DESIGN LIMITATIONS TO POTENTIAL LEAF AREA IN URBAN FORESTS

Nat asha Michel le Duffy

A thesis submitted in conformity with the requirements for the degree of Master of Science in Forestry Graduate Department of Forestry University of Toronto

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Design limitations to potential leaf area in urban forests

MScF 1999

Natasha M. Du@

Faculty of Forestry

University of Toronto

Sample plots (90) were located in 10 different land use types in the pre-amalgamated City of

Toronto. The potential leaf area densities (PLAD)were calculated for each of these plots under four different buffer width scenarios: control (no buffers), minimum buffers, average buffers, and maximum buffers. The feature composition of each of the land use types and the impact of each feature's buffer on the loss of soft surface was also determined. There were significant differences in PLAD between the ten different land use types and between the four different buffer scenarios. Potential leaf area densities fell into three main categories: low (1 1-26 m2/1 000m2)(Low, Medium, and High htensity Commercial), medium (648- LOO0 m2/1000m2)(in Residential Medium and High Densities, Industrial, Institutional, and

Transportation), and high (1629-2083 m2/1000m2)(in Residential Low Density and

Exhibition Lands). Finally, alternative locations for planting trees in cities were discussed. iii

Acknowledgements

1 would like to thank my supervisor, Dr. W. Andy Kenney, for allowing me to work with him and supporting me throughout this research. He has introduced me to the concept of urban forestry and has illustrated the importance of this discipline. His guidance has been invaluable. 1 have thoroughly enjoyed coming to work for the past two years.

1 would also like to thank the members of my supervisory cornmittee, Dr. Rodney White, for his comments on my thesis; Dr. Sandy Smith for her valuable suggestions; and Dr. Brad Bass for his contributions and interest in my topic.

A great part of this thesis was completed in the GIS software ArcView with which 1 had no previous experience. I would like to thank Danijela Puric-Mladenovic who spent many hours teaching me the intricacies of GIS.

Thank you also to the City of Toronto employees who provided me with data used in this research: Murray Boyce, Rebecca Kay, Erika Horvath, and the Public Ut il ities Coordinat ing Cornmittee, especially Wally Kowalenko.

Finally 1 would like to thank my family for their support and love during my studies. 1 am the person 1 am today due to their guidance.

Financial suppon for this research was provided by the Ontario Graduate Scholarship and the Faculty of Forestry at the University of Toronto. Table of Contents

... Acknowledgements...... m...... m...... mm...... m..m....mm..m...... m...... m..mm.m...... 111

Table of Contents...... m...m...... mmmm...... m...... iv

.. List of Figures...... vil List of Appendices ...... m...... m...... m..mm...... mm.m..m...... m...... m...... ix 1. Background Information ...... ******.**...... *.*..*..*...... **..**.....*1 1.1 Urban Forestry ...... 1 1.2 Urban Forest Benefits ...... 7 1.3 Leal Area and Leaf Area Density ...... 4 1.4 Available Growing Space ...... 8 1.5 Urban Forest Structure ...... , ...... -9 1.6 Urban Forest Classification System ...... 12 1.7 Objectives ...... 14 2 . Materials and Methods ...... m...... mm.m...... m...... m..m.m..m...... mm.m.m...... 16 2.1 Study Area ...... 16 2.2 Geographic Information Systems (GIS) ...... 16 2.3 Sample Plot Selection...... 17 2.4 Data Collection ...... 23 2.5 Impact of Features' Buffers on Sofi Surface ...... -29 2.6 Prototype Trees ...... -30 2.7 Detennination of Potential Leaf Area Density ...... 31

3.1 Canopy Cover Categories ...... 33 3.2 Accuracy Assessrnent ...... 33 3.3 Land Use Type Distribution ...... 33 3.4 Average Feature Composition for each Land Use Type ...... 36 3.5 Impact of Features' Buffers on Sofi Surface within each Land Use Type ...... 50 3.6 Prototype Trees ...... -63 3.7 Potential Leaf Area Density: Land Use Types and Buffer Width Analyses ...... 65

4.1 Land Use Type Distribution ...... 70 4.2 Feature Composition of Sample Plots ...... 70 4.3 Impact of Features' Buffen on Available Soft Surface ...... -74 4.4 Land Use Types and Potential Leaf Area Density ...... 77 4.5 Buffers and Potential Leaf Area Density ...... 81 4.6 Urban-Biotopes ...... -83 4.7 Future Directions ...... 87 5. Alternative planting locations ...... 90 5.1 Rooftop and Vertical Gardens ...... 90 5.2 Sidewalk Planting...... 92 5.3 Planters ...... -94 5.4 Hard Surface Rernoval ...... 95 6. Surnrnary and Conclusions ...... 97 7. Literaturc Cited ...... 99 8. Appendix A ...... 104 9. Appendix B ...... 116 10. Appendix C ...... 118 List of Tables

Table 1: Bufferwidths inmeters foreachofthe features found in the sampleplots ...... 29 Table 2: Similarities and differences in rank levels of feature composition in the ten different land use types in the City of Toronto...... 48 Table 3: Summary of Spearman Rank Correlation coefficients (p) of feature composition similarities for each of the land use type pairs in the City of Toronto...... 49 Table 4: Similarities and differences in rank levels of soft surface loss due to features' buffers in the ten different land use types in the City of Toronto...... -51 Table 5: Summary of Speannan Rank Correlation coetfcients (p) of sofi surface decrease similarities for each of the land use type pairs in the City of Toronto...... 52 Table 6: Prototype trees used to determine potential leaf area in the City of Toronto within each of the 90 sample plots...... 63 Table 7: Species lists used to determine the four potential leaf area category types in the City of Toronto...... 64 List of Figures

Figure 1: Example of one hectare plots that have the same percent canopy cover (50%) but different leaf area ...... 7 Figure 2: Orthophotos fiom Region 13 (Etobicoke/Toronto West) and Region 14 (Toronto East) which encornpasses the entire pre-amalgamation City of Toronto ...... 18 Figure 3: Methodology procedure for creating the 90 digitized sample plots in the City of Toronto used in this study ...... 20 Figure 4: The fifieen land use types in the City of Toronto...... 22 Figure 5: Location of the 90 sample plots to detemine potentiai leaf area in each of ten land use types in the City of Toronto ...... 24 Figure 6: Examples of canopy cover classification ...... 34 Figure 7: Correlation between 127 actual feature sizes in the sample plots and the digitized feature sizes ...... 35 Figure 8: Distribution of land use types in the City of Toronto ...... 36 Figure 9: Percent feature composition for each land use type ...... 37 Figure 10: Percent decrease in soft surface due to the buffers on each feature type within each land use in the City of Toronto...... 53 Figure 1 1: Dendrogram of cluster analysis of the 57 tree species used to detemine the prototype tree classes ...... 65 Figure 12: Effects of land use types (X-axis) and buffer sires (2-axis) on potential leaf area density (Y-axis) in the City of Toronto...... 67 Figure 13: Negative impact of buffers on potential leaf area density among the 10 different land use types in the City of Toronto...... 82 Figure 14: Example of building placements that impact the amount of overall planting space for trees ...... 83 Figure 15: Urban-biotopes found in the City of Toronto: Low. Medium, and High potential leaf area density...... 86 Figure 16: Examples of Hundertwasserhaus in Vienna, Austria...... 91 Figure 17: Example of raised tree planter design used by the City of Toronto on St . George Street, University of Toronto ...... -92 viii

Figure 18: Example of tree pit design used by the City of Toronto on Spadina Avenue ..... 93 Figure 19: Example of iron tree grates used on the ground for mil protection in the United Kingdom ...... 94 Figure 20: Exampie of tree planters used on University Avenue by the City of Toronto .... 95 List of Appendices

Appendix A. Summary data of feature composition for each land use type...... IO4

Table 1 : Percent feature composition of each land use type with associated standard errors. Figure 1: Percent feature composition of each land use type with associated standard errors.

Appendix B. Summary data of sofl surface decrease due to feature's buffer...... 1 16

Table 1: Percent loss of soft sutface due to features' buffers with associated standard emors.

Appendix C. Summary data of potential leaf area densities for each land use type.. .Il8

Table 1 : Potential leaf area density in each land use type with associated standard errors. Figure 1: Potential leaf area density in each land use type with associated standard errors with (a) no buffers, (b) minimum buffers, (c) average buffers, and (d) maximum buffers. 1. Background Information

1.1 Urban Forestry

Urban forestiy is a very broad terrn that applies to both public and private trees and their associated biotic and abiotic features. It is dificult to define urban forestry since the boundaries of what is considered urban are ofien vague. Dunster and Dunster (1996) define urban forestry as a specialized form of forest management concerned with the cultivation and management of trees in the entire area infîuenced andlor utilized by the urban population. It includes trees on streets, in parks, on private properiy, as well as watershed areas such as ravine systems.

The concept of urban forestry did not evolve until the mid-1960's (Johnston, 1996). Until this time, vegetation management in urban areas concentrated on tree planting, tree maintenance and landscape architecture (Deneke, 1978). Urban forestry incorporated a more holistic approach to vegetation management by including the cultivated and civilized aspects of the city and the rural and unmanaged forests (Jorgensen, 1986).

The discipline of urban forestry can be divided into urban or peri-urban areas. Pen-urban areas are transitional zones between urban and rural landscapes that are significantly influenced by urbanization. Urban areas are city centres or areas where development has occurred and where high proportions of people are present. This research is concerned with urban rather than peri-urban areas. Urban areas pose the greatest threat to growing space needed for trees due to the large amount of paved surface in the city. Peri-urban areas pose less of a threat to tree growth since these areas are cornposed of less hard surfaces. This work on urban areas will allow for the understanding of patterns in urban forestry to be seen, and then these patterns can be extended out into the peri-urban areas in the future.

The question then arises, why be concemed with urban forestry and understanding the patterns found in Our urban forests? The United States Department of Agriculture (USDA) Forest Service is concerned with urban forestry to help improve the quality of life for their people. They define the management of the urban forest as "the planning for and management of a comrnunity's forest resources to enhance the quality of life". Quality of life is enhanced by the many social, economic, and physical benefits that the urban forest provides to the surrounding areas.

1.2 Urban Forest Benefits

Individually, and more importantly collectively, trees in urban areas provide many benefits to the surrounding area. Effective management of the urban forest is critical to realize these many benefit S.

The urban forest is oflen promoted as being a natural air filter due to its absorption of gaseous pollutants, sequestenng of carbon, and filtering of particulate matter (Nowak, 1994a; Nowak, 1994b; Nowak et al., 1994). Improving the air quality in a city requires a forest structure that includes pollution tolerant species placed in high pollution areas. The USDA Forest Service has quantified benefits such as air quality for certain areas of the United States. Quantifying these benefits allows people to understand the importance and cost effectiveness of trees in the urban environment in improving air quality. In Chicago, trees are estimated to remove 15 metric tons (t) of carbon monoxide, 84 t of sulfur dioxide, 89 t of nitrogen oxide, and 191 t of ozone annually (Nowak, 1994a). Chicago's trees also store an estimated 855,000 t of carbon (Nowak, 1994b). The estimated values of the pollution removal in 1991 in Chicago is %US 1 million (Nowak, 1994a). Carbon storage by urban forests in the United States is estimated to be between 400 and 900 millions t (Nowak, 1994b). Trees in the Chicago area also removed an average of 8.9 t/day of particulate maner less than 10 Pm in site (PMIO) (Nowak, 1994a).

Urban forests also provide hydrological benefits such as storm water attenuation. The land cover in cities has a high proportion of impervious surfaces such as roads parking lots, buildings, etc. Water is not absorbed into these surfaces but rather nins off into stom sewers. It is estimated that 80 percent of annual rainfall results in runoff (McPherson, 1994a). The increased ninoff found in cities results in the need for increased sewerage, drainage channels and treatment capacities (Sanders 1986). Hydrological simulations have indicated that tree cover reduces urban stormwater runoff by four to eight percent (Sanders, 1986).

Urban forests have the potential to Save cities millions of dollars through energy conservation. The three main ways that urban forests conserve energy are through shading, through evapotranspiration, and through windbreaks. Shading buildings and other features results in a direct decrease in the surface temperature of the feature that can lessen the need for air conditioning. Computer models suggest that by using trees to shad a house, a 3 1% decrease in annual cooling energy use in Chicago could be realized (McPherson, 1994b). Evapotranspiration by trees also lowers air temperatures, but estimates Vary as to the magnitude of this evapotranspiration effect (McPherson, 1994b). It is estimated that large numbers of trees in urban areas can reduce local air temperatures by 0.5 to 5OC (McPherson, 1994b). Trees also provide a windbreak which reduces wind speeds by as much as 50% (McPherson, 1994b). The reduction in wind speed provide additional protection that reduces the amount of cold outside air that infiltrates the building (McPherson, 1994b). These three ways that urban forests conserve energy are al1 related to the extent of the canopy. The larger the canopy, the greater the shadi ng, evapotransp kat ion, and windbreak.

Urban forests also increase property value. It is estimated that the retention of trees on private property can increase property values by more than twenty percent (City of Vancouver, 1995), and result in a quicker sale of the property. Newspapers include advenisements for new housing developments. Many of these advertisements include some reference to the natural environment, either b y cal l ing themselves "Maple Estates", "Deerfield Forest", "Rosebank Forest", "Willow Ridge", etc., or by associating their location to woodlots and ravine systerns. Obviously, this "naturai" feature is a selling point for the consumer.

The benefits provided fiom Our urban forests also include psychological well being (Kaplan, 1995). Talbott et al. (1976) found that the introduction of flowering plants in a psychiatnc ward resulted in an increase in social interaction and food consurnption among the patients. Another study by Ulrich (1978) recorded the alpha brain waves of viewers to measure the amount of wakeful relaxation when slides of urban and rural landscapes were shown. The relaxation state of the viewer increased as the percentage of green and water on a slide increased (Ulrich, 1978). Urban foreas provide a relaxing and more peacefùl environment for people which results in an increase in psychologicai well being.

