Ecological Modelling 220 (2009) 3612–3620

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Ecological Modelling

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Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, Chinaଝ

Jian-zhou Gong a,b, Yan-sui Liu b, Bei-cheng Xia c, Guan-wei Zhao a,∗ a School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China b Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China c School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China article info abstract

Article history: Forecasting changes in urban ecological security could be important for the maintenance or improvement Available online 11 November 2009 of the urban ecological environment. However, there are few references in this ﬁeld and no landmark research work has been reported, particularly quantitative research. A forecasting model for ecological Keywords: security based on cellular automata (CA) was developed using preliminary spatial data from an ecological Urban ecological security security assessment of Guangzhou conducted previously (1990–2005). The model was constrained using Assessment transformation rules based upon proposed planning for 2010–2020. A simulation accuracy of 72.09% was Forecasting acquired. Using a one-bit assessment grid for 2005 as the starting state for the simulation, the model CA Guangzhou was used to forecast ecological security for 2020. This revealed that although the ecological security status would be improved relative to current trends, there would still be an overall decline in ecological security over the next 15 years. Even if new urban plans were implemented, landscape pattern analysis suggested a more scattered and homogenous distribution in the urban landscape of Guangzhou and signiﬁcant variation in landscape characteristics among districts. This suggests that further measures must be adopted to reverse the current trends in Guangzhou’s ecological security. The model highlights the need to make ecological protection an integral part of urban planning. This study demonstrates the potential of CA models for forecasting ecological security. Such models could make an important contribution to decision-making for regional governors and to the development of urban planning incorporating assessment and prediction of ecological security. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Traditionally the concept of security denotes safety from injury, harm, or danger, and in most cases it infers no damage to the life, Since the industrial revolution, humans have notably created health, property or territory of a region or nation. Many existing and and been impacted by a variety of forms of pollution and ecosystem potential problems (economic, ecological and social) are consid- degradation. Nevertheless, until recently much of this ecosystem ered within problems of security. Moreover, ecological security has degradation has been ignored (Shi et al., 2006). In the middle of the been linked with national security, economic security and human last century, concerns began to mount over problems caused by well-being (Edward, 2002). ecological degradation, and regional and global ecological security The term ecological security was first proposed by the gov- has become a common interest of many countries and organiza- ernment of the United States (Ezeonu and Ezeonu, 2000). Since tions. At the Johannesburg 2002 World Summit on Sustainable then, although it has been widely considered (Solovjova, 1999; Development participating countries shared a common concern Kullenberg, 2002; Ma et al., 2004), a universally accepted defi- over ecological security. Ecological security was also clearly named nition has not been agreed upon, nor have standard assessment as a fundamental component of state security. parameters or research methods been accepted (Zhao et al., 2006). Ecological security is temporally dynamic and has spatial structure and thus any study must embrace these dynamics. Feedback of ଝ assessment results to support decision-making for planning future The study was supported by the following foundations: The National Natural Science Foundation of China (No.40635029); The National Science Foundation for developments is also desirable. Ecological security modeling may Post-doctoral Scientists of China (No. 20080440511); Science & Technology Project fulfill these criteria and provide useful insight. Nevertheless, there of Guangzhou (No. 08C027). ∗ is lack of prior or similar studies to inform the choice of method. Corresponding author at: Box No. 126A, No. 230 Waihuanxi Road, University Urbanization has profoundly changed land use and thus land Town, Panyu District, Guangzhou, Guangdong Province, China. Tel.: +86 20 34156412; fax: +86 20 39366890. cover around the globe (Courage et al., 2008; Long et al., 2009). E-mail address: [email protected] (G.-w. Zhao). Many ecological and environmental problems are thought to result

