Environment and Ecology at the Beginning of 21 st Century

Transcription

1 Environment and Ecology at the Beginning of 21 st Century Editors Prof. Dr. Recep EFE Prof. Dr. Carmen BIZZARRI Prof. Dr. İsa CÜREBAL Prof. Dr. Gulnara N. NYUSUPOVA ISBN ST. KLIMENT OHRIDSKI UNIVERSITY PRESS SOFIA

2 Environment and Ecology at the Beginning of 21 st Century Editors Prof. Dr. Recep Efe Balikesir University, Faculty of Arts and Sciences Department of Geography Çağış, Balıkesir-Turkey Prof. Dr. Carmen Bizzarri European University of Rome Via degli Aldobrandeschi Roma, Italy Prof. Dr. Isa Cürebal Balikesir University, Faculty of Arts and Sciences Department of Geography Balıkesir-Turkey Prof. Dr. Gulnara Nyussupova Al-Farabi Kazakh National University, Faculty of Geography and Natural Management, Department of Geography Almaty-Kazakhstan St. Kliment Ohridski University Press ISBN The contents of chapters/papers are the sole responsibility of the authors, and publication shall not imply the concurrence of the Editors or Publisher Recep Efe, Carmen Bizzarri, İsa Cürebal, Gulnara N. Nyussupova All rights reserved. No part of this book may be reproduced, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission of the editors and authors 2

3 Chapter 11 Simulating the Impacts of Future Policy Scenarios on Urban Land Use in Izmir Metropolitan Area Using the SLEUTH Urban Growth Model Engin NURLU, Hakan DOYGUN, Hakan OGUZ and Birsen KESGIN ATAK INTRODUCTION From local to global scale, monitoring land use has had great importance in discovering the causes, effects and trends of changes in our world. Changes in land use are vitally important in terms of exposing land s future development trends. In order to develop effective planning and management approaches that will guide these changes, realistic predictions are needed for potential results of proposed plan and strategies next to the existing data (Barredo et al., 2003). In this context, models and simplification of reality have been used in monitoring land use and developing future scenarios in recent years. Batty (1976) demonstrated the importance of models for helping scientists to understand urban phenomena through analysis and experiment, for helping planners, politicians, and the community to predict, prescribe and create urban future by demonstrating the limitations of theory and the potential of simulation in his book titled Urban Modelling: Algorithms, Calibrations, Predictions. Land use change is driven by interaction in space and time between humans and the environment that can be captured by computer simulation models (Veldkamp & Verburg, 2004). In the last few decades, land use change models have played an important role in understanding the causes, mechanisms and consequences of land use dynamics. The models have provided an opportunity to explore and evaluate land use policies, and have helped to visualize results of these policies (Chaudhuri & Clarke, 2013). During the last two decades, the methodological approaches for modeling have been profoundly enhanced and thereby, several modeling approaches such as Markov chain analysis (MCA), neural networks (NN), cellular automata (CA), multi-agent systems (MAS), fuzzy logic, and logistic regressions have been developed and applied in land use change and urban growth studies based on time series (Clarke, 2014; Moise, 2012; Paegelow & Camacho Olmedo, 2008). Land use models reinforced with spatial, multi-scale and dynamic modeling approaches have been used effectively as a tool to create alternative urban growth scenarios for the future (Veldkamp & Lambin, 2001). Today s digital modeling techniques are designed to be used in various subjects along with digital technology in modeling the interactions between humans and the environment, especially where anthropogenic land use change is a central focus. Monitoring land use, explaining in the context of cause and effect, and modeling studies regarding the development of future policy scenarios that may occur in the future indicated a significant increase in the last two decades (Overmars & Verburg, 2006). 166