It is through the combination of al1 of these benefits that the urban forest provides cities with extremely valuable services. Cities can estimate the value of the trees in their cities and the services that these trees provide. The Guide for Plant Appruisd (Council of Tree and Landscape Appraisers, 1992) published by the International Society of Arboriculture (ISA), provides a rnethod of tree valuation. Using this method, the city of Kingston, Ontario is estimated to have public trees (city owned street trees and park trees) valued at $18.3 million (Kenney and Puric-Mladenovic, 1999). If a city had $18.3 million in other assets such as in a fieet of trucks, there would most definitely be a management plan for these resources. Thus the urban forest is a valuable resource that must be recognized and managed properly by cities.

1.3 Leaf Area and Leafiirea Density

The mechanisms involved with most of the benefits provided by the urban forest relate to the leaf area density of the urban forest canopy. Leaf area density (LAD) is the total combined area of al1 leaves per unit land area (e.g. per hectare). For exampie, this can be represented as 1235 m2 of leaf area per 1000m2 of ground area, or as a leaf area density of 1.235. For this research, leaf area densities will be represented as m211000m2. Leaf area index (LAI) is another commonly used tenn and it refers to the total combined area of a11 leaves on a tree relative to the dripline area of that tree.

Understanding leaf area is critical since leaves are the surface area that provides the majority of the benefits provided by the urban forest. Leaves are where the trees intercept pollutants and rainfali, shade buildings, and cool the air though evapotranspiration (McPherson, 1998). Since most of the benefits provided by the urban forest can be attributed to the leaf area of the urban forest canopy, the management objective should be to maximite leaf area density. The urban forest canopy has traditionally been represented as the percentage of the community that is covered by trees or shrubs (% canopy cover). McPherson (1998) states that, "quantifjing current tree canopy cover, tree health, and the potential for additional canopy cover provides a basis for estimating impacts of community forestry programs on the future economic and environmental vitality of our cities". 1 suggest that considering leaf area density is another, more holistic way to look at the urban forest because the dimension of height is added to traditional canopy cover.

The main difference between canopy cover and leaf area density is that canopy cover is only a two dimensional approach whereas leaf area density is a three dimensional approach which involves canopy volume, not only canopy cover. Two trees can have the same percent canopy cover yet have very di fferent leaf area. A columnar maple (Acer plantmoides L.) and a crabapple (Malru cororiaria (L). Mill.) may have the same percent canopy cover, but the columnar maple has a much greater leaf area due to the increased height of the canopy.

Leaf area is estimated from tree rnorphological data such as crown volume and diarneter at breast height (dbh). Two regression equations for predicting leaf area for an open grown deciduous tree were created hy Nowak (1994c, 1996) using different tree morphological variables: 1) Based on dbh: In Y = b, + b,X + b,S 2) Based on crown parameters: ln Y = -4.3309 + 0.2942H + 0.73 12D + 5.7217Sh - 0.0148s where: Y isIeafarea(m2) X isdiameterofthestem(dbh)at1.4mabovetheground(cm) ba b, are regression coefficients H is crown length (m) D is maximum crown diameter (m) Sh is the shading coefficient (average shading factor for the individual species (% light intensity intercepted by Foliated tree crowns)) S is crown surface area ( x D [H + D]/2 )

These relationships between dbh and leaf area, and between crown parameters and leaf area, explain 64% and 91% of the variation in leaf area, respectively.

These regression equations were derived from data on healthy, full crown, open grown urban trees (Nowak, 1994a). Hence, the equations are estimating near-maximum leaf area for individual open grown urban trees. Solving the equation for an individual tree will calculate the maximum leaf area that an individual could accommodate for its present size, assuming perfect health. Therefore using the information fiom mature individuals in these equations will result in the derivation of the maximum potential leaf area possible for a panicular tree species.

As was mentioned earlier, two areas of land can have the same percent canopy cover and have different leaf area densities. For example, visualize two plots of land, each being one hectare in size and with a canopy cover of 50% (Figure 1). In the first plot, the canopy cover is composed of mature small trees (avg. height=7m, avg. crown diarneter=5.4m; 25 m2 of canopy cover per tree) with a leaf area of 100 m2 per tree or a total leaf area density of 20,000 m21ha. The second plot has its canopy cover composed of 33% mature srnall trees, 33% mature medium trees (avg. height43.4111, avg. crown diameteHorn; 80 m2 canopy cover, 330 m2 leaf area), and 33% mature large trees (avg. height=22m, avg. crown diameter= l6m; 200 m2 canopy cover, 1 150 m2 leaf area). The leaf area density is 23,124.5 m*/ha, which is a 15.6% increase in leaf area density fiom the plot composed of small trees only. Therefore, the two plots with the same canopy cover in this example, have a 15.6% difference in leaf area density.

Consequently, the benefits derived hmthe forest in the second case could be expected to be substantiaily greater than the fint. Considering leaf area density rather than canopy cover also makes it possible to recognize species differences since the equations inciude a species specific shading coefficient. Figure 1: Example of one hectare plots that have the same percent canopy cover (50%) but different leaf area. (a) is cornposed of 200 mature smali trees with a total leaf area of 20,000 m2. (b) is composed of 67 mature small trees, 21 mature medium trees, and 8 mature large trees with a total leaf area of 23,124.5 m2.

Leaf area density can be considered in two different ways, existing leaf area density (LAD) and potential leaf area density (PLAD). Existing leaf area density is the leaf area that is supporied by an area whereas potential leaf area is the leaf area that coiild be supponed by an area. Potential leaf area density can be seen as the upper limit to the amount of leaf area available at a site.

Potential leaf area density is the focus of this research. Many factors govem potential leaf area density such as the percentage of hard surfaces, utility placement, sight-line requirements, etc. These factors limit what species and where a tree can be planted. It should be noted that public opinion wouid most likely not allow al1 available planting places for trees to be realized. Open space is desirable in some areas such as sports fields.

"Potential" can be seen as a vague term that can have different meanings. In this study, only the existing hard surface/soft surface composition of the City of Toronto is considered. However, an alternative way to look at potential leaf area density is to consider where hard surfaces in the city can be changed to soft surfaces to accommodate tree growth. This includes rooftop gardens where a hard surface (i.e. a roof) has been convened to a sofl surface through vegetation planting which will increase the potential leaf area density of that area. Chapter 5 of this thesis will elaborate on "Alternative Planting Locations" to increase leaf area. There is an important relationship between leaf area and tree site. Leaf area is exponentially higher in large trees than in srnaIl trees (based on leaf area equation in Nowak, 1994b). Nowak (1994a) noted that large trees have up to 1,000 times greater carbon storage than small trees and up to 90 times greater carbon sequestration rates. This is due to the exponentially higher amount of leaf area in large trees that trap and sequester more carbon.

Leaf area is maximized by allowing trees to reach their potential in size, which requires excellent health of the tree. Attaining a large size is often dificult since urban areas can be extremely stressful for tree survival. It is estimated that 500/0 of the tree canopy of Washington D.C. is replaced every 6 years (Bradshaw et al., 1995). By ensuring that a tree is provided with the essentials such as water, soil, nutnents, and very importantly, space to grow, leaf area will have a chance to reach its potential.

1.4 Avaiiab le Growing Space

To obtain the maximum potentiai leaf area for a site, the amount of available growing space should also be rnaximized to ensure the utmost in tree health and growth. Available growing space is affected by the morphology of the city and it is defined as areas of plantable land such as tree, shnib, grass, bare soil, or other cover types (Sacarnano et al., 1995). The increased amount of hard surfaces in a city result in a decreased land area to plant trees. These non-plantable hard surfaces include buildings, roads, sidewalks, walkways, driveways, laneways, decks and paved courtyards.

Conflias with below ground utiiities such as electricity (hydro) and cable television lines, as well as conflicts above ground with overhead utilities, also decrease the amount of space available for tree growth. Sufficient below and above ground growing space is required to achieve long term growth and health of trees (City of Toronto, 1995). Below ground utilities intempt the rooting zone of the trees. One major problem with below ground utilities is the need to access these utilities by trenching, which results in root severance (Morell, 1992). Above ground utilities limit the growing area for trees because tree branches are often cleared around utility wires to maintain line reliability (Bieller, 1992). Buffers are areas of soft surface unavailable for tree planting due to the proximity to other hard surfaces, structures or utilities. Available growing space is also impacted by buffer widths. Trees are not planted directly beside a feature but rather they are placed a distance away so that the tree has space to grow. This distance from the feature is the buffer width and this indicates where the stem of a tree cannot be planted. For exarnpie, a buffer width of 3 m around a fire hydrant indicates that the stem of a tree cannot be within this 3 m radius. However, a tree planted directly outside of this 3 m radius can have its canopy overhang the fire hydrant and its buffer area. Similarly, root systems can enter some buffer areas.

When available growing space is decreased (due to buffer widths or due to the composition of features such as buildings, walkways, etc.), the potential leaf area of an area is also affected since the trees are then growing under less than optimal conditions. Confined root growth will refiect in a decrease in crown density and hllness, hence a decrease in the amount of leaf area available from that site. Therefore, to maximize potential leaf area density, non-plantable surfaces and conflicts with utilities need to be minimized so that trees have the greatest amount of growing space possible.

1.5 Urban Forest Structure

The previous sections have explained the benefits of the urban forest, how these benefits are related to the extent of leaf area density, and how the leaf area is related to the arnount of growing space available. Simply understanding that urban forests provide benefits to surrounding areas is not enough. Understanding the stmcture of the urban forest and its distribution allows urban foresters to manage the urban forest in a comprehensive manner to maximize those benefits.

Urban forest structure refers to the spatial arrangement of vegetation in relation to other objects, such as buildings, within urban areas (Rowntree, 1984a). The structure of urban forests is determined by thtee broad factors: 1) natural factors, 2) human management systems, and 3) urban rnorphology (Sanders, 1984). Natural factors determine what types of vegetation will be found in a city. These factors include thermal and moisture regimes, soils types, and seed sources (Sanders, 1984). Only species that are adapted to the natural factors of an area will prosper. For example, Honey locust (Gledirsa triacmthos L.) will not be found in areas outside of Southem Ontario since it can not tolerate a colder climate range outside of the Plant Hardiness Zone eight (Farrar, 1995).

Human management systems include actions taken to achieve specific objectives with vegetation and open spaces by any private or public landowner (Sanders, 1984). These management systems are impacted by the different cultural perspectives of the urban forest (Fraser, 1997). Fraser (1997) found that in the City of Toronto, British communities prefer abundant shade, Chinese communities prefer fewer trees, and Italian and Ponuguese prefer hit trees and gardens. Including the whole diverse community in urban forest management is beneficial since their perspectives will determine the success of the urban forest (Iles, 1998). This social aspect of cultural differences on urban forestry does not fa11 into the scope of this research.

Human management systems involve decisions about the utility and desirability of urban trees. These utilities and desires are pnmady based on urban land uses (Sanders, 1984). Sanders (1984) asks, "What kinds and amounts of vegetation are regarded as suitable by different land uses?". The relationship between land use type and vegetation will determine the management plans for the urban forest. For example, it is desirable to have trees in residential land use types, yet it may not be possible to include trees in certain industrial areas. The land use predicts, or in some cases determines, the urban forest.

Urban morphology is "the pattern or geometry of urban development in horizontal and vertical dimensions, a pattern ihat creates or forecloses opportunities for vegetation within the city" (Sanderq 1984). In essence, urban morphology restricts or encourages growing areas for trees and it is composed of al1 features within the city which include buildings, sidewalks, lawns, courtyards, below ground utility lines, above ground utilities, etc. Urban morphology is affected by the age of the city. Older cities tend to have intensively developed interiors with little open or vegetation space due to lack of population dispersion (Sanders, 1984). In contrast, younger cities have less variation in available growing space throughout the city due to low density development (Sanders, 1984).

The USDA Forest Service uses canopy cover and land use types to describe urban forest structure. Rowntree (1984b) studied the relationship between forest canopy cover and ten different land use types in four cities in the eastem United States. The cities of Syracuse, NY; Dayton, OH; Cincinnati, OH; and Birmingham, AL were selected since they are al1 found in the eastern mixed hardwood region which minimized gross ecological differences (Rowntree, 1984b). There was significant variation in percent canopy cover for the ten land use types. The ten land use types and their conesponding percent canopy cover averaged from the four dies were: Residential - 1 to 2 families (32%), Residential - multifamily (14%), Commercial (5%), Industrial (6%), Institutional (21%), Parks (40%), Vacant (60%), Transportation (8%), and Miscellaneous ( i 6%).

Even though a correlation was found between land use types and canopy cover, there was variation among the four cities studied. For example, canopy cover ranged from 1% (Dayton, OH) to 13% (Syracuse, NY) in the "transportation" land use type (Rowntree, 1984b). The variation between cities is due to many factors such as growing season and precipitation differences (Rowntree, 1984b). The variation seen between the ten land use types may be due to factors other than strictly land use zoning. These other factors include population density, percent hard surface, uti lity placement, and building density.

Therefore, urban forests are not homogenous with respect to tree distribution and structure. Trees have definite pattems throughout a city and identifying these patterns illustrates the structure of the urban forest. It is through understanding the stmcture of the urban forest that enables the implementation of proper management. It follows that a classification system is one of the necessary tools for urban forest management. 1.6 Urban Forest Classcfication System

To assist urban foresters in managing the urban forest, an urban forest classification system is needed. A resource can not be successfully managed if its size, health, and distribution are unknown.

ClassiQing vegetation means identifying individual units of vegetation and ananging them in an orderly and meaningful way (Küchler, 1973). Vegetation is usually classified to find patterns in nature for management purposes. The Ecological Land Classification (ELC) is used in Ontario to classify land types. The ELC was developed to standardize the way natural communities and features are identified, narned, inventoried and mapped for management purposes (Lee et al., 1998).

Management of urban forests is difficult since they do not have a clearly defined classification or management system. Urban forests found within cities are excluded from the ELC for Southern Ontario since the procedure focuses on more natural, less anthropogenic communities (Lee et al., 1998). The smallest classification unit in the ELC for Southern Ontario is the "ecoelement" which has an appropnate scale of 12,000 to 1 : 10,000 (100 to 100,000 m) (Lee et ai., 1998). This scale range is obviously too large to accommodate the high spatial variability found within urban forests illustrating the necessity to provide a framework for urban forest classification that can be used for management purposes.