0304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2009.10.018 J.-z. Gong et al. / Ecological Modelling 220 (2009) 3612–3620 3613 from land use cover change (LUCC) (Meyer and Turner, 1992; tions. Geographical studies have developed CA models to represent Stephan et al., 2005; Stevens et al., 2007). Thus urban/regional plans spatial and time-dependent processes of complex systems, such as ought to support sustainable development and promote ecologi- forest dynamics or planning (Lett et al., 1999; Anne-Hélène et al., cal security. Existing as well as newly introduced environmental 2008), urban growth (Dragicevie et al., 2007) and land use change problems need to be incorporated into urban development plans (Stevens et al., 2007). such that ecological security is a priority in any acceptable urban Ecological security theory informs us that the internal structure development scheme. As a consequence, urban growth plans, their of ecosystems is logical and stable and that the service func- resulting spatial impact and their associated forcing factors, have tion of ecosystems is positive and tends towards health (Liao et become key areas of planning and urbanization research (Batty al., 2004). Like other complex geo-phenomena, ecological secu- and Xie, 1994; Al-Ahmadi et al., 2008). Methods to assess urban rity includes many complex processes, is typically space- and planning and resultant growth scenarios as well as to forecast the time-dependent and is affected by the states of neighboring areas resultant urban ecological security are an absolutely requirement and relative social-natural factors. Thus, CA may be particularly for sustainable development of an area. However, relevant research suitable for investigating ecological security, but there are no is limited, and few studies have investigated forecasting ecological prior applications of CA models for the assessment of ecological security during urbanization. Thus the question of how ecological security. security for a particular region should be determined is open to dis- Guangzhou is a rapidly developing super megalopolis. Its eco- cussion. Here, quantitative models to describe regional ecological logical security has been assessed for the past 20 years (Gong and security could be helpful. Xia, 2008a,b). In this study an urban ecological security CA model In cellular automata (CA) models local interactions reﬂect global was developed by coupling an ecological security assessment properties and determine the evolution of system. These models are model with a CA framework model, and the ecological security of discrete both in space and time (White and Engelen, 1997; Lena Guangzhou was taken as a case study. There were two phases to and Margara, 2008) employing regular two-dimensional lattices this investigation. First, the urban ecological system of Guangzhou such as grids (Fonstad, 2006). Generally, CA models are charac- for 2005 was simulated and compared with ecological security terized by a matrix space, where each cell possesses a state. Time assessment results for 2005 obtained from prior studies, thus the is discrete and progresses through iterations. With each iteration parameters of the model were regulated. Second, the model was cell transition rules are applied which redeﬁne cell states (André used to forecast the future ecological security for 2020. and Danielle, 2007). Transition rules describe functions of a series The primary objective was to couple CA with ecological security of independent variables on the dependent variable (cell state). assessment to identify insecure cells within the region and simu- Thus CA models have an immense capacity to simulate complex late their future development. At minimum, this provides another processes or systems through building and applying different func- view of ecological security and some suggestions for urban plan-

