Information for Urban Forest Planning and Management - Springer Link

Chapter 14

Information for Urban Forest Planning and Management

Part IV

Jasper Schipperijn · Werner Pillmann · Liisa Tyrväinen · Kirsi Mäkinen · Rory O’Sullivan

14.1

Introduction Different traditions of management, planning and design of urban forests and other green spaces each have their own specific information needs and knowledge cultures (see Chap. 13). Management strategies provide a framework for management decisions, based on available information, which means that reliable, comparable and up-to-date information is crucial for decision making. The need for reliable information on various aspects of urban forest resources and their use has led to the development of different methods, tools and systems to help collect, compile and use available information. Information in urban forestry is needed to develop management concepts (see Chap. 13), make policy decisions (see Chap. 5), to determine the benefits of urban green space (see Chap. 4), to determine how green space should look (see Chap. 6), to decide which trees to plant where and how (see Chap. 9–12), and for many other reasons. However, depending on its purpose information is needed on different scales and in different levels of detail. Local, more detailed information about, for example: tree and plant species, the number of users, and management costs, is primarily useful for green-space and tree management. An overview of all green space in a city is more useful for city development plans and city green-space policies. Information on national or even international level can be used in urban development strategies, health strategies, etc. Besides the difference in scale, information is quite often available and used for certain topics only. For example, information on the biodiversity of a city’s green spaces can be available and used in great detail, while information on environmental benefits such as reduction of air-pollution is virtually non-existent in the same city. This chapter will start with defining what information is required for urban forestry planning and management. Three main types of information are used in this chapter, being essential basic green-space information, environmental and ecological information, and socio-cultural information. It then continues with a description of suitable methods to collect the different types of information. The chapter concludes with examples of the application of this information in urban forest management and planning.

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14.2

Information in Urban Forestry

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14.2.1 Data and Information Data is in this chapter seen as the most basic form of information. Data is understood as the direct result of measurements, recordings or calculations. Data becomes information as soon as is it put in a wider context and a first analysis of the meaning of the results can be preformed. For example, recording the dbh of a tree is data until the number is entered into the inventory system together with other tree characteristics and made accessible and comparable with data on other trees. Producing information that can easily be used in planning, management and decision-making for urban forestry is very important, as it is this information that will primarily be used in the decision making process. The logical procedure to reach a significant aggregation of data, producing usable information for decision-makers, can be described as (partly based on Tyrväinen et al. 2002): Collecting data by means of recording, measuring, calculating, or extracting data from existing sources Processing and compile of available data Assessing missing data Modeling or estimating missing data Extrapolating or aggregating data to be useable on different (geographical) scales Analyzing data and producing information Making information available and accessible Using information to develop scenarios to predict the implications of urban development 14.2.2 High Quality Information It is particularly important to recognise that urban forest information needs to be prepared in a way so that it can effectively support urban planning, public involvement, and decision-making processes that have a strong influence on urban green spaces. High quality information is assumed to be objective, reliable, representative and comparable. Seen from an urban forest planning and management perspective, has several characteristics. First of all, it should be available and easily accessible, as mentioned above. Furthermore, it should be easy to use and easy to understand and it should not be too expensive to collect, use or update, both in terms of time and resources. 14.2.3 Information Types The wide range of information needed for urban forest management can be divided in different information types in various ways. In this chapter the following main types of information are distinguished:

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Basic green-space information includes, as the name suggests, the most elementary information to be used in urban forest management, such as the location of the green area, green element or street tree, and characteristics such as vegetation type or (tree) species, age, size, height, and so forth. Basic resource information also includes information for green-space maintenance, i.e. information on which management or maintenance activities are undertaken or planned for a certain green space. Environmental and ecological information encompasses all information on environmental conditions that influence green spaces, as well as on the environmental benefits of green space (see also Chap. 4). It includes information on abiotic factors such as climate, water and hydrology, geology, air, soil and water pollution, noise, and solar radiation. This category also includes information on vegetation, plant and animal species. Socio-cultural information includes socio-economic, psychological, aesthetic and cultural information. It deals with people’s views, attitudes and preferences. User preferences, cultural values, aesthetics, information on health and other social services, as well as the economic benefits (see also Chap. 4) of green space are covered by this type of information. 14.2.4 Selection and Evaluation of Information The decision-maker needs to be aware of what kind of information should be taken into account and how it can be valued within the specific decision-making context. Precise and comprehensive information can support transparent and successful decisionmaking. Furthermore, the values of different types of information should be considered, ranging from scientific information to local knowledge. New participatory planning approaches rely on diverse sources of information, which means that professional or expert knowledge alone will not suffice. Ideally, information should cover scientific, professional and public fields of knowledge (Tyrväinen et al. 2002). In reality, this ideal situation does not exist because policy-making and planning situations in urban forestry are characterized by limitations in time, available skills and resources (Konijnendijk 1999). Moreover, it should be stressed that the decision what kind of information is taken into consideration is an important part of the decision-making process. In decision-making, data, information and knowledge are important resources. Because of the broad spectrum of different aspects, needs and information sources, information for urban forestry planning and management must be selected, popularized and, often, simplified. During recent years, the use of indicators has been promoted in policy-making and natural resources management (e.g., Bossel 1997). Indicators can be seen as concise expressions of information or as tools to deliver information to decision makers in a usable, understandable form. Indicators are not the only type of information tools. Especially public participation and involvement of several interest groups have led to the development of new