Brady et al. (1979) created an urban ecosystem classification based on a variety of biological and physical attributes. This classification was created to facilitate research on the ecological processes of urban areas (Brady et al., 1979). Twelve Land Types are the main classification level such as clinlorganic detritus, derelict/weedy grasslands, derelidsavanriah, urbanlforest plantation, etc. (Brady et al, 1979). The problem with using this classification system for urban forestry management purposes is that it is too general for use by an urban forester. It is a very good holistic ecological classification system for urban areas, but it does not easily incorporate urban forestry. An urban forester requires a classification systern that is focused on woody vegetation.

a classification system that is adapted to built environments (i.e. urban areas) a high resolution classification system (which has a classification unit smaller than the 1:2000 to 1:100 000 range of the ELC ecoelement) which enables more detail to be included in the classification standard tenninology that is well defined to minimize confusion a classification system that is easy to use a classification system that uses climax or potential natural vegetation as its management unit as opposed to arictly the vegetation that is present. a classification system that can be used in cleared areas, that is, areas where the vegetation has been removed and requires planting.

These characteristics provide the basis for the creation of an urban forest ecosystem classification system.

The classification system that was developed and used in this study was based on urban- biotopes. A biotope is defined as the smallest geographical unit of land or habitat in which environmental conditions (soil and climate), and the associated organisms dwelling there, are uniform (Dunster and Dunster, 1996). Urban-biotopes can be defined by many factors such as hard surface cover, soft surface cover, utility placemect, etc., al1 of which impact the available growing space in the urban forest. If land use alone accounts for the differences seen in potential leaf area, then the urban-biotopes can be divided along the lines of land use. If not, then the urban-biotopes will need to be modified by other factors that influence potential and existing leaf area (e.g. % hard surface, building density, etc.).

1.7 Objectives

This research will examine urban forest structure in a manner different than the research completed by the USDA Forest SeMce. Instead of looking at canopy cover, the relationship between potential leafarea and urban morphology within the City of Toronto will be studied. The city will be divided into land use types and the potential leaf area within each type will be compared. Land use types with similar potential leaf area will be combined and land use types that are dissimilar can be fùrther divided by the factors that are affecting potential leaf area. Effectively, the City of Toronto will be divided into urban-biotopes based on potential leaf area density.

The key objectives of this research are to: 1. Detemine the potential leaf area density in the ten land use types in the City of Toronto. &: There is no significant difference among land use types with respect to potential leaf area density. 2. Detemine potential leaf area density under different buffer sizes in urban areas. Ho: There is no significant difference in potential leaf area density when buffer sizes are manipulated in each of the ten land use types. 3. Identify and compare the hard surface features such as buildings, parking lots, walkways, etc., which limit the amount of soft surface in our cities in each of the ten land use types. 4. Quantify the impact of each feature's buffer on the soA surface in each of the ten land use types. 5. Develop an urban forest ecosyaem classification methodology based on potential leaf area density. This will be done by dividing the city into urban-biotopes based on land use types. Fuither refinement of these urban-biotopes can be based on the factors outside of land use that influence potential leaf area density.

This research will provide information required to develop guidelines aimed at maximizing potential leaf area for various urban-biotopes in southem Ontario. This is a first approximation of potential leaf area in varying urban-biotopes based on the City of Toronto. This research will not attempt to explain the ditference found between potential and existing leaf area within a biotope, however, potential leaf area must be identified before any work is done on the existing to potential ratio. This information can then be used to assist planners and developers to identify appropriate mixes of urban-biotopes within new developments to maximize the benefits from the urban forest. Observing the change in potential leaf area of an urban-biotope over time can be used as an indicator of sustainability, whereby once potential leaf ara decreases (e-g. by paving an area), it is permanently lost. 2. Materials and Methods

2.1 Siudy Area

This research has been completed in the pre-amalgamation City of Toronto. This area is bounded by the Humber River to the West, Lake Ontario to the south, Victoria Park Avenue to the east, and Highway 40 1 to the nonh, and this area will be referred to as the City of Toronto for the rest of this thesis. Data were collected during 1998 and during the spring of 1999.

The City of Toronto was chosen as the study area due to its location and sirnilarities to other cities in Ontario. The University of Toronto is located in the centre of downtown Toronto which enabled easy access to data for its collection. The City of Toronto has fifieen different land use types which provide a wide range of urban environments that pose a variety of growing environments for trees. These land use types are likely to be found in other Ontario communities.

The information in this thesis obtained from the City of Toronto will be applicabie to other cities due to the large sire and environments found within the city. Other Canadian cities will likely have similar land use types and feature composition as that of Toronto, just on a smaller scale.

2.2 Geographic Information Sysrems (GIS)

Geographic Information System (GIS)were used extensively in this research project. GIS are mapping technologies that have the ability to store locational data (such as a traditional map) as well as attribute data (e.g. elevation, census data, mils information, etc.). The major advantage of GIS programs over traditional map drawing software is the ability to query or ask questions for spatial relationships.

Information is stored in GIS as different "layers" or "themes". Each layer overlays other layers to provide more detail about a particular area. For example, the layers that were used in this research for the City of Toronto included a street layer, a utility layer, a canopy cover layer, etc.

The GIS software package that was used for this project was ArcView 3.1 (1998). This sof'tware was created by Environmental Systems Research Institute Inc. (ESRI), the creators of ARC/DEO.

2.3 Sample Plot Selection

Colour Digital Orthophotos for the area encompassing the City of Toronto were used as a backdrop (as one of the GIS layers) to digitize new layers of thematic information and to overlay existing maps obtained fiom the City of Toronto. They were obtained from the University of Toronto Map Library. Onhophotos combine the geometric accuracy of a map wiih the high visual information content of a photograph (Triathlon Mapping Corp., 1998). The Orthophotos used in this study are colour aerial photographs taken at a scale of 130 000 that are accurate to 1:20 000 by using Ontario Base Maps as a control during their production. The Orthophotos cm be enlarged to about 1:2000 before deterioration of the image impairs their use. The Onhophotos used were photographed on September 15, 28 and 29, 1995 by Triathlon Mapping Corporation. The resolution of each image pixel is one meter.

Sixteen Orthophotos in TIFF format were used ranging in size from 39 352 KB to 48 621 KB. These Onhophotos were taken fkom Triathlon's Region 13 and Region 14 of the Greater Toronto Area (Figure 2).

The digital Orthophotos files that were used for each region are: Region13 16392.tif Region 14 164 1 1.tif 18401 .tif 1738 1.tif 164 12.tif 18402.tif 17382.tif 17402.tif 18421.tif 1740 1.tif 17421.tif 18422.tif 1838 Ltif 17422.tif 18382.tif 1744 1.tif Each of the TIFF Orthophotos has three positioning data files associated with them. Tney are: 1. Image Report File (IRP). The IRP file provides technical details about the individual TIFF files including pixel size. number or rows and columns of pixels in the file. angle of rotation of the images. etc. (Triathlon Mapping Corp.. 1998). 2. World File (TFW). The TFW file contains the transformation matrix that is required to register the TIFF file to ArcAnfo coverage (Triathlon Mapping Corp.. 1998). 3. TAB File. The TAB file allows automatic registering of the image to map data by providing geo-referencing information about the TIFF file relative to the datum used. These files are used in iMaplnfo (Triathlon Mapping Corp.. 1998).

TORONTO 13341

Figure 2: Orthophotos From Region 13 (Etobicoke/Toronto West) and Region 14 (Toronto East) which encompasses the entire pre-amalgamation City of Toronto (Triathlon. Mapping Corp.. 1995)

These three file types are georeferenced using Universal Transverse Mercator (UTM) plane gnd projection in North Amencan Datum (NAD 27 and 83), as well as Geographic (NAD 27 and 83) and 3TM (NAD 27 and 83) (Triathlon Mapping Corp., 1998). Each of the 16 Orthophotos was hand digitized into a combined total of 871 polygons based on the appearance of tree canopy cover (Figure 3a). Hand digitizing involved the creation of these joining polygons which covered the entire area of the City of Toronto. The polygons were divided into three broad categories: low canopy cover, medium canopy cover and high canopy cover. The land use type for each of the 871 polygons was also noted. Areas where there was no or very Metree cover defined low canopy cover. Areas where there was tree cover and also a built up environment defined medium canopy cover. Areas where there was substantial tree cover and very little or no built environment defined high canopy cover. Quantification of these categories was completed by randomly locating 33 one hectare sample plots in each of the three canopy cover categories on the Orthophotos. Canopy cover was determined by manually outlining the canopy of the trees that fell within the sample plot. This was then calculated as a percentage of the total area within the sample plot.

The sample plots were located by numbering al1 of the polygons from one to 871. Each polygon was classified as one of the fifieen land use types. The fifieen land use types are (Figure 4): Low Density Residential: maximum gross fîoor area that does not exceed 1.0 times the area of the lot (City of Toronto, 1996). Medium Density Residential: maximum gross floor area that is greater than 1 .O times the area of the lot but does not exceed 2.0 times the area of the lot (City of Toronto, 1996). High Density Residential: maximum gross floor area that exceeds 2.0 times the area of the lot (City of Toronto, 1996). Low Intensity Commercial: shopping district with Street parking, smaller single-unit stores of one or two stories Medium Intensity Commercial: small indoor shopping malls or stnp malls with dedicated parking lots High Intensity Commercial: high rise office towers, large indoor shopping malls + (overlay) Select 90 sampte plots

Georeferenced City of Toronto 1 :SOO Aerial photographs Orthophotos Digitized Data for for each (shown in grey) each plot (includes sample plot to get detailed with a sample of utilities and information superimposed building such as wahays which numbered footprints) cannot be seen Pol ygons black circle on the Orthophotos represents sam ple plot

(d) Digitize sample plot and utilities in ArcView

Transfer of digitized plot and utilities (e) to the Onhophotos (for georeferencing)

Figure 3: Methodology procedure for creating the 90 digitized sample plots in the City of Toronto used in this study. Industrial: factories, rail-yards, warehousing facilities institutional: hospitals, schools, universities, coileges, etc. Passive Recreation Park: treed parks Active Recreation Park: baseball diamonds, football fields, soccer fields, playgrounds, etc. Cernetery Ravine/Natural Area TransportationRTtility Comdors: major roadways with eight or more lanes of active trafic, railway lines, hydro transmission comdors Exhibition Lands Airport Land

The focus of this research has been to examine areas in the city that can provide places for tree planting. The land use types of parks, cemeteries, ravinehaturai areas, and airport lands were not considered in this study. Parks and cemeteries were discarded from this study since available growing space for trees in these land use types is less of an issue than in other land use types. The ravines and natural areas are assumed to be highly stocked therefore they do not require planting. Airport lands are not considered as potential planting spots in this study since they cannot have trees that may interfere with the hnction of the airport. This results in the remaining ten land use types to be considered in this study as: 1) Residential Low Density, 2) Residential Medium Density, 3) Residential High Density, 4) Commercial Low Intensity, 5) Commercial Medium Intensity, 6) Commercial High Intensity, 7) Industrial, 8) Institutional, 9) Transportation, and 10) Exhibition Lands.

The polygons that fell within these ten land use types were then available for locating the sample plots. A random number generator in Microsoft Excel 97 was used to select three polygons as a representative sample from each land use type. Figure 4: The fifieen land use types in the City of Toronto. Within each of the 30 chosen polygons, three sample plots were randomly iocated in ArcView (Figure 5). This procedure was done by identiQing the selected polygons and then "hiding" the Orthophoto layers so that there were no visual cues and hence no bias in sample plot location. The sample plots were hlly contained within the polygon and non-overlapping with polygon edges and with other sample plots. By locating the sample plot clearly within the polygon, the area within the sample plot is then representative of the land use type. Therefore, there are a total of 90 sample plots throughout the City of Toronto (30 polygons with three sample plots per polygon).

Sample plots were 1000 m2 (1110 ha) circular plots (radius 17.84 m). This plot size was chosen since one tenth of a hectare is a convenient and manageable size and it falls within the range of commonly used urban forestry plot sizes (Nowak, 1994b; Grimmond et al., 1994). if there were factors outside of the 17.84 m radius that had an impact on the available growing space within the sample plot, these areas were mapped within the sample plot. For example, if a stop sign was present just outside of the sample plot, it would have an impact on the available growing space within the sarnple plot since a 15 rn radius must be preserved sunounding stop signs as a buffer for sightline purposes.

2.4 Data Collection

Information on the composition of each of the sample plots was needed. This included the feature composition such as where the buildings, parking areas, walkways, lawns, etc. were located. As well, both below ground and above ground utilities were added to the plots. The composition of the 90 sample plots was not clearly discemable from the Orthophotos alone due to the low resolution of the Onhophotos (1 m pixel size). Sample plot composition was determined by the City of Toronto data (property data maps and underground utilities) as well as by using 1500 aerial photographs. Sample Plots ,h~'Streets

Figure 5: Location of the 90 sample plots to determine potential leaf area in each of ten land use types in the City of Toronto. The City of Toronto provided two types of digitized data for this research project: property data maps, and underground utilities on public areas (Figure 3 b). The property data maps contained surveyed information on road curbs and building footpnnts. They were originally digitized between 1988 and 1990 and they have an accuracy of approximately lm (M. Pawlowski, pers. corn., October 1998). Property data maps files are updated daily as new information becomes available. The complete set of property data maps that were provided by the City of Toronto for use in this research are fiom May 20, 1997.

The underground utilities on public areas contained surveyed information fiom each of the following utility companies or services: Bell Canada, Toronto Hydro, Ontario Hydro, Consumer Gas, Toronto Water, Storm Sewers, Sanitary and Combined Sewers, Toronto Transit Commission, and Miscellaneous.

These utilities were originally digitized in 1993 and range in accuracy from "extremely high accuracy" up to lm accuracy (L. Leeworthy, pers. com., December 1998). These files are updated constantly from each of the utility companies. Like the property data maps, the files used in this research are from May 20, 1997. The utility information obtained from the City of Toronto is stnctly on public land. With regards to private lands, this research assumes that utilities enter ont0 properties along property lines and split into a T joint to feed into each house (J. McNeil, pers. corn., January 1999; L. Leeworthy, pers. corn., December 1998).