4 Engin Nurlu et al. Since the 1980 s, cellular automata (CA), one of the artificial intelligence based models that has components that are interacting at the local level according to elementary rules to simulate a complex and dynamic system over space and time has been utilized to design future scenarios for modeling future urban land use scenarios (Barredo et al., 2003; Paegelow & Camacho Olmedo, 2008). CA represents the phenomenon under study using a grid space of cells, cell-states, neighborhood effects and transition rules (Silva et al., 2008). CA-based models are dynamic models used to investigate fundamental principles of system evolution and self-organization. The capacity of these models to simulate local behavior according to a set of rules of neighborhood, several cell states, and time constraints, is being recognized as valuable when planning complex systems, while simultaneously assuring the portability and adjustability of the model to local characteristics (Silva et al., 2008). Therefore, it has been widely used in modeling urban dynamics (Batty & Longley 1994; Batty et al., 1999; Couclelis, 1985; Couclelis, 1997), urban growth or expansion (Clarke & Gaydos, 1998; Clarke et al., 1997; Landis & Zhang, 1998; Openshaw & Openshaw, 1997), local economy, social statistics, land use (White & Engelen, 1997), location selection (Roy & Snickars, 1998), and integrated environmental assessment (Köhler et al., 2014) studies. From this point of view, the cellular automata-based SLEUTH (an acronym based on the data inputs of Slope, Land use, Excluded, Urban, Transportation, and Hillshade) urban growth model developed in order to predict potential future urban growth with the help of historic land use data, that is today one of the most popular simulation models of urban growth and land use change all over the world. The model uses two tightly coupled cellular automata models, one of for urban growth and the second for land use change. It includes self-modification of control parameters, and has a self-calibrating capacity built into the computer code for the model (Clarke, 2008). It requires six gridded raster maps used as input data layers in the model. The SLEUTH urban growth model predicts urban expansion by a number of growth rules which control urbanization probability of each pixel. The SLEUTH urban growth model has been applied to a variety of regions from local to regional scale over the last two decades to simulate future urban growth and land use change (Chaudhuri & Clarke, 2013; Clarke et al., 1997; Nurlu et al., 2013; Oguz et al., 2007; Oguz et al., 2008; Oguz et al., 2010; Oguz et al., 2011; Silva et al., 2008; Silva & Clarke, 2002). The aim of this study is to explore the potential impacts of different regional management scenarios on urban land use in Izmir metropolitan area, Izmir Province in Turkey, using SLEUTH urban growth model. Being a major trade, residential, and tourism center, and having mild climate conditions, the urban areas have begun to sprawl along the transportation network and the coastline in the metropolitan area of Izmir. MATERIALS AND METHODS In this study, urban growth projections for 2040, based on two policy scenarios, were developed using the SLEUTH urban growth model. Mainly, remotely sensed data with ancillary data such as topographic maps, ASTER Global Digital Elevation Maps (GDEM), printed and visual documents such as, photos taken from the study area have been used as secondary data. Both input dataset preparation and analysis were carried out using geographic information system and remote sensing techniques 167

5 Simulating the Impacts of Future Policy Scenarios on Urban Land Use in Izmir and software tools such as ArcGIS 10, ERDAS IMAGINE 9.0 and ENVI 4.1 were used to derive input dataset for the calibration of the model and also to create future policy scenarios. Study area: Izmir is located in the west of Turkey, on the Aegean coastline and is encircled by rich plains of the Aegean Region. It is an ancient city dating back to 6000 B.C. It is recognized for its natural beauties and rich history. Having experienced rapid land use change because of the diversity of economic activities, population growth and urbanization, Izmir is the country s third most populous city and the second largest port, thus one of the immigrant attraction centers at national scale. Between 1927 and 2011, the city of Izmir experienced a population increase of more than 3 million. The study was carried out on eleven districts within the boundary of Izmir metropolitan area, Izmir Province in Turkey (Fig. 1). The study area covers an area of 95,880 hectares (IZKA, 2008) and has a population of according to 2011 Population Census of Turkey. It covers %7 of the total provincial area of Izmir Province and 70% of population of the province lives in the study area (TSI, 2012). Izmir has a Mediterranean climate which is characterized by long, hot and dry summers; and mild winters. Figure1: The location of the study area The SLEUTH model The SLEUTH urban growth model, developed by Dr. K.C. Clarke at the University of California, Santa Barbara, Department of Geography, was used to assess and predict future land use changes and to create future policy scenarios in the study area. The model has the capacity to simulate urban/non-urban dynamics as well as urban land use dynamics. The dual ability has led to the development of two subcomponents within the framework of the model, one that models urban/non-urban growth, the Urban Growth Model (UGM), and the other Land Cover Deltraton Model (LCD) that models land use change dynamics (Chaudhuri & Clarke, 2013; Clarke, 2008; Clarke et al., 2007; Dietzel & Clarke, 2007; Silva & Clarke, 2002). Model 168