Fig. 1. The study area. 3614 J.-z. Gong et al. / Ecological Modelling 220 (2009) 3612–3620 ning. Potentially, its findings could prompt improvements in the 3. A CA model for ecological security forecasting of urban ecological environment, as well as inform new planning for Guangzhou Guangzhou in the next decade. 3.1. Motivation 2. Urban ecological security history and status and urban planning for Guangzhou The latest Guangzhou comprehensive master plan was pub- lished in 2008, and a major goal was to direct further urban Guangzhou (Fig. 1), the biggest megalopolis and the economic, development to achieve environmental sustainability within that political and cultural center of South China, is located at the period. The incorporation of factors from this plan into a sim- center of the Pearl River Delta, between 22◦26–23◦56N and ulation model (CA model), may provide an auxiliary planning 112◦57–114◦3E. It covers an area of approximately 7434.4 km2. decision-support tool. This could assist understanding of the urban Guangzhou has a southern subtropical ocean monsoon climate, environment and the relationships between urban expansion and with an average annual temperature 22.4 ◦C. The topography is environmental change. Such a model could forecast possible eco- characterized by mountains and hills in the north and northeast logical security problems due to future developments or assess the and by basins and alluvial plains in the south. Guangzhou’s infras- health of an urban system. The model presented in the current study tructure includes 10 districts and two counties based on the new does not provide reliable solutions to these problems, but does pre- zoning scheme. These are: Liwan, Yuexiu, Haizhu, Tianhe, Huangpu, dict possible problems and thus provides some insight into urban Luogang, Baiyun, Panyu, Nansha and Huadu districts and, Conghua planning and sustainable development. The ultimate potential of and Zengcheng counties. ecological security forecasting is only superficially explored in the Over the last 20 years, Guangzhou has experienced high eco- current study, leaving huge scope for further study in this area. nomic development and unprecedented urbanization, which has Cellular automata (CA) are a class of numerical models based been a leading factor in the implementation of the Reform and Open on a discrete space-time grid (Fonstad, 2006; Cheng et al., 2008). A Policy in China. In 1990, 57.4% of people lived in urbanized districts. simple CA is defined by a lattice L, a state space Q, a neighborhood This increased to 61.1%, 62.2% and 69.3% in 1995, 2000 and 2005, template D and a local transition function F (Balzter et al., 1998), respectively. Correspondingly, the gross domestic product (GDP) of namely a space (L, Q, D and F)(Long et al., 2007). An elementary the population for 1990 was 3.1959 billion RMB which increased framework CA comprises the following elements: space, always to 12.4307, 23.7591, 51.1575 billions of RMB, in 1995, 2000 and expressed as an array of cells; a discrete number of states; and 2005, respectively. GDP increased 17-fold in the last 15 years. The neighborhood and transition functions (Jose et al., 2003). CA have most obvious result of this increasing population and GDP has been powerful modeling capabilities to simulate spatio-temporally com- an increased demand for land, both for habitation and production. plex systems when coupled with GIS (Li and Yeh, 2000; Yassemi et Consequently, a concomitant result has been a rapid change of al., 2008). The integration of CA and GIS has been widely adopted land use/cover (Gong and Xia, 2007). Ecological and environmen- in the field of dynamic analysis (Peixoto et al., 2008), such as urban tal problems are usually associated with such changes in land use dynamics (Batty et al., 1999; Jose et al., 2003; Stevens et al., 2007), (from natural or half-natural to urbanized). These changes include forest planning problems (Anne-Hélène et al., 2008), vegetation increased exposure to air pollution and water quality deteriora- dynamics (Balzter et al., 1998). tion. These urban problems limit further development and damage Urban ecological security could be described as a synthesis the quality of life for urban inhabitants. Academically, ecological of components of eco-environmental quality. It exhibits complex security has become a focal point in ecological and environmental spatial and temporal processes and own system characteristics, sciences. such as fractal dimensionality, self-similarity across scales and Research on Guangzhou’s ecological security has been ongoing self-organization. In general urban development processes are the (Gong and Xia, 2008a,b). Assessment models have been applied and integrated result of physical geography and human activities, with model parameters have been determined using RS (Remote Sens- decision-making lying at the centre of urban dynamics. ing) and GIS (Geographical Information Systems) techniques. The Nevertheless, ecological security has hardly been simulated ecological security of Guangzhou is generally at an intermediate using CA despite the obvious applicability. Therefore, a forecasting level. Of the whole region, 79.7–98.9% can be classified as having CA model for ecological security (CAFMES) was developed. In addi- intermediate ecological security. However, some spatial variation tion to natural factors, the Guangzhou urban development plans is apparent. Ecological security reaches higher levels in the north- were considered when constructing the ecological security trans- ern part of the region, such as in Conghua and Zengcheng Counties, formation rules for the CA. where there is less urbanization. Ecological security assessments of Guangzhou to date have provided spatial, numerical and graph- 3.2. The components and function of CAFMES ical results. Three main phases in ecological security status have been revealed, a decreasing phase (1990–1995), a slightly increas- CA framework models have been described by many reports (Al- ing phase (1995–2000), and rising phase (2000–2005) (Gong Ahmadi et al., 2008; Cheng et al., 2008). Here a succinct and key and Xia, 2008b). The urban ecological security of Guangzhou description of the model CAFMES is given. was