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Basic green-space information Environmental and ecological information Socio-cultural information

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instruments (see, e.g., Kangas and Store 2003). Information/knowledge resources are demanded to be increasingly based on scientific information and measured data that is popularized. Various information tools have been developed to serve decisionmaking. These include analytical analyses of hierarchical processes (Schmoldt et al. 2001), cost-benefit analyses (McPherson 1992; Tyrväinen 2001), criteria and indicator schemes, multicriteria evaluation methods (Beinat and Nijkamp 1998), models of recreational demand (De Vries and Goosen 2002), visualization programs (Karjalainen and Tyrväinen 2002; Tyrväinen and Uusitalo 2004), and geographic information systems (GIS) (Pauleit and Duhme 2000; Van Herzele and Wiedeman 2003; GermannChiari and Seeland 2004). 14.3

Collection of Information 14.3.1 Planning the Collection of Information Before starting with the collection of information it is important to set clear goals and objectives (Wandall and Randrup 1999). Which information is exactly needed, and in what level of detail? Furthermore, it is important to determine what will be done with the information after collection; how and by whom will it be used? How often will the information be used and updated is another question that influences the collection and storage of information. Information will need to be updated in order not to become outdated, and a strategy for updating needs to be developed from the beginning. This also means that sufficient resources need to be allocated not only for initial recording of data, but also for future updates. 14.3.2 Recording Basic Green-Space Information Information on green-space characteristics can cover a wide range of urban forest features and attributes such as size, location, vegetation type and structure, soil types, hydrology, land use, infrastructure and a range of technical aspects related to forest management. Information on the amount and location of urban woodlands is important, for example, to protect them against urban development projects or compensate the loss of green spaces; also, the implementation of laws requires precise definitions and boundaries. Several countries and cities of Europe apply norms for the per-capita provision of minimum areas of open space. Basic information on public and other green space is a prerequisite to assess current levels of provision against these greenspace standards (see also Chap. 3). Basic resource information is also required for planning of maintenance activities. Cities increasingly prepare a green-space inventory and monitoring system. A wide range of methods, tools and systems have been developed (e.g., Pauleit and Duhme 2000; Pillman and Kellner 2001). However, still many local authorities have only limited information available on green spaces in general, and specifically on the urban forest resource (Pauleit et al. 2003, see Chap. 3).


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With the availability of aerial and satellite images and the development of methods for urban land use and land cover analysis (e.g. see papers in Jürgens 2003), a new potential for surveys on green space arises. The use of remote sensing methods has offered the chance for the implementation of a financially feasible and repeatable method for the assessment of a city’s vegetation resources (Sukopp and Wittig 1998; Beisch 1998; Golibersuch and Wessels 1999; Pillmann and Kellner 2001). A green-space classification can be made based on an aerial or satellite image while only a limited number of field surveys are necessary to verify the results, thus greatly reducing the cost of a full inventory (see Box 14.1 and Fig. 14.1). The main problem with the system currently lies in difficulties with the classification of the aerial images, which can still be a rather time-consuming and complicated task (e.g., Myeong et al. 2002, see also various papers in Jürgens 2003). In the project MURBANDY the development of urban areas in Europe was investigated (Joint Research Centre/Ispra 2000). Extensive datasets were used to study past and current land uses, trying to understand urban dynamics on the basis on indicators and forecast Box 14.1 Biotope mapping in Vienna (Pillmann and Kellner 2001) BiotopMonitoring Vienna was launched in 1991 with the goal of creating an information system designed to provide in depth information on Vienna’s green spaces and urban forests. In a surveying flight over Vienna in 1991, aerial color images and air-borne multi-spectral scanner data were taken simultaneously for the whole city area (413 km2). To create a time series on the status of green spaces, surveys were also carried out in 1997 and 2000. During each flight the entire city area was depicted on color infrared aerial pictures, taken from a height of 2 000 and 3 700 m respectively, resulting in 650 images with a scale of 1 : 7 800 (23 × 23 cm resolution) and 100 images with a scale of 1 : 25 000. Green spaces were distinguished as units with respect to their geometric form, function and surrounding areas. In the whole city 35 600 biotope areas, so-called phytotopes, were identified. The stereoscopic interpretation of aerial photographs enabled the identification and description of a wide variety of object features. Six main feature classes were used to describe the phytotopes:

Green space structure type (describes the characteristics of green space within a city) Biotope type (describes the habitat function) Vegetation type (e.g. deciduous and coniferous trees, shrubs, meadows, lawns) Estimated number of trees in 5 stem diameter classes Crown condition (classes of tree vitality; crown defoliation, leaf discoloration) Surface cover types (percentage areas covered by vegetation, built-up areas, other impervious surfaces, etc.)

