an ice storm event due to the unpredictability of this
type of extreme weather (Shi et al., 2013; Irland, 2000). Post- ice storm disturbance is measured
based on the difference in canopy cover, change in species and age composition, tree stem
density, and/ or vegetation health (Hauer et al., 2011). Data after an ice storm event can be
collected on site, or through the use of satellite imagery and remote sensing. Efforts to determine
ice storm damage using remote sensing by Shi et al. (2013) found that local topography and
species distribution impacted the variation in tree damage. Following the 1998 ice storm,
extensive permanent field monitoring plots and aerial photography analysis was used to
determine the distribution of the damaged area.
With the intensity and frequency of ice storm events expected to increase as a result of
climate change, it is important to incorporate strategies to mitigate the impact of ice storm
damages in areas that experience frequent extreme weather events (Hauer et al., 2006). Tree
species that are more resilient to ice storm damage should be included in the species selection
process of tree planting programs. While ice storm resilience has not always been a factor in tree
species selection, the increased frequency of these events has resulted in this being a factor in
species survival (Irland, 2000). Also, trees should be planted in locations that will not pose major
damage, such as near utility lines (Forests Ontario, 2014). Hauer et al. (2006) argue that the most
beneficial way to minimize the impacts of ice storm damages are to encourage tree pruning and
maintenance, which promotes healthy tree growth.
14
Measuring the Urban Forest
4.1 Tools to Measure the Urban Forest
The management of urban forests is not possible, or successful, unless the structure and
distribution of urban trees are known. Field based analyses, such as the Urban Forest Effects
(UFORE) and the iTree-Eco model yield valuable information about the structure of urban
forests (Nowak et al., 2008; i-Tree User’s Manual, n.d.). These plot-based analyses collect
metrics about tree species, age distribution, DBH, tree height, and overall tree health, which can
be used to quantify the benefits produced by that tree (Nowak et al., 2008). Using statistical
methods and GIS to model the results allows for estimates about the distribution of the urban
forest to be evaluated.
The creation of thorough tree inventories has become much more efficient with the use of
GIS, aerial imagery, and remotely sensed data (Nowak et al., 1996; Kenney et al., 2011). Spatial
urban forest data analyzed with GIS reduces the time needed for map development, increases the
amount of information that can be combined to make the map, and allows for comparisons into
the state of the urban forest structure over time (Dwyer et al., 1999). As a large proportion of
urban trees are on private properties where field access is challenging, and due to the sheer size
of large cities, aerial and satellite imagery is able to overcome potential barriers of plot-based
inventories and data collection (Mathieu, 2007; Hauer et al., 2006). Through geospatial data, the
distribution of the urban canopy can be determined.
Remotely sensed imagery has proven to be effective in gathering information about the
distribution of urban features, including urban forests. Urban areas have numerous land covers,
all with unique electromagnetic reflectance values (spectral signatures), which may lead to
confusion due to the large number of small objects concentrated within a small area (Myint et al.,
2011). However, with commercially available high resolution satellite imagery, classification of
urban land cover features has become much more efficient (Bhaskaran, Paramananda &
Ramnarayan, 2010; Myint et al., 2011).
Recent applications of remote sensing include basic mapping, policy evaluation, land use
relationships, and change detection. For example, Landry and Pu (2010) used imagery to create
canopy cover maps to evaluate the effectiveness of tree protection policies based on existing tree
15
canopy proportions on residential lands. Using a vegetation index to identify the distribution of
urban vegetation along an urban-rural gradient, Conway and Hackwork (2007) were able to
determine socio-demographic and land use correlates to determine patterns of urban forest
distribution. Using multiple GIS datasets, satellite images, and historical data, Hostetler et al.
(2013), were able to map canopy changes and attribute the sources of tree canopy loss from
2008-2010 in central Massachusetts. By combining high spatial resolution imagery, active
LiDAR data, and field based inventories, Tooke et al. (2009) were able to identify vegetation
characteristics, such as tree species and vegetation health. The impact of biophysical factors,
such as tree density, leaf area, elevation, and albedo within parks on urban temperature regimes
has also been examined using remotely sensed imagery (Ibrahim, Samah & Fauzi, 2014).
Applications determining the impact to urban forests from disturbances using GIS and
imagery have also proven to be effective. A study based in Pittsburgh, PA by Pfeil-McCullough
et al. (2015), used spatial data to determine the impact of Emerald Ash Borer (EAB), an invasive
insect, on landslide potential. By mapping the impact of EAB- related tree loss, and its effect on
modifying the local topography, they were able to identify areas where slope instability could
result in damage to urban infrastructure and urban forest distribution (Pfeil-McCullough, Bain,
Bergman & Crumrine, 2015). Intense wind storm events impact forests as well, and a process
identifying areas of tree loss due to high winds was developed using Landsat remotely sensed
data (Baumann et al., 2014). A combined GIS and field based plot sampling project design was
developed to determine vegetation recovery following a hurricane in Nova Scotia (Burley,
Robinson & Lundholm, 2008). Finally, using airborne laser scanning and GIS data to create
digital elevation models (DEM) and digital surface models (DSM), Rahman & Rashed (2015)
were able to determine tree canopy diameter and height before and after an ice storm event in
Oklahoma. By comparing the DEM (elevation data) and DSM’s (surface data) from before and
after the ice storm, they were able to identify how much damage was incurred by the trees
(Rahman & Rashed, 2015).
4.2 Object Based Image Analysis
Satellite imagery is an accessible and powerful source of spatial data for collecting and
analyzing land use and land cover. Analyses of satellite imagery yields valuable information for
meteorological, ecological, and environmental data. Traditionally, the unit of analysis is the pixel
16
level, where the spectral characteristics are limited to the spatial resolution of each pixel.
However, a shift towards image object analysis, or groups of pixels has occurred since early
2000. Image object analysis began with industrial and medical imaging in the 1980s (Dey, Zhang
& Zhong, 2010). After the year 2000, through advancement of satellite remote sensing platforms
and the resulting increase in spatial resolution, geospatial object based image analysis (GEOBIA
or OBIA) has emerged as a powerful approach to interpreting satellite imagery (Dey et al., 2010;
Blaschke, 2010).
When the features of analysis are larger than one pixel, OBIA is an effective alternate
approach to pixel-based analysis. This process transforms a heterogeneous image into small
image objects, which are homogenous in pixel characteristics (Blaschke, 2005), and are much
more meaningful units for analysis and interpretation (Baatz & Schäpe, 2005). With increasing
spatial and spectral resolution, OBIA is capable of processing highly complex scenes, which was
not previously possible with traditional pixel analysis (Dey et al., 2010). eCognition, developed
by Definiens Developer, has emerged as a powerful software program to facilitate OBIA
(Blaschke, 2010).
The process of OBIA analysis involves two stages: image object segmentation and
classification. In order to create appropriate image objects for subsequent analysis, they must be
created using a process of segmentation. The segmentation process creates contiguous groups of
pixels (image objects) that share similar spectral characteristics (Mathieu et al., 2007; Blaschke,
2004). To do this, each pixel is iteratively merged with surrounding pixels that share similar
features. Due to the heterogeneous nature of land covers, not all image objects will be the same
size. A multiresolution segmentation algorithm can produce image object clusters of varying size
in order to account for the diversity of land covers or other objects of interest (Baatz & Schäpe,
2005); these image objects correspond with meaningful ground features (Mathieu et al., 2007).
The size and scale of the image objects are determined based on user defined parameters of
multispectral layer, scale, shape, and colour (Blaschke, 2004; Baatz & Schäpe, 2005). These
parameters are determined based on trial and error, until appropriately sized image objects have
been created (Mathieu et al., 2007). One of the benefits of OBIA is that multiple sources of
spatial data can be integrated into the analysis (Baatz & Schäpe, 2005). This means that a series
of georeferenced imagery and GIS data can be used to include boundaries or topology
17
information into the segmentation process (Salehi, Zhang, Zhong & Dey, 2012; Kosaka,
Akiyama, Tsai & Jojima, 2005).
Once the image objects are created, there are a variety of classification methods that can
be used to assign the image objects into appropriate classes. With a membership- based
classifier, user defined membership rules or criteria, must be met by the image objects in order to
be assigned into each particular class (Myint et al., 2011). Alternatively, a nearest neighbour
classifier utilizes training samples for each classification that identify and define the criteria
required for the remaining unclassified image objects to be sorted into the appropriate classes
(Myint et al., 2011). For any classification method, the accuracy directly depends on the scale of
the image objects (Bhaskaran et al., 2010); classification error will result from poorly segmented
image objects.
A hierarchical analysis involves multiple levels of image object segmentation and
classifications. First, larger image objects classified into broad categories, such as “water”,
“urban”, and “vegetation”. A second level in the hierarchy will have more detailed image object
levels for the classes; for example, the “vegetation” class can be separated into “soil”, “shrub”,
“grass”, and “tree” (Mathieu et al., 2007). For example, Kosaka et al. (2005) used a hierarchical
object- based segmentation with a supervised nearest neighbour classification approach to
identify tree species in Japan. Another study used a multiresolution, hierarchical segmentation
process with membership based classification algorithms to identify different tree types
(conifers, broadleaf, mixed) to understand forest structure (Hájek, 2006). While these studies
used different classification methods, they both produced finely detailed distribution maps that
would be challenging to create with pixel-based classification methods.
OBIA is particularly effective in mapping land use and land cover in urban areas. Urban
areas have complex and diverse land covers at fine scales, so accurate maps from resulting image
analysis has been challenging to produce (Stueve, Hollenhorst, Kelly, Johnson & Host, 2015).
Aerial photographs were a primary source for urban land use mapping, however interpretation is
time consuming, based on manual digitization, and relies on the interpreter’s expertise (Mathieu
et al., 2007). With the increased availability of very high spatial resolution satellite imagery, it is
much easier to automate land cover classification processes for larger urban areas (Mathieu et al.,
2007; Myint et al., 2011).
18
For urban land cover mapping, the spatial resolution is more important than the spectral
resolution, meaning that high spatial resolution multispectral imagery can be effectively used
(Myint et al., 2011). With increasing spatial resolution, spectral variation within classes also
increases due to the heterogeneous nature of urban land cover (Mathieu et al., 2007). However,
many small objects are easier to distinguish apart from each other with finer spatial resolution
imagery (Myint et al., 2011).
Many urban land covers are composed of similar materials (such as asphalt roads and
asphalt parking lots), which share similar spectral characteristics, but serve different functions. A
pixel- based analysis would have difficulty interpreting these different land covers based on the
spectral characteristics alone. However, with the textural information associated with image
object segmentation, more meaningful units of analysis can be produced (Myint et al., 2011).
OBIA is highly effective in producing urban vegetation and urban tree canopy
distribution maps (O'Neil-Dunne, MacFaden & Royar, 2014). Trees in urban areas have complex
pixel characteristics, and are influenced by differences in illumination and background effects
(Pu & Landry, 2012). This often results in mixed pixel characteristics between the the tree and
the surrounding non-tree pixels, resulting in a misclassification (Pu & Landry, 2012). OBIA
segmentation results in image objects representative of the entire tree crown, which may have a
variety of pixel characteristics within the image object cluster, yet are distinct from the
surrounding non-tree vegetation pixels (O’Neil-Dunne et al., 2014). Despite the heterogeneity of
an image object for a tree, it can still be classified as a meaningful ground feature (Bhaskaran et
al., 2010). These methods can produce tree canopy maps that visually reflect the reality of tree
canopy shape and distribution in a visually coherent way (O’Neil-Dunne et al., 2014).
There have been a number of successful applications of OBIA for urban forestry
mapping. A study by Bhaskaran et al. (2010) used a pixel-based and object-based analysis of the
same high resolution image to map the distribution of urban features in New York City. They
found that the pixel based approach was successful, but accuracy was lowest for certain roof
types and vegetation. However, by using a multiresolution segmentation and membership based
OBIA, there was improved accuracy of vegetation, and tree canopy mapping specifically
(Bhaskaran et al., 2010). A similar result was found buy Myint et al. (2011), where the
classification of urban features was much more accurate with an object-based approach than a
19
pixel-based analysis. However, they found that within the same imagery, and using the same
image object groups, some classes were more accurately classified using a rule based classifier,
and other classes were more accurately classified with the nearest neighbour approach (Myint et
al., 2011).
An urban vegetation map of New Zealand produced by Mathieu et al. (2007) was able to
accurately classify distinct vegetation features, even though it was not as detailed as vegetation
maps from aerial imagery. However, the process of automating OBIA of satellite imagery was
more efficient than aerial image analysis (Mathieu et al., 2007). A multisource OBIA of imagery
from two different satellite platforms (Quickbird and Iknonos), with urban spot height data, was
used in a hierarchical rule-based classification of urban features in New Brunswick (Salehi et al.,
2012). The results indicated high accuracy for both maps, although the Quickbird imagery had a
slightly higher accuracy, likely due to increased spatial resolution (Salehi et al., 2012).
An urban land use map created by Stueve et al. (2015) for Minnesota using OBIA found
success where tree canopy overlapped with impervious surfaces to aid in urban management
decision- making (Stueve et al., 2015). Finally, an automated urban tree canopy program was
developed by the University of Vermont and USDA to use a multisource OBIA for land use
mapping (O’Neil-Dunne et al., 2014). This process classifies urban areas into distinct land use
classes, which identifies existing tree canopy distributions, as well as areas suitable for future
tree planting on both impervious and non-treed vegetated areas (O’Neil-Dunne et al., 2014). This
study shows how useful OBIA urban feature maps are in assisting with urban planning and urban
forestry decision-making.
4.3 Change Detection
One commonly used application of remote sensing technologies is change detection; a
process of identifying differences in land covers at different times (Chen, Hay, Carvalho &
Wulder, 2012). Through remote sensing, change in land cover will result in identifiable changes
in spectral reflectance values, which is valuable to monitor for environmental management and
decision making (Hall & Hay, 2003; Chen et al., 2012).
When comparing multiple images, it is important to take into account the spatial
resolution of each image. While there is much more detail in high-resolution images, there is
20
greater opportunity for spectral variation and mixed pixels, which may alter the change detection
results (Chen et al., 2012). With higher spatial resolution, and associated smaller pixel size, it is
often more difficult to achieve accurate registration between multiple images (Chen et al., 2012).