seriously challenged during 1990–1995. This trend, however, was reversed and, due to ecological interventions, a period of 3.2.1. Cell space continuous improvement occurred from 2000 to 2005. Neverthe- The cell space in CAFMES was a digital space composed of less, ecological security was still only at an intermediate level square grids, each 100 m × 100 m. Based on prior ecological security by 2005. assessment data (Gong and Xia, 2008b) and the calculation capac- In Guangzhou, the period of decreasing ecological security cor- ity, the total digital Guangzhou area was firstly split into “subplot responds with the period of most rapid urbanization, from 1990 to sets” for different areas, with equal-area analysis units. Six subplots 1995. After this rapid development, the government implemented were generated: Huadu district, Panyu district, Nansha district, the several measures to control blind urban expansion including urban city center, Conghua County, and Zengcheng County. Then, each comprehensive planning, urban detailed planning, and urban sub- subplot was a cell space of simulation of CA and was divided into ject planning (PBG, 2008). These activities have made a great cells, and included 96203 cells, 72743 cells, 54279 cells, 150903 contribution to increasing ecological security. cells, 197419 cells, 171999 cells, respectively. A status of each cell J.-z. Gong et al. / Ecological Modelling 220 (2009) 3612–3620 3615 was assigned as 1 for non-security and 0 for security (Li and Yeh, security changed to non-security, where 1 indicated true and 0 2002). indicated false. According to the prior ecological security assessment of 3.2.2. Neighborhoods Guangzhou (Gong and Xia, 2008b), ecosystem service (sev), vege- Moore neighborhoods were adopted. The neighborhood effect tation fraction (cov), transportation (trans) and urban built-in area was calculated for each central cell within 3 × 3 cell range. The (ursu) were the four main factors influencing ecological security. result of Moore neighborhoods weighs against the function com- The CA model thus incorporated these factors as layers, operat- putation of the focalmean (grid, neighborhood) in Arc/Info. The ing a logistical regression model (Long et al., 2007) among spatial computation formula for the neighborhood effect value is: grids, Pg was calculated using this logistic regression. The logistic (focalmean(grid, rectangle, 3, 3) ∗ 9 − grid) regression model was expressed as following (Yu and He, 2004a,b): P = (1) n 8 1 Pg = (3) where Pn is the neighborhood effect value in relation to the central + − 1 exp( aixi) cell, grid is the state space map. Whilst CAFMES was running, the grids were made to change dynamically with each temporary sim- where xi (i = 1, 2, 3, 4) represents the above factors, including ulation map being imported into the model as the start map of the ecosystem service (sev), vegetation fraction (cov), transportation next iteration. (trans) and urban built-in area (ursu); ai (i = 1, 2, 3, 4) are the relevant regression coefficients, which indicate the contribution of 3.2.3. Discrete time each factor to each dependent variable; the larger the value of ai, the The data used for the model was obtained from remotely sensed greater the contribution to non-security since values of Pg increased data or other sources between 1990 and 2005. These data were towards 1. discrete, and the parameters of CAFMES were derived from these It has been reported that constraint CA models simulate urban data. The forecasting duration of CAFMES is discrete also, for each conformation better, in that a large number of factors are incorpo- year 2005 and 2020. rated into models as a complex coefficient Pc (Li and Yeh, 1999). Here, Pc is the constraint probability that prevents cells changing 3.2.4. Transition rules state from security to non-security during the study period. Two Transition rules are the core parts of any CA model includ- factors, including river state and urban planning, were defined and ing CAFMES. Following the theoretical probabilistic function of used to acquire values for Pc. River state was taken as an un-changed traditional CA models (Jose et al., 2003), an integrated vector of natural constraint, urban planning was taken as a social constraint transition probability (one probability for each cell) was calculated that prevented retrogression in the cell state of ecological security from the cell status, neighborhood effect and urban plan factor. A deterministic value was then acquired by incorporating a stochastic perturbation which altered most values a very little but changed (i) Value 0 was assigned to cells where rivers occurred, and value a few significantly. The transition rule framework could be summa- 1 was assigned to others: this then created a layer named rized as: River. (ii) Based on Guangzhou’s urban growth plans and their impact, an Pt = Pg × Pc × Pn × RA (2) expert evaluation method was adopted to assign values to cells that fell into different areas. Values 0, 0.3, 0.5 or 1 were assigned where Pt is the deterministic value of the integrated probability of to cells that fell into protected areas, limited areas, generally change, Pg is the probability of change for the whole system, Pc is limited areas, and non-limited areas respectively, and a layer the probability of constraint, Pn is the neighborhood effect value in named ‘PlanningBelt’ was created. relation to the central cell, and RA is a stochastic perturbation item. Pg was determined using a spatially logistic regression model (iii) Finally, Pc was calculated as follows: with a hypothesis of urban non-security due to a series of fac- Pc = River × PlanningBelt (4) tors. The details of the logistic regression technique are available in the literature (Yu and He, 2004a,b). In the regression equation, In addition, to make the simulation better approach realistic values the dependent variable (Pg) was set as a binary value (1 for non- for ecological security a parameter RA was imported (Li et al., 2007). security and 0 for the others) and the layer of Pg was acquired using RA was a stochastic perturbation item which can be expressed as: a spatial overlapping operation in Arc/Info. The processing was as follows: RA = 1 + (− ln rand) (5)