Both the property data maps and the underground utilities are not georeferenced to the same coordinates as the Onhophotos, therefore they do not overlay accurately on top of the sample plots which are located on the Orthophotos in ArcView. These City of Toronto layers were rnanipulated to overlay on the Orthophotos by lining up features at ground level that could be determined on both the City of Toronto data and on the Orthophotos. The City of Toronto data were not distorted in the process, only resized and rotated as needed.

Due to the low resolution of the Orthophotos (lm pixel size), and the lack of feature information on the City of Toronto data, aerial photographs (1500) were needed to detemine plot features (e.g. sidewalks, walkways, lawns, etc.) (Figure 3c). These aerial photographs were taken between 1988 and 1993 (Northway Map Technology Limited, 1993). The other information used in this research such as building footprints, road widths, and below ground utilities were obtained using the City of Toronto data.

The aerial photographs were used in conjunction with the accurate property data rnaps from the City of Toronto due to the distortion involved in the aerial photographs. A tall building on an aeriai photograph will cover and hide one side of the ground surrounding the building dependhg on the angle that the photograph was taken from the airplane. The City of Toronto data were overlaid on top of the aerial photographs and each plot was digitized (Figure 3d). Any discrepancies between the City of Toronto data and the aerial photographs were elirninated by groundtmthing which will be explained further below.

Digitizing was completed on top of the cornbined aerial 1300 photographs with the City of Toronto data. The information for each of the sample plots was digitized into two categories, the digitized plot layer and the utilities layer.

1. The digitized plot layer was composed of: Roads: includes al1 types of public roads. Sidewalks Lawns: defined as al1 soft surfaces such as gardens, yards, forested land, etc. Walkways: includes paved areas leading to property entrances. Buildings A: defined as buildings equa! to or Iess than three stories in height. Buildings B: defined as buildings greater than three stones in height. Parking lots Driveways: defined as paved or unpaved areas on private property generally used for vehicles. Laneways: defined as paved or unpaved areas that are used by a select group of the public for access to individual properties. For example, this includes the roadways behind residential houses in the city where parking is in the backyards of the residences. Decks Courtyards: defined as paved public areas. For example, the paved areas between buildings that are larger than a simple walkway.

2. The utilities layer was cornposed of Bell Canada (underground and above ground wires, O.P.I.(telephone maintenance) boxes, telephone poles) Toronto Hydro (underground and above ground wires, hydro poles, hydro boxes) Ontario Hydro (underground and above ground wires hydro poles, hydro boxes) Consumer Gas (underground lines) Toronto Water (underground pipes, fire hydrants) Storm Sewers (underground pipes) Sanitary and Combined Sewers (underground pipes) Toronto Transit Commission (streetcar tracks, budstreet car shelters, budstreet car signs) Miscellaneous (underground linedpipedwires) Signs (e.g. no parking) Sto psigns Stoplights Railway Lines (non-TTC)

Note that the subway lines were not identified due to the depth at which the subway lines are located. The City of Toronto plants trees on top of subway lines (E. Horvath, pers. corn., August 1998).

The digitized plot layers and utility layers were then transferred to the Orthophotos for the City of Toronto (Figure 3e). This resulted in the georeferencing of each of the sample plots, so that each plot was true in scale. Georeferencing enabled areas to be calculated for each of the plots, which allowed available growing space to be determined.

An accuracy assessment of the digitized sample plots was pedormed. Random samples of 127 digitized features within the sample plots were compared to their actual value on the ground. These features were taken from a variety of land use types. A linear regression analysis was then performed to quanti& the error involved in digitizing.

The feature composition (buildings, lawns, sidewalks, parking, etc.) of each of the 90 sample plots was completed in ArcView. The features were calculated as a percentage of the total area of the 1000m2sample plots.

Having the digitized plots separated from the utilities enabled different buffers to be placed on the features. Four case studies have been completed on the effect of varying buffer widths on potential leaf area density. These four case studies included determining potential leaf area density in the sample plots with: a) no buffers, b) minimum buffers, c) average buffers, and d) maximum buffers. The buffer widths used in this study were based on the buffer sizes that are used in municipalities around Southern Ontario. These buffer sizes were obtained fiom the cities of London, Toronto, Richmond Hill, Markham, Mississauga, and Oakville. Considerable variation existed among the rnunicipalities with respect to buffer widths. The average bufier is the average of the municipalities, the minimum buffer is the smallest buffer width used among these municipalities, and the maximum buffer is the largest buffer width used arnong these rnunicipalities. Some features did not have published buffer widths fiom the are3 municipalities. In these cases, consultation with the urban foresters in these municipalities resulted in estimating the minimum, average and maximum buffers for these features. The buffer widths used in this study are found in Table 1. Buffer widths that are underlined are buffers that were created based on estimates from the area rnunicipalities.

Once the areas of hard surfaces and their associated buffer areas are removed from each sample plot, the remaining area of soit surface becornes the available planting space for the urban forest. Table 1: Buffer widths in meters for each of the features found in the sample plots. The minimum, average, and maximum buffer widths derived from the six area municipalities are shown for each of these features. Buffer sizes that are underlined are buffers that were created based on estimates from the area municipalities. Feature Minimum Average Bu ffer Maximum Buffer Buffer Bell Canada Box Below Ground Utilities Building A (less than 3 stories) Building B (greater than 3 stories) Cable Box Courtyard Deck Driveway Fire Hydrant Hard- packed Grave l Hydro BoxPoles Laneway Parking Parking Meter Railway Tracks Roads Sidewalks Signalized Street Lights Stop Sign Streetlights Street Signs Walkway

2.5 Impmî of Features ' Buffers on Sofi Su fice

The impact of each feature's buffer on available soft surfaces using the average buffer widths was also determined. The amount of soft surface at each plot was calculated without any buffers. Then average buffer widths were added individually to each feature to determine its impact on the amount of soft surf'ace at the site. In essence, the decrease or loss of soft surface due to each buffer was calculated.

Simply calculating the buffer area for each feature would not have indicated the loss in soft surface. It would have been complicated by the fact that the buffer area would also have covered hard surfaces. For example, a house with a buffer of 2m al1 around it will have an impact on the lawn that is surrounding the house, however, the same 2m wide buffer area will also cover parts of the driveway attached to the house. This buffer area on top of the driveway would not have any eRect on the available soft surface area at the sample plot.

2.6 Prototype Trees

Once the sample plots were hlly digitized with both feature and utility information, and the variety of buffer widths applied, the potential leaf area density for the plot had to be determined. Prototype trees were used to estimate potential leaf area density. Each prototype tree has a known leaf area density as well as a known area of required growing space. The number and size of these prototype trees determined the potential leaf area density for each of the 90 sample plots.

Fifiy-seven broad-leaved tree species found in urban areas were used to develop prototype tree classes (e.g. Srnall trees, Narrow Medium trees, Medium and Nanow Large trees, and Large trees). Cluster analysis was used to determine the prototype trees because it provides an explicit way to identify groups in raw data and allows stmcture to be found in the data (Jongman et al., 1995). The variables tree height and tree crown diameter were used in the cluster analysis resulting in the prototype trees varying from each other based on these two variables (Davis, 1986; Crockett, 1984). Broad-leaved trees were used in this study since they are the most common group of trees planted in the urban forest. As well, conifers have low crowns therefore they are considered poor choices for tree selection with regards to site lines and sidewalks (Grey and Deneke, 1992). Each of the four prototype trees has a known potential leaf area as well as a known required growing space. It is assurned that the dripline ara of the prototype trees will provide enough root extension for healthy trees (Ray, 1987; Helliwell, 1985). These prototype trees were developed by obtaining information from the literature for each of the 57 species on: Tree height at maturity Crown diameter at maturity Crown height (length of the canopy). This was calculated by exarnining photographs and diagrams of the tree species at maturity and recording the percentage of crown height to the total height of the tree. Shading coefficient (Nowak, 1996) is the average shading factor for the individual species (% light intensity intercepted by foliated tree crowns). For example, Norway maple (Acer plontanoides L.) is a densely leafed species and it has a shading factor of 0.88; Honey locust (Gleditsia triacanthos L.) is a sparsely leafed species and it has a shading factor of 0.69. Dripline area (derived from tree crown diarneter at matunty, assuming a circular dripline) Crown surface area (derived fiom tree crown diameter and tree height)

2.1 Determination of Potential Leaf A rea Density

Using the variables collected for each of the 57 tree species, the potential leaf area was calculated using Nowak's (1 994a) equation for eaimating leaf area for open grown deciduous trees. This equation is based on crown parameters: ln Y = -4.3309 + 0.2942H + 0.73 12D + 5.7217Sh - 0.0148s (r2 = 0.9 1) w here: Y is leaf area (ml) H is crown height (m) D is maximum crown diameter (m) Sh is the shading coefficient S is based on the outer sudace area of the tree crown ( K D [H + Dl12 )

PIease note that the regression equations were derived from data on healthy, full crown, open grown urban trees. Hence, the equations are estimating near-maximum leaf area for individual open grown urban trees resulting in the values used for maximum potential leaf area for a particular species.

Trees that exceeded the limitations of the equation (H > 12 m, D > 12 m, WD >3, S > 500) had their leaf area calculated by using an extrapolation of leaf area for similar trees (Nowak and Crane, in press). This modified leaf area was caiculated by inputting the height to diameter ratio of the crown (WD) and the shading coefficient of the trees that exceeded the limitations of the regression into a table provided by David Nowak (pers. com, 1999). Trees with a WD ratio that was above the maximum (2) or below the minimum (0.5) value for this table were proportionately scaled down or up to reach either the maximum or minimum WD value (Nowak and Crane, in press). The leaf area was then calculated and proportionately scaled to reach the correct leaf area value for the tree.

The four prototype trees must be related to the amount of available planting space to determine how many prototype trees can fit into each sample plot. First, the Large prototype trees are fit into the available planting space. Large trees (at maturity) are favoured since they have the most leaf area density and they provide the most benefits. Then, the Medium- Narrow Large size prototype trees are insened into the rernaining areas, followed by the Narrow Medium prototype trees and then the Small prototype trees. The number and size of each prototype tree is then multiplied by its calculated leaf area to determine the potential leaf area density of the plot.

The substrate composition at each sample plot results in two different placements of the prototype trees. The different substrates within the sample plot are either non-obstructive or obstructive to the crown of the prototype tree. Non-obstructive substrates are those that the canopy of the prototype trees cm overhang the substrate. This includes most features such as sidewal ks walkways, parking lots, courtyards, below ground utilities, etc. Obst mct ive substrates mean that the canopy of prototype trees cannot overhang the substrate. This includes features such as buildings greater than three stories in height. Placement of the prototype trees in the sample plots reflected these constraints. 3. Results

3.1 Canopy Cuver Categories

The Orthophotos used in this research were divided into 871 polygons based on the appearance of canopy cover. The three canopy cover ranges used in this research were low=O-25% canopy cover; medium=26-74% canopy cover; and high=75- 100% canopy cover. Figure 6 illustrates a range of canopy covers from 0% through to 100% canopy cover. To ensure that the 871 polygons fell imo these three categories, the canopy cover in 33 one hectare sample plots were manually identified. Out of the 11 sampled one hectare plots for each canopy cover category, the low canopy cover ranged fiom 0% to 24%. The medium canopy cover ranged from 26% to 69%, and the high canopy cover ranged fiorn 75% to 99%.

3.2 Accuracy Assessrnent

The digitized feature sizes in the sample plots were highly correlated to the actual feature sizes with a ? value of 0.995 (SE=0.811, n=127) (Figure 7). The dope was almost 1.O therefore no correction was required. The dependent variable was the actual feature size measured on the ground and the independent variable was the digitized feature size. The high ? value indicates that using the City of Toronto data and the 1500 aenal photographs is a reliable way to determine the composition of the sample plots.

3.3 Land Use Type Distribution

The distribution of land use types in the City of Toronto range From residential low density land encompassing 46% of the city, to commercial medium density and airport lands encompassing less than 1% of the city area each (Figure 8). Figure 6: Examples of canopy cover classification. The highlighted region on the left column represents I ha plots on Orthophotos. right column represents canopy cover within the plot. Digitized Lengths (m)

Figure 7: Correlation between 127 actual feature sizes in the sample plots and the digitized feature sizes. Exhibition 1% Transport.

4% /'

Res. Low 46%

1 Corn. Med. 1% 1% - Res. Hig h Corn. Low 4% 5%

Figure 8: Distribution of land use types in the City of Toronto. Pas.Rec.Park and Act.Rec.Park stands for Passive and Active Recreation Parks, respectively.

3.1 Average Fearrrre Contposirionfor each Land Use Type

The following pie graphs illustrate the average feature composition for each of the ten land use types (Figure 9a-j, Appendix A for table and graphs that include standard errors). Note that the amount of lawn in the sample plots was highest in Residential, Institutional, Transponation, and Exhibition land use types (27-45% soft surface). The land use types with the lowest percentage of sofi surface were Commercial lands (1-8% sofi surface). (a) Residential Low Density

Sidewalk Walkway 1 3% i 3% BGU

Par king - 2%

Other - 1%

Lanew ay - 3%

Figure 9: Percent feature composition for each land use type. BGU stands for below ground utilities. Other stands for the combination of features that make up less than one percent each. (a) Residential Low Density, (b) Residential Medium Density, (c) Residential High Density, (d) Low Intensity Commercial, (e) Medium Intensity Commercial, (9 High Intensity Commercial, (g) Industrial, (h) Institutional, (i) Transportation, and 6)Exhibition Lands. See Appendix A for table listing standard errors. @) Residential Medium Density

Sidew alk 4% Building A

ilding B 18%

Drivew ay - 3% Lanew ay * 2% (c) Residential High Density

Par 1(

Lanew ay [Xivew ay 5% 1% (d) Low Intensity Commercial

Walùway 8GU Sidewalk 5% 7%

Parkin Building A 39% 16%

Other - 0.01 %

Lawn -I

Building B G ravel - 9% Laneway 1% (e) Medium Intensity Commercial

Walkw ay 1% Sidew al k BGU

Law n -. Building B 3% Lanew ay - 4% (f) High lntensity Commercial

Sidew alk Road *% BGU 7 4% 5% Building A

Parking 39%

Law n 1% (g) Industrial

Road 2% i Walkw ay Raittracks : 7 j 1.. 1% 2% ',.