For management purposes additional information was necessary characteristic of alleys (density, regularity, gaps, number of sections), use of open spaces (e.g. playgrounds, sport, car parks, market places etc.), establishment and maintenance. One major goal in the further development of BiotopMonitoring will be to improve the cost efficiency of this method through automation. Scanner data and image processing methods can be used for change detection purposes. The data presently available to us through BiotopMonitoring could provide an excellent basis for the evaluation of such new techniques. Now a rectified multispectral scanner image of Vienna is available with a resolution of 2.5 m in 11 spectral bands (visible, infrared and thermal) for further change detection using image processing methods. Highly resolved satellite images promise to be a source of data in the future.

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Remote Sensing


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Fig. 14.1. High resolution image of classified green-space vegetation (Image: USDA Forest Service, Northeastern Research Station, Syracuse, NY)

the development of urban areas. In the follow up study MOLAND – Monitoring Land Use/ Cover Dynamics, detailed GIS data sets have been developed of land use types and transport networks at a mapping scale of 1 : 25 000, typically for four dates (early 1950s, late 1960s, 1980s, late 1990s), over the last fifty years, or for two dates (mid 1980s, late 1990s) in case of larger areas. Some of the results related to urban green spaces are reported in Chap. 3. Urban Forest and Tree Inventory Systems Recording green-space characteristics such as size, location, species, vegetation condition, and so forth sounds relatively straightforward but can be difficult in practice. A comprehensive inventory can be time-consuming and expensive. Surveyors need sound training in order to be able to collect high quality data. While measuring the height of a tree or the area of an urban park may be learnt more easily, determining the health status of street trees requires considerable expertise and a good classification system in order for measurements by different staff members to be comparable (see Box 14.2, Fig. 14.2 and also Chap. 15). Since trees are a very important part of the vegetation in urban green spaces, many inventory systems are (street) tree based. Available systems typically collect field data using standardized data recording sheets, and increasingly laptop or palmtop computers for data recording in the field (Fig. 14.3). The data are compiled in customized database software. Links with other applications, such as standard databases and GIS are expected to become increasingly common.


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In the UK, an interesting approach was developed to provide a representative overview of the urban tree population. Trees were sampled in 66 towns and cities based on a classification of urban land uses (called urban morphology types, Department of the Environment 1993). This approach was further developed and applied to inventory the urban forest resource in the north west of Greater Manchester (Handley et al. 2000). Urban morphology units and types were distinguished and mapped in a geographic information system. Urban forest attributes such as the number of trees were recorded from aerial photographs in 200 m by 200 m plots selected at random in each of the urban morphology types. A field survey was then undertaken in a sub-sample of these plots for a more detailed assessment of the tree population. Attributes recorded included tree species, girth (diameter in breast height), height and spread, condition and location of the tree. Such a survey produces a very good information base for strategic planning and management of the urban forest resource. For instance, Fig. 14.1 shows the density of trees in the different morphology units of the sample area. A wide variance could be observed, with inner city areas being particularly deficient of trees whereas low density suburbs proved to be particularly important for protecting the urban tree resource. Such information in combination with an assessment of environmental, social and economic benefits can be used to target specific programs and measures to protect and improve the provision of trees in urban areas.

Fig. 14.2. Tree density in urban morphology units in the north west of Greater Manchester, UK (source: Handley et al. 2000)

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Box 14.2 Tree registration in the UK

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Fig. 14.3. Example of a handheld computer with special software to record green space characteristics (image: USDA Forest Service, Northeastern Research Station, Syracuse, NY)

Three main types of tree registration systems can be distinguished (Wandall and Randrup 1999): 1. Number based registration systems. A system based on a certain number of trees that is registered, e.g. the entire population, per area, per species or a representative sample 2. Information-based registration systems. A system focusing on recording a specific type of information, e.g. about spread of diseases or the health conditions. 3. Frequency-based registration systems. A system based on how often data is recorded, e.g. periodically or continuous registration. Electronic Tree Recognition A recently introduced tool in urban tree management is the use of a computerized label, a so-called transponder. The transponder assists in identifying each individual tree and can store all main tree characteristics, including management treatments given to it over time. The use of transponders allows for quick consulting and updating of information on individual trees. However, there are still problems with the durability of the system, as some transponders are no longer readable after a few years due to growth of the tree. Also the initial investment costs needed for the system might be problematic, especially for larger cities. GPS The use of the Global Positioning Systems (GPS) is becoming more and more common as a tool to record the exact location of trees. The accuracy of the system is improving so that sub-meter precision can now be achieved. A GPS receiver needs to locate a minimum of three satellites to determine the horizontal position. When the receiver locates four or more satellites, the vertical position can also be determined.