(i) Based on the ecological security assessment layers data from where rand is a random variable with a value between 0 and 1, 1990 and 2005 (Gong and Xia, 2008a), two recoded (0 and 1) and is a parameter that allows the size of the perturbation to be layers were created first by using a reclassification method, 1 adjusted, for example, a large value of results in a minor change. being assigned to a cell with an ecological security value rang- ing from 0 to 50, and 0 being assigned to others (as described in: 3.3. Running CAFMES Zuo, 2004; Zhang and Ren, 2006; Gong and Xia, 2008a). Thus, two new one-bit layers were created (named secu1990 and To optimize the calculation capacity, dependent and indepen- secu2005, respectively). dent series maps were split into “subplot sets” for different areas, (ii) A CON function was used to output the ecological security with equal-area analysis units. Six subplots were generated: Huadu changing layer (named change90-05), which performed a con- district, Panyu district, Nansha district, the city center, and Conghua ditional evaluation on a cell-by-cell basis. County, Zengcheng County. All cell spaces were set 100 m × 100 m for each layer using the resample method, which maximizes the The parameters and expression were: amount of spatial detail captured, but ensures adequate computation capacity. change90-05 = con(secu1990 < secu2005, 1, 0) CAFMES codes were compiled in Visual Studio.net 2005 with C#. where change90-05 was the changing layer, 1 denoted a change After the first alterative, a median value was acquired by sorting from security to non-security and 0 the other case. The argument all grids. The median was then set as a threshold for determining ‘secu1990 < secu2005’ was the condition used to judge whether cell state, making comparisons cell-by-cell. If the value of a cell 3616 J.-z. Gong et al. / Ecological Modelling 220 (2009) 3612–3620 was equal or greater than the median, 1 was assigned to the cell, at the landscape level: (1) aggregation index (AI), (2) patch den- otherwise 0 was assigned. In this fashion, in the course of a sin- sity (PD), (3) fractal index distribution (FRAC MN), (4) largest patch gle iteration, growth types were applied sequentially; one cell at a index (LPI), (5) landscape division index (DIVISION), (6) Simpson’s time, and the entire grid was updated. The updated status of a grid diversity index (SIDI). These were calculated in FRAGSTATS3.3. Three was then used as the starting state for growth in the subsequent one-bit grids of ecological security were used for these calcula- iteration (Alexandra et al., 2005). The threshold value should be tions for the study periods 1990, 2005 and 2020. Whilst grid 2020 dynamic and in the second and subsequent iterations the threshold resulted from CAFMES the other two grids came from prior ecolog- value should decrease (Batty et al., 1999). CAFMES was tested using ical security assessments. a range of thresholds and comparing results between the simulation and previous assessment data. A ramping decrement of 0.015 4. Results gave the best comparison. The one-bit layer from the ecological security assessment of 4.1. CAFMES calibration 1990 (secu1990) was used as the start map for the simulation of non-security. This operation and its analysis were repeated and it CAFMES was successfully calibrated by analyzing the accu layer was found that the number of non-security status cells reached rel- resulting in an acceptable accuracy of 72.09%. ative stability after about ten iterations. No significant difference was found in maps of subsequent simulations, and thus a value of 4.2. Logistic regression coefficients 20 iterations was chosen as a standard. The simulation map and the one-bit assessment for 2005 were used to calibrate the model Logistic regression is useful for prediction of the presence or (see next section). Finally, this one-bit layer (named secu2005) was absence of some characteristic or outcome (in the form of proba- used as the start map for prediction of non-security in Guangzhou bility) based on values of a set of predictor variables. Each logistic for 2020. regression coefficient describes the size of contribution of the corresponding independent variable to the dependent variable in the 3.4. Model calibration model. Here, the presence of non-security is viewed as the dependent variable, four factors were used as variables and logistic An accuracy matrix, based on comparison between the simula- regression coefficients were acquired by running logistic regres- tion layer generated and the one-bit assessment result for 2005, sion operations in the Grid. The regression coefficients of the four was derived using the assessment result grid as a check-point. factors are listed in Table 1, where ‘sev’ stands for ecosystem ser- Accuracy was expressed as the percentage similarity between the vice, ‘cov’ for vegetation cover, ‘trans’ for transportation, and ‘ursu’ two grids. The CON function on the Arc/Info platform was used for urban built-up area. and an output grid (accu) was generated. For binary data, the The greater the value of the regression coefficient, the greater is simplest measure is the percentage of observation units between its contribution to Pt (non-security) within the model (Eq. (3)). For the two grids which are the same. Hence, matching accuracy example, ‘trans’ has the greatest coefficient of the four factors and (percentage) could now be simply calculated from the ‘accu’ exhibits greatest effect on Pt in the city center. ‘Trans’ is followed by grid. ‘cov’, and then ‘sev’, and the last ‘ursu’. In other words, the sorting In this CA model in three parameters were changeable: order of coefficients for the city center, starting with the highest RA, Pg and Pn, whilst Pc was stable. RA was adjusted to get coefficients is transportation, vegetation cover, ecosystem service, achieve a reasonable level of accuracy. Pg and Pn were dynamic and then urban built-up area. Thus transportation has the greatest parameters. Model adjustment was iterative; at each stage accu- contribution to non-security within the city center and urban built- racy was assessed until an acceptable percentage similarity was up area the least. achieved. Overall regression analysis revealed heterogeneity in contribu- The constraint layer, Pc acted as a mask in some areas, where tion among factors and within districts (Table 1). Only ‘cov’ in the cells could neither be changed nor developed. For example, some Huadu District had a positive coefficient value (Table 1) indicat- cells were assigned to protected areas and made to retain a value ing that this factor had the greatest effect on Pt in this location. of 0. Because these cells were not and could not be changed, they Nonetheless, the same factor, made relatively lesser contributions were not counted in the accuracy calculations. to Pt in the City Center and in Zengcheng County, and made an intermediate contribution in Conghua County. 3.5. Landscape indices analysis 4.3. Ecological security dynamics from 1990 to 2005 Landscape indices have been used to describe landscape change through time or to compare landscapes including composition or One-bit assessment maps of ecological security in 1990 and pattern (Alexandra et al., 2005). In this study, two landscape indices 2005 are shown in Fig. 2a and b. The decline in ecological security were used at the class level: (1) percentage of landscape (PLAND), is obvious. The gray area with a value 1 is much larger in 2005 than (2) number of patches (NP), and six landscape indices were used that in 1995. Furthermore, there is clear heterogeneity, such that