6 Engin Nurlu et al. execution takes place in the form of a growth cycle and a series of growth cycles making up the whole simulation process. In order to develop these policy scenarios, the model was implemented in three general processes: input dataset preparation process, calibration process, where historic growth patterns are simulated and prediction process, where historic patterns of growth are projected into the future (Jantz et al., 2009; Jantz et al., 2010). Input dataset The SLEUTH urban growth model requires six input data layers of historic urban extent and transportation for at least four time periods; land use layers for two time periods; a slope, a hillshade, and an excluded layers for calibration (Table 1). In input dataset preparation process, urban extent layers were used to represent the historical pattern of growth during the model s calibration and application. Transportation layers were used to determine the probability/tendency of urban development depending on accessibility of the location. Land use layers were used in calculating the class-toclass transition between different land use classes. Hillshade layer was used to form user-controlled background for the urban growth predictions. The excluded layer was used to control urban growth in areas where urbanization is restricted according to the land use policies. Table 1: SLEUTH input dataset for the study area MODEL INPUTS DATA YEARS DATA TYPES Slope 2009 ASTER GDEM Land use 1984, 2009 Landsat 5 TM Excluded 2009, 2012 Landsat 5 TM & Protected Area Map Urban extent 1984,1990, 2000, 2009 Landsat 5 TM Landsat 5 TM & Digital Topographic Transportation 1984,1990, 2000, 2009 Map Hillshade 2009 ASTER GDEM The SLEUTH urban growth model requires that all input data layers must be in the same map extent, same projection and same resolution. Layers of urban extent and road network as transportation from four different time periods and land use layers from two different time periods were derived from LANDSAT 5 TM images, acquired July 05, 1984, August 07, 1990, June 07, 2000, and July 26, 2009 with 30m resolution applying supervised classification technique according to the CORINE LCC (Fig. 2). Eight dominant land use/cover classes of the study area were identified: urban areas, agricultural areas, forests, semi natural areas, saline, salt marshes, water bodies and other land use/cover. Additionally, transportation layers were retrieved by digitizing roads from 1: scale topographic maps. Slope and hillshade layers were derived from ASTER Global Digital Elevation Map (GDEM). The excluded layers were developed from the land use layer of the year 2009 and maps indicating protected areas (including wetlands, natural and archaeological protection sites) under the authority of both Ministry of Culture and Tourism and Ministry of Environment and Urbanization for each scenario (Fig. 3). SLEUTH urban growth model allows binary classification of excluded areas: totally excluded from development or totally open for development. Excluded layers were built with arbitrarily chosen resistance scores for urbanization, indicating 169

7 Simulating the Impacts of Future Policy Scenarios on Urban Land Use in Izmir probabilities of exclusion between 0 and 100, where 0 represents areas with no exclusion at all and 100 represents areas that are completely excluded from development A value of 50 represents areas that neither attract nor repel development, values from 51 to 100 represents increasing levels of repulsion and values 49 to 0 represent increasing values of attraction. This convention allows the exclusion layer to be used more broadly as a suitability layer for new urban development, allowing users to capture factors that both attract and exclude urbanization (Jantz et al., 2014). Areas where urban development was considered impossible, water bodies or protected areas, were given a value of 100, and locations that were available for urban development had a value of 0 in the study area. Figure 2: Model inputs: urban extent, transportation, hillshade, slope, and land use layers Future urban growth was projected for the year 2040, based on two different policy scenarios (Fig. 3). The current trends scenario reflects the current policies and urban growth trend as history. Saline areas, salt marshes and water bodies were fully protected from development. Higher levels of protection were used for natural and archaeological protection areas. The managed growth with protection scenario reflects stricter set of protective policies aimed toward limited urban development and more natural resource protection. Saline areas, salt marshes, water bodies, natural and archaeological protection areas were fully protected from development. Higher levels of protection were chosen for agriculture, forests and semi natural areas. The excluded layer for the current trends scenario represented areas giving values (0, 50, 60, 80, and 100) that were partially protected from urbanization/excluded from development and the other excluded layer for the managed growth with protection scenario represented areas giving values (0, 80, and 100) were more resistant to urbanization (Table 2). 170