However, there is greater detail with high resolution imagery, which is useful for change
detection of urban areas (Al-Khudhairy, Caravaggi & Giada, 2005).
As change detection involves images from multiple dates, the temporal resolution refers
to the length of time between each image (Chen et al., 2012). The temporal resolution varies
based on the type of change of interest, as a longer temporal resolution can be used for land use
change, while shorter temporal resolution is required for monitoring events such as forest fire
damage or hurricanes (Chen et al., 2012). Sensors vary in their return time to similar places,
which also results in differences in temporal resolution of the same sensor to the same location
(Al-Khudhairy et al., 2005).
As change detection involves direct comparison between multiple images, the angle of
acquisition and solar illumination impact the resulting accuracy. Ideally, all images would be
captured at nadir (0° look angle), which results in the top of all surfaces being captured.
However, many sensors capture images with up to 20° look angles, which results in images
including the sides of tall features. In urban areas, this results in the tops and sides of tall
buildings and trees being captured, and differences in look angle between images may skew the
change detection results (Chen et al., 2012; Al-Khudhairy et al., 2005). Additionally, solar
illumination varies based on time of day and angle of image acquisition, and skews the degree of
shadows and brightness of certain features (Desclée, Bogaert & Defourny, 2006; Chen et al.,
2012). Trees in urban areas often have one side of the crown appearing much brighter, due to the
influence of shadows from the look angles and solar brightness.
With high resolution satellite imagery, change detection accuracy is more effective using
textural data rather than spectral data (Chen et al., 2012). Thus, image object based change
detection (OBCD) is much more effective than pixel-based methods when using high resolution
imagery (Desclée et al., 2006; Chen et al., 2012; Zhou, Troy & Grove, 2008). Using image
objects for change detection are a solution to mitigate errors from misregistration, angle and
illumination effects (Chen et al., 2012).
21
OBCD can be used to identify changes in spectral information of created image objects to
determine change in image object characteristics between multiple years (Hall & Hay, 2003).
Another method is to compare already classified image objects to determine “from- to” change
using, which identifies the type of land use change (Chen et al., 2012; Zhou et al., 2008). This
post-classification approach is much more of a GIS-based analysis than purely remote sensing,
and can be more efficient for processing using simple image differencing functions (Al-Khudairy
et al., 2015).
By conducting change detection from classified objects, impacts of vegetation
biophysical differences (as expressed though different spectral reflectance) are minimized as the
classification is already complete (Zhou et al., 2008). As discovered by Desclée et al. (2006) the
heterogeneous nature of image objects representing tree crowns did not impact the result of an
OBCD analysis of forests areas, as “from-to” change results were accurately identified. The
benefits of post-classification OBCD include information of which land use changes have
occurred, specifically of what land covers have change to, which are essential for informing
policies and decision making.
Research Objectives
The purpose of this research is to determine tree canopy distribution and the associated
changes in canopy cover following an ice storm event in Toronto and Mississauga. Specifically,
changes in tree canopy resulting from the ice storm will be identified. Through a post-
classification change detection analysis of high resolution satellite imagery, this study will
identify the spatial distribution of canopy change to determine which areas were particularly
vulnerable to canopy loss.
This research will test the following hypothesis: urban forest canopy damage from the ice
storm can be identified from the spatial patterns of canopy spectral signal change measured using
high spatial resolution satellite imagery.
In order to test the hypothesis, the following objectives and methodology have been
implemented. The first objective was to process the multispectral imagery with an OBIA
approach. This involves image object segmentation and classification of high spatial resolution
imagery of the target area from 2007, 2011, and 2014. This resulted in the creation of three tree
22
canopy distribution maps for each image, based on the spectral properties of tree canopy and
non-tree land cover.
The next objective was to compare the classified images to characterize canopy change.
A comparison of the 2007 and 2011 images to the 2014 image was used to quantify the change in
canopy following the storm event. A post-classification change analysis was used to identify ‘to-
from’ change to determine where there was canopy losses and canopy gains. Survey responses
by residents were used to attribute reason for canopy change, specifically determining ice storm
related canopy change resulting from the ice storm at the property level. Changes in vegetation
presence, through a vegetation index, was additionally identified to determined ice-storm related
impact.
23
Chapter 3 Study Area, The Ice Storm, & Data Used
Introduction
This analysis focuses on Toronto’s and Mississauga’s urban forests. While the December
2013 ice storm impacted many municipalities across southern Ontario, this analysis is limited to
these cities due to the reported amounts of concentrated damage, high population, and the
presence of urban forest management plans seeking to grow the urban forest within these cities.
Toronto and Mississauga are within the Mixed Woods Plain Ecozone (7E), bordered by the Oak
Ridges Moraine, the Ontario Greenbelt, and Lake Ontario. The following section describe the
demographics of each city, existing urban forest management policies, the December 2013 ice
storm, as well as describes the data and software used in this analysis.
Study Area
2.1 City of Toronto
The City of Toronto is Canada’s largest city, with a diverse population of 2.79 million and an
area of ~63,000 ha (Statistics Canada, 2011). This study does not encompass the entirety of
Toronto, focusing on the urban forest within Etobicoke. Etobicoke has a population of 620,000
and is dominated by single- family houses and other residential land uses (Statistics Canada,
2011). The management of trees in Etobicoke (as part of Toronto) is done in collaboration with
the City of Toronto and the Toronto and Region Conservation Authority (TRCA).
Toronto is characterized by its network of rivers and ravines throughout the city, where
the development of business and residential areas radiate from these natural features (City of
Toronto, 2013b). The ravines were recognized as an essential ecological network within the city,
as they contain a large portion of the city’s trees; in 2002 the Ravine Protection By-law was
established for their protection. Shortly afterwards, in 2004, a Private Tree By-law was
implemented to regulate private tree removal to ensure trees on private properties have removal
and pruning standards to ensure longevity. Toronto has recognized the functional benefits of the
urban forest, addressing the need to increase the urban forest in the Strategic Plan “Our Common
Grounds” (City of Toronto, 2004) and Toronto’s Official Plan (City of Toronto, 2010).
24
Additionally, maintaining and increasing the urban forest was included as one of the Potential
Actions to be taken to mitigate the impacts of climate change within the city in the report
“Change is in the Air” (City of Toronto, 2007). There are also a number of heritage trees that are
protected in the Ontario Heritage Act and Ontario Planning Act. In order to create an effective
management plan, an evaluation of Toronto’s urban forest entitled Every Tree Counts was
published in 2010, with updated revisions that monitor the state and structure of Toronto’s trees
(City of Toronto, 2013a). Toronto’s Strategic Management Plan outlines a detailed plan for
managing and maintaining the city’s trees from 2012-2022 (City of Toronto, 2013b).
Based on the findings of Every Tree Counts, Toronto has an urban canopy cover of 26-
28%, which is made up of over 10.2 million trees. While it is difficult to attribute a dollar value
to every benefit of urban trees, Toronto’s trees have a structural value of $7.1 billion (City of
Toronto, 2013a). This forest is unevenly distributed across the city, with 60% located on private
properties, 34% within ravines and parks, and 6% in the form of city street trees (City of
Toronto, 2013a & City of Toronto, 2013a). Average tree mortality of 3-4% is being offset by tree
planting initiatives to maintain canopy levels, however available planting space may be limited.
The age structure is uneven, with approximately 68% of trees having a DBH less than 15.2 cm
(Nowak et al., 2012). The Norway, Sugar, and Manitoba Maple (Acer negundo), as well as Green
Ash (Fraxinus pennsylvanica) and White Spruce (Picea glauca) make up the largest proportion
of leaf area, while the most common species by number of stems are Eastern White Cedar (Thuja
occidentalis), Sugar Maple, and Norway Maple (City of Toronto, 2013a). Toronto has many
diverse and exotic species, with approximately 64% of the trees representing species native to
Ontario.
Many of the reports produced by the City of Toronto and the Parks, Forestry and
Recreation department since 2004 had identified a goal to increase canopy cover to 30-40%, a
range recommended by urban foresters across Canada and the United States (City of Toronto,
2013b). The 2013 Strategic Plan includes goals to increase canopy cover by creating a
biodiverse, structurally strong urban forest equitably distributed throughout the city. This plan
identifies areas across the city on public and private lands that are suitable for tree planting.
While available planting spaces are limited across the city, reports estimate that an
increase in canopy cover by 18% is possible on single- and multi- family residential areas, open
25
park spaces, industrial, and institutional land uses (City of Toronto, 2013b). With a large portion
of the urban forest located on single-family residential land, highly suburban neighborhoods in
Toronto, such as Etobicoke (the focus of this study) are important areas to monitor as they are
neighborhoods where a healthy, dense canopy can exist.
There are many threats to Toronto’s urban forest that may limit efforts to increase canopy
cover. Many invasive insects have threatened, and continue to threaten, a number of tree species
across the city. The European Gypsy Moth (GM) outbreak in 2007 and 2008 threatened up to
16% of Toronto’s trees, valued at a possible loss of $1.5 billion (City of Toronto, 2013a). Aerial
and ground Btk spray treatments were successful in saving the vast majority of vulnerable trees,
and GM is no longer a significant threat city-wide. The Asian Long-Horned Beetle (ALHB)
confirmed in 2003 could have cost the city 42% of its tree population (value of $4 billion), but
was successfully contained as of 2013 (City of Toronto, 2013a). Currently, Emerald Ash Borer
(EAB) poses a threat to Ash trees across the city, which provide 24-26% of the city’s canopy
cover. The loss of Ash Trees would cost the city $570 million; treatment and Ash tree removal
are in progress.
Development pressures from an increasingly urbanized downtown core, expansion of
residential areas, and increased pressure and demand for recreational areas are additional threats
(City of Toronto, 2013b). Finally, there are also a number of climate change impacts that are
detailed in the plan, such as changing growing seasons, change in temperature and precipitation
norms, and increased severity of storms. However, within the plan, there is no direct strategies
for preventing or mitigating the impacts from ice storm events.
2.2 City of Mississauga
Mississauga, a lower-tier municipality located in Peel Region, is the 6th largest city in
Canada, and is the second largest municipality in the Greater Toronto Area. As of 2015, the city
had a population of over 710,000 people (City of Mississauga, 2016b). Mississauga has a high
immigrant population, resulting in an ethnically diverse city, like Toronto. Residential land uses
make up a large proportion of the city, with 30% of land use designated as residential, 12% of
which contains semi-detached homes. There is also a large area of industrial and commercial
land. Unlike Toronto, Mississauga lacks a distinct network of ravines. However, the Credit
26
River, and many other smaller rivers, flow through the city and provide a number of public parks
and greensapces.
As Mississauga is a municipality within Peel Region, regional and municipal
stakeholders must collaborate in the development of policies and plans to best manage natural
resources and urban forests. Plans to manage Mississauga’s Natural Heritage System (or Natural
Areas System) were established in 1996. Existing plans for urban forest management in Peel
Region include the Regional Official Plan (reviewed 2014) which provides direction for
municipalities to create UFMP’s, and the Peel Climate Change Strategy (2011), which
emphasizes the role of urban forest in climate change mitigation. For the city of Mississauga,
plans that include specific targets to maintain and manage the urban forest were formalized in the
city’s Strategic Plan: Our Future Mississauga (2009b), Official Plan (2011), and the Parks &
Natural Areas Master Plan (2009a). A Natural Heritage & Urban Forest Strategy was adopted in
2014, which included objectives for both natural heritage areas as well as the urban forest. This
was published in conjuncture with the City of Mississauga: Urban Forest Management Plan
(2014-2033) (2014c), which includes detailed targets and strategies for managing and increasing
the urban forest in the city. Three by-laws exist for the management of trees in residential areas;
the Tree Permit By-law, the Street Tree By-law, and the Encroachment By-law. These by-laws
are in place to help govern residents, as the majority of canopy cover is located on private
properties.
The City of Mississauga Urban Forest Study- Technical Report (2011) presented the state
of the distribution and structure of the urban forest in 2007, upon which the official UFMP was
created (TRCA, 2011). This study used two main approaches, similar to Toronto, to evaluate the
canopy distribution across the city. The i-Tree Eco Model used a point-based random sampling
of many plots across the city to determine urban forest structure, and Urban Tree Canopy (UTC)
distribution was determined from classifying high resolution satellite imagery. By combining
these methods, it was found that Mississauga had 2.1 million trees, resulting in a canopy cover of
15% that is unevenly distributed across the city and valued at $1.4 billion in structural benefits
(TRCA, 2011). An updated UTC assessment for 2014 identified that tree canopy increased to
19% across the city, an increase of 4% since the implementation of the UFMP (Plan-it Geo,
2014). More than half of the city’s canopy cover is located on residential areas, a third is found
in natural woodlands and natural heritage areas, and the remainder is found across institutional,
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industrial, and commercial land uses. Based on the number of tree stems, Mississauga was
dominated by Sugar, Manitoba, and Norway Maple, White Ash (Fraxinus americana), and
Eastern White Cedar species. The species that provide the greatest proportion of leaf area are
Sugar Maple, Norway Maple, and Green Ash (TRCA, 2011). Compared to similarly sized cities,
Mississauga has a low proportion of canopy cover, and a low species and structural diversity in
its urban forest (TRCA, 2011; City of Mississauga, 2014c).
Within Mississauga’s UFMP, the canopy cover goals from 2014-2033 are to maintain a
healthy urban forest with a canopy cover of 15-20%. While many cities in North America aim
for a canopy cover target or 30-40% for optimal sustainability, it is believed that 20% is more
realistic for Mississauga. At the time of the creation of the UFMP, many of the trees in the city
had a small DBH and efforts will be made to monitor tree growth, as they will provide a larger
canopy cover contribution in 10 to 20 years (City of Mississauga, 2014c). Due to space
availability and zoning permits, the space to increase planting is limited across the city. There is
also tree mortality due to natural causes, poor growing conditions, and associated climate change
impacts that tree planting efforts may offset. Mississauga’s One Million Trees initiative,
implemented in 2012, is a city-wide program to increase awareness and stewardship of the urban
forest through public outreach and education. Through the One Million Trees program, and
continued efforts by Mississauga’s urban foresters, the canopy cover goals presented in the
UFMP seek to sustain a productive urban forest, in order to maximize the benefits of the urban
trees.