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The combination of GPS, palmtop computers and GIS is likely to develop further in the near future. This development will also simplify the computerization of data collection within urban forestry (Brockhaus et al. 2003).

Urban forests provide many environmental and ecological benefits to the urban population (see Chap. 4), for example by meliorating the urban climate, reducing air pollution, noise levels, storm-water runoff, controlling erosion, and protecting drinking water sources. Also urban forests play an important role in maintaining and increasing biodiversity in urban areas. However many of these benefits are threatened by urban development such as building activities and road construction. Reliable information on the environmental and ecological benefits is important for the protection of the urban forest. Moreover, urban areas are mostly characterized by difficult growing conditions for trees (see Chap. 11 and 12), and information about environmental conditions and stresses of trees is needed for tree protection and management. Measuring the Environmental Effect of Urban Vegetation Many European cities have environmental monitoring systems in place. For instance climate data, and data on air and water pollution are regularly collected. In some cases, detailed measurements of noise levels exist, especially related to traffic. Measurement methods are well established, however, the impact of green spaces on air quality, etc. has rarely been measured or modeled (see Chap. 4). Currently, several European research projects are working on methods and tools for green-space planning, including the assessment of their environmental benefits (e.g. the EU projects BUGS – Benefits of Urban Green spaces, and RUROS – Rediscovering the Urban Realm and Open Space). Models have been developed to estimate the environmental benefits of urban green space such as increase of rainwater infiltration and improving the urban climate (see Chap. 4). Land cover data obtained from aerial photographs is the main input to run these models. For instance, in a study in Merseyside, UK, the following land cover classes were distinguished: built, paved, trees, shrubs, rough grass, amenity grass, flowerbeds, open soil, water (Whitford et al. 2001; Pauleit et al. in press). A stratified random sampling approach can be applied to collect this information for the different urban land uses (e.g., Akbari et al. 2001). Biodiversity During recent years, more attention has been given to the specific values of urban nature (e.g., Gilbert 1989; Goode 1998; Wittig 1998; Florgård 2000). Several cities have been able to maintain tracts of nature areas in or close to their boundaries and created opportunities for the spontaneous development of vegetation (see, e.g., Chap. 13).

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14.3.3 Environmental and Ecological Information

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The presence of natural vegetation and/or indigenous vegetation in urban areas have been studied in Europe as well as elsewhere (e.g., Florgård 2000; Breuste et al. 1998). A wide range of scientific studies on flora and fauna of European cities is available, including data on urban forests (e.g., Gilbert 1989; Sukopp and Wittig 1998; Breuste et al. 1998). Plant species, bird and butterfly species have been recorded in a number of cities. Biotope mapping is frequently used to survey habitats for urban wildlife. For instance, the city of Vienna (Austria) carried out an extensive survey of the biotopes within its borders (see Box 14.1). In a Danish study (Attwell 2000), vegetation cover and habitat quality were identified for a number of Danish towns revealing a potential for increased habitat and biodiversity values especially in low density residential and industrial zones. Additional information on biodiversity is often supplied by extensive networks of volunteers and environmental NGOs operating in urban areas. In The Netherlands, a popular project called ‘Nature Calendar’ is coordinated by Wageningen University. People are asked to report their first sighting of selected bird and other animal species, as well as flowering of plants, to a web site. The study aims, for example, to establish the impacts of climate change on wildlife (for more information www.natuurkalender.nl). Thus it presents an interesting example of how different types of environmental/ecological information from scientific and ‘local’ knowledge can be brought together. 14.3.4 Socio-Cultural Information Historically, especially in Europe, many benefits of urban forests are associated with social, aesthetic and economic functions of urban green space (see Chap. 4). However, the use of this type of information in urban forest management is still relatively new. General information on the social and cultural values of urban forests does exist in many countries but this information does not always reach decision-makers. Furthermore, the use of area specific information, e.g. the social values of a specific city park, is still rare. In the past years more comprehensive methods to assess the social benefits of urban green spaces have been developed in various European countries and these tools and methods now start to reach urban forest management (see Chap. 4 and also Tyrväinen et al. 2004). One of the main aims of collecting social information is to find out how people think and feel about urban green spaces. Central questions are: What do they use green areas for?; how often?; when?; which areas do they prefer and why?; how should the management be done in their eyes? 14.3.5 Collecting User Information The most used methods to find out how people use green spaces are by means of interviews and questionnaires. By means of for example on site or telephone interviewing, or through mailed, door-to-door, or even Internet surveys, users and non-users of green spaces can be asked about their use (or non-use) of specific green spaces and preferences.

