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

ii

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.

iii

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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1

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).

4

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

5

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

8

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.,

11

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

12

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).

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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

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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).

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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.

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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.

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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.

55

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

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