8 Engin Nurlu et al. Figure 3: Model inputs: excluded layers used in each scenario (a) Current trends scenario (b) managed growth scenario with protection The SLEUTH urban growth model simulates urban dynamics through the application of four growth rules: spontaneous new growth, new spreading center growth, edge growth, and road-influenced growth which are applied sequentially during each annual growth cycle. Each type of growth is controlled through the interactions of five growth coefficients of diffusion, breed, spread, road gravity, and slope that can range in value from 0 to 100, which indicates the relative influence of each parameter on development patterns, with higher values producing a stronger influence. These growth coefficients define the growth pressure within the urban system. While areas to be protected either completely of partially were determined through the excluded layer, a resistance against growth was established using the slope layer and growth was limited (Clarke & Gaydos, 1998; Jantz et al., 2009; Jantz et al., 2010). Table 2: Excluded levels used in each scenario EXCLUSION LEVELS (%) EXCLUDED LAYERS SCENARIO 1 (*) SCENARIO 2 (**) Natural protection areas (1.grade) Archaeological protection areas (1&2. grades) Salines, salt marshes and water bodies Natural protection areas (2.grade) Archaeological protection areas (3. grade) Natural protection areas (3.grade) Agricultural areas & Forests&Semi natural areas Other land cover/land uses 0 0 * current trends scenario ** managed growth with protection scenario Model calibration The calibration process, where various combinations of growth coefficient values are tested, was carried out to derive a set of values for the growth coefficients that can accurately reproduce historic land use change within the study area from 1984 to Due to the extensive computational requirements of calibrating the model, the Brute Force calibration technique, the most common technique for SLEUTH calibration, was used to derive coefficient values. This technique involves calibrating 171

9 Simulating the Impacts of Future Policy Scenarios on Urban Land Use in Izmir the model to the data in three steps; coarse, fine and final, sequentially narrowing the range of coefficient values and the data resolution (Chaudhuri & Clarke, 2013; Clarke et al., 1997; Silva & Clarke, 2002). Spatial resolutions were taken as 120m for coarse, 60m for fine and 30m for final calibration. At the end of each calibration, thirteen metrics (such as the number of urban pixels, urban cluster edge pixels, the number and size of urban clusters and other fit statistics like Lee-Sallee) were recorded to compare actual growth with simulated growth and used to evaluate which set of coefficient values were best to recreate the historical urban growth. In this study, Lee-Sallee metric, which is the ratio of the intersection and the union of the simulated and actual urban areas, and also one of the most common spatial measures of growth, was used as primary metric to evaluate the performance of the model (Clarke et al., 1997). After each calibration phase, the top set of Lee-Sallee scores determining the range of values was used as primary metric to evaluate the performance of the model. The calibration results for experiment ranges from 1984 to 2009 are given in Table 3. As seen from the table above that slope and diffusion coefficients do not have an effect on urban growth due to having low values of 1 and 5 respectively. The most influential coefficients were found to be breed, spread, and road gravity with the value of 100. This indicates that urbanization tends to spread outward from main nucleus and occur along with the transportation network. Besides, breed coefficient shows that new urban nucleus tends to occur on other land uses. Table 3: Calibration results for experiment ranges from 1984 to 2009 CALIBRATION Coarse Fine Final Driving Forecast Monte Carlo Iterations Lee-Sallee Metrics 0, , , Image Dimensions 411x x x x1122 Growth Coefficients Range Step Range Step Range Step Final Coefficient Values Diffusion Breed Spread Slope resistance Road-gravity Prediction Forecasts of future urban growth were created through modifications of the excluded layers and by applying different future growth rates using SLEUTH s selfmodification function (Jantz et al., 2009). Self-modification function, which allows the typical S-curve growth rate of urban expansion in the model, is critical to reflect accurate growth rate because urban growth is not always linear or exponential. At the prediction phase, two future policy scenarios were simulated: current trends assuming no changes in policies or land use change drivers and managed growth with protection assuming full protection of all legislation related to environment and urban. Excluded layers served as the primary instrument to derive scenarios. An excluded layer was 172