In a Danish study a questionnaire was mailed to 3600 randomly selected persons and 1 900 randomly selected associations and institutions in 6 Danish cities to reveal the use and importance of urban parks (Holm 2000). 98% of the population had visited an urban park a least once in the previous year. The results showed furthermore that the average citizen visited urban parks twice a week. The distance to the nearest park, having a dog and the age of the respondents had the strongest influence on the number of park visits. In Vienna telephone interviews were conducted over a number of years to reveal how the population of Vienna used its urban woodlands (Bürg et al. 1999). The results for the period 1989–1997 showed that 68% of the randomly selected interviewees used the woodlands. The study provided a good overview of the average visitor, the most common activities and the revealed different types of users. A Swiss study used the ‘Theory of Planned Behaviour’ and focused on the public acceptance of planned restrictions of recreational activities as result of the establishment of a nature reserve (Seeland et al. 2002). The study showed how different usergroups react on restrictions imposed on their use of urban woodlands. It seemed that most user groups were rather indifferent, but hunters and berry-pickers were opposed to restrictions.

Box 14.3 Social value mapping in Helsinki (Tyrväinen et al. 2004) The Swedish social mapping method was adapted and tested in Helsinki in 2003 as part of an EU funded research project, NeighbourWoods. The case study area consisted of three housing areas in Eastern Helsinki. The total number of households in the case study area was approximately 9 000. The total population consisted of nearly 20 000 inhabitants. The study area was located 10–15 km from the city centre. The area had been selected primarily because of its variety, both in types of green areas and in housing types. It has high-rise blocks from the 1960s and 1970s, a large area with single-family homes dating from the 1920s to the 1950s, combined with many buildings built during the last decade. The city was planning to construct new homes in the area, but the exact locations for building had not yet been determined. It was decided to use a mailed questionnaire with pre-coded questions to study the social values of the green areas in the case study area. A thousand questionnaires were sent out to randomly selected residents, with a good distribution between the different residential areas and different ages of the residents. The questionnaire consisted of four parts: a b c d

respondents’ opinion on green area values and functions in general values and characteristics of green areas use of green areas and opinion on management background information on the resident

For part b, a map of the case study area showing the different green spaces with a number was presented to the respondents. The respondents were asked to identify their favorite area, and also to describe area characteristics such as forest feeling, beautiful landscape, nice park, and so forth. This resulted in a range of maps of the area displaying the different values as identified by the residents (see Fig. 14.4a–c). The response rate was around 40% and the collected information on social values provided a valuable insight in how the residents of these housing areas value their green areas, but also where development should definitely not take place.

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The Regional Planning and Transport Office in Stockholm has developed a method to describe and map the experience values of local residents by means of questionnaires and interviews and combine the results with other planning data (Regionplane– och trafikkontoret 2001). The method used the following steps: 1. Different open spaces were identified and named, primarily based on existing classes such as parks, woodlands, grasslands, and so forth. 2. Background information on these areas was collected. Traffic noise models were used, for example, to determine noise levels. Moreover, cultural historic elements were identified, plant species data was recorded and tree age was assessed. 3. Questionnaires and/or interviews were used to obtain area-specific information from local residents about the social values of green spaces. 4. The information from these dialogues with the residents was combined with the other information into socio-cultural values for green spaces. Since all information was related to real areas, a map could be made displaying the specific (combination of) socio-cultural values of each green spaces. The method has successfully been used in Stockholm and, in adapted form, also in Helsinki (see Box 14.3). Other Swedish and Finnish cities have expressed their interest in using the method as it provides, in a relatively easy and cost effective way, an overview of social values and can easily be integrated with other planning methods.

Fig. 14.4a. Percentage of respondents identifying one specific area as their favorite green area within the study area in Eastern Helsinki, divided in four classes (Tyrväinen et al. 2004)

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Fig. 14.4b. Percentage of respondents experiencing one specific area as a valuable nature area, divided in four classes (Tyrväinen et al. 2004)

Fig. 14.4c. Percentage of respondents experiencing one specific area as a noisy area, divided in four classes (Tyrväinen et al. 2004)