Other 1% Building 6 17%

Law 190,

Gravel 24% (h) Institutionai

Sidew alk - BGU 1% 1% Waikay Road -. 4% Building A 2% .x~.k 8%

Law n 40% (i) Transportation

Gravel 8%

Road 56% (j) Exhibition Lands

ûther 2% - Road

Law n 44%

Parking 14% The feature composition data were not normal and they were heteroscedastic, therefore non parametric statistics were used to test the hypothesis of no significant differences among land use types with respect to the feature composition. The similarities or discrepancies in feature composition between the ten land use types can be seen by ranking the features within each land use type based on each features proportion of the land area. Table 2 illustrates the differences in rank levels of some of the most dominant features among the land use types.

Using these ranked data, a Speannan's Rank Correlation was performed on al1 of the land use type pairs (Table 3) to indicate similarities among land use types. Land use type pairs with a correlation coefficient (p) of equal to or greater than 0.75 indicating a strong relationship were: Residential Medium Density and Residential High Density jp = 0.78)

Residential Medium Density and Exhibition Lands (p = 0.87)

Residential High Density and Medium lntensity Commercial (p = 0.77)

Residential High Density and High Intensity Commercial (p = 0.79)

Residential High Density and Exhibition Lands (p = 0.75)

High Intensity Commercial and Exhibition (p = 0.85) Table 2: Similarities and differences in rank levels of feature composition in the ten different land use types in the City of Toronto. Features at the top of each column are ranked first, with decreased ranking down the column. The solid line illustrates the differences in ranking of lawn composition between the ten land use types. The dashed lines illustrate the differences in ranking of Building A and Building B composition.

Res Low Res Med. Res High LwCorn Med. Corn High Corn Industrial Institutional Transportation Exhibition

LM

Building A .,. Building 6 / C >... Road

Drivewy

BGU

Walkway

Sidewlk

Laneway

Parking Sidewalk Walkvmy Laneway Walkway Stoplight Sidewlk Gravel Deck BGU

Deck Oriveway Drivewy Gravel Stoplight Courtyard BGU Streetlight Oriveway Deck Table 3: Summary of Spearman Rank Correlation coefficients (p) of feature composition similarities for each of the land use type pairs in the City of Toronto.

Res. Res. Res. Low Medium High lndustrial Insti- Trans. Exhib. Low Med. High lntensity Intensity lntensity tutional Derisity Density Density Corn. Corn. Corn. Res. Low X 0.596 0.633 0.462 0.559 0.540 0.431 0.473 0.181 0.512 Oensrty Res. Md. X 0.775 0.649 0.684 0.735 0.563 0.696 0.182 0.870 Density Res. High X 0.670 0.765 0.790 0.616 0.500 0.147 0.752

Law lntensity

Med. lntensity

High lntensity

Industrial X 0.646 0.596 0.701

Trans. X 0.241

Exhibition X 3.5 Impact of Features ' Buffers on Sofi Surface within eoch Land Use Type

The contribution of each feature's buffer to the decline in soft surface was determined within each of the ten land use types (Figure 10a-j, Appendix B for table that includes standard errors). Only average buffer widths were used for comparative purposes

The soft surface decline data were not normal and they were heteroscedastic, therefore non parametric statistics were used to test the hypothesis of no significant differences among land use types with respect to the decrease in sofi surface due to features' buffer. These data were also ranked and are listed in Table 4 which illustrates the differences in rank levels of some of the most dominant features whose buffers caused the greatest loss in soft surface among the land use types. The features whose buffer had the largest impact on soft surface arnong the ten land use types were buildings where eight out of ten land use types ranked it first in soft surface decline.

A Spearman's Rank Correlation was performed on the ranked data in the land use type pairs (Table 5) to indicate sirnilarities among land use types. Land use type pairs with a correlation coefficient (p) of equal to or greater than 0.75 indicating a strong relationship were: Residential Low Density and Residential Medium Density (p = 0.78)

Residential Low Density and Residential High Density (p = 0.88)

Residential Low Density and Low Intensity Commercial (p = 0.78) Residential Medium Density and Residential High Density (p = 0.82) Residential Medium Density and Exhibition Lands (p = 0.81)

Residential High Density and Low Intensity Commercial (p = 0.80) Table 4: Similarities and differences in rank levels of soft surface loss due to features' buffers in the ten different land use types in the City of Toronto. Features at the top of each colurnn are ranked first, with decreased ranking down the column. The solid line illustrates the differences in ranking of Building buffer impact on sofl surface between the ten land use types.

Res Low Res Med. Res High Low Corn Med. Corn High Corn Industrial Institutional Transporlation Exhibition Building -Building - Building -Building -- Building -Building- - BuiIdin\Walkwa y Railway Building Walkway Walkway Sidewalk Walkway Oriveway Parking Railway Sidewalk Parking Walkway Sidewalk Sidewalk BGU Parking BGU Road Parking Parking Light Sidewalk Gravel BGU Walkway Oriveway BGU Laneway Light Parking Courtyard Walkway Sidewalk Buildin Parking Laneway Sidewalk BGU BGU Walkwa y Oeck BGU Light Courtyard Sidewalk Light Laneway Driveway Courtyard Laneway Oriveway Courtyard Gravel Deck BGU Parking Driveway Road Deck Hydro Pole Fife Hydrant Oeck Road Driveway Oeck Road Courtyard Light Driveway Courtyard Gravel Driveway Fire Hydrant Fire Hydrant Driveway Deck Fire Hydrant Stopsigti Fire Hydrant Oeck Hydro Pole Fire Hydrant Deck Hydro Pole Fire Hydrant Table 5: Surnmary of Speannan Rank Correlation coeficients (p) of sot? surface decrease similarities for each of the land use type pairs in the City of Toronto.

Res. Res. Res. Low Medium High lndustrial Insti- Trans. Exhib. tow Med. High lntensity lntensity lntensity tutional Density Density Density Corn. Corn. Corn. Res. Low X 0.779 0.884 0.778 0.639 0.574 0.137 0.495 -0.335 0.476 Density Res. Med. X 0.817 0.661 0.440 0.629 0.284 0.521 -0.162 0.807 Density Res. High X 0.797 0.ô53 0.675 0.269 0.431 -0.254 0.585 Density Low lntensity X 0.581 0.709 0.305 0.530 -0.417 0.607 Corn. Med. lntensity X 0.328 0.267 0.150 -0.321 0.227 Cam. High lntensiiy X 0.398 0.353 -0.236 0.579 Cam.

Industrial X 0.213 0.388 0.325

Trans. X -0.016

Exhibition X (a) Residential Low Density

Features

Figure 10: Percent decrease in soft surface due to the buffers on each feature type within each land use in the City of Toronto. Each bar represents a mean of three sampled polygons with standard error bars. (a) Residential Low Density, (b) Residential Medium Density, (c) Residential High Density, (d) Low lntensity Commercial, (e) Medium lntensity Commercial, (f) High lntensity Commercial, (g) Industrial, (h) Institutional, (i) Transportation, and (j) Exhibition Lands. See Appenrlk B for table listing averages and standard errors.

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3.6 Prototype Trees

Five prototype trees were identified using a cluster analyses: Small, Narrow Medium, Medium, Narrow Large, and Large (Table 6, Table 7, Figure II). Each of these prototype trees has a potential leaf area, as well as an average canopy area, or dripline as indicated in the table.

Table 6: Prototype trees used to determine potential leaf area in the City of Toronto within each of the 90 sarnple plots. Tree Size Heght Range Diameter Range Potential Leaf Area Average Dri line Area (ml (ml (m2/1000m2) cm? Mean SE(+/-) n Mean SE (+/-) n Small 5-9 2-6 100.67 12.3 15 24.03 2.14 15 Nartow 10-12 5-8 209.83 23.17 12 35.53 4.23 12 Medium- Medium 9-16 8-1 2 331 -81 38.79 9 82.12 8.57 9 Narrow 18-30 6-1 2 459.64 49.37 11 75.18 Large 9.59 11 Large 18-25 14-30 1159.37 107.92 10 197.92 13.86 10

Average leaf area increases fiom Small trees through to Large trees, as does average dripline area except for the Narrow Large tree size (Table 6). Narrow Large trees have a smaller average dripline area than Medium trees due to their columnar shape. This would suggest that in a situation where either of these two tree sizes could be planted due to the amount of available growing space, the Narrow Large tree would always be selected over the Medium prototype tree since the Narrow Large tree has a higher leaf area. This will result in the avoidance of planting the Medium size class that would cause a decrease in the biodiversity of the urban forest. To ensure diversity, it is recommended that no species occupies more than five to ten percent of the community (Moll, 1989). To remedy this problern, these two prototype tree classes have been cornbined. The average leaf area for this Medium-Narrow Large size class is: 402.12 m2 with a standard error of +/- 34.69, n = 20. The average dripline area of the Medium-Narrow Large size class is: 78.30 m2 with a standard error of +/- 6.42, n = 20. Table 7 lists the species that created the four prototype tree sizes. Table 7: Species lists used to determine the four potential leaf area category types in the City of Toronto. Srnall Trees Narrow Medium Trees Medium - Narrow Large Trees Large Trees Acer campestre Alnus glutinosa Aesculus x camea Acer plantanoides Acer griseum Betula jacquemontii Carpinus betulus Acer pseudoplantanus Acer negundo Betula papyrifera Catalpa bignonioides Accer rubrum Amelanchier laevis Betula pendula Corylus colurna Aescul us hi ppocastanum Cercis canadensis Cornus sp. Gleditsia triacanthos Fagus sp. Crataegus oxyacantha Magnolia x soulangeana Liquidambar styraciflua Juglans regia Kalopanax pictus Ostrya virginiana Prunus pennsylvanica Populus alba Labumum anagyroides Quercus robur Sassafras albidum Populus nigra Malus sp. Salix matsudana Sorbus aucuparia Pterocarya fiaxini folia Morus sp. Sorbus americana Ailanthus altissima Salix babylonica Prunus sp. Sorbus sp. Alnus incana Mus typhina Ulmus sp. Carya sp. Salix sp. Fraxinus excelsior Syringa sp. Fraxinus sp. Gymnocladus dioicus Liriodendron tulipifera Plantanus x acerifolia Populus balsami fera Quercus coccina Tilia sp. Large Narrow Small Medium Narrow Trees Large Trees Trees Medium Trees Trees

Figure Il: Dendrogram of cluster analysis of the 57 tree species used to determine the prototype tree classes.

3.7 Potential LeafArea Density: Land Use Types and Bufler Width Analyses

There were distinct differences in potential leaf area density between the ten land use types and between the four buffer widths (Figure 12, Appendix C for table and graphs that include standard errors).

Normal distributions and homoscedastic data are assumptions in using the parametric ANOVA. The potential leaf area density data were teaed for normality using the Kolmogorov-Smimov Nonnality test, and they were tested for homoscedasticity by Bartlett's and Levene's tests. The data were neither normal nor homoscedastic. Transformation (log or square root) did not significantly improve the non-normality or heteroscedasticity. The residuals for the data were plotted to identiS> any outiiers that may be skewing the normality and variance of the data. The land use type Residential Low Density was identified as being highly variable. This land use type was then eliminated corn the normality and hornoscedasticity tests to see its effect on these values. Removing the Residential Low Density land use type did not significantly improve the non-normal or heteroscedastistic distributions of the data, therefore the land use type is included in the rest of these analyses. Since the assumptions of the ANOVA were violated, non-parametric statistical analyses were used for this research.

A Friedman test for potential leaf area by land use type blocked by bufTers was used. This is a non-parametric statistical method that is used in lieu of a two-way ANOVA. There was very strong significant difference among the 10 land use types (S=34.70, de9, p=0.000). Freidman tests only indicate whether there are significant differences, they do not indicate where these differences are seen. To find the data which are significantly different, a poa- hoc Multiple Cornparisons test among the treatment means was used (Sokal and Rohlf, 1995). Potential leaf area density (per 1000 m2)

Figure 12: ERects of land use types O(-axis) and buffer sizes (2-axis) on potential leaf area density (Y-mis) in the City of Toronto (See Appendix C for table and graphs which include standard errors). The Multiple Comparisons test is based on U, the Mann-Whitney statistic. The formula for the critical value of Usis: U .ru, bl = (b2/2)+ Q a [,, 1 b [(2b+ 1)/24]l" Where: a = signi ficance level(0.05) a = number of treatments (10) b = number of blocks (4) Q = significance level for the studentized range for a groups and a degrees of freedom (3.633)

Therefore 16.9 is the critical value for Us.All Usvalues equal to or greater than this number are significant. Based on the observations of the data counts, the maximum Usvalue that the data can attain is 16. Therefore none of the cornparisons can be declared significant, despite the high overall significance of the Freidrnan analysis (Sokal and Rholf, 1995).

The data were then analyzed by painvise Mann-Whitney U tests, also known as Wilcoxon two-sample test (Sokal and Rholf, 1995). Forty-five Mann-Whitney U tests were perfomed between each of the land use types when the potential leaf area density was calculated with an average buffer (e.g. Res-Low and Res-Med, Res-Low and Res-High, Res-Low and Com- Low, etc.). There were no significant differences found between the land use types. The p value was recalculated using the Bonferroni correction due to the high number of statistical tests that were penormed on the same set of data. If no Bonferroni correction had been applied there would be a -901 (90.1%) chance of finding one or more significant diflerences by chance alone in the 45 tests. The alpha value for each test was lowered to 0.00114 to bring the overall alpha level back to 0.05.

Six Mann-Whitney U tests were performed among the four different buffer scenarios on each of the 10 land use types. There were no significant dif'i'erences found between the bufTers for each of the 10 land use types. The p value was recalculated by the Bonferroni correction. If it had not been applied, there would be a .265 (26.5%) chance of finding one or more significant differences by chance alone in the 6 tests. The alpha value for each test was lowered to 0.0085 to bring the overall alpha level back to 0.05.