Mississauga faces many of the same developmental and climate change stressors that
Toronto experiences which reduce available planting space and limit the availability of resources
to accommodate these pressures. Similarly, GM threatens 15% of Mississauga’s trees, potentially
costing the city $370 million (TRCA, 2011). The ALHB could result in a loss of up to $702
million in structural value, as 56% of Mississauga’s trees may be susceptible (TRCA, 2011).
However, ALHB has been contained in certain areas, and is monitored by the Canadian Food
Inspection Agency (CIFA) (City of Mississauga, 2014c).
Like Toronto, the Emerald Ash Borer (EAB) poses a significant threat to the urban forest
in Mississauga. EAB was discovered and confirmed in 2008, and as of 2014, EAB was found
throughout the entire city. EAB threatens 16% of stems within the city, which makes up 10% of
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the urban canopy cover across private and public land uses, and is estimated to cost Mississauga
over $51 million for managing, removal, and replanting (Marchant, 2012; City of Mississauga,
2014c). The City of Mississauga Emerald Ash Borer Management Plan was implemented in
2012, which outlined immediate and long term goals for managing the invasive pest (Marchant,
2012). Initial efforts involved a biological control agent, Tree Azin, which was injected into
publicly owned trees. Over 5,000 Ash trees that are being treated with Tree Azin; they were
treated in 2012, 2014, and will receive another treatment in the summer of 2016 (City of
Mississauga, 2016a). Over 6,500 ash trees were removed in high risk areas, and trees will be
replanted at a 1:1 rate over the next several years. As of October 2015, 2,366 trees have been
replanted. Ash trees on private properties, which contribute a significant amount of the city’s
canopy cover, are the responsibility of the property owner. By 2022, it is anticipated that ash
mortality will reach 100%, and will have resulted in significant changes to the urban forest and
canopy structure (Marchant, 2012). Like Toronto, Mississauga’s UFMP also has no specific
strategies for anticipating or responding to ice storm damage on trees in the city.
The December 2013 Ice Storm
Recognized as one of the most damaging ice storms in Canada since the 1998 event, the
December 2013 ice storm devastated trees throughout southern Ontario. The areas significantly
impacted were the regions of Peel, York, and Durham, along with the City of Toronto. From
December 20 to 21, two waves of freezing rain produced up to 40 mm of freezing precipitation,
resulting in ice accumulation of 20-30 mm on above ground utility wires, tree branches, and
other exposed surfaces (Armenakis & Nirupama, 2014; Davies Consulting, 2014).
Thousands of 311 service requests (requests for non-emergency city services) were filed
following the ice storm event as a result of property damage and safety concerns from downed
branches and trees, overwhelming response crews across Mississauga and Toronto (City of
Toronto, 2014; City of Mississauga, 2014a). As a result of the ice storm, there was an increased
rate of emergency room visits and injuries reported in Toronto compared to Ottawa within the
same amount of time, and a 10% increase in emergency room visits in Toronto during the ice
storm compared to the same days relative to past five years (Rajaram et al., 2016). Additionally,
98 cases of carbon dioxide poisoning were reported in Toronto as a result of power loss during
the ice storm (Rajaram et al., 2016). Immediately following the ice storm, efforts to address
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concerns to public safety were prioritized by restoring lost power, clearing blocked roadways and
paths, and removing tree debris (City of Mississauga, 2014a).
The damage to infrastructure, properties, and power lines was primarily a result of fallen
tree branches or entirely downed trees damaging or breaking utility lines (Toronto Hydro, 2015).
Within the City of Toronto, about 60% of Toronto Hydro customers experienced loss of power
for some duration of the storm, affecting about 1 million residents (Davies Consulting, 2014).
However, many Toronto residents went over 4 days without power being restored, while some
residents waited up to two weeks until power returned (Davies Consulting, 2014). As a result of
the loss of power experienced in Toronto, and the length of time it took to restore power across
the city, discussions of underground electrical utility networks have been presented. However,
due to the increased frequency of flooding events and the price tag of $11- 16 billion, this is not a
likely scenario for Toronto (City of Toronto, 2014a; Armenakis & Nirupama, 2014).
Recommendations to increase the budget for canopy trimming around the overhead utility
networks have also been made (City of Toronto, 2014a). Mississauga experienced a much lower
rate of residents losing power, with a peak of 22,000 customers losing power, which was reduced
to 12,800 within the day, and 500 customers without power were restored within the following
days (City of Mississauga, 2015).
In addition to hydro, a number of other services were affected by the ice storm.
Community centers were open to provide energy and warmth to residents without power.
Pearson Airport experienced major delays, and two major hospitals relied on generators due to
loss of power (Armenakis & Nirupama, 2014). Road conditions were extremely difficult to
navigate, and drivers were recommended to stay off the road. A number of closed businesses
experienced revenue loss. The province of Ontario established an Ice Storm Assistance Program
to help municipalities with the cost of the ice storm.
The ice storm has cost Toronto over $77.2 million, which accounts for forestry response
clean up crews, infrastructure repairs, and the cost associated with tree loss and tree damage. It
was reported that 40,000 tonnes of tree debris were removed following the storm, the same
amount collected over five summer months of 2013 (Alamenciak, 2014). A review of publicly
owned trees was conducted by Davies Resource Group, who found that most of the damage was
located in the north-east areas of Toronto (not included in this analysis); areas of low density,
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small trees and mature hardwood species did not experience widespread damage; Siberian elm
was the most damaged species; and ice accumulation was so severe across the city that trees
experienced damage regardless of proactive pruning efforts (Davies Resource Group, 2014).
They also concluded that trees in the city may have residual damage that will be identified in
subsequent growing seasons.
For Mississauga, the cost of the ice storm is valued at $9.4 million, which accounts for
response crews, damaged tree removal, and tree replacement (City of Mississauga, 2015).
Widespread damage was experienced throughout the city to public trees, with 15,000 trees
requiring damaged branch removal; 8,000 trees being extensively pruned; and 2,000 trees being
fully removed all as a result of ice storm damage. Publicly owned trees are being replanted
across the city at a 1:1 ratio during the summers of 2015 and 2016.
The damage from the ice storm on private properties is the responsibility of the property
owner, and it is difficult to determine how many trees were damaged. Due to the large proportion
of the urban forest that is contained on residential land uses, it is likely that significant losses
were incurred on these properties.
Data & Geospatial Software
4.1 Satellite Imagery
For this analysis into the changes in tree canopy across Mississauga and Toronto, three
years of classified satellite imagery were used to identify the distribution of canopy change:
images from 2007, 2011, and 2014 (Figure 1). All images were taken during the summer with
leaf-on conditions.
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Figure 1: Extent of Satellite Imagery
In order to represent baseline conditions for tree canopy across the study area, 2007 existing
classified imagery was used. This was classified by the University of Vermont’s Spatial Analysis
Lab using an automated classification method. This was done in collaboration with Toronto and
Region Conservation Authority, the City of Mississauga, and the City of Toronto in an effort to
determine the distribution of urban forests within many municipalities across the Greater Toronto
Area (TRCA, 2011).
This analysis used classified 2007 Quickbird imagery, because of it’s high spatial
resolution (0.6 m) (Pelletier & O’Neil-Dunne, 2010a; Pelletier & O’Neil-Dunne, 2010b; Digital
Globe). Ancillary datasets were used in the automation process to classify land cover into 7
categories: tree, grass/ shrub, water, roads, building footprints, paved surfaces, and other. This
study also identified existing canopy, areas that could possibly sustain future canopy on
vegetated and impervious surfaces, and areas not suitable for canopy expansion. For this year,
the original imagery was not available for use, and only the classification results can be used in
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this analysis. The classified data were available for the entirety of Toronto and Mississauga,
although the processing extent was cropped to the same extent of the 2014 image.
The 2007 classified images were used to represent the distribution of urban canopy with
‘baseline’ conditions. This represented the urban canopy prior to the implementation of formal
UFMP’s by Toronto and Mississauga, and prior to tree planting initiatives such as Every Tree
Counts and One Million Trees. This dataset also displays tree canopy prior to major disturbance
events, including the presence of several invasive insect species (ALHB, EAB) and the ice storm.
In order to gain insight into changes in canopy over time, a 2011 satellite image was used
to provide tree canopy data for an intermediate year between 2007 and 2014. The 2011 image
was acquired June 19, 2011 with the Ikonos satellite sensor (Image metadata file). The Ikonos
imagery has a 1 m panchromatic (black and white) layer, and 4 m resolution multispectral data
(Digital Globe, 2013). The multispectral bands contain data in the blue, green, red, and near
infrared portion of the electromagnetic spectrum. This image has a slightly coarser spatial
resolution, and was used to identify areas with trends of canopy growth or loss along with the
images for the other dates. This image displays the distribution of the urban forest shortly after
UFMP’s were established, and during the EAB management process.
The third satellite image was acquired with the GeoEye- 1 satellite platform, which
contains a panchromatic layer (0.46 m resolution) and 4 multispectral layers (1.84 m resolution)
(Digtal Globe, 2014). The image was taken June 28, 2014. This data will be used to display tree
canopy following the impact of the EAB and the December 2013 ice storm. Specifications of the
2011 and 2014 images can be seen in the following table (Table 1).
Both the 2011 and 2014 imagery were acquired in June, although taken on different dates
and likely reflect slightly different stages of seasonal tree growth. However, while the differences
in the date of acquisition may result in classification differences, as certain tree species may not
have developed full tree canopies at the same time of year. However, the differences in growing
conditions vary daily, as well as annually, based on different climate and growing conditions, the
differences in image acquisition date are likely small enough that they do not significantly
impact the subsequent tree canopy distribution maps.
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Table 1: Satellite Imagery Specifications Sensor Name IKONOS 2 Geo Eye- 1
Image Type Panchromatic/ Multispectral Image Panchromatic/ Multispectral Image
Image Date June 19, 2011 June 28, 2014
Area Covered 219 km2 177 km2
Cloud Cover 0 0
Bits per pixel 11 11
Projection Universal Transverse Mercator (UTM) Zone 17 N
Universal Transverse Mercator (UTM) Zone 17 N
Spectral Range Panchromatic band: 526-929 µm Blue band: 445-516 µm Green band: 506- 595 µm Red band: 632- 698 µm Near Infra-red band: 757-853 µm
Panchromatic band: 450-800 nm Blue band: 450-510 nm Green band: 510-580 nm Red band: 655-690 nm Near Infrared: 780-920
4.2 GIS Data
Additional GIS data sets are used to aid in the analysis of the tree canopy maps. Some
data were also accessible through the University of Toronto Map and Data Library.
Street tree data are available for the City of Toronto, and can be used to identify areas
that experienced street tree loss. While this is useful, there is no reason attributed to street tree
loss, and it cannot be used to identify loss related to the ice storm or EAB. For the City of
Mississauga, land use data were used to determine the proportion of canopy cover on each land
use category. Property parcel data from Toronto and Mississauga were also used to determine
canopy changes at the property level.
4.3 Survey
A written existing survey was conducted in 2014 to gain an understanding of residents’
experiences during the ice storm (Conway & Yip, 2016). This survey focused on the trees on
their properties, their experience with the December 2013 ice storm, and tree management after
the ice storm. The part of the survey relevant to this study, limited to reported tree damage from
the ice storm, is used. Responses from residents from the two neighborhoods selected to
participate in the survey that overlap with the available satellite data are incorporated into the
analysis. The specific neighbourhoods (Figure 2) are within the top quartile of canopy cover on
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residential lands, as these areas where likely hardest hit by the storm. For the Toronto
neighbourhood, 389 surveys were distributed, with 202 responses received. For the Mississauga
neighbourhood, 335 surveys were distributed, with 208 responses returned. The neighbourhoods
contain mostly detached, single-family houses. These survey results were geocoded to the
property parcel data to facilitate analysis.
Figure 2: Location of surveyed neighbourhoods
4.4 Software
A variety of geospatial software was used in this analysis. Detailed descriptions of how
each program was used is explained in subsequent sections. Erdas Imagine V13.00 was used for
the preprocessing of the 2011 Iknonos Image. Definions Developer 7.0.9 (formerly eCognition)
was used for the image object segmentation and classification of the 2011 and 2014 images. This
program has very powerful segmentation algorithms that was integral in the processing of the
images. The ArcGIS suite of programs, specifically ArcMap 10.3.1, was extensively used in this
project to display the resulting tree canopy maps, and for the canopy change analyses.
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Chapter 4 Creating Urban Tree Canopy Maps
Introduction
Using high spatial resolution satellite imagery, tree canopy maps were created using an
Object Based Image Analysis (OBIA) approach. These tree canopy maps were created for 2011
and 2014, using the satellite images detailed in Table 1. This involved image preprocessing,
image object segmentation and classification, followed by an accuracy assessment. The results
are the creation of maps displaying the distribution or urban tree canopy cover.
Methods
2.1 Preprocessing
Before the tree canopy maps were created, the images were preprocessed in order to proceed
with the OBIA. Preprocessing was completed using Erdas Imagine and ArcMap 10.3.1.
2.1.1 Pan Sharpening the Imagery
The 2011 Ikonos image was pan-sharpened before it was used in the analysis. The
original satellite data contained a panchromatic, or greyscale, image with 1 m spatial resolution,
while the multispectral image contained the blue, green, red, and near-infrared spectral
information, with 4 m resolution (DigitalGlobe, 2013). The process of pan-sharpening fused the
coarser multispectral data to the finer textural detail of the panchromatic image (Erdas, 2010).
The result was a multispectral image with the spatial resolution of 1m.
Specifically, a subtractive resolution merge pan-sharpening algorithm was applied to the
2011 Ikonos image in Erdas Imagine (Erdas, 2010). This method was selected as it was designed
to be used for imagery with a multispectral to panchromatic pixel ratio of 4:1. (Erdas, 2010). The
2014 image used in this analysis was acquired with the pansharpening process having been
previously completed.
2.1.2 Georeferencing the Imagery
The 2011 and 2014 images were captured on different dates, using different satellite
platforms, resulting in slightly misaligned imagery. Using ArcMap, the images were
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georeferenced using control points between the two images to align ground features, thereby
ensuring the images overlap appropriately. This was done to guarantee accuracy when comparing
the changes in canopy cover between multiple images.