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Information Systems During the course of the COST Action E12 Urban Forests and Trees a questionnaire was sent out to selected European cities and towns to reveal if and which information systems were used to collect green-space characteristics. This pilot study was by no means exhaustive, but it rapidly became clear that many different systems are being used for many different purposes by both researchers and urban forest managers. Table 14.1 gives an overview of the most commonly used tools and systems. 14.4.1 Databases and GIS Comprehensive recording of green-space characteristics is only useful when the data is stored systematically and made easily accessible. The backbone of each inventory and information system is therefore formed by a database. Having a database with relevant green-space information that is (or could be) updated forms an important basis for all further use of the information. Furthermore, more and more cities realize that the location of green spaces in relation to each other and to other urban land uses is important. This is likely to result in an increasing use of geographic information systems (GIS), optionally combined or integrated with a database system. In a small COST E12 survey ‘computer applications in urban forestry’ 53 organizations, primarily city administration departments, were asked which software was used for database and GIS applications. Smaller cities tended to use standard desktop software, whereas larger cities were more likely to have customized systems. The software packages that were most frequently mentioned in this pilot study were: MS Access, Dbase, Oracle, MapInfo and ArcView. 14.4.2 Green Structure and GIS On city or even agglomeration level, it is important to look at the entire green structure, made up by all green elements in and around cities, i.e. individual trees and other vegetation elements, medium sized urban green spaces such as parks and urban and peri-urban woodlands (Konijnendijk and Randrup 2002). Besides the benefits of each green element as such, additional benefits occur because of connections and corridors between elements, which for instance, favor longer recreational routes and also provide additional ecological benefits. Also, a well-designed and developed green structure usually makes for a better spatial distribution of urban green (e.g., Pauleit and Duhme 2000), which, in turn, makes the benefits available to a larger part of the urban population (e.g., Van Herzele and Wiedeman 2003). Within urban forestry various studies have been done to study accessibility (e.g., Van Herzele and Wiedeman 2003; De Vries and Goosen 2002), distance to green spaces (e.g., Præstholm et al. 2002; De Vries and Goosen 2002) and recreational preferences and benefits (e.g., Van Herzele and Wiedeman 2003; De Vries and Goosen 2002). Through using GIS, different aspects can be combined into more integrative and informative information.

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Box 14.4 A monitoring tool for the provision of accessible and attractive urban green spaces (Van Herzele and Wiedeman 2003) The methodology used in this study consisted of two main steps, firstly examining the preconditions for use, primarily accessibility, and secondly accessing the qualities that make a green space attractive for use. To be able to calculate the accessibility of green spaces, four map layers were derived from various thematic maps. The densely built urban core area was identified, as well as relevant neighborhoods within the city. All green spaces larger than 10 ha as well as barriers and crosswalks (locations where the barriers are permeable) were identified. The accessibility of green areas was calculated with the cost distance module of ArcView and by mapping the sizes and distances of green areas. The attractiveness of green spaces was assessed with the help of various parameters linked to physical features of green spaces. Parameters used were size, naturalness, cultural and historic values, quietness and the availability of facilities. Values for each of the parameters were assigned by experts. The tool was used in four Belgium cities and the results showed that the provision of different types of green areas was not always sufficient. Especially the availability of so-called ‘neighborhood green’, an area of at least 10 ha (or 5 ha for a well designed park) within 800 m from home, was very limited. In the city of Kortrijk 95% of the population had no access to quarter green. The method allows to compare different cities which each other, and also to reveal problems with green area provision within a city. Thanks to the combination of quantitative and qualitative information on urban green spaces with social information on a neighborhood level, the model gives an accurate overview of the recorded deficiencies in different parts of the city. Since the analysis can be performed at different functional levels, it can be detected to what extent problems and changes result from conditions and politics on different levels of decision making, e.g. local, regional, and so forth. Consequently, specific green-space policies and measures can be designed at the relevant scale. The use of aggregated quality groups also allows for relating the results to different policy domains, e.g. traffic, nature, among other.

Accessibility of green spaces has been mentioned as key to its social use. Several studies show that increasing distance to recreation areas decreases their use (e.g., Tyrväinen 1999; Hörnsten and Fredmand 2000). The key factor for active use is easy access to areas, which should be located preferably within walking distance of the home environment. The benefits of green spaces for urban quality of life greatly depend on the accessibility and qualities which people perceive from them. In Belgium, a GIS-based model has been applied to determine the supply and accessibility of green spaces (Van Herzele and Wiedeman 2003, see Box 14.4). The model was designed to allow the monitoring of the urban green spaces status through time and space against quantitative and qualitative targets. The tool is particularly aimed at assessing the effects of future policy scenarios. 14.4.3 Decision-Support Systems Information becomes particularly useful if it can be used directly to support the decision making process. An important question many decision-makers like to see answered is ‘what will happen if we change this or do that?’ In order to make these predictions for the future, several models and other decision support tools have been developed


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Because of the difficulties of comparing the value of urban green areas with the value of other urban land uses for decision makers, American Forests, one of the oldest NGOs in the USA has developed a GIS tool that can calculate the value of urban green areas. Calculations are based on extensive research done by USDA Forest Service in combination with the creation of a so-called green data layer (American Forests 2002). The system is developed to analyze the effect of urban green areas on: storm water management, air quality, energy use, carbon sequestration and furthermore the system also incorporates a tree growth model that can be used to predict future scenarios. If all factors can be taken into account depends on the availability of data. Tree canopy data is the most important information. Analyses can be undertaken for both small and large areas. For small-scale projects, a detailed inventory is possible, and required to give the best results. For application on a larger scale, it is necessary to use satellite or aerial images to determine urban forest canopy cover. This makes it possible, with good classification of the images, to develop a green infrastructure data layer (equivalent to e.g. data layers on road infrastructure, housing and utilities) for which benefits can be quantified for larger areas. The system seems especially suitable to show, in a cost effective way, that is easy to understand for non-experts, how preserving green areas will lead to reduced expenses for storm-water retention and energy consumption. Furthermore air quality improvement and carbon sequestration can be estimated.