10 Engin Nurlu et al. generated for each scenario to represent the corresponding spatial changes to policies or drivers of land use change. RESULTS In this study, the potential impacts of two future policy scenarios; current trends and managed growth with protection, were explored by creating two excluded layers. The current trends scenario was based on existing trends, policies, and practices played out to It reflects partial protection over semi natural areas such as forests and agricultural areas. The managed growth with protection scenario was based on fully protecting natural and cultural heritage areas such as natural and archaeological protected areas, forests and semi natural areas. It reflects maximum protection and resists any development within the excluded boundary. The SLEUTH urban growth model employs the brute force technique during calibration, where all the search space is investigated exhaustively to find the best set of calibrated rule values. The technique, which steps through the coefficient space in large, and then increasingly smaller steps, was conducted in three phases: coarse, fine, and final calibration. Initially the model was calibrated using hierarchical spatial resolutions, beginning with data of coarser resolution, narrowing the range of the parameters that most accurately described the growth of the system, and then using a finer resolution to narrow the parameter values to one distinct set. After the calibration phase, growth coefficient values to be used in the prediction phase to forecast the future urban growth was obtained through derived forecasting coefficients. The final derived forecasting calibration values were 5 for diffusion, 100 for breed, spread, and road gravity, 1 for slope resistance respectively (Table 3). Future urban growth predictions for current trends and managed growth with protection scenarios for the year 2040 were created by applying the SLEUTH urban growth model to the study area (Fig. 4 and Fig. 5). The results of the scenario predictions showed more dispersed urban development patterns for the current trends scenario than the managed growth with protection scenario due to the lack of protection over forests and semi natural areas occurring mostly in and around existing urban centers as expected, while the urban growth occurs mainly in north, east, and south of the study area especially along the main roads. Due to the higher levels of protection, the growth rates for the managed growth with protection scenario reduces, producing a much lesser loss of land resources as illustrated in Fig. 5 and Table 4. The statistical simulation results for these two scenarios indicated more dispersed development patterns in urban areas than in agricultural, forests and semi natural areas from 2009 to 2040 for the study area (Table 4). While a significant growth rate was observed in urban areas in current trends scenario, the growth rates for the managed growth with protection scenario reduced due to the higher levels of protection. Under the current trends scenario, the total urban area for 2040 would be ,19 ha. As a result of such dramatic growth, urban area would occupy 43 percent of the total modeled land by Under the managed growth with protection scenario, by 2040, the total urban area would be ,31 ha, in other words, 37 percent of the entire modeled area. While the current trends scenario results for the year 2040 shows that agricultural areas would cover 1 percent (1.115,91 ha), forests and semi natural areas would cover 34 percent (32.729,58 ha) of the study area in total, the managed growth with protection scenario results for the year 2040 revealed that agricultural areas would 173