2.1.3 Defining the Processing Area
The different images extents were also cropped in order to reduce processing time,
focusing only where there was overlapping data (Figure 1). The area of the 2014 image was
selected to include the two survey neighbourhoods and most of Mississauga (Figure 2). This was
done to correspond with the Mississauga coverage of the 2011 image, which was cropped to the
northern bounds of the 2014 image.
2.1.4 Compute NDVI layers
In order to determine the presence of vegetated land covers, a vegetation index was used
in image processing. The Normalized Difference Vegetation Index (NDVI) uses the red and
near-infrared bands to create a simple index ranging from -1 to +1, using the equation: NDVI =
(NIR- Red) / (NIR + Red). Vegetation absorbs light energy in the visible portion of the
electromagnetic spectrum (represented by the red band), and is highly reflective in the near-
infrared range. The output ratio of these bands results in a range from -1, representing non-
vegetated surfaces, to +1, representing dense and healthy vegetation. The NDVI layer was
created using the NDVI function in ArcMap’s Image Analysis Window.
Due to the varied surfaces in urban areas, some non-vegetated features may result is
unusually high NDVI values, such as bright roofs, high variation in moisture levels, or sun glare
on water (Walton, et al., 2008). As NDVI is a simple ratio, it is effective in identifying areas of
vegetation, but cannot be the only metric used to conclusively determine the presence of
vegetation.
2.1.5 Layer Mixing
For visualization purposes, a false colour scheme was applied to the display the satellite
images in the various software programs. As vegetation is highly reflective in the near-infrared
wavelength range, it is much easier to distinguish between different vegetation types when the
NIR band is displayed in the red colour scheme (Jensen, 2007).
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2.2 Processing
Due to the very high spatial resolution of the imagery and the complexity of urban land
covers, an image object based approach is more suitable than a pixel- based analysis (Zhang,
Feng & Jiang, 2010). An image object is a group of pixels, which is a distinct cluster within the
scene (Definiens, 2008). The image objects were then classified into either a “Tree” or “Non-
Tree” class. All image processing was done using Definiens Developer 7.0 (formerly
eCognition), due to its powerful segmentation algorithm and classification capabilities, as well as
ArcMap 10.3.
2.2.1 Subset Selection
Due to the large size of the images and computer processing ability, it was not possible to
process either image in one piece; smaller subsets of each image were defined when importing
the image into eCognition in order to accommodate for the processing power available. For the
2011 image, these subsets were 1000 x 1000 pixels, with each subset overlapping by 20 pixels on
all sides to ensure complete coverage. For the 2014 image, each subset was 2000 x 2000 pixels,
with 20 pixels overlapping. The subsequent processing was done on each subset to ensure the
complete extent was processed.
2.2.2 Segmentation
A multiresolution segmentation algorithm was applied to the images, as this procedure
minimizes the average heterogeneity of the image object features, and produces image objects of
multiple resolutions (Blaschke, 2005). The bottom- up algorithm iteratively merges a pixel with
surrounding pixels containing similar pixel characteristics based on the defined parameters.
A process of trial and error was employed to determine the following values in order to result
in the optimal size of the image objects (Myint, et al., 2011, Mathieu, et al., 2007). As the
purpose was to create a tree canopy map, the parameters were optimized for the creation of
image objects that best segmented tree features.
• Layer Weights: The multispectral layers were given different weights, based on their
relative importance or value for the resulting segmentation. The layer with the highest
value has more information used in the segmentation process. The NIR layer was given
the highest value (2), due to the high reflectivity of vegetation in this wavelength range.
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The visible colour bands of Blue, Green, and Red, were all equally weighted (1). Layers
that do not contain information intended for the segmentation receive a lower weighted
value. The panchromatic and NDVI layers received the lowest value (0) and were not
used in the segmentation procedure. These layer weights were the same for the 2011 and
2014 images.
• Scale Parameter: An abstract value, the scale parameter determines the amount of
allowed heterogeneity of the image objects. Based on the complexity and heterogeneity
of urban scenes, a smaller scale value is used, relative to a more homogenous scene. A
small scale value will result in smaller image objects (Zhang, Feng & Jiang, 2010). After
trial and error, an appropriate scale value was selected for each image to produce
appropriately sized image objects, while a smaller scale value produced too-finely
detailed image objects (Figure 3). Scale values of 20 and 40 were used for the 2011 and
2014 images, respectively.
Figure 3: Scale parameter selection (2014 image)
• Colour and Shape: The input of the colour parameter is the digital number associated
with the spectral values. The shape parameter relates to the image object shape’s
homogeneity. These two value weights will always equal 1, so any modifications to the
shape value will change the colour. A preference was given to the colour input (0.8), as
the spectral numbers were more important than the shape (0.2) for both the 2011 and
2014 imagery. Generally, more meaningful objects result from a stronger influence of the
colour criteria (Mathieu, et al., 2007).
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• Smoothness and Compactness: The previously mentioned shape criteria is refined
through the smoothness and compactness parameters. Smoothness optimizes the
smoothness of the image object borders, while compactness refers to the compactness of
the image object spectral contrast. The smoothness and compactness criteria were both
given an equal weight of 0.5 for the 2011 and 2014 years of imagery.
2.2.3 Nearest Neighbour Supervised Classification
Once the image objects have been successfully segmented, they must be categorized into
the correct class. Using the Nearest Neighbour (NN) Supervised Classification method, a
relationship is defined using sample image objects to assign membership criteria for each class
(Definiens, 2012; Myint, et al., 2011). All image objects are then organized into the class that
best matches the image object features, in this case, either Tree or Non-Tree.
2.2.3.1 Create Classification Categories
The desired classification types must be created before defining the NN feature space and
the representative samples (Definiens, 2008). The primary objective was to determine changes in
canopy cover over time, therefore tree canopy was the focus of the classification. The “Tree”
class included all tree canopy features and/ or dense shrubs. The “Non-Tree” class included all
other urban land cover that was not tree canopy. This included all non-tree vegetation (ie grass,
fields, etc), all built covers (houses, roads, commercial areas, etc), rivers and swimming pools,
and anything else that does not fall within the tree cover class.
2.2.3.2 Define the Nearest Neighbour Feature Space
Once the classifications categories were created, the criteria used in the NN classifier were
selected. Using the Feature Space Optimization tool, the program selects the combination of 10
image object features that will best represent the class from a larger pool of possible features
(Definiens, 2012). The Feature Space Optimization produces an output matrix that applies a
value to the most suitable features. A series of multiple features were run through the Feature
Space Optimization tool before choosing the most suitable features to be used. The criteria were
adapted from features used in previous research utilizing the same imagery (Shakeel, 2012). A
Standard NN expression was used, meaning that the same feature space was used in all
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classifications. The criteria selected in Feature Space Optimization were various features at the
image object level, associated with image object layer means, shape, and texture.
• Layer Means: Properties associated with the pixel values within each image object based
on spectral information (Definiens, 2007).
- Mean NIR, Mean NDVI: mean value calculated by the pixels within each layer of the
image object
- Brightness: mean value of the spectral mean values of all layers in an image object.
- Maximum Difference: the minimum mean value within an image object is subtracted
from the maximum mean value, then divided by the brightness. Max. Difference uses
all layers within the image object
• Shape: Shape properties calculated on the distribution and layout of pixels at the image
object level (Definiens, 2007).
- Area: With the georeferenced imagery, the area of each image object is the ground area
covered by one pixel multiplied by the number of pixels in the image object.
- Area (including inner polygons): The area of an image object including the area of
internal image objects
- Area (excluding inner polygons): The area of an image object without including the
area of internal image objects.
- Average Branch Length: based on the image object skeleton, which takes into account
the main line and branches from that line to the image object borders. The average
branch length takes into account all the branch segments to determine the mean branch
value for each image object.
- Border Index: A border length is the sum of edges of an image object that are shared
with other image objects. To determine the border index, a small rectangular box
enclosing the image object is created. The ratio of the border length to the smallest
enclosing rectangular border is the border index.
- Compactness: Using a similar enclosing box as the border index, the area ((length *
width) / Pixels) determines the compactness of the image object.
- Density: Determined by the image object area divided by its radius. This is related to
the compactness of the image object.
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- Main Direction: The main direction of am image object is the direction of the
eigenvector. The eigenvector relates to the spatial distribution of the image object.
• Texture: image object texture is determined based on the relationship between and within
image objects (Definiens, 2007).
- GLCM Homogeneity (all directions): the gray level co-occurrence matrix (GLCM)
value determines the frequency of different combinations of pixel gray levels within an
image object. All directions (0°, 45°, 90°, and 135°) were used to show GLCM in all
horizontal and vertical directions.
The image objects of the tree canopies are made up of a diverse set of pixels, which contain
many different pixel values, as there are some pixels with high reflectance values, as well as
lower reflectance values due to shadows. As pixel, or layer values alone, would not be able to
correctly classify the tree image objects, the shape and textural image object criteria were used to
inform the nearest neighbour classifier (Walton, et al., 2008).
2.2.3.3 Sample Selection
Sample image objects were selected to be representative of each classification. The
selected samples train the NN classifier to identify the features chosen in the NN Feature Space
that result in membership criteria (Myint, et al., 2011). A higher number of samples selected will
result in a more accurate classification (Definiens, 2007). Using the Select Sample brush, a large
number of samples were manually selected for each classification. The Select Sample brush is an
interactive tool that selects each sample by clicking on the image object (Definiens, 2012). The
number of samples varied based on the land cover of the scene, as urban areas have diverse land
covers (ie residential, dense urban, dense road network, commercial, rivers, etc).
When selecting sample image object for the Tree classification, efforts were made to
ensure a variety of representative image objects were selected, based on the shape, size, colour,
and location. The selection of Non-Tree Samples included a much more diverse set of samples,
as it included non-tree vegetation (such as fields, and residential lawns), roofs, roads, water, and
many other features that may fall in this category.
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2.2.3.4 Application of the Nearest Neighbour Classification to the Scene
Before the NN Classification algorithm (a membership based classification) was used on
the scene, a rule- based classification was first applied. This rule set involved the NDVI layer.
This rule set was used to classify any image objects with an NDVI value of less than 0.1 into the
Non Tree classification. NDVI should not be exclusively used to identify vegetation land covers,
so this tool was used to get most, but not all of, the Non-Tree image objects into the correct
classification. By first using the NDVI rule-based algorithm, it greatly reduced the processing
time of the NN classifier, which only had to classify the remaining unclassified image objects.
As the application of the NN classifier progressed on the subsets, a series of trial and
error revealed that the GLCM homogeneity criteria in the Feature Space significantly increased
the processing time of the algorithm. Once this feature was removed, the time requirement was
reduced and the quality of the classification was not negatively impacted.
2.2.4 Manual Edits
While powerful, and very successful, classification techniques do not always correctly
assign image objects into the appropriate class (O’Neil- Dunne, et al., 2014). This may be the
result of not enough sample image objects selected, a bias in the samples, or samples that were
unrepresentative of the entirety of land covers within the scene.
In order to correct for the misclassified image objects, extensive manual editing was done
to ensure high accuracy. As there are only two classification categories, it was a streamlined
process to gain user-familiarity with manually editing the objects into the correct category.
Image objects that were commonly misclassified included objects of shadows (both tree and non-
tree shadows), certain fields and non-tree vegetated areas (lawns), as well as commercial areas
with complex spectral and spatial characteristics.
Extensive manual edits were done to ensure high accuracy with the 2011 and 2014
images, which will result in greater confidence with the subsequent tree canopy analysis. The
process of image segmentation and classification can be seen in Figure 4.
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Figure 4: Methods used in eCognition A) Subset Selection B) Layer Mixing C) Segmentation D) Sample Selection E) Classification F) Manual Edits
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2.2.5 Export to Shapefile
Once the image objects were correctly categorized into the appropriate classification,
each subset was given a final review. The classification results were then exported from each
subset into a shapefile, and imported into ArcMap. These subsets were merged into one final
canopy map, displaying a unified map of tree and non-tree coverage for each image’s total
coverage area. The final raster tree canopy maps display Tree Canopy, with a value of 1, and
Non-Tree land cover, with a value of 2.
2.3 Accuracy Assessment
An accuracy assessment was done to determine the quality of the classification for the
2011 and 2014 imagery. Ideally, an accuracy assessment would have been done at the image
object level, as that was the unit of analysis for the classification. This involves the creation of
Training and Test Area masks of the image objects, which matches the image objects with their
classification result. However, due to the large number of subsets for each image, and the
duration of time required for this type of accuracy assessment, this method was not used.
Within ArcMap, a pixel based accuracy assessment was used, using the unclassified
imagery and the classified tree canopy maps. For each classification type, the same number of
random points were created on the image. The 2011 image had 500 random points generated per
class (1000 total), while the 2014 image had 800 random points generated per class (1600 total).
The large number of points was chosen due to the large study area and high spatial resolution.
More points were used for the 2014 image, as it covered a larger processing area than the 2011
image. Using only the point data and the original imagery, each point was compared to the land
cover, and assigned into either the Tree (value = 1) or Non- Tree (value = 2) category. These
values were then compared to the classification results.
Using an Error Matrix, the correct and incorrect matching of the original imagery and
classification result shows the quality of the classification. This displays the overall accuracy,
producer’s and user’s accuracy (errors of omission/ exclusion and commission/ inclusion,
respectively), and the Kappa index (accuracy associated to chance) (Table 2) (Bhaskaran et al.,
2010). The overall high accuracy of both images likely resulted from only have two
classifications and extensive manual editing.
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Table 2: Error Matrix in percentages for 2011 and 2014 classification accuracy 2011 Image Tree Non- Tree
Producer’s Accuracy 91.9 89.1
User’s Accuracy 88.9 92.1
Overall Accuracy 90 Kappa Coefficient 0.81
2014 Image Tree Non- Tree Producer’s Accuracy 93.5 90.5
User’s Accuracy 90.1 93.892.1
Overall Accuracy 92 Kappa Coefficient 0.84
Results
Using the classified tree canopy maps, the distribution of tree canopy across parts of
Mississauga and Toronto in the study area can be determined. The area of interest is determined
by the spatial extent of the 2011 and 2014 images, which do not represent the entirety of each
city. The proportion of canopy cover within the area of interest is higher than total canopy for
each city, as the land use in the area of interest is dominated by residential and open spaces,
which contain more trees than commercial and industrial land uses, which were not the focus of
this analysis.