with natural resource management, but only very few have been adapted to an urban forestry context. This may be related to the complexity of urban forestry in terms of, for example, the diversity of green-space elements, the many different interests, and the difficulty to fully assess various urban forest benefits. Although most of the earlier mentioned methods also include an ‘advise’ or ‘prediction’ part, there often is a long way to a comprehensive decision-support tool. One of the few examples of a decision-support tool used in urban forestry is CITYgreen (American Forests 2002), developed in North America (see Box 14.5). Dwyer and Miller (1999) tested the use of this tool in Stevens Point, Wisconsin, USA and concluded that it was a useful tool for obtaining a sound overall impression of the status of the urban green spaces in a city. 14.5

Conclusion In many European cities there is wide range of information on urban green spaces available. And suitable methods, tools and information systems for measuring, collecting and compiling information are becoming more widespread. However, the available information is often spread over many different city departments and decision makers are not always aware of the existents of all information. Bringing all available and necessary information together and making it accessible to the wide range of professionals and decision makers that are involved with the management and planning of urban green spaces will be a major challenge in the coming years. Scientist and practitioners can together work on facing this challenge and improving the situation by developing and using more integrated information systems.

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Box 14.5 CITYgreen, a GIS based decision support tool (American Forests 2002)

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References Akbari H, Rose LS, Taha H (2003) Analyzing the land cover of an urban environment using high-resolution orthophotos. Landscape Urban Plan, 63(1):1–14 American Forests (2002) CITYgreen – calculating the value of nature. American Forests, Washington Attwell K (2000) Urban land resources and urban planting – case studies from Denmark. Landscape Urban Plan 52:145–163 Beinat E, Nijkamp P (eds) (1998) Multicriteria analysis for land-use management. Kluwer Academic Publishers, Amsterdam, pp 17–31 Beisch Th (1998) Städtische Baum- und Grünflächeninformationssysteme. (Urban tree- and green-space Information systems). Dissertation Georg-August-Universität Göttingen Bossel H (1997) Deriving indicators of sustainable development. Environmental Modeling and Assessment, Vol. 1, pp 193–218 Breuste J, Feldman H, Uhlmann O (eds) (1998) Urban ecology. Springer-Verlag, Berlin Brockhaus J, Manry D, Jones R (2003) Using field-portable GPS/GIS data acquisition systems. Arborist News June 2003:49–51 Bürg J, Ottitsch A, Pregernig M (1999) Die Wiener und ihre Wälder. Schriftenreihe des Instituts für Sozioökonomik der Forst- und Holzwirtschaft. Band 37. Universität für Bodenkultur, Vienna Department of the Environment (1993) Trees in towns. A survey of trees in 66 towns an villages in England. Research for Amenity Trees No. 1, HMSO, London De Vries S, Goosen M (2002) Modelling recreational visits to forests and nature areas. Urban For Urban Green 1(1):5–15 Dwyer MC, Miller RW (1999) Using GIS to assess urban tree canopy benefits and surrounding greenspace distributions. J Arboriculture 25:102–106 Florgård C (2000) Long-term changes in indigenous vegetation preserved in urban areas. Landscape Urban Plan 5:101–116 Germann-Chiari C, Seeland K (2004) Are urban green spaces optimally distributed to act as places for social integration? Results of a geographical information system (GIS) approach for urban forestry research. For Policy Econ 6:3–13 Gilbert OL (1989) The ecology of urban habitats. Chapman and Hall, London, New York Golibersuch W, Wessels K (1999) Grünflächeninformationssysteme. Konzeption – Aufbau – Einsatz Goode D (1998) Integration of nature in urban development. In: Breuste J, Feldman H, Uhlmann O (eds) (1998) Urban ecology. Springer-Verlag, Berlin, pp 589–592 Handley J, Wood R, Ruff A (2000) The Red Rose Urban Timber Initiative: a report on the sampling of the street, park and garden tree population. Unpublished Report to the Red Rose Forest, Salford, UK Holm S (2000) Anvendelse og betydning af byens parker og grønne områder (Use and importance of urban parks). Forest and Landscape Research No. 28-2000. Danish Forest and Landscape Research Institute, Hørsholm Hörnsten L, Fredmand P (2000) On the distance to recreational forests in Sweden. Landscape Urban Plan 51:1–10 Jürgens C (ed) (2003) Remote sensing of urban areas. Proceedings of the ISPRS WG VII/4 Symposium, Regensburg, June 27–29, 2003. The International Archives of The Photogrammetry, Remote Sensing and Satellite Information Sciences. Vol. XXXIV-7/W9 Karjalainen E, Tyrväinen L (2002) Visualization in forest landscape preference research: a Finnish perspective. Landscape Urban Plan 885:1–16 Konijnendijk C (1999) Urban forestry in Europe: a comparative study of concepts, policies and planning for forest conservation, management and development in and around major European cities. Tiedonantoja 90, Joensuun yliopisto, Metsätieteellinen tiedekunta Konijnendijk CC, Randrup TB (2002) Editorial. Urban For Urban Green 1:1–4 Kangas J, Store R (2003) Internet and teledemocracy in participatory planning of natural resources management. Landscape Urban Plan 62:89–101 McPherson EG (1992) Accounting for benefits and costs of urban greenspace. Landscape Urban Plan 22:41–51