Table 1 Regression coefﬁcients for different districts.

District Factors RMS error

Ursu Trans Sev Cov Constant

Center −0.108 −0.002 −0.004 −0.039 −3.05 0.202 Conghua district −0.068 −0.034 −0.005 −0.001 −2.591 0.168 Huadu district −0.139 −0.003 −0.007 0.008 −1.755 0.173 Zengcheng district −0.37 −0.168 −0.008 −0.047 −7.025 0.105 Panyu district −0.011 −0.004 −0.007 −0.009 −1.28 0.17 Nansha district −0.017 −0.015 −0.009 −0.015 −0.364 0.113 J.-z. Gong et al. / Ecological Modelling 220 (2009) 3612–3620 3617

Fig. 2. Results from eco-security assessment and simulation for Guangzhou. (a) and (b) show results of image assessment for 1990 and 2005. (c), (d), (e) and (f) show simulation results after 1, 5, 10 and 20 iterations, respectively.

the City Center, Huadu District, Panyu District and Nansha District County. An exception to this pattern was observed for Zengcheng have declined markedly, Conghua County is also somewhat worse, County. but Zengcheng County has improved. On the other hand, some simulated results did not match with A series of simulation layers are displayed in Fig. 2(c–f) for the assessment results. An example: is the simulation for Baiyun different numbers of iterations (1, 5, 10 and 20, respectively). district as is apparent in Fig. 2(b) and (f). This discrepancy may These layers also indicate obvious changes in ecological security have occurred for two reasons: in Guangzhou County. First, there was a decline in ecological security during the past 15 years; second, there was an (i) the adopted constraint condition of the city Planning Belt from expansion in non-security from the city center to peripheral 2005 to 2020 in the model transformation rules was not con- areas and then to suburbs. Moreover, there is a multiple cen- sidered in 2005; ter pattern of non-security, highlighting developments such as (ii) the limited simulation accuracy (72.09%) indicates that further the inner ring road of the city, Huadu district and Conghua modiﬁcation of parameters could improve the model accuracy. 3618 J.-z. Gong et al. / Ecological Modelling 220 (2009) 3612–3620

Table 2 Number of patches of value 1 (NP) and the percentage of areas (PLAND).