For baseline tree canopy conditions, prior to the implementation of the UFMP and before
any major disturbances, the 2007 image was used. Within the area of interest, specifically the
extent as the 2014 image, there was an overall canopy cover of 23.4% (Figure 5; Table 3). For
the 2011 image, which has a smaller extent than the 2014 image, canopy cover across the image
was 31.1% (Figure 6). Finally, for the extent of the 2014 image, which was used to represent
canopy following the implementation of the UFMP and ice storm event, canopy cover across the
area was 22.2% (Figure 7). The higher canopy cover in 2011 is likely due to the larger pixel size
of the imagery, relative to the 2007 and 2014 images, which results in a larger area mapped as
canopy cover, and overestimated canopy cover, as well as the difference in the extent of the
image area.
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Figure 5: 2007 Tree Canopy Distribution
47
Figure 6: 2011 Tree Canopy Distribution
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Figure 7: 2014 Tree Canopy Distribution
49
Where the images overlapped and shared spatial extents for all three images, canopy can be
compared across the same area. Within this common extent, the canopy cover was 21.6% for
2007, 28.1% for 2011, and 23.1% for 2014 (Table 3).
Table 3: Distribution of Canopy Cover Canopy Cover within the Extent of
Total Image Canopy Cover within the Common
Extent 2007 23.4% 21.6%
2011 31.1% 28.1%
2014 22.2% 23.1%
Based on the proportions of land use within the common extent area, this smaller area
contains much more residential and open parks/ greenspaces than the entirety of Mississauga
(Table 4). The majority of canopy cover is found on residential land use, followed by open
spaces/ green lands and Right of Way (transportation) routes. The proportion of canopy that falls
in the right of way/ transportation routes is likely due to nature of tree growth over roadways;
when a tree is located on a residential property, it’s canopy is not limited to the property
boundary and will often cover many roads and pedestrian paths.
Table 4: Proportion of Land Use & Canopy Cover
Land Use
Proportion of Land Use
for All of Mississauga
(%)
Proportion of Land Use
within Overlapping Study area
Proportion of 2007 Tree
Canopy Coverage per
land use
Proportion of 2011 Tree
Canopy Coverage
per land use
Proportion of 2014 Tree
Canopy Coverage
per land use Residential 29.1 40.2 45.5 47.9 44.9
Right of Way/ Transportation 20.5 22.6 16.5 16.9 15.9
Industrial 15.2 5.1 0.9 0.9 0.8 Open Space/ Greenland 11.6 14.7 26.8 24.6 28.5
School/ Public Institution 9.2 4.4 3.5 3.3 3.6
Commercial/ Mixed Use 6.6 6.1 1.6 1.5 1.5
Vacant/ Farm 4.3 2.2 2.4 2.1 2.1 Utility/ Public Works 2.3 3.7 1.7 1.7 1.7
Community 0.9 1.1 1.1 1.1 1.1
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Discussion
Using the tree canopy maps, the distribution of tree canopy in the study area was
determined. These results can be compared to previous efforts to map tree canopy. The City of
Mississauga has used two methods of measuring tree canopy: the iTree Eco Model; point based
ground collected data, and Urban Tree Canopy (UTC) mapping; detailed land use maps produced
from satellite imagery (City of Mississauga, 2011). Based on these methods, canopy cover across
all of Mississauga was calculated at 15% for 2007 and 19% for 2014. However, slight
differences in data and methods in the initial calculation of canopy cover suggests that the
change in canopy cover is more likely 2-4% over that time frame (Plan It Geo, 2014).
The tree canopy maps produced in this study focused on the same time frame as the City
of Mississauga’s studies, although using different spatial extents, and followed methods similar
to the UTC methodology. Within the larger study area, canopy across decreased by about 1%.
However, in the smaller study extent (equal to the extent of the 2011 image), canopy increased
by about 2% in the same span of time. This shows that the distribution of canopy depends on the
extent of the area being studied and the spatially uneven dynamic of canopy changes. A more
detailed exploration in the changes in canopy cover will be discussed in the subsequent chapter.
There are trends in canopy cover distribution across all three dates. Where there is canopy
data for Toronto (in the north and north-eastern portion of the images) for the 2007 and 2014
dates, there are large clusters of canopy coverage. This coincides with mostly residential land
uses and river corridors. This is similarly seen in the southern portions of all three images, where
the Credit River flows through the processing extent. This area is also dominated by a river
corridor, residential land uses, as well as parks and greenspaces. These areas are characterized by
large, contiguous patches of canopy coverage. In the interior of the study area, another river
flows through, surrounded by large canopy patches. Radiating away from these river corridors,
canopy cover decreases, as there are patches of very low canopy cover associated with
commercial/ industrial land uses in the eastern portion of the classified images. Also on the
western extent of the images, dominated by newly developed residential lands, tree canopy is
sparser, and there are smaller patches which are also spaced further apart.
Canopy is absent, or sparse, in the middle of the image, which is associated with
commercial and industrial land uses. The low proportion of canopy cover is likely due to the
51
nature of commercial areas to fully develop and expand, rather than for the preservation of urban
forests. Along the major roads and transportation networks there is also an absence of trees, as
they obstruct views and are not locations typically associated with high canopy cover.
The study area is not reflective of the entirety of Toronto or Mississauga, as land use in
the study areas is dominated by older, established residential neighborhoods, open parks and
ravines. These land use categories contain the majority of canopy cover across all three years of
tree canopy data. It is unclear if efforts to increase urban canopy cover on these areas have been
successful, through the implementation of tree planting program, like One Million Trees
Mississauga, as trees planted through these programs as still very young.
One caveat to the mapping efforts in this study is the different sources of imagery used in
the creation of the tree canopy maps, which has a potential impact on the results. A few areas of
tree canopy were not measured for the 2007 map, which results in a slight underestimation in
some areas.
Also the 2011 imagery had a coarser spatial resolution than the 2007 and 2014 images.
This coarser spatial resolution likely resulted in slightly larger image objects for the tree canopy
classifications, which resulted in an overestimation on canopy cover. Also, there may be
differences in the growing conditions, climate variables, and date of imagery acquisition that
may have also contributed to the increased proportion of canopy cover in 2011. Local climate
variables, particularly temperature and precipitation levels, impact tree canopy growth
throughout the growing season and also possibly contributed to the increased canopy area in
2011. However, the extensive manual editing sought to minimize some of these errors.
Interestingly, the proportion of tree canopy per land use for 2011 is consistent with the other two
tree canopy maps. This likely resulted from the overestimation of canopy cover, however, this
overestimation was equal across all land covers.
Conclusion
Using high resolution satellite imagery, tree canopy maps were created using an image
object based approach. These maps were successful in identifying the distribution urban forest
cover across parts of Mississauga and Toronto, which are dynamic urban environments. These
forests face many stressors and threats, but continue to exist under stressful growing conditions.
52
It is evident that the urban forest is unevenly distributed across different land uses, with private
properties and parks containing the majority of Mississauga’s urban trees, which is inline with
previous studies looking at the relationship between land use and canopy cover (TRCA, 2011;
Pelletier & O'Neil-Dunne, 2011a). Further research would benefit from including the entirety of
each city in the analysis to more fully understand municipal-wide dynamics, and to continue to
regularly map canopy cover in the future to understand the long-term dynamics of the urban
forest.
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Chapter 5 Change in Urban Canopy Cover
Introduction
Using classified tree canopy maps, analysis into the changes in canopy distribution that
occurred from 2007 to 2014 were determined. Increases or decreases in canopy cover were
identified across the entire study area at a broad level, and additional data were used to identify
the changes in canopy that could be attributed to the ice storm. Specifically, survey responses
were used to identify ice storm related canopy loss at the property level in two neighbourhoods.
Methods
2.1 Identifying Changes in Urban Canopy Cover
Changes in urban canopy cover were identified using ArcMap for a post-classification
change detection analysis. Change in urban canopy cover can be experienced as both an increase
in canopy cover (canopy growth) and as a decrease in canopy cover (canopy loss) when using
two years of classified imagery. In this analysis, tree canopy change from 2007-2011, 2011-
2014, and 2007-2014 were examined.
The preclassified 2007 land use map included seven land use classifications (Tree, Grass/
Shrub, Water, Roads, building footprints, and other). This was reclassified into the Tree and Non
Tree categories to be consistent with the 2011 and 2014 images. The three tree canopy maps
were used in a raster format, and for each year of data, the Tree and Non Tree class was given a
unique value. To determine tree canopy change, the two target tree canopy maps were multiplied
by each other. This was repeated for each combination, for a total of three canopy change maps;
(A. 2007- 2011, B. 2011- 2014, C. 2007- 2014). The result of the raster calculation for each
canopy change map was reclassified into the following:
1) No Change in Canopy (no change from Tree to Tree) 2) Canopy Growth (change from Non- Tree to Tree) 3) Canopy Loss (change from Tree to Non-Tree) 4) No Change in Non-Tree Cover (no change from Non-Tree to Non-Tree)
54
From these canopy change maps, spatial patterns of canopy change, as experienced by canopy
losses and growth, can be determined, but there is not enough information available to attribute
the reason for those canopy changes.
2.2 Change in NDVI Values
Changes in NDVI values within the ‘No Change in Canopy’ class were identified from
2011 to 2014 to provide insight to canopy change associated with branch loss from the ice storm,
as that type of loss would result in canopy thinning. The change in NDVI values where there was
overlapping canopy coverage was used to provide insight into how the state of the canopy varied
between the two years. Where there was an increase in NDVI, it likely indicated positive canopy
growth, while a decrease in NDVI values would indicate canopy loss or canopy thinning.
To calculate change in NDVI, where canopy cover overlapped in the two images, the
Image Differencing tool in ArcMap was used to create a map displaying areas of increased or
decreased NDVI values.
2.3 Attributing Canopy Change Resulting from the Ice Storm
Canopy change maps alone cannot provide insight into the reason for change, as there are
numerous factors that actively contribute to canopy changes from 2007-2014. Tree mortality
contributes 3-5% of tree loss in the urban forest, and likely only significantly alters the canopy
when larger, mature trees reach the end of their timeline (Nowak et al., 2012). Invasive species,
specifically EAB, threatens 10% of Mississauga’s street trees, which constitutes 16% of the leaf
area within the city (City of Mississauga, 2014b). The ice storm resulted in 2,000 street trees
being removed, at 8,000 trees being extensively pruned, as well as many trees on private,
residential properties removed or losing branches (City of Mississauga, 2014a).
Tracking canopy changes on private properties is difficult, as residents have the authority
to manage trees on their property to suit their needs. Homeowners in residential areas often
remove trees for the construction of swimming pools, to expand their gardens, or for smaller
construction projects. Residents with Ash trees on their properties were responsible for the
removal of the trees following the EAB outbreak. Due to the large number of Ash trees and the
haste to remove them to prevent further spreading, permits were not required for residents, which
makes it difficult to determine the extent and location of Ash trees loss on private properties.
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Supplemental data sources are often limited or unavailable to track tree removal of both
public and private trees, and few studies have been able to attribute change in the urban forest. In
order to attribute canopy losses to the ice storm specifically, survey responses from residents
were used to identify ice storm related losses at the property level for two neighbourhoods in
Toronto and Mississauga.
2.3.1 Geocoding Survey Responses
In order to link residents’ survey responses with the canopy change maps, the survey
results had to be geocoded by joining the survey responses to a street address file through a
unique identifier code established as part of the survey. Using ArcMap’s Geocode Addresses
function, the survey responses were imported into ArcMap into a point shapefile. Based on Peel
Region and City of Toronto parcel property data, the property parcels were linked to the street
addresses through manual editing to verify proper placement. This resulted in the polygons
representing the property parcels having the survey response attributes.
2.3.2 Selecting Survey Responses to Identify Canopy Change
Within the survey, residents were asked how many trees and large branches (<10 feet)
were lost during the ice storm; whether they had small branches fall on their property; and the
number of trees planted and removed during the previous year and previous five years. Properties
were excluded where the number of trees removed in the past year exceeded the number of trees
planted to account for canopy loss from planned tree removal and not due to the ice storm. Based
on this criteria, 183 and 182 properties were used in this analysis for the Mississauga and
Toronto neighborhoods out of a possible 202 and 208, respectively.
2.3.3 Identifying Canopy Loss and NDVI Change from the Ice Storm
In order to attribute canopy loss from the ice storm, survey responses that indicated losing
at least one large branch and/ or losing at least one tree on the property during the ice storm were
identified. From the properties, the amount of canopy loss that occurred within each property
parcel was extracted from the canopy change maps, limited to canopy changes only on
residential land uses, to identify ice storm related canopy loss proportional to total canopy
changes. This provided the canopy change that is likely attributed to ice storm damage.
56
Based on tree morphology and vulnerability to ice storms, the most common type of
damage is branch loss (Hauer et al., 2006). This type of damage may not necessarily result in
drastic changes in total canopy cover, yet it will result in canopy thinning. Properties were
identified in which residents reported branch loss (including both small and large branch loss).
These selected properties were used to examine the magnitude of the NDVI change that occurred
within the bounds of the parcel properties to identify canopy thinning due to branch loss from the
ice storm.
Results and Discussion
3.1 Total Canopy Change
3.1.1 Change in Canopy Cover
The results of the canopy change maps can be seen in Figures 8, 9 and 10. The proportion
of canopy change, in terms of total land cover, are displayed in Table 5.
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Figure 8: 2007-2011 Canopy Cover Change
58
Figure 9: 2011-2014 Canopy Cover Change
59
Table 5: Changes in Canopy Cover
Canopy Change 2007-2011
Canopy Change 2011-2014
Canopy Change 2007-2014
No Change in Canopy Cover 16% 18% 15% Canopy Growth 12% 5% 8%
Canopy Loss 6% 10% 8% No Change in Non-Tree Land Cover 66% 67% 69%
Figure 10: 2007-2014 Canopy Cover Change
60
As seen in the canopy change maps, the distribution of canopy gains and losses are
unevenly distributed across the study area. Across all three canopy change maps, it appears that
canopy remained unchanged in contiguous, dense clusters of trees. These densely grouped trees
are most commonly found within parks and greenspaces, and along the course of rivers and
ravines. These are more natural areas within the urban landscape, and appear to be the places
where canopy cover is consistently unchanged (Figure 11A).