Myeong S, Nowak DJ, Hopkins PF, Brock RH (2002) Urban cover mapping using digital, high-spatial resolution aerial imagery. Urban Ecosystems 5:243–256 Pauleit S, Duhme F (2000) GIS assessment of Munich’s urban forest structure for urban planning. J Arboriculture 26(3):133–141 Pauleit S, Slinn P, Handley J, Lindley S (2003) Promoting the natural greenstructure in towns and cities: the Accessible Natural Greenspace Standards Model (ANGSt). J Built Environ 29(2):157–171 Pauleit S, Golding Y, Ennos R (in press) Modelling the environmental impacts of urban landscape change – a study in Merseyside, UK. Landscape Urban Plan Pillmann W, Kellner K (2001) Monitoring of green urban spaces and sealed surface areas. In: Proceedings of the 2nd Int. Symposium: ‘Remote Sensing of Urban Areas’, University of Regensburg Price C (2003) Quantifying the aesthetic benefits of urban forestry. Urban For Urban Green 1:123–134 Præstholm S, Jensen FS, Hasler B, Damgaard C, Erichsen E (2002) Forests improve qualities and values of local areas in Denmark. Urban For Urban Green 1(2):97–107 Regionplane- och trafikkontoret (2001) Upplevelsevärden, Sociala kvaliteter I den regionala grönstrukturen (Experience values, social qualities in the regional green structure). Regionplane- och trafikkontoret, Rapport 4, Stockholm Seeland K, Moser K, Scheuthle H, Kaiser FG (2002) Public acceptance of restrictions imposed on recreational activities in the peri-urban Nature Reserve Sihlwald, Switzerland. Urban For Urban Green 1(1):49–57 Schmied A, Pillman W (2003) Tree protection legislation in European cities. Urban For Urban Green 2(2):115–125 Schmoldt DL, Kangas J, Mendoza GA, Pesonen M (eds) (2001) The analytical hierarchy process in natural resource and environmental decision making. Managing forest ecosystems. Kluwer Academic Publishers, Dordrecht Sukopp H, Wittig R (1998) Stadtökologie. Urban and Fischer, München Tyrväinen L (1999) Monetary valuation of urban forest amenities in Finland. Academic dissertation. Finnish Forest Research Institute, Research papers 739 Tyrväinen L (2001) Economic valuation of urban forest benefits in Finland. J Environ manage 62:75–92 Tyrväinen L, Mäkinen K, Schipperijn J, Cuizzi D, Salbitano F (2002) Information for decision making. NeighbourWoods state of the art report. available at http://www.sl.kvl.dk/euforic/docs/NeighbourWoods/NBW-good-practice-information.pdf Tyrväinen L, Silvennoinen H, Kolehmainen O (2003) Ecological and aesthetic values in urban forest management. Urban For Urban Green 3(1):135–150 Tyrväinen L, Mäkinen K, Schipperijn J, Silvennoinen H (2004) Mapping social values and meanings of green areas in Helsinki, Finland. NeighbourWoods project report. University of Helsinki, Helsinki, available at: http://www.sl.kvl.dk/euforic/docs/NeighbourWoods/D10a%20Helsinki.pdf Tyrväinen L, Uusitalo J (2004) Role of landscape simulators in forestry: a Finnish perspective. In: Bishop ID, Lange E (eds) Visualization in landscape and environmental planning. Spon Press, London, pp 125–132 Van Herzele A, Wiedeman T (2003) A monitoring tool for the provision of accessible and attractive urban green spaces. Landscape Urban Plan 966:1–18 Wandall B, Randrup TB (1999) Registrering af bytræer – mål, midler og praktiske eksempler (Registration of city trees – objectives, tools and examples). Park- og Landsskabsserien 24, Skov and Landskap, Hoersholm Whitford V, Handley J, Ennos R (2001) City form and natural process – Indicators for the ecological performance of urban areas. Landscape Urban Plan 57:91–103 Wittig R (1998) Urban development and the integration of nature: reality or fiction? In: Breuste J, Feldman H, Uhlmann O (eds) (1998) Urban ecology. Springer-Verlag, Berlin, pp 593–600

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