Even though the potential leaf area density data in this study were considered to be strongly significantly different, there is no robust non-parametric statistic that can differentiate where the differences lie in relation to one another. This is because non-parametric statistics are based on ranking of the data, not on using actual values for the calculations. Since the ample size of the data was low (three averaged values per land use type), the ranking of the data which is done in non-parametric statistics rnasks the significantly different trends that are seen when looking at the raw values. 4. Discussion

4.1 Land Use Type Distribution

The City of Toronto is comparable to other North Arnerican cities with regards to the distribution of land use types. The residential lands in the City of Toronto encompass 51% of the total urban area. This is similar to the amount of residential land seen in an average of four cities in the northeastern United States (Syracuse, NY; Dayton, OH; Birmingham, AB; and in Cincinnati, OH) where residential land covers 50% of the total urban land area. (Rowntree, 1984b). Sirnilarly, the commercial land use types were similar at 12% in the City of Toronto, and 7% in the four city average; the industrial land was 12% in the City of Toronto and 8% in the four city average; parkland was 7% in the City of Toronto and 8% in the four city average; and finally, transponation land at 4% was equal between the City of Toronto and the four city average (Rowntree, 1984b).

4.2 Feature Composition of Sampie Plots

There was a wide variety of feature compositions based on the ten different land use types. There were high standard errors associated with the feature compositions of the sample plots since the three polygons that were used to detemine the feature composition for each land use type did not always share the same features (Appendix A). This resulted in the standard error for a feature being the same size as the mean feature in some cases. Recognizing that there is a high standard error among the features within these ten land use types, the rest of this section explains some of the variation seen between the land use types.

The Residential Low Density land use type had one of the highest amounts of lawn relative to the other land use types at 39% of the sample plot area (Figure 9a). Lawn is the marked feature of this land use type (Nelson, 1994). The high amount of lawn that is found in this land use type is a result of peoples desire to have a yard and garden associated with their house. The second largest portion of the sample plots in this land use type is buildings under three stories in height which composes 3 1% of the sample plot area. Bungalows or two story homes are the dominant featwe among residential low density housing (City of Toronto, 1996). The remaining third of the sample plot is composed of a variety of other hard surfaces such as driveways, ut ilities, walkways, parking, etc.

The Residential Medium Density and the Residential High Density land use types were quite similar in their feature ccmposition (p=0.78), especially in their soft surface feature composition (Figure 9b and 94. Lawn represented 28% and 27% of the area respectively. This similarity in percent lawn composition is due to the many small gardens that are found in Medium Density Residential housing, and in the few large lawns that surround High Density Housing. in one scenario, the lawn is fiagmented into many parcels of ownership, the other scenario is one large common land area that is used as open space for the residents. Although they are fiagmented differently, they involve the same amount of sof€ surface for tree growth.

The main difference between these two land use types is that Residential High Density housing has a much higher percentage of buildings greater than three stories in height (City of Toronto, 1996). These are the high rise apartment buildings that house the residents. The Residential Medium Density housing tends to be more of a mix of building heights ranging mostly between two stories and four stories.

The trend seen in residential areas of al1 densities is that the lawn and the buildings on these land use types occupy the majority of the land area (between 60% to 70%).

The three types of commercial land uses (low, medium, and high intensity) have similar feature composition in their land area except for their amouni of soft surface (Figures 9d, 9e. 90. Al1 three of these commercial land use types are mostly composed of buildings and parking (between 63% to 78%). The main difference between these three land use types is in the amount of soft surface that composes the land area. Law Intensity Commercial has an average of 8% lawn cover. Medium Intensity Commercial has 3% lawn cover, and High Intensity Commercial has only 1% lawn cover. The differences seen in lawn cover between these commercial land use types can be attributed to the type of customer that is visiting thes3 land use types. In the Low Intensity Commercial land use, the proprietor relies on people walking by the store and making a purchase. One way to attract people to an area is to provide green areas that can suppon tree growth (Miller, 1997). This is a more beautiful and cornfortable design than stnctly concrete walkways and sidewalks. In the Medium and High Intensity Commercial land uses, the proprietor relies cn customers driving to their stores and parking. This appears to put pressure on the land use to maximize parking at the expense of lawns.

High Intensity Commercial land is also similar to Residential High Density land use @=O. 79). This is due to the high proportion of buildings that are greater than three stories in height (39% and 35% respectiveiy). The High Intensity Commercial buildings are shopping malls around four stories in height. The Residential High Density buildings are multistory apartment buildings. One of the main differences between these land use types is the amount of lawn present which is only 1% in the commercial land use and it is 27% in the residential land use. As was mentioned earlier, the High Intensity Commercial land use type has incentive to increase parking space at the expense of lawn. The High Density Residential land use favours lawns as parks and open spaces for the tenants.

There are two main categories of industrial lands, light industrial and heavy industrial. Light industry is usually housed in small factories and warehouses that are often found in business parks which have landscape as a integral part of their design (Nelson, 1994). Heavy industry requires large buildings and storage yards. These heavy industry sites are often unfavourable for tree growth due to lack of available growing space, high pollution levels, and the movement of goods and transport (Nelson, 1994).

The sampled Industrial land use plots were located in heavy industrial lands. Light industrial lands would have been grouped into commercial lands when located in business parks. A high proportion of Industrial lands in the City of Toronto are composed of hard packed grave1 that is unavailable for tree planting (23%) (Figure log). These areas are ofien used as storage space. The second largest proportion of Industrial lands is composed of lawn at 19% This large portion of lawn is an untapped resource for urban foresters since most of the sofi surface land in Industrial areas are not treed or cared for. Pedestrians do not generally fiequent heavy Industrial areas, therefore owners may try to Save rnoney by avoiding landscaping and the maintenance cost of trees. These open areas also allow for easy expansion of the industry. The other main feature that composes Industrial areas are buildings which make up 35% of the land area.

Institutional lands have a very high proportion of their land as lawn (Figure 8h). The problem with planting this soft surface is that a high proportion of this lawn is found in soccer and football fields surrounding schools. These open spaces around schools are used for play and education (Nelson, 1994). The second largest feature in Institutional lands is courtyards. These paved areas are used as outdoor meeting places around hospitals and universities, as well as play areas for activities around elernentary schools. The courtyards provide an opportunity to plant greenery such as trees, shnibs and flowers in planters to increase the beauty and shade found in these concrete areas (See Section 5.3). Another alternative to increase the amount of vegetation in Institutional lands is to remove some of the courtyard area and convert it to a soft surface where trees and other vegetation could be planted (See Section 5.4).

Not surprisingly, Transportation lands are dominated by roads at 56% (Figure 89. This land use type is the most unique with respect to feature composition in that it has the lowest correlation coefficients with al1 of the residential and commercial land use types (pc0.20). The lawns surrounding these transportation corridors may not be planted easily since these lawns are often very close to the highways and railway lines.

Finally, Exhibition lands are the largest of al1 of the land use types with regards to the amount of land that is Iawn (45%) (Figure 8j). This is due to the amount of open lands around exhibition areas where people can walk. Parking lots are second in abundance in this land use type at 14%. The rides and games are found on the courtyards or in the buildings, which make up the 22% of the total land area when combined. This land use type is not commonly found in other cities and may be seen as an exceptional land use type located in the City of Toronto.

Exhibition lands are very similar to Residential Medium and High Density land uses as well as very similar with the High Intensity Commercial land use. This is due to the similarities in ranking of the different features within these land use types.

4.3 Impoct of Features ' Buffers on Available Sofi Surjàce

Each of the features found in the sample plots were studied with regards to their impact on available soft surfaces for planting, and not the amount of potential leaf area density. This was in part due to time constraints. The calculation of the impact of each features' buffer was a manual exercise where each plot was individual ly studied, and each buffer was added independently. If the system of prototype tree placement was automated to determine the area possible to plant trees, then potential leaf area density could have been calcuiated. Since this methodology is not autornated, 90 sample plots tintes 18 buffer scenarios would have had to have the four prototype tree sizes placed manually. Therefore for this section of the thesis, sot? surface is used as an estimate of the amount of potential leaf area density.

Caution must be taken when relating soft surface area to potential leaf area density. Directly relating the total amount of soft surface at a site to the amount of potential leaf area density could not be done. That is, X amount of soft surface at a plot does not equal Y amount of leaf area density. This is due to the composition and fragmentation of the soft surface. For example, a site that has 100m2 of soft surface can be composed of a single area 10m x 10m area where trees could definitely be planted, or it could be composed of a very long narrow strip of soft surface of 0.10m x lOOOm where trees could not be planted. Also, the 100m2of sofi sudace could have been composed of one hundred fragments of lm2 areas (high fragmentation). Therefore the composition and fragmentation of the sofi surface is also critical in detemining the amount of potential leaf area density available at a site.

The average decrease in soft sudace for each buffer size was an average of the three polygons that make up each land use type. In some land use types, only one out of the three polygons contained a particular feature. In these cases, the standard enors and the average percent decrease in sofl sudace are the same size. Although the standard errors are very large in some cases, this section examines the patterns seen in the land use types with respect to soft sufiace decrease.

The feature whose buffer caused the highest decrease in soft surface among the land use types (except for Institutional and Transponation lands) was buildings. Since buildings less than three stories and those greater than three stories have the same average buffer size (lSm), we can combine these two types of buildings. There are two factors that cause this relationship between building buffers and soft surface. First, buildings are large in size. It is logical that when a large feature is buffered, the buffer sunounding the feature will also be large and cover a large surface area. Second, buildings are oflen surrounded by lawn. If buildings were surrounded by hard surfaces, then buffering the building would have no effect on soft surface. However, since the buffer added to the building overlays lawn, this relationship will necessarily cause a large decrease in sofl surface.

The soft surface in Institutional lands was most impacted by the buffers on walkways. This is due to the high number of walkways found in this land use type. There are many walkways surrounding the universities, hospitals, and schools in this land use because of the high number of pedestrians found in these areas. The large decrease in soft surface is also due to the mal1 size of the walkway feature. A small feature of only a meter in diameter, will have a increased its diameter three times if a one meter buffer is added on either side. Therefore even a small feature, if prevalent enough, can cause a large decrease in sofl surface when buffered.

The decrease in soft surface in Transportation lands (24%) was due mostly to railway line buffers. Railway lines require a large buffer (4.Sm) since high speed trains would damage any trees that were planted closer to the rails. These trees could also interfere with the functioning of the railway thereby being consideted a hazard. In addition, there were often more than one railway line side by side which resulted in a large decrease in the soft surface surrounding each of the lines. Buffered below ground utilities had a relatively small impact on soft surface (a soft surface loss no greater than 9.5%). Most of the below ground utilities were below roads or sidewalks such that adding a buffer to the below ground utility was still masked by the larger hard surface above the utility (e.g. a road). Some of the below ground utilities such as Toronto Water and Subways are too deep in the ground to effect trees (T. Horvath, pers. corn., August 1998). Trees experience problems when these utilities are accessed by means of trenching. Other below ground utilities that are included in this study were abandoned and a conflict between trees and these utilities would not matter since access would not be required for abandoned lines.

Above ground utilities (fire hydrants, streetlights, stopsigns, hydro poles, stoplights) had a very small impact on the soft surface within the ten land use types (a soft surface loss of no greater than 4.3%). This is due to the location of these above ground utilities and the soft surface at a site. The above ground utilities were located far enough away frorn the lawns to not have an impact. This small impact of above ground utilities on sofi surface is in part due to the location of the sample plots. In general, few sample plots contained above ground utilities. Therefore, the data is an underestimation of the impact of above ground utilities on sofi surface.

One alternative to fûnher minimize the impact of above and below ground utilities on soft surfaces is to locate al1 utilities on one side of a street (Koch, 1999). This would allow one side of the street to be accessible to al1 the utility companies, but the trees will be conflict fiee on the other side of the street.

There is an ongoing debate as to whether it is better to have utilities placed above or below ground with respect to available growing space and urban trees. The tradeoff appears to be either tree rwts are being compromised by trenching access to below ground utilities, or the compromise is in the tree canopy which is continually being topped to avoid interferhg wiih the above ground utilities. If an existing above ground utility system is in place and operating, it is unlikely that it would be economically viable to place those utilities below ground (Goodfellow, 1990). Below ground installation is feasible in newly constructed areas. Cost wise, it is much cheaper tu have above ground utilities. The wire of the above ground cable costs $0.15 per foot vs $2.20 per foot for below ground insulated wire (Goodfellow, 1990). It coa S132,800h to install overhead 15kV local distribution lines, vs the S348,000/kmto install the same service below ground (Goodfellow, 1990). There is no clear answer as to the best set up for utilities. Ideally, utilities shouid be placed in a way to have a cost effective, reliable semice that minimites conflicts with trees.

The two features whose buffers did not impact soft surface in any significant way were Street signs and stoplights. Street signs are positioned close to the road for parking and driving information. They were not located in close proximity to a soft sufiace to impact the availability of this soft surface. The signs tended to be found on sidewalks. The stoplight feature did not impact the sofi surface in the sample plots since the buffer is only impacting areas advancing towards the feature. That is, a 15 m buffer on a stoplight affects the on- coming traffic side of the stoplight. This buffer does not impact the sofi surface behind or to the side of the feature. In the sampled plots, the stoplights did not have any sofi surface in advance of these features which explains the zero loss in sofl surface due to this features buffer.

Stopsigns were very similar to stoplights however they did result in a 0.1% decrease in sofi surface in the residential high density land use type.

4.4 Land Use Types and Potential LeafAreca Density

The amoum of soft surface at a site was critical in detemining the potemial leaf area density. The number and size of prototype trees that fit into the sofl surface of the sample determined potential leaf area density. An average buffer on each feature was used to estimate the potential leaf area density in the sample plots.

There were significant differences in potential leaf area density between the ten land use types, but the location of those differences could not be identified therefore patterns were identified in potential leaf area density. There are three main categories of potential leaf area density that can be determined. These are high, medium, and Iow potential leaf area density.