On the commercial and mixed-use areas, there is significantly less canopy coverage than
on any other land use (Table 4). These commercial areas also experience more canopy loss than
canopy increases (Figure 11B). This is likely due to the nature of this land use to fully convert to
non-vegetated cover, sacrificing canopy cover for the expansion of the commercial activities.
Within residential areas, which make up the majority of the land use in this study area,
there is an uneven and mixed distribution of canopy growth and loss. It appears that some
neighborhoods experienced significantly more canopy losses or gains than others. In
neighbourhoods with high proportions of canopy cover, some areas experienced more losses
(Figure 11C), while others appear to have increased in canopy cover (Figure 11D). In
neighbourhoods with a low proportion of canopy cover, there appears to be much more canopy
growth than loss (Figure 11E). This may be the result of the canopy structure, species
distribution, and tree age. A more in-depth evaluation of two specific neighborhoods, and reason
for canopy change, will be discussed in the subsequent section.
Based on the proportion of each type of canopy change, as seen in Table 5, the
classification of the 2011 image likely resulted in some skewed results. Due to the
overestimation of canopy, there appears to be much more canopy increase from 2007-2011, and
significantly more canopy loss from 2011-2014. However, looking at the relationship between
canopy increases and decreases from 2007-2014, the canopy losses were offset by an equal
amount of canopy growth. This suggests that in spite of the many sources of canopy loss, efforts
to maintain the existing urban forest have been successful, but have not yet resulting in actual
increases in canopy.
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Figure 11: Examples of Canopy Cover Change, 2007-2014 A) Park/ Greenspace B) Commercial Area C) Residential- High Coverage (Loss) D) Residential- High Coverage (Growth) E) Residential- Low Coverage
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While the distribution of canopy losses and gains can be seen through the aforementioned
figures, it is difficult to attribute the reason for canopy changes. Gains in canopy cover can result
from increased tree planting efforts, natural growth, and healthy tree development, while tree
mortality, invasive species, and extreme weather have caused canopy loss.
3.1.2 Change in NDVI Values
Where the spatial overlap of the 2011 and 2014 images was available, the change in
NDVI values can be seen within the study area (Figure 12). The NDVI values displayed in Table
6 show the range of minimum and maximum NDVI values of all land covers in the study area.
Figure 12: Change in NDVI from 2011-2014
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Table 6: Change in NDVI Values from 2011-2014 2011 NDVI Value 2014 NDVI Value 2011-2014 Change
Minimum NDVI -0.95 -0.97
Maximum NDVI 0.98 0.99
Average NDVI 0.46 0.20 -0.24
In order to compare tree canopy NDVI values, NDVI was clipped to the extent of the tree
canopy classification, which resulted in the average NDVI value of the tree canopy to be slightly
lower in 2014 than 2011. This indicates that the vigor of the treed areas may have been reduced,
likely due to tree thinning.
As seen in Figure 12, the changes in NDVI are displayed on a stretched scale. Broadly
speaking, it appears that some areas experienced healthy canopy growth, while other areas
suffered from canopy thinning. On the left-hand side of the image, along ravines and bordering
the course of the river where are are dense tree stands, it appears that the NDVI values remain
either unchanged or have experienced some thinning. However, on residential land uses which
dominate the scene, there is variation across the study area, with some neighborhoods
experiencing a net increase in NDVI, while others areas tend towards canopy thinning. On
residential properties, the decrease in NDVI is most commonly seen around the edges of where
tree canopy overlaps.
Similarly, to the many causes of canopy and tree loss, there may be many sources for the
differences in NDVI value and canopy thinning. It may be the result of different growing
conditions, such as variations temperature and precipitation that affected each season. However,
there was likely canopy thinning that resulted from the ice storm, as reports estimate that at least
15,000 street trees required maintenance for hanging and broken branch removal following the
ice storm, and 8,000 trees being extensively pruned for publicly owned trees in Mississauga
(City of Mississauga, 2014). While it is difficult to estimate how many trees on private properties
also experienced significant branch loss, it is likely that many trees on all land uses across the
city suffered broken branches.
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3.2 Neighbourhood Canopy Change
Using supplemental data from residents’ survey responses, amount of damage experienced at the
property level can be identified in the two focus neighbourhoods.
3.2.1 Change in Canopy Cover
The uneven distribution of canopy growth and loss that occurred across the study area is
similarly experienced within the neighborhoods. From 2007 to 2014, canopy cover in the
Toronto neighborhood changed from 44% (89.38 ha of tree canopy) to 35% (69.58 ha). Of the
89.38 ha of 2007 tree canopy; 51.19 ha remained unchanged to 2014; 38.08 ha was lost; while it
was partially offset by an increase in 18.32 ha of added canopy. In the Mississauga
neighbourhood, canopy cover changed from 46% (89.24 ha) to 45% (86.11 ha). Of the 89.24 ha
of 2007 tree canopy; 60.06 ha remained unchanged to 2014; 28.97 ha was lost; while it was
almost completely offset by an increase in 24.97 ha of canopy in 2014. Relative to the
Mississauga neighbourhood, the Toronto neighborhood experienced much more canopy loss
from 2007 to 2014, as seen in Figure 13 and 14.
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Figure 13: Canopy Cover Change 2007-2014 (Toronto)
66
Figure 14: Canopy Cover Change 2007-2014 (Mississauga)
67
Based on the images, the Mississauga area contains more parks and greenspaces, which
have more contiguous areas of forest. However, in the Toronto area, there appears to be fewer
parks; it is almost entirely residential. The distribution of trees in residential areas is much more
fragmented than in greenspaces, which may be associated with the higher canopy loss.
3.2.2 Change in Canopy Cover Resulting from the Ice Storm
As seen in Table 7, branch loss was the most common type of damage reported by the
survey respondents resulting from the ice storm, impacting the vast majority (90%) of properties.
More than half of all respondents reported that large tree branches (> 10 feet) were lost from
trees on their properties (60%), and a minority (10%) of residents reported losing at least one
tree. When residents knew the species of tree damaged, the most common species reported as
impacted by the storm were Maple, Birch (Betula) and Spruce in Toronto; and Maple, Pine, and
Birch in Mississauga. The spatial distribution of this reported damage to trees (Figures 15 and
16) show that impacts resulting from small branches damage to tree loss is distributed throughout
both neighborhoods, and there is no major clustering of intense damage.
Table 7: Survey Results of Damage to Trees on Private Properties Toronto Neighborhood Mississauga Neighborhood
Loss of Small Branches 94% 89%
Loss of Large Branches 67% 61%
Loss of Tree 9% 10%
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Figure 15: Ice Storm Damage to Trees Reported by Toronto Residents
69
Figure 16: Ice Storm Damage to Trees Reported by Mississauga Residents
70
By isolating the canopy loss that occurred on the parcel of properties that reporting losing
at least one large branch or the loss of a tree, that canopy loss can be used as a proxy for ice
storm related canopy loss. The distribution of canopy loss that occurred within the boundary of
each parcel property can be seen in Figures 17 and 18.
Since the surveys were limited to residential properties, the amount of canopy cover and
canopy loss on other land uses (transportation networks, institutional land, and parks) are
excluded from attributing canopy losses to the ice storm. As previously stated, the Toronto
neighbourhood experienced a total loss of 38.08 ha (from a tree canopy of 89.38 ha) in canopy
cover from 2007-2014. Of that distribution limited to all surveyed residential properties, 26.15 ha
of canopy were lost from a prior canopy cover of 61.39 ha. From that canopy loss, 2.67 ha fall
within the boundaries of the surveyed property parcels reporting large branch of tree loss from
the ice storms, which accounts for 10% of the canopy loss experienced over this time.
The Mississauga neighbourhood experienced a total loss of 28.97 ha (from a tree canopy
of 89.1 ha) in canopy cover in the same timespan. Of that distribution, similarly limited to
residential land use, 20.27 ha of canopy were lost from a prior canopy cover of 67.33 ha. Of the
total canopy loss, 2.84 ha are within the property parcels, which accounts for 14% of the canopy
loss experienced.
Proportional to the previously existing canopy cover in 2007, the amount of canopy loss
that resulted from the ice storm is 4% for the Toronto and Mississauga neighborhoods. To
account for the dynamic nature of both the impacts of the ice storm and forest growth, a range of
3-5% of canopy loss is more likely for ice storm related loss on residential properties.
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Figure 17: Toronto Canopy Loss Attributed to the Ice Storm within Surveyed Property Parcels
72
Figure 18: Mississauga Canopy Loss Attributed to the Ice Storm within Surveyed Property Parcels
73
Based on common structure of residential land use, with houses and driveways unsuitable
for trees to be located, residential trees are most commonly located at the front and/ or back of
houses, with some trees located at the sides, space permitting. While canopy cover may be
present on the interior of properties, based on the relative size of the house and tree, the majority
of canopy cover is located at the back and front of residential properties. This tree placement
results in canopy loss on the property parcels occurring along the periphery of the properties,
with canopy losses only occurring in the interior of the parcels in minimal instances.
Within each property parcel that experienced canopy losses, the type of canopy loss was
determined for both neighborhoods, specifically large branch loss or uprooting of a tree. There
were no trends of increased canopy loss occurring on properties reporting lost trees, as there
were also significant canopy losses on properties only reporting loss of large branches. This
suggests that the cumulative effects of losing a tree, as well as small and large branches, all
contribute to changes in canopy coverage.
Following the ice storm, media reports initially reported that 20% of the tree canopy was
lost to the ice storm (Oved, 2013; Rainford, 2014; Alcoba, 2014), which was quickly revised to
anywhere from 5-20% (The Toronto Star, 2014), as there was no information readily available to
accurately identify the amount lost. A range of 3-5% of canopy loss across residential land uses,
which are dominant land uses across the study area, is more likely than 20% loss as not all land
uses experienced the same amount of damage across the city. Also, many areas of Toronto and
Mississauga were not included in this analysis, which may have also experienced significant
canopy losses that were not accounted for in this analysis.
A loss of 3-5% of canopy cover is consistent with the canopy loss experienced by
similarly sized ice storm events. For example, in December 2009, an ice storm with 25- 35 mm
of ice accumulation resulted in the loss of 6% of the Worcester’s urban forest (Hostetler et al.,
2013; Frank & DelliCarpini, 2009). The 2007 ice storm event in Oklahoma, characterized by 25-
38 mm of ice accumulation resulted in 7% of medium to large trees being completely damaged
or cleared by the ice storm (Rahmed & Rashed, 2015).
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3.2.3 Change in NDVI
Due to the extent of the 2011 image, the change in NDVI values was only available for
the Mississauga neighborhood, as displayed in Figure 19. Within this area the overall average
value of NDVI change was -0.18 on properties that reported branch loss, which coincides with
the NDVI change value of the full study area. This suggests that there was some decrease in the
presence of healthy vegetation, which could indicate that there was canopy thinning. Based on
the survey responses, approximately 90% of respondents indicated that there was some branch
loss on the properties, resulting in the thinning of tree crowns, likely contributing to a decrease in
NDVI.
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Figure 19: Change in NDVI in the Mississauga Neighbourhood
76
Conclusion
In order to identify the changes in urban forest cover, classified tree canopy maps were
analyzed to determine the distribution of canopy growth and loss across the study area. Within
the study area, uneven distributions were found between and among different land uses. Due to
the large area of interest and the variety of sources that can impact canopy changes, survey
responses were used to attribute the proportion of canopy loss that occurred in residential areas.
From this analysis, it is likely that 3-5% of the tree canopy was lost due to the ice storm, and that
branch loss, the most common type of damage, resulted in canopy thinning across the entire
scene.
It is important to note that the entirety of Mississauga and Toronto were not included in
this analysis due to the extent of the imagery available. Further studies would benefit from
examining canopy loss within the entire city boundaries. Also, it would be beneficial to attribute
canopy losses to the variety of sources, such as tree removals due to mortality, trees removed
from EAB, and specific trees removed resulting from the ice storm. Due to spatial data
accessibility, this information was not available for this study, but ideally would be used to help
aid in understanding the sources of canopy change.
Overall, it is clear that there was a widespread impact of the ice storm, and all areas with
tree coverage were susceptible to some type of tree loss. It is also possible that many trees
suffered damage from the ice storm, and declining tree health is exacerbated over time, so
canopy thinning may be even more prevalent in the years following the ice storm. Up to date tree
inventories of public street trees, and increased communication and education with Toronto and
Mississauga residents on how to best manage trees in the city will likely reduce the impact of
future ice storms.
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Chapter 6 Conclusions & Recommendations
Conclusions
The research objectives of this analysis were to develop tree canopy distribution maps,
determine patterns of canopy change for Toronto and Mississauga, and attribute canopy losses to
the December 2013 ice storm at the property level.
The first objective involved the implementation of GIS and remote sensing analysis in the
creation of tree canopy distribution maps. This approach was used, rather than a field or plot-
based approach, to determine canopy cover distribution due to the large size of the study area and
the lack of accessibility to private and residential properties. It also allowed for the creation of
canopy distribution maps for years prior to the ice storm event.
Specifically, an OBIA approach was implemented to create the tree canopy maps. This
was achieved through the processes of image object segmentation and classification of high
resolution satellite imagery. OBIA is an effective and efficient approach for mapping urban
features, particularly urban vegetation, due to the complexity of urban features. The properties of
image objects produce more meaningful analyses than that of pixels, due to the complex spectral
and textural characteristics of urban land use.
The result of the multi-resolution segmentation and nearest-neighbour supervised
classification produced detailed maps of land cover, specifically tree canopy coverage and non-
tree land cover. The proportion of canopy cover per land use type was also identified. The
resulting maps had high accuracies (90-92%), resulting from a point-based accuracy assessment.
These tree canopy distribution maps suggest that OBIA is an effective and powerful approach for
classifying urban tree distribution.
The second objective was to determine the change in canopy cover over time within the
entire study area and to identify changes within the canopy through a vegetation index; while the
third objective was to attribute reason for those canopy changes using supplemental data at the
property level.
78
A post-classification change detection analysis was conducted using the classified tree
distribution maps. This post-classification approach was used, as it allowed for the nature of
canopy change to be identified, such as canopy growth or loss. The resulting canopy change
maps displayed an uneven distribution of canopy increases and decreases throughout the city,
likely resulting from land use and socio-demographic factors. Parks and greenspaces with large,
contiguous patches of trees experienced less canopy loss, commercial areas had more canopy
loss (and lower canopy cover), while residential land uses experienced both losses and gains in
canopy cover.