Year PLAND NP

1990 10.8 4836 2005 20.0 3466 2020 25.8 2235

NP, number of patches of value 1; PLAND, the corresponding percentage of area.

4.5. Analysis of landscape indices for 1990, 2005 and 2020

The primary purpose of the simulation was to predict future ecological security and to investigate the dynamics of ecological security during the study period. Landscape indices were employed as another means to understand these dynamics. Table 2 and Fig. 4 give textual and graphical descriptions of dynamics. NP decreased monotonously from 4836 patches in 1990 to 3466 patches in 2005 and to 2235 patches in 2020. NP indicates the level of fragmentation of patches with a value 1 (non- security), with a high value of NP suggesting many fragments. Nevertheless, the NP metric provides simply a basic analysis of the landscape or an ecological process since it does not provide information of area, distribution, or density of fragments (fragstats). PLAND describes the proportion of the whole area associated Fig. 3. Simulation result on eco-security in Guangzhou, 2020. with each patch type. PLAND increased monotonously (converse to NP) (Table 2). Thus, these two indices suggest aggregation of patches of non-security. This directly reflects the general pattern of 4.4. Simulated ecological security for 2020 expansion of non-security observed and is not necessarily in oppo- sition to the multiple center pattern described previously (Section Using the one-bit assessment grid for 2005 as the starting state 4.3). for the simulation, a one-bit simulation layer of ecological security Six indices were employed to characterize the ecological secu- for 2020 was modeled. The simulation layer after 20 iterations was rity landscape composition and the landscape configuration. The analyzed. six indices were grouped into four groups each measuring a dif- As presented in Fig. 3, several districts, including the city center, ferent aspect of the landscape, including area/density/edge, shape, the north of Baiyun district and most areas of Huadu district, are contagion/interspersion and diversity. covered in gray, indicating an increase in non-security by 2020. Significant discrepancies were found between indices (Fig. 4). In Conghua County, the key town exhibited a similar pattern of Nonetheless, some clear response patterns could still be found decline. Fortunately, in Zengcheng County the contrary occurred among indices. They were grouped into four general types: and ecological security status improved. Type I—polyline dynamics from decreasing to increasing; Type

Fig. 4. Landscape index curves for eco-security in Guangzhou, 1990, 2005 and 2020. J.-z. Gong et al. / Ecological Modelling 220 (2009) 3612–3620 3619

II—polyline dynamics from increasing to decreasing; Type measures incorporated into new urban planning can reduce the III—decreasing monotonously; Type IV—increasing monotonously. decline in regional ecological security by 5% (since the decline The aggregation index (AI) was calculated from an adjacency was 9.2% at the previous stage). This implies that urban ecological matrix showing the frequency with which different pair-wise patch security can improve and progress positively if urban plans favor types appeared side-by-side on the map. AI showed a Type I the environment and ecological processes. response with an initial decrease followed by an increase, exhibit- Landscape indices were employed with the CA model to investi- ing an unstable probability of like adjacencies of non-security gate the dynamics and spatial characteristics of ecological security patches. in the study area. NP and PLAND indicated a high degree of frag- In contrast to the Type I response, FRAC MN and PD exhibited a mentation of the landscape from 1990 to 2005 whilst the other Type II response with the opposite curve, indicating similarly unsta- six indices varied from one another significantly among different ble dynamics for average area or patch density in the landscape. periods. A Type III response dynamics was exhibited by Simpson’s diver- Overall the model CAFMES is both effective and accessible for sity index (SIDI) and the landscape division index (DIVISION). The forecasting regional ecological security. With progressive develop- SIDI curve ran almost parallel to the DIVISION curve, showing a ment, CAFMES could form the basis of a decision-support system monotonous increase. SIDI is equal to zero when the landscape for regional governors. 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