The land use types that have high potential leaf area densities are Residential Low Density and Exhibition Lands. The potential leafarea density ranges from 1629 m2/1000 m2 to 2083 m2/1000 m2. Residential Low Density is not surprisingly one of the highest potential leaf area density land use types. Low density residential areas provide one of the most favourable tree planting locations in an urban matrix (aside from parklands and natural areas) due to the high amount of SOA sudace found sunounding the buildings as lawns (Nelson, 1994).

It is particularly important to nute that the land use type with the highest potential leaf area (Residential Low Density) is also the rnost cornmon in the City of Toronto with 46% of the area. This suggests that the city rnay have considerable opponunity to maintain or expand leaf area density. Rowntree (1984b) states that if residential lands occupy about 50% of the city's area, then zction by these groups (especially single family residence) are important in determining a city's canopy stmcture.

Exhibition lands also have a very high potential leaf area density of 2083 m2/1000m2due to the large amount of sofl surface areas (44%). However, this land use type only encompasses 1.4% of the land area of the City of Toronto and may be even less in other communities. Planting trees in this area may realize the amount of leaf area density that could be supponed there but due to the small size of the land use type, the impact on the overall leaf area density for the city would be small.

The land use types that have medium potential leaf area density are Residential Medium Density, Residential High Density, Industrial, Institution, and Transportation. These potential leaf area densities range from 648 m2/i000 m2 to 953 m2/1000 m2. As was mentioned in Section 4.2, Residential Medium and Residential High Density land uses have a very similar soft surface composition in spite of differences in property ownership. Residential Medium Density houses are often cosperative housing provided by the govemment with small property lots. Residential High Density housing is apartment style accommodations with a sofi surface area surrounding the building. These two land use types have similar potential leaf area densities even though their layout is quite different.

A problem rnay arise in reaching the potential leaf area density of the Residential Medium Density land use type relative to the Residential High Density land use type. The high Fragmentation of the Residential Medium Density land use type lawn may result in dificulty of planting trees. There will be more property lines that will decrease the areas where trees can grow. Many neighbours in close proximity may have different perspectives of the urban forest. These different perspectives result in potential conflict over the presence of trees on their property. One resident may want a large tree in their backyard, but their neighbour does not want any tree overhanging their property. To contrat, the soft surface land surrounding Residential High Density housing does not belong to any one individual in the building, and is therefore cared for by the property manager as a common resource.

Industrial lands have an average potential leaf area density of 648m2/1000m2and are in the medium category. Rowntree (1984b) stated that growing space in industrial areas could be as high as 40%. Tree stocking in this land use is low, therefore there is a high potential in Industrial lands to convert to tree covered lands if desired (Rowntree 1984b). Industrial lands also have a high proponion of hard surfaces such as buildings. These areas would benefit frorn alternative areas to plant trees such as rooftop gardens (see Section 5.1).

Institutional land only has medium potential leaf area density (722m2/1000m2)due to three main factors. First, the size of the building footprint is quite large and eliminates planting space for trees. Second, the open land found on institutional properties often cannot be planted with trees since they are used as soccer/football fields, and as active recreation areas. Third, areas surrounding the institutions have a high proportion of paved areas such as courtyards for use as a meeting place or as a play area.

Transportation lands have a potential to support a medium leaf area density. This is found around highways, but even more sa around railway lines. Railway lines provide an area where trees could be planted to provide a visual and noise buffer to the surrounding land uses.

The third category is low potential leaf area density. The land use types that have low potential leaf area density are the three commercial land uses: low, medium and high intensity. The potential leaf area density ranges from 1 1 m2/1000 m' to 26 m2/1000m2 in these land use types. These areas are unsurprisingly low in potential leaf area density due to the very small amount of soft surface where a tree could be planted. However, these areas are not treeless. Alternative planting locations are often used in these areas of very low sofl surface to accommodate trees (Chapter 5).

There is a decrease in the potential leaf area density from Low Intensity Commercial to High Intensity Commercial. This decrease can be attributed to the type of customer that is visiting this land use type. Low Intensity Commercial lands tend to rely on attracting customers as they walk by the store. It is to the owners benefit if they provide a visuaily appealing place to walk by as a pedestrian. Trees provide aesthetic beauty as weil as shade which attracts shoppers (Miller, 1997). The Medium and High Intensity Commercial areas do not rely as heavily on pedestrian shoppers. Rather, most shopping mal1 customers drive to the maIl which increases the need for parking areas, and hence minimizes the need for green spaces.

Many large indoor shopping malls mimic an outdoor streetscape by including store fronts and in some cases Street signs and trees (e.g. Ficzcs benjarnina) growing in pots. In essence, the architects have moved the favourable outdoor attributes to indoors. In these cases, the trees do not contribute to the environment. It would be beneficial to place these recreated "natural" environments outside of the shopping malls. 4.5 Buffers and Potential Leaf Area Densiîy

The location of tree placement in cities will strongly affect the distribution of the urban forest. The proximity of trees to vanous features will determine the number of trees that can be planted and the sire that these trees cm attain. This distance between a tree and a feature is the buffer width. Three different buffer sizes (minimum, average, and maximum buffer widths) were looked at and compared to the leaf area density when no buffers were used.

There is a strong decrease in potential leaf area density between the control (no buffers) and the maximum buffers because of the loss of available growing space associated with the latter. The potential leaf area density decreased from the controi scenario to maximum buffers in al1 of the land use types. However, the rate of decline in potential leaf area density varied. The land use type that had the strongest effect due to buffers was Residential Low Density (Figure 13). The potential leaf area density for the other nine land use types al1 declined at a similar rate. The Residential Low Density land use type was most affected by the buffers since it was the land use type which contained the highest variety of features to be buffered, and the highest frequency of these features. Residential Low Density lands have buildings, parking, driveways, decks, tire hydrants, below ground utilities, above ground utilities, sidewalks, etc.; most of which appear more than once in a sample plot. Other land use types have fewer features to be buffered (e.g. Transportation lands do not have decks), and a lower frequency of each feature-type (i.e. only one building, one walkway, etc.).

The potential leaf area that was determined when buffers were not added (the control scenario) resulted in an overestimation of potential leaf area density. This occurs because it is assumed that the health of the tree will not be affected by the close location of the tree stem to the feature. In reafity, placing a tree directly beside a feature such as a sidewalk without providing the tree with even a small buffer will negatively affect the health and vigour of that tree. Vrecenak et al. (1989) found that the extent of house setback (analogous to buffer width) was significantly correlated with the growth ratio index (dbh/age) which is used as an indicator of tree health. This indicates that an increased buffer width is beneficial to tree health. The leaf area densities of the prototype trees were not decreased to accommodate the decline in health of the tree when placed in undesirable settings. 4 Res Low +Res Md +Res High *Corn Low +Corn Med +Corn High

.- .- Indus. -Instit. -Trans . 4 Ex hib.

Control Minimum Average Maximum Buffers

Figure 13: Negative impact of buffers on potential leaf area density among the 10 different land use types in the City of Toronto. Note the strong negative impact of the various buffers on the Residential Low Density land use type.

Municipalities should take caution in designating buffer sizes for different features. One feature whose buffer has a large effect on tree planting is buildings. Consider the example in Figure 14. The north facing side of a building cannot support tree growth very well due to the lack of sunlight (figure on left). One option is to plant shade tolerant trees in this location. Another alternative is to alter the placement of the building during the constmction stage. If a building is placed against the northem edge of their lot, then a larger bufier cm be applied on the remaining three sides of the building which will better accommodate tree growth (figure on right). It is important to note that buildings placed at a closer setback to the road or to other lots should not enable more buildings to be crowded into smaller places. Rather, buffer widths should be considered to be a rule of thumb, with the recognition that each planting space is unique and should be treated as such. \ Lot footprint f Figure 14: Example of building placements that impact the arnount of overall planting space for trees.

4.6 Urban-Biotopes

Urban-biotopes are areas within a city that have similar potential leaf area densities. Many different factors wili affect potential leaf area density including land use types, utility placement, buffer sizes, building densities, cultural perspectives of the urban forest, age of the community, soils, microclimate, etc. One of the objectives of this research was to create and provide a first approximation of urban-biotopes, which can be used as management units by urban foresters and plannen.

Urban-biotopes satisfy the favourable characteristics in urban forest classification systems that are outlined in Section 1.6. Urban-biotopes are created for use in built environments like those found in urban areas. The urban-biotope classification system is a smaller scale classification than the ELC ecoelement that enables higher degrees of detail to be included in the classification. Urban-biotopes are easy to use and they have standard tenninology based on potential leaf area density. Finally, the potential leaf area density that the urban-biotopes are based on allows this system to be used in cleared areas where vegetation is not present.

The factors that were studied in this research to have affected potential leaf ara density were buffer size, utility placement, and land use type. It is assumed the buffer size scenario will remain constant within a particular city. This does not mean that each feature will have the same buffer width (e.g. building with 2m buffer, walkway with 2rn buffer, etc.). Rather, a city is assumed to have either an aggressive tree protection and planting policy, or a lenient one. The city with an aggressive policy will protect ail of their trees very well by having buffer sizes that are close to the maximum buffers scenario used here. This will enable trees to grow to their maximum size, and hence, achieve maximum leaf area. The city with a lenient policy will have small bufTer sires for each feature that will reflect a minimum-buffer scenario.

It has been shown that above ground utilities did not play a major role in decreasing the amount of soi? surface within the sample plots. The impact of above ground utilities on potential leaf area density could be reduced even funher if urban foresters looked at the entire urban forest as planting opportunities and not just public land. The rnajority of urban forestry departments are concerned stnctly with public land that only comprises 10% of the urban forest (Kenney et al., 1996). Public lands include parks, street rights-of-way, highway and railroad rights-of-way, public buildings and grounds, and riparian areas (Grey and Deneke, 1992). Pnvate land makes up to 90% of the urban forest and it includes al1 of the residential, commercial, and industrial lands (Grey and Deneke, 1992). If the responsibility of urban foresters included planting on private propeny, the conflict with trees and above ground utilities would be minimized since trees would no longer be competing for space in the strip of public land surrounding private property.

Aside fiom the Residential Low Density land use, the addition of buffers to below ground utilities caused a maximum 5.5% decrease in soft surface. This is a small impact on soft surface. In Residential Low Density lands where below ground utility buffers caused a 9.5% decrease in soft surface, it is recommended that the buffer impact on soA surface could be minimized if below ground utilities were placed under other hard surfaces (e.g. roads, sidewalks). Another alternative is to remove the utilities fiom below ground and place them above ground. This leaves the principal factor determining potential leaf area density to be land use type in this first approximation of urban-biotopes. There are three main categories of potential leaf area density that have already been mentioned: low, medium, and high. The City of Toronto cm be divided into these three urban-biotopes. This is represented in Figure 15 as the three different classes of potential leaf area density. Within each urban-biotope, the land use type is still used as an important subdivision of the urban-biotope. Using the land use type as a further refinement of the urban-biotopes allows for easier management of the urban forest since this will subdivide the size of the urban-biotope into smaller, more manageable units.

The classification of the urban forest into urban-biotopes will allow for easier management of the urban forest since management plans can be catered to each urban-biotope. Each urban- biotope cm have specific planting and maintenance schedules. Urban foresters could then identiQ patterns in the urban forest and attempt to manage the benefits that the urban forest provides. Urban-biotopes with low potential leaf area densities would be identified and attempts to increase leaf area in these areas would be low. Rather, urban-biotopes with high potential leaf area densities would be identified with detailed management plans to maximize the leaf area of the urban forest, and hence, maximize the benefits obtained from the urban forest. High 1 Res. Low Exhibition Res. Med. Res. High Med. Industrial Institutional Trans. Low. Corn. Low / Med. Corn. High Corn.

Figure 15: Urban-biotopes found in the City of Toronto: Low (red), Medium (blue) and High (green) potential leaf area density. Res. represents Residential lands, Corn. represents Commercial lands, and Trans. represents Transportation lands. This research has been a first approximation of estirnating potential leaf area density, and creating urban-biotopes. In the future, there are many directions this work can go to further improve and refine the methodology of examining potential leaf area density in cities.

The lack of automation of prototype tree placement resulted in too small a sample size for normal and homoscedastic results. It would be beneficial to have automated and mathematical placement of the prototype trees within the sample plots. This was not done since the programming involved was beyond the scope of this study. In the future, work on these areas of automation would greatly benefit the mode1 by decreasing the time involved in determining the amount of potential leaf area density. This would also allow potential leaf area density to be calculated at al1 times rather than just available growing space, since prototype trees would be automatically placed. Automation of the prototype tree placement would also permit a wider range of prototype tree classes to be used that would give a closet estimate to potential leaf area values. Automation would also enable more sample plots and buffer size scenarios to be calculated thereby better estimating potential leaf area density.

Another area where this research could be expanded is the relationship between rooting volume and potential leaf area. When prototype trees overhang certain features such as sidewalks, the dnpline area of the tree is impacted. This impact results in less than ideal situations for maximum leaf area growth since the amount of available growing space for the rooting system is compromised. Further research into the impact of constricting the dripline area of trees and their resulting decrease in leaf area should be considered

The research into rooting volume can be extended into the relationship between the depth of the rooting environment and potential leaf area. A shallow rooting environment will add stress to the tree resulting in a decrease in the amount of leaf area compared to that of a similar tree grown in an open field environment. Nowak's leaf area equation used in this study is based on city, open-grown trees therefore the leaf area values derived fiom his equation should accommodate this leaf area decrease. Refinernent of the potential leaf area detemination should include canopies that overhang features that were not considered in this study. For example, very ta11 trees may overtop buildings that are just greater than three stories in height. These overhanging trees are not included in this study since it is assumed that these trees are rare in the urban forest.

Shrubs and other non-tree vegetation should be included in the determination of potential leaf area. Shbsand ground vegetation also provide many benefits and they are not included in David Nowak's leaf area equation.

The potential leaf area determination did not consider multi-layered canopies. It was assumed in this mode1 that each tree's canopy did not overlap those of other trees. This minimized or eliminated the competition between trees at maturity enabling them to grow to their full potential. However, tree canopies can overlap to some degree and by not including this feature leaf area density will be underestimated since fewer trees will be added to each sample plot. Enabling a degree of overlapping during prototype tree placement could hrther refine the methodology.