Where NDVI data was available, change in the NDVI values were used to indicate
change within canopy cover from 2011-2014. Areas where NDVI remained unchanged or
increased suggest healthy canopy function. However, there was an overall decrease in NDVI
values, indicating that the presence of vegetation decreased. As branch loss is the most
widespread and common type of damage impacting trees from ice storms, the change in NDVI
suggests that there was canopy thinning.
While the canopy change maps indicate areas of canopy increases and decreases, it does
not provide reason for canopy change. Supplemental survey data from residents about their
experience with the ice storm was used to identify ice-storm related canopy losses on property
parcels. Through supplemental data, the portion of canopy losses from the ice storm in two
residential neighbourhoods was determined to be 3-5%.
In spite of the increased developmental pressure, natural tree mortality, invasive species,
and extreme weather events, all of which have contributed to decreases in canopy, the urban
forest in Toronto and Mississauga continue to thrive. The significant impact of EAB and the ice
storm have resulted in widespread canopy losses (and canopy thinning), although continued
efforts to maintain current canopy coverage, and increase tree distribution through tree planting
initiatives have been successful. With continued urban forest management, both cities are likely
to maintain their canopy, although there is little evidence at this point that canopy cover
increases needed to achieve their canopy cover goals are occurring. Recommendations to explore
species selection and planting location strategies that can mitigate the disservices associated with
ice storms are needed to reduce the social and ecological impacts in the event of future ice
storms.
79
This research contributes insight into the nature of tree distribution and canopy change
within Toronto and Mississauga. Specifically, this research has identified canopy losses on
residential properties as a direct result of the December 2013 ice storm. This addresses a gap in
our knowledge about the impact of to Toronto and Mississauga’s tree coverage directly related to
the ice storm event on residential land uses. This research also contributes to the body of
literature about the strengths of OBIA as it applies to mapping the distribution or urban canopy
cover, as well as in post-classification change detection analyses.
Recommendations for Urban Forest Management
Knowing that ice storm events impact urban trees by changing the canopy structure and
distribution, it is essential for on-going management strategies to address current maintenance
and future planting efforts in relation to ice storm impacts. As stated, many of the ecosystem
services provided by trees are directly related to the canopy, including mitigating UHI impacts,
infiltration, runoff, air quality, and energy savings.
Urban forest management plans would benefit from including specific strategies for
anticipating and reacting to ice storm events. Both Toronto and Mississauga have UFMP’s that
recognize the impact of climate change and extreme weather events, however, there are no direct
plans for anticipating such events. Also, in these plans ‘extreme weather events’ include a variety
of weather phenomena, such as heat stress, drought, floods, wind storms and ice storms. These
are highly varied in the time of year they occur and how they impact urban trees. While the
various types of extreme weather are recognized, there should be management strategies tailored
to each type of extreme weather. While the adaptive management approach adopted by urban
forest managers can react to these weather events, proactive planning for mitigating the impacts
and subsequent response should be developed for more effective management.
Increased frequency of pruning trees will encourage healthy tree growth, of both city-
owned trees and privately owned trees. Efforts to communicate the benefits of increased pruning
to residents must increase, as the majority of trees are found on residential land uses. Different
tree species have different vulnerabilities, such as wide branching patterns and branch strength.
Pruning for reducing ice-storm impacts should be tailored for different tree species. As certain
tree species are more vulnerable for branch breakage due to weak branching structure, pruning
efforts should focus on maintaining these tree species. Increased pruning is also necessary
80
following an ice storm, as some tree damage may not be evident immediately after the storm.
Weak branches may threaten public safety if not identified right away, and may fall in high
winds in subsequent seasons if the tree was previously weakened.
Also, the location of tree planting and placement should include strategic decision
making. Trees should be planted in locations to reduce the impact of branch loss from damaging
hydro and communication wires, as that directly results in many health and safety impacts.
Future planting locations efforts should identify areas where there is a lot of available space,
such as residential properties. There should also be increased communication with residents on
existing tree by-laws, pruning practices, and tree benefits.
Future tree planting efforts should also take into account tree species that are structurally
hardy and more resistant to the impacts of ice storms, such as conifers. While tree species should
not be selected solely for the purpose of ice-storm resistance, it should be a factor that is strongly
considered. Based on data availability, tree species identified as being resilient to ice storms
should be compared to Toronto and Mississauga’s planting guidelines and species recommended
for future tree planting.
Similarly, there should be an increased diversity of tree species selected for future
planting efforts. While there is generally high species diversity on residential land uses, city-
owned street trees tend to lack diversity. Increased species diversity not only reduces the impact
of pest-vulnerability, but also results in a more structurally complex forest distribution and will
include a variety of trees that are able to withstand the impacts of extreme weather events.
Recommendations for Future Research
Future research projects focusing on OBIA for urban vegetation mapping, and for
attributing canopy losses from ice storm events, would benefit from the following
recommendations.
During the image preprocessing, some method of shadow removal would improve the
accuracy of the image classification. Due to the images being acquired off-nadir, the influence of
shadows from tall features results in mixed spectral characteristics of some pixels. Urban areas
contain many tall features, particularly buildings, which may obscure some trees at the ground
81
level. Also, tree canopies themselves contain shadows due to illumination effects and the shape
of tree crowns. While the segmentation process often includes shadowed areas in the creation of
the image objects, some portions of the tree crown may be segmented into non-tree image
objects.
Additionally, using consistent satellite imaging sources would benefit future studies. This
study made use of three different sources of satellite imagery, all with different spatial
resolutions. Consistent imagery sources would result in more similar image objects, as the Ikonos
image (which had a coarser spatial resolution) resulted in an overestimation of canopy cover
relative to the Quickbird and GeoEye-1 images. However, many of the issues resulting from the
different imagery sources were reduced by using the post-classification change analysis.
To assist in determining changes in tree canopy, particularly at the property level,
additional LiDAR data would have provided tree canopy density and height data to assist in
identifying changes in the canopy. However, LiDAR data availability is limited, and would be
challenging to acquire data for previous canopy conditions due to the unpredictability of ice
storms occurrence.
If possible, expanding the study area to include the full extent of each municipality would
produce results on canopy changes that reflect canopy conditions city-wide. This would result in
canopy distribution and changes to be identified across all land uses and socio-demographic
conditions. While ice storms do not limit their impact to municipal boundaries, it would be easier
to determine the impact relative to the structure and distribution of trees in each city.
While there is valuable information in the tree distribution and canopy change maps, it is
difficult to attribute reason for each type of canopy change. Publicly available spatial data were
limited from each municipality, resulting in differences in supplemental data from Toronto and
Mississauga. Increased collaboration with each municipality for street tree, land use, and tree
plantings/ removal spatial data would aid in the interpretation of the canopy maps. In particular,
it would have been beneficial to identify the location and amount of canopy that was lost to
EAB, a factor likely contributing to a significant amount of canopy loss.
Also, an increase in the area and number of surveys conducted would provide more
information on residents’ experience with the ice storm. While four neighbourhoods were
82
targeted for this survey, only two fell within the extent of available imagery. Additional surveys
distributed within the study area would have provided more spatially diverse responses to assist
in attributing canopy changes from the ice storm.
A follow-up survey or field-based plot study, in conjuncture with another canopy
distribution analysis, would also provide valuable information on the impact of the ice storm.
Some of the impacts of the ice storm may lag in their impact, and branch loss and tree crown
thinning may not be fully realized in the growing season immediately after the ice storm. A
subsequent study, to determine the state of trees impacted by the ice storm to account for delayed
damage, would provide further insight into the storm’s impact.
83
References
Alberti, M. (2005). The Effects of urban patterns on ecosystem function. International Regional
Science Review(2), 168-192.
Alcoba, N. (2014, January 2). The bill for cleaning up after Toronto’s ice storm? More than $75-
million, officials say. Retrieved from http://news.nationalpost.com/.
Al-Khudhairy, D., Caravaggi, I., & Giada, S. (2005). Structural damage assessments from Ikonos
data using change detection, object-oriented segmentation, and classification techniques.
Photogrammetric Engineering & Remote Sensing, 71(7), 825-837.
Alamenciak, T. (2014, June 18). Toronto’s trees continue to suffer ice storm damage. Retrieved
from https://www.thestar.com
Armenakis, C., & Nirupama, N. (2014). Urban impacts of ice storms: Toronto December 2013.
Natural Hazards(74), 1291-1298.
Baatz, M., & Schäpe, A. (2005). Multiresolution segmentation: an optimization approach for
high quality multi-scale image segmentation. Angewandte Geographische Informations
verarbeitung(58), 12-23.
Baumann, M., Ozdogan, M., Wolter, P. T., Krylov, A., Vladimirova, N., & Radeloff, V. C.
(2014). Landsat remote sensing of forest windfall disturbance. Remote Sensing of
Environment(143), 171-179.
Bhaskaran, S., Paramananda, S., & Ramnarayan, M. (2010). Per-pixel and object-oriented
classification methods for mapping urban features using Ikonos satellite data. Applied
Geography(30), 650-665.
Blaschke, T. (2005). A framework for change detection based on image objects. (113), 1-9.
Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry & Remote Sensing(65), 2-16.
84
Bourne, K. S., & Conway, T. M. (2014). The influence of land use type and municipal context on
urban tree species diversity. Urban Ecosystems(17), 329-348.
Burley, S., Robinson, S. L., & Lundholm, J. T. (2008). Post-hurricane vegetation recovery in an
urban forest. Landscape and Urban Planning(85), 111-122.
Chen, G., Hay, G. J., Carvalho, L. T., & Wulder, M. A. (2012). Object-based change detection.
International Journal of Remote Sensing, 33(14), 4434-4457.
City of Mississauga. (2009a). Future Directions: Master Plan for Parks and Natural Areas. City
of Mississauga.
City of Mississauga. (2009b). Strategic Plan: Our Future Mississauga. City of Mississauga.
City of Mississauga. (2011). Official Plan. City of Mississauga.
City of Mississauga. (2014a). Corporate Report: Ice Storm, December 2013- Preliminary
Report. City of Mississauga: City Manager and Chief Administratice Officer.
City of Mississauga. (2014b). Natural Heritage and Urban Forest Strategy. City of Mississauga.
City of Mississauga. (2014c). Urban Forest Management Plan. City of Mississsauga: Parks &
Forestry.
City of Mississauga. (2015). Corporate Report: Ice Storm Assisstance Program Grant
Agreement. City of Mississauga: Commissioner of Corporate Services and Chief
Financial Officer.
City of Mississauga. (2016a). Emerald Ash Borer Management Plan: Update. City of
Mississauga: Parks & Forestry.
City of Mississauga. (2016b). Population, Demographics & Housing Profile. City of
Mississauga: Population, Demographics & Housing.
City of Toronto. (2004). Our Common Grounds: Strategic Plan. City of Toronto: Parks and
Recreation.
85
City of Toronto. (2007). Change in in the Air: Toronto's Commitment to an Environmentally
Sustainable Future. City of Toronto: Toronto Environment Office.
City of Toronto. (2010). Toronto Official Plan. City of Toronto.
City of Toronto. (2013a). Every Tree Counts: A Portrait of Toronto's Urban Forest. City of
Toronto: Parks, Forestry and Recreation, Urban Forestry.
City of Toronto. (2013b). Sustaining & Expanding the Urban Forest: Toronto's Strategic Urban
Forest Management Plan. City of Toronto: Parks, Forestry and Recreation, Urban
Forestry.
City of Toronto. (2014). Staff Report: Impacts from the December 2013 Extreme Winter Storm
Event on the City of Toronto. City of Toronto: City Manager.
Conway, T., & Hackworth, J. (2007). Urban pattern and land cover variation in the greater
Toronot area. The Canadian Geographer(1), 43-57.
Conway, T., & Yip, V. (2016). Assessing residents' reactions to urban forest disservices: A case
study of a major storm event. Landscape and Urban Planning(153), 1-10.
Davies Consulting. (2014). Final Report: The Response of Toronto Hydro-Electric System
Limited to the December 2013 Ice Storm. Davies Consulting, LLC.
Definiens. (2007). Definiens Developer 7- Reference Book. Definiens.
Definiens. (2008). Definiens Developer 7- User Guide. Definiens Developer.
Definiens. (2012). Definens Developer XD 2.0.4- User Guide. Defniens Developer.
Degelia, S. K., Christian, J. I., Basara, J. B., Mitchell, T. J., Gardner, D. F., Jackson, S. E., . . .
Mahan, H. R. (2016). An overview of ice storms and their impact in the United States.
International Journal of Climatology, 36(8), 2811-2822.
Desclée, B., Bogaert, P., & Defourny, P. (2006). Forest change detection by statistical object-
based method. Remote Sensing of Environment(102), 1-11.
86
Dey, V., Zhang, Y., & Zhong, M. (2010). A review on image segmentation techniques with
remote sensing perspective. ISPRS TC VII Symposium – 100 Years ISPRS (pp. 31-42).
IAPRS.
Digial Globe. (2013). Digital GlobeL Satellite Information. Retrieved from
https://www.digitalglobe.com/resources/satellite-information.
Digital Globe. (2014). Digital Globe: Satellite Information. Retrieved from
https://www.digitalglobe.com/resources/satellite-information.
Duinker, P. N., Ordóñez, C., Steenberg, J. W., Miller, K. H., Toni, S., & Nitoslawski, S. A.
(2015). Trees in Canadian cities: Indispensable life form for urban sustainability.
Sustainability(7), 7379-7396.
Dwyer, J. F., McPherson, E. G., Schroeder, H. W., & Rowntree, R. (1992). Assesing the benefits
and costs of the urban forest. Journal of Aboriculture, 18(5), 227-234.
Dwyer, M. C., & Miller, R. W. (1999). Using GIS to assess urban tree canopy benefits and
surrounding greenspace distributions. Journal of Aboriculture(2), 102-107.
Erdas. (2010). ERDAS Field Guide. EERDAS.
Escobedo, F. J., Kroeger, T., & Wagner, J. E. (2011). Urban forests and pollution mitication:
Analyzing ecosystem services and functions. Environmental Pollution(159), 2078-2087.
Forests Ontario. (2014). Homeowner's Guide: Maintaining Your Trees Following Ice Storms and
how to Prevent Future Damage.