11 Simulating the Impacts of Future Policy Scenarios on Urban Land Use in Izmir cover 2 percent (1.812,51 ha), forests and semi natural areas would cover 38 percent (36.440,46 ha) of the study area in total. With these results, it is obvious that when the study area employs higher levels of protection for natural areas, the urban growth rates are reduced, producing a much lower predicted loss of forests, semi natural areas, and agricultural areas (Table 4; Fig. 4 and Fig. 5). Land Use / Land Cover Table 4: Statistical simulation results for future policy scenarios SCENARIO (*) 2. SCENARIO (**) ha % ha % ha % Urban areas , , ,31 37 Agricultural areas 3.059, , ,51 2 Forests and semi natural areas , , ,46 38 Saline and salt marshes 4.284, , ,91 4 Water bodies 3.138, , ,34 3 Other land use / land cover , , ,47 16 TOTAL , , , * current trends scenario ** managed growth with protection scenario DISCUSSION AND CONCLUSIONS A comparison between the current land use for the year 2009 and the current trends scenario results for the year 2040 revealed a significant increase in urban areas. On the other hand, a considerable decrease was predicted in agricultural areas, forests and semi natural areas. According to the current trends scenario, it's predicted that urban sprawl will affect agricultural areas at first, and then spread over the forests and semi natural areas, and other land uses. Sprawl of urban areas was observed especially in the southeast and north of the study area. Despite the protection measures in the second scenario, increase in urban growth for 2040 has been observed in and around existing urban centers. Due to the higher level of protection, managed growth with protection scenario produced low density development patterns compared to the current trend scenario. In conclusion, a comparison between current land use for the year 2009 and both scenario results for the year 2040 revealed that the maximum growth rate is predicted on urban layers compared to other uses (Fig. 6). For the year 2040, fully protected areas in both scenarios, such as, saline areas and salt marshes remained completely free of any development remaining one of the significant wetlands of Turkey (Table 4). Shown as maritime wetlands in Fig. 6, no changes for these areas were predicted, because a part of this area was designated and managed as Ramsar Site and protected according to the Convention on Wetlands of International Importance called the Ramsar Convention. The SLEUTH urban growth model has been used for building future policy scenarios to predict urban development for policy making, planning and decision making purposes. The main objective of this study was to evaluate the consequence of alternative future policy scenarios through the SLEUTH model to reveal urban 174

12 Engin Nurlu et al. Figure 4: Model prediction for 2040 based on the current trends scenario 175

13 Simulating the Impacts of Future Policy Scenarios on Urban Land Use in Izmir Figure 5: Model prediction for 2040 based on the managed growth with protection scenario 176

14 Engin Nurlu et al. Figure 6: Comparison of growth rates in the study area for future policy scenarios. development mechanism and help decision makers decide on an optimal planning scenario on the coastal areas of Izmir metropolitan area, Turkey. In the study, two future policy scenarios, current trends and managed growth with protection scenario were designed and incorporated into the SLEUTH model for eleven districts within the boundary of Izmir metropolitan area, Izmir Province, in Turkey. The results for quantitative analysis showed that the urban area would expand continuously from 2009 to 2040 under the current trends scenario. More land resources such as forests and semi natural areas, and agricultural areas would remain under the managed growth with protection scenario unlike the current trends scenario. Results indicate that urbanization is most likely to occur around the edges or in the vicinity of already established urban centers. Urban settlements and roads have the most influence among the newly urbanized areas for both scenarios. Considering these results from an ecological point of view, optimal future urban growth scenarios in the study area should be of managed growth with protection scenario. Findings of this study show that level of protection affects the output and it is important for application of the SLEUTH urban growth model to understand future policy scenarios and its environmental impact. The predictions highlight urban sprawl between 2009 and 2040 which has impacts on agricultural, forests and semi natural areas for Izmir metropolitan area. Considering the output of these predictions, local decision makers will be able to identify areas of concern for the future and implement protective measures in advance. ACKNOWLEDGEMENT: This study was part of the research supported by the Scientific and Technological Research Council of Turkey-TUBITAK [Grant Number: CAYDAG 109Y210] for COST Action TU0902: Integrated assessment technologies to support the sustainable development of urban areas. We would like to thank the project research team and the funding organization TUBITAK that have made this work possible. REFERENCES Barredo, J. I.; Kasanko, M.; McCormick, N.; Lavalle, C. (2003). Modelling Dynamic Spatial Processes: Simulation of Urban Future Scenarios through Cellular Automata. Landscape and Urban Planning 64: (3), Batty, M. (1976). Urban Modelling: Algorithms, Calibrations, Predictions, Cambridge 177

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