This work has been dealing stnctly with the potential of an urban-biotope to have leaf area. The next step in this research would be to calculate the existing leaf area in these biotopes and compare them to the potential leaf area density. This will illustrate where urban foresters should be concentrating their efforts. The ideal scenario would be the existing leaf area density equaling the potential leaf area density. The potential and the existing leaf area density will very rarely be equal since this would require ail trees at that particular site to be mature and in perfect health to attain their maximum leaf area. Urban foresten should be focusing on the areas where the existing leaf area density is very different from the potential since this indicates where planting efforts and tree maintenance should be focused.

The urban-biotopes can be further refined to include other features such as building density, cultural perspectives of the urban forest, age of the community, soiis, microclimate, etc. Each of these factors will further refine and subdivide the urban-biotopes that were created in this research. The uhan forest of the City of Toronto was studied with regards to potential leaf area density. This city was chosen to aa as a mode! from which the land use types and characteristics of the city would reflect other cities in Canada. In the future, modeling other cities could hrther refine the rnethodology created in this study. 5. Alternative planting locations

This research has provided information on tree pianting locations in cities based on available so8 surface planting areas. A lack of available soft surface planting spots is often seen in urban areas. This chapter provides alternatives to planting trees on soft surfaces when these locations are not available.

5.1 Rooftop and Vertical Gardens

Rooftop and vertical gardens are alternatives to tradit ional Street tree plantings. They take advantage of othenvise unused planting spaces in urban areas.

A roofiop garden consists of vegetation piaced on the roofs of buildings. This requires the addition of a growing substrate for the vegetation to be added to the rooftop. There are two different types of roofiop gardens. The first type of rooftop garden is "extensive" where only 5 cm - 15 cm of substrate is used, and the weight of the substrate and plants does not exceed 169 kg per square meter (Peck et al., 1999). This type of rooftop garden cm be installed without major structural changes to the existing building due to the low weight of the garden. However, due to the small amount of substrate, this type of rooflop garden can only support light vegetation, not trees. The other type of rooftop garden is "intensive" and has between 20 cm - 60 cm of substrate, and between 290 kg - 967 kg per square meter (Peck et al., 1999). This type of rooftop garden requires additional stmctural strength of the building, however due to the increased arnount of substrate, trees can be carefully planted on these rooftop gardens.

Vertical gardens are plants and/or trees growing on or against the façade of a building (Peck et al., 1999). This includes vines, climbing plants, as well as trees. The trees are located in balconies that enable the tree to grow alongside the exterior of the building, shading the building and windows.

One example of successfûl rooftop and vertical gardens is the Hundertwasserhaus in Vienna, Austria (Figure 16). This 51 dwelling apartment building was built in 1983-1985 by the artist Friedensreich Hundertwasser (Koiler, 1994). More than 530 trees and shbs grow on the roof, terraces. balconies. sills and inner courtyards of this building (Koller, 1994). There is more than 900 tons of earth on top of the roof surfaces to support this vegetation (Koller. 1994). There are a few tenants who share their apartments widi trees whereby the trees are anchored in deep troughs inside the windows and they grow outwards dong the façade of the building (Koller. 1994).

Figure 16: Exarnples of Hundertwasserhaus in Vienna. Austria (Koller. 1 994).

Examples of green roofs in Canada include the new Vancouver Public Library in Vancouver. BC: the Environmental Sciences Building at Trent University in Peterborough. ON: the Legislative Building in the Northwest Territories: the Mary Lambert-Swale Housing Project in Toronto. ON: Roundhouse Park in Toronto. ON: and the Residence and Day Centre for the YMCA's Environmental Leaming Centre in KitchenedWaterloo, ON (Kuhn, 1996).

Caution must be used to ensure that our cities do not have their forests removed tkorn the ground level and elevated to rooftops and vertical gardens. We do not want our urban areas to be devoid of greenspace at the ground level especially in the urban canyon where it is already difficult to plant trees. These gardens cm increase the benefits obtained fiom the urban forest. However, it is still critical that trees are fint planted in the available soft surface planting areas within cities, and then rooftop gardens and vertical gardens seen as an addition to the traditional urban forest. If there are no natural planting environments for trees on lawns and other soft surfaces. alternative places must be looked at. .Approximately 20% of al1 of the trees planted by the City of Toronto are planted in these alternative locations (E. Horvath. pers.com.. May 1999). Close to 1800 residential trees are planted by the city each year on lawns. An additional 400 trees are planted each year by the city on other land use types such as commercial land uses. These remaining trees are mostly not planted on lawns. but rather in raised tree planters and tree pits (E. Horvath. pers. corn.. May 1999).

The first option a City of Toronto urban forester attempts when there is inadequate soti surface for tree planting. is a raised tree planter. Raised tree planters are 1.8m by 12m in length with a height variable up to 400mm (City of Toronto. 1995). This allows approximately two trees to be planted in each raised container. An example of raised tree planters is seen dong St. George Street in the University of Toronto (Figure 17). Benctits to this system include a long (1Zm) area where tree roots cm extend. As well. thrsr trees are not watered by maintenance crews since there is sufficient permeable surface to capture water.

Figure 17: Example of raised tree planter design used by the City of Toronto on St. George Street, University of Toronto. If planting on lawns and planting in raised tree planters are not possible, a last resort for urban foresters is to plant trees in tree pits. The common tree pit size used by the City of Toronto is 1.2m by 2.4m (City of Toronto. 1995). These tree pits are found in areas that are inhospitable for trees such as along Spadina Avenue (Figure 18). Trees are planted in a pit in a sidewalk with a precast concrete tree pit cover 5Omm above the soil to protect soil compaction. The small pit size forces the tightly packed roots to encircle and strangle the stem of the tree (Mol1 and Urban. 1989).

Figure 18: Example of tree pit design used by the City of Toronto on Spadina Avenue.

These trees have a hose protruding along the tree stem for watering since there is no permeable surface to collect rainwater. Due to budget cutbacks at the City of Toronto. it is dificult for maintenance crews to visit these trees even once a year for watering (E. Horvath. pers. corn.. August 1998). These trees are also susceptible to vandalism due to the close proximity of the trees to pedestrians. It is estimated that one of the main causes of urban tree mortality is due to vandalism (Bradshaw et al., 1995).

Another option for tree pit covers that are not cornmonly used in the City of Toronto but are used elsewhere, are cast iron grates (Figure 19). Cast iron grates can be placed around the ground surrounding the tree that will minimize soi1 compaction and maximize soil aeration. Soi1 condition will be improved since people will not be walking directly on top of the roots. The grates also facilitate watenng and fertilization of the tree. However. the grates also pose a problem in urban areas by collecting Iitter that is difficult to remove.

Figure 19: Example of iron tree grates used on the ground for soi1 protection in the United Kingdom (Bradshaw et rd., 1995).

In the past. when there was no lawn area to plant trees, tree planters were used. These tree planters were four feet by four feet, and two feet high. They have an open bottom to allow roots to extend below the planter. These planters are seen along University Avenue in Toronto. south of College Street (Figure 20). The planters are no longer installed in the City of Toronto for two main reasons. First, there was a problem with freezing and hawing action which would cause the planters to crack. Second, the small size of the planter box stunted tree growth (E. Horvath, pers. corn., August 1998). Figure 20: Example of tree planters used on University Avenue by the City of Toronto.

The most drastic (and improbable) way to increase tree plantings in cities when lawns are unavailable. is to remove the hard surfaces that are impeding tree planting. This would involve the removal of parking lots, buildings. courtyards. walkways. sidewalks. etc. that limit tree planting. These areas are not immediately ready for tree planting once the hard surfaces are removed. The soil at the site must be prepared for tree planting by having ail rubble removed, and a more organic soil irnported.

The removal of hard surfaces is a very expensive approach to increasing the arnount of plantable surfaces. It is improbable that this approach would be taken at a wide scale throughout a city. This approach is much more feasible on a small scale such as within a particular land use type or on a landowners private property. Every small portion of land that is converted to a possible tree planting location will contribute to increasing the potential leaf'area density of the urban forest.

One land use type that is having hard surfaces converted into sofi surfaces is Institutional land. The Evergreen Foundation is a non-profit organization that is "dedicated to connecting people with nature through the enhancernent of healthy natural environments in schools and cornmunities" by increasing the amounts of vegetation in these areas (Evergreen, 1999). They are responsible for rernoving hard surfaces in schoolyards and converting them to outdoor gardens and environmental learning centres.

Other land use types that could increase the amount of urban trees through hard surface removal are the Low, Medium and High Commercial lands and the Industnal lands. The Commercial land use types can convert some of the parking areas into SUA surfaces for tree planting. Industrial lands have 23% hard packed grave1 as a surface cover which could also be converted into sofi surfaces.

Another alternative to increase sofi surface is to not use pavement for a feature that is traditionally seen as a hard surface. In the city of Surrey, BC, pavement was not used in an area where a sidewalk was being eaended (Dunster, 1999). Mulch was used as the sidewalk substrate to protect the stand of very large trees that would have been impacted. The mulch protected the tree roots since mulch can be added directly to the lawn surface. Sidewalks require digging to set the face of the sidewalk flush with the lawn surface. This digging destroys the feeder roots of the trees dongside.

This research has illustrated the areas where trees can be planted in cities to maximize the amount of leaf area density. The ideal condition for tree planting is a soft surface location such as a lawn that has a minimum amount of conflicts with utilities and with other features such as buildings. Al1 of the trees that are planted within cities do not fail into this category. This chapter has introduced other areas that are being planted include sidewalks, roofiops, trees in planten located anywhere, etc. These alternative places to plant trees are only second in favour to planting in a soft sufiace location. However, these alternative planting locations provide a way to increase the number of trees in areas of our cities where there would otherwise be none. 6. Summary and Conclusions

The overall goal of this study was to investigate the relationship between urban design and the potential leaf area of the urban forest.

The first objective was to determine potential leaf area density in the ten land use types in the City of Toronto. There were significant differences in potential leaf area density between the land uses. Land use types that had similar potential leaf area densities were Residential Low Density and Exhibition Lands (1629-2083 m2/1000m2);Residential Medium and High Density, Industrial, Institution, and Transponation (648-953 m2/1000m2); and Low, Medium, and High Intensity Commercial ( 1 1-26 m2/1000m2).

The second objective was to detemine potential leaf area density under different buffer sizes in urban areas. There were significant differences between the buffer sizes in each of the ten land use types. As the buffer widths increased from none through to maximum buffer widths, the potential leaf area density of the urban forest decreased. The land use type that was most impacted by the increase in buffer size was Residential Low Density lands. This was due to the high nurnber and fiequency of features that are found, and hence buffered, in this land use type.

The third objective was to identify and compare the hard surface features such as buildings, parking lots, walkways, etc., that limit the amount of soft surface in cities in each of the ten land use types. There were a high variety of feature compositions between the ten land use types. Land use types that had the highest amount of soft surface were Residential Low Density, Institutional, and Exhibition Lands (3844% of area). Land use types that had the lowest amount of soft surface were Low, Medium, and High Intensity Commercial lands (1- 8% of area).

The fourth objective was to quanti& the impact of each feature's buffer on the sofi surface in each of ten land use types. The feature whose buffer had the largest impact on soA surface throughout the entire city was buildings (eight out of ten land uses ranked it first in impact on soft surface). The buffers on above ground utilities and below ground utilities did not have a large impact on sofi surface decline (less than 10% decrease in sofi surface).

The fifth objective was to develop an urban forest ecosystem classification methodology based on potential leaf area density and the factors that influence potential leaf area density. This was a first approximation of an urban forest ecosystem classification and it was based on land use types. There were three urban-biotope categories for the City of Toronto: High ( 1629-2083 m2/1000m2), Medium (648-95 3 m2/1 000rn2), and Low ( 1 1-26 m2/1 000rn2), based on potential leaf area density. These urban-biotopes are further subdivided by land use type for assisting urban foresters in further subdividing the urban forest. It should be noted that funher refinement of these urban-biotopes should include other factors that impact potential leaf area density such as building densities, cultural perspectives of the urban forest, age of the community, soils, microclimate, etc.

The urban forest provides many benefits to surrounding areas and the majority of these benefits are related to the extent of leaf area. Understanding the relationship between leaf area and urban design will enable urban foresters to plan and manage their urban forest appropriately to maximize these benefits. An urban forest ecosystem classification is needed to provide a working plan for urban foresters. 7. Literature Cited

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Average Feature Composition - Summary Data

Table 1: Percent feature composition of each land use type with associated standard erro rs.

Figure 1: Percent feature composition of each land use type with associated standard errors. c. E ag u cm2 a d e PPZ te,: PPcf,. ,Il 0"~JZJIBP~II~~ZZ~H~~Z~~~ .J'ptol&. Percent composition Percent Composition Percent composition Percent composition Percent composition Percent composition Percent composition Percent composition Percent composition Percent composition 9. Appendix B

Decrease in Soft Surface due to Features Buffers - Surnmary Data

Table 1: Percent loss of soft surface due to features' buffers with associated standard errors. (II 10. Appendix C

Potential Leaf Area Density -Summary Data

Table 1 : Potential leaf area density in each land use type with associated standard errors.

Figure 1 : Potential leaf area density in each land use type wit h associated standard errors with (a) no buffers, @) minimum buffers, (c) average buffers, and (d) maximum buffers. Table 1 : Potential leaf area density in each land use type with associated standard errors.

Control - No Buffers Minimum Buffers Averaqe Buffers Maximum Buffers Land Use Type PLAD SE PLAD SE PLAD SE PLAD SE Res. Low Density 3094.46 502.28 2543.61 551.83 1629.37 660.48 1173.77 605.41 Res. Med. Density Res. High Density Corn. Low Intensity Corn. Med. lntensity Corn. High lntensity Industrial Institutional Transporation Ex hibition (a) Control - no buffer

Potential leaf area density in each land use type. Each bar represents a mean of three sampled polygons with standard error bars. (a) Control - No Bufiers, (b) Minimum Buffers, (c) Average Buffers, and (d) Maximum Buffers. Potential leafarea density (m2/10aOm2) Potential leaf area density (m2/1000m2) Po tential leaf area density (m211000rn2)