Frank, H., & DelliCarpini, F. (2009). Analysis of the December 11-12, 2009 Destructive Ice
Storm across Interior Southern New England. WFO Taunton Storm Series Report.
Gauthier, S., Bernier, P., Burton, P. J., Edwards, J., Isaac, K., Isabel, N., . . . Nelson, E. A.
(2014). Climate change vulnerability and adaptation in the managed Canadian boreal
forest. Environmental Reviews(22), 256-285.
87
Goddard, M. A., Dougill, A. J., & Benton, T. G. (2010). Scaling up from gardens: biodiversity
conservation in urban environments. Trends in Ecology and Evolution, 25(2), 90-98.
Godefroid, S., & Koedam, N. (2007). Urban plant species patterns are highly driven by density
and function of built-up areas. Landscape Ecology(8), 1227-1239.
Hájek, F. (2006). Object-oriented classification of Ikonos satellite data for the identification of
tree species composition. Journal of Forest Science, 52(4), 181-187.
Hall, O., & Hay, J. G. (2003). A multiscale object-specific approach to digital change detection.
International Hournal of Applied Earth Observation and Geoinformation, 4(4), 311-327.
Hauer, R. J., Dawson, J. O., & Werner, L. P. (2006). Trees and Ice Storms: The Development of
Ice Storm-Resistant Urban Tree Populations, Second Edition. USDA Forest Service-
Urban Forest Center for Midwestern States.
Hauer, R. J., Hauer, A. J., Hartel, D. R., & Johnson, J. r. (2011). Rapid assessment of tree debris
following urbab forest ice storms. Aboriculture & Urban Forestry, 37(5), 236-246.
Hopkin, A., Williams, T., Sajan, R., Pedlar, J., & Nielsen, C. (2003). Ice storm damage to eastern
Ontario forests: 1998-2001. The Forestry Chronile, 79(1), 47-53.
Hostetler, A. E., Rogan, J., Martin, D., Delauer, V., & O'Neil-Dunne, J. (2013). Characterizing
tree canopy loss using multi-source GIS data in Central Massachusetts, USA. Remote
Sensing Letters, 4(12), 1137-1146.
Ibrahim, I., Samah, A. A., & Fauzi, R. (2014). Biohysical factors of remote sensing approach in
urban green analysis. Geocarto International, 29(7), 807-818.
Irland, L. C. (2000). Ice storms and forest impacts. The Science of the Total Environment(262),
231-242.
iTree Eco. (n.d.). iTree Eco User's Manual. iTree Eco.
Jensen, J. R. (2007). Remote Sensing of the Environment: An Earth Resource Perspective 2nd
Ed. . Pearson Prentice Hall.
88
Kenney, W. A., & Idziak, C. (2000). The state of Canada's municipal forests- 1996 to 1998. The
Forestry Chronicle, 76(2), 231-234.
Kenney, W. A., van Wassenaer, P. J., & Satel, A. L. (2011). Criteria and indicators for strategic
urban forest planning and management. Aboriculture & Urban Forestry, 37(3), 108-117.
Klima, K., & Morgan, M. G. (2015). ice storm frequencies in a warmer climate. Climate
Change(133), 209-222.
Konijnendijk, C. C., Ricard, R. M., Kenney, A., & Randrup, T. B. (2006). Defining urban
forestry- A comparative perspective of North America and Europe. Urban Forestry &
Urban Greening(4), 93-103.
Kosaka, N., Akiyama, T., Tsai, B., & Jojima, T. (2005). Forest type classification using data
fusion of multispectral and panchromatic high-resolution satellite imageries. Geoscience
and Remote Sensing Symposium, 2005. IGARSS '05 (pp. 2980-2983). 2005 IEEE
International.
Landry, S., & Pu, R. (2010). The impact of land use development regulation on residential tree
cover: Am empirival evaluation using high-resolutino IKONOS imagery. Landscape and
Urban Planning(28), 94-104.
Locke, D. H., & Baine, G. (2015). The good, the bad, and the interested: how historical
demographics explain present-day tree canopy, vacant lot and tree request spatial
variability in New Haven, CT. Urban Ecosystems(18), 391-409.
Loughner, C. P., Allen, D. J., Zhang, D.-L., Pickering, K. E., Dickerson, R. R., & Landry, L.
(2012). Roles of urban tree canopy and buildings in Urban Heat Island effects:
Parameterization and preliminary results. Journal of Applied Meteorlogy and
Climatology(51), 1775-1793.
Lowry Jr, J. H., Baker, M. E., & Ramsey, R. D. (2012). Determinants of urban tree canopy in
residentia neighbourhoods: Household characteristics, urban form, and the geophysical
landscape. Urban Ecosystems(15), 247-266.
89
Luley, C. J., & Bond, J. (2006). Evaluation of the fate of ice storm-damaged urban maple (Acer)
Trees. Aboriculture & Urban Forestry, 35(5), 214-220.
Marchant, K. (2012). City of Mississauga Emerald Ash Borer Management Plan. City of
Mississauga.
Mathieu, R., Aryal, J., & Chong, A. K. (2007). Object-based classification of Iknonis imagery
for mapping large-scale vegetation communities in urban areas. Sensors(7), 2860-2880.
McPherson, E. G., Nowak, D., Heisler, G., Grimmond, S., Souch, C., Grant, R., & Rowntree, R.
(1997). Quantifying urban forest structure, function, and value: the Chicago Urban Forest
Climate Project. Urban Ecosystems(1), 49-61.
McPherson, E. G., Simpson, J. R., Peper, P. J., & Xiao, Q. (1999). Benefit-cost analysis of
Modesto's municipal urban forest. Journal of Aboriculture, 25(2), 235-248.
Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs.
object-based classification of urban land cover extraction using high spatial resolution
imagery. Remote Sensing of Environment(115), 1145- 1161.
Nowak, D. J., Crane, D. E., Stevens, J. C., Hoehn, R. E., Walton, J. T., & Bond, J. (2008). A
ground-based method of assessing urban forest structure and ecosystem services.
Aboriculture & Urban Forestry, 34(6), 347-358.
Nowak, D. J., Hoehn III, R. E., Bodine, A. R., Greenfield, E. J., Ellis, A., Endreny, T. A., . . .
Henry, R. (2012). Assessing Urban Forest Effects and Values: Toronto’s Urban Forest.
USDA Forest Services.
Nowak, D. J., Kuroda, M., & Crane, D. E. (2004). Tree mortality rates and tree population
projections in Baltimore, Maryland, USA. Urban Forestry & Urban Greening(2), 139-
147.
Nowak, D. J., Rowntree, R. A., McPherson, E. G., Sisinni, S. M., Kerkmann, E. R., & Stevens, J.
C. (1996). Measuring and analyzing urban tree cover. Landscale and Urban
Planning(36), 49-57.
90
O'Neil-Dunne, J., MacFaden, S., & Royar, A. (2014). A Versatile, Production-Oriented
Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes
Using GEOBIA and Data Fusion. Remote Sensing(6), 12837-12865.
Ordóñez, C., Duinker, P. N., & Steenberg, J. (2010). Climate Change Mitigation and Adaptation
in Urban Forests: A Framework for Sustainable Urban Forest Management. 18th
Commonwealth Forestry Conference, (pp. 2-14).
Oved, M. C. (2013, December 27). Ice Storm: New power outages plague hydro reconnections.
Retrieved from https://www.thestar.com.
Pan, Y., Chen, J. M., Birdsey, R., McCullough, K., He, L., & Deng, F. (2011). Age structure and
distrubance legacy of North American forests. Biogeosciences(8), 715-732.
Pasher, J., & King, D. J. (2006). Landscape fragmentation and ice storm damage in eastern
ontario forests. Landscape Ecology(21), 477-483.
Pelletier, K., & O'Neil-Dunne, J. (2011a). A Report on the City of Mississauga's Existing and
Possible Tree Canopy. University of Vermont.
Pelletier, K., & O'Neil-Dunne, J. (2011b). A Report on the City of Toronto's Existing and
Possible Urban Tree Canopy. University of Vermont.
Pfeil-McCullough, E., Bain, D. J., Bergman, J., & Crumrine, D. (2015). Emerald ash borer and
the urban forest: Changes in landslide potential due to canopy loss scenarios in the City
of Pittsburgh, PA. Science of the Total Environment(536), 538-545.
Pham, T.-T.-H., Apparicio, P., Séguin, A.-M., Landry, S., & Gagnon, M. (2012). Spatual
distribution of vegetation in Montreal: An uneven distribution or environmental inequity?
Landscape and Urban Planning(107), 214-224.
Pisaric, M. F., King, D. J., MacIntosh, A. J., & Bemrose, R. (2208). Impact of the 1998 ice storm
on the health and growth of sugar maple (Acer Saccharum Marsh.) dominated forests in
Gatineau Park, Quebec. Journal of the Torrey Botanical Society, 135(4), 530-539.
91
Plan-It Geo, LLc. (2014). An Assessment of Urban Canopy: Mississauga, Ontario. City of
Mississauga: Plan-it Geo.
Pu, R., & Landry, S. (2012). A comparative analysis of high spatial resolution IKONOS and
Worldview-2 imagery for mapping urban tree species. Remote Sensing of
Environment(124), 516-533.
Rahman, M. A., Armson, D., & Ennos, A. R. (2015). A comparison of the growth and cooling
effectiveness of five commonly planing urban tree species. Urban Ecosystems(18), 371-
389.
Rahman, M. T., & Rashed, T. (2015). Urban tree damage estimation using airborne laser scanner
data and geographical information systems: An example from 2007 Oklahoma Ice Storm.
Urban Forestry & Urban Greening(14), 562-572.
Rainford, L. (2014, January 29). Toronto tree canopy suffers huge loss during ice storm; deputy
mayor calling on millions for restoration efforts. Retrieved from
http://www.insidetoronto.com/.
Rajaram, N., Hohenadel, K., Gattoni, L., Khan, Y., Birk-Urovitz, E., Li, L., & Schwartz, B.
(2016). Assessing health impacts of the December 2013 ice storm on Ontario, Canada.
BMC Public Health(16), 1=9.
Region of Peel. (2011). Peel Climate Change Strategy. Region of Peel.
Region of Peel. (2014). Official Plan. Region of Peel.
Rowntree, R. A., & Nowak, D. J. (1991). Quantifying the role of rurban forests in removing
atmospheric carbon dioxide. Journal of Aboriculture, 17(10), 269-275.
Roy, S., Byrne, J., & Pickering, C. (2012). A systematic quantitative review of urban tree
benefits, costs, and assessment methods across cities in different climatic zones. Urban
Forestry & Urban Greening(11), 351-363.
Rustad, L., & Campbell, J. L. (2012). A novel ice storm manipulation experiment in a northern
hardwood forest. Canadian Journal of Forest Research(42), 1810-1818.
92
Salehi, B., Zhang, Y., Zhong, M., & Dey, V. (2012). Object-based classification of urban areas
using VHR imagery and height points ancillary data. Remote Sensing(4), 2256-2276.
Sanders, R. A. (1984). Some determinants of urban forest structure. Urban Ecology(8), 13-27.
Shakeel, T. (2012). Homeowners as Urban Forest Managers- Examining the Role of Property-
level Variables in predicting Variations in Urban Forest Quantity Using Advanced
Remote Sensing and GIS Methodologies. University of Toronto.
Shi, L., Wang, H., Zhang, W., Shao, Q., Yang, F., Ma, Z., & Wang, Y. (2013). Spatial response
patterns of subtropical forests to a heavy ice storm: a case study in Poyang Lake Basin,
southern China. Natural Hazards(69), 2179-2196.
Smith, K. T. (2015). Tree recovery from ice storm injury. Ontario Aborist, 24-26.
Stagoll, K., Lindenmayer, D. B., Knight, E., Fischer, J., & Manning, A. D. (2012). Large trees
are keystone structures in urban parks. Conservation Letters(5), 115-122.
Statistics Canada. (2011). City of Toronto Community Council Profiles. Toronto: Statistics
Canada, Census 2011.
Stueve, K. M., Hollenhorst, T. P., Kelly, J. R., Johnson, L. B., & Host, G. E. (2015). High-
resolution maps of urban-forest watersheds present an opportunity for ecologists and
managers. Landscape Ecology(30), 313-323.
Tooke, T. R., Coops, N. C., Goodwin, N. R., & Voogt, J. A. (2009). Extracting urban vegetation
characteristics using spectral mixture analysis and decision tree classifications. Remote
Sensing of Environment(113), 398-407.
Toronto Hydro. (2015). Status of Recommendations from December 2013 Ice Storm Report. City
of Toronto: Toronto Hydro.
TRCA. (2011). City of Mississauga Urban Forest Study: Technical Report. City of Mississauga:
Toronto and Region Conservation Authority.
93
United Nations, Department of Economic and Social Affairs, Population Divisions. (2014).
World Urbabanization Prospects: the 2014 Revisions, Highlights (ST/ESA/SER.A/352).
Walton, J. T., Nowak, D. J., & Greenfield, E. J. (2008). Assessing urban forest canopy cover
using airborne or satellite imagery. Aboriculture & Urban Forestry, 34(6), 334-340.
Wang, Y., & Akbari, H. (2016). The effects of street tree planting on Urban Heat Island
mitigation in Montreal. Sustainable Cities and Society, (28) Article in Press
Weeks, B. C., Hamburg, S. P., & Vadeboncoeur, M. A. (2009). Ice storm effects on the canopy
structure of a northern hardwood forest after 8 years. Canadian Journal of Forest
Research(39), 1475-1483.
Zhang, Q., Hong, Y., Zou, F., Lee, T. M., Song, X., & Rao, J. (2016). Avian responses to an
extreme ice storm are determined by a combination of functional traits, behavioural
adaptations and habitat modifications. Scientific Reports(6), 22344.
Zhang, X., Feng, X., & Jiang, H. (2010). Object-oriented method for urban vegetation mapping
using IKONOS imagery. International Journal of Remote Sensing, 31(1), 177-196.
Zhou, W., Troy, A., & Grove, M. (2008). Object-based Land Cover Classification and Change
Analysis in the Baltimore Metropolitan Area Using Multitemporal High Resolution
Remote Sensing Data. Sensors(8), 1613-1636.
Zhu, P., & Zhang, Y. (2008). Demand for urban forests in United States cities. Landscape and
Urban Planning(84), 293-300.