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Landscape and Urban Planning 117 (2013) 57– 65

Contents lists available at SciVerse ScienceDirect

Landscape and Urban Planning

jou rn al hom ep age: www.elsev ier .com/ locate / landurbplan

esearch paper

etermining socioeconomic drivers of urban forest fragmentationith historical remote sensing images

hongfeng Gonga,1, Shixiao Yua,∗, Heather Joestingb, Jiquan Chenc,d

Department of Ecology, School of Life Sciences/State Key Laboratory of Biocontrol/Guangzhou Key Laboratory of Urban Landscape Dynamics,un Yat-sen University, Guangzhou 510275, ChinaGulf Coast Research Laboratory, University of Southern Mississippi, Ocean Springs, MS 39564, USAInternational Center for Ecology, Meteorology and Environment (IceMe), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaLandscape Ecology and Ecosystem Science, Department of Environmental Sciences, University of Toledo, Toledo, OH 43606, USA

i g h l i g h t s

Forest area in Shenzhen was restored to 85% of the pre-urbanization level by 2005.Yet, forest fragmentation in Shenzhen continued from 1979 to 2005.Forest fragmentation rates were found to be nonlinear over the study period.Socioeconomic drivers explained 75.9% of the changes in the fragmentation rates.Both positive and negative socioeconomic influences were found to be drivers.

a r t i c l e i n f o

rticle history:eceived 22 March 2012eceived in revised form 14 April 2013ccepted 21 April 2013

eywords:rban forest fragmentationoupled human and natural ecosystemuman-related factorsandsat

a b s t r a c t

Urban forests are valuable resources in coupled human and natural urban systems where green spacesare essential in maintaining ecological benefits and services of the landscape. In southern coastal China,the Shenzhen Special Economic Zone (SEZ) was established as a new city in 1979 and developed to be amegacity from an agriculture-dominated landscape. To quantify the land-use change during this rapidurbanization process and explore the underline drivers, nine sets of Landsat images from 1973 through2005 were used to calculate the landscape metrics of forest patches. We found that the forest in Shen-zhen SEZ had been restored to 85.85% of pre-urbanization coverage by 2005, but was characterized withsmaller, isolated patches across the landscape. The changes in patch density, distribution, and shapeduring the 30-year study period were nonlinear and defined by episodic periods. The stepwise multiple

andscape metricshenzhen

regression models with socioeconomic drivers provided further explanation for fragmentation rates inpatch density, distribution, and shape, with modeled R-squared of 0.837, 0.759, and 0.985 and P-valuesof 0.011, 0.035, and 0.004, respectively. Among the drivers, urban structure change, industry-relatedeconomic booming, and the increase of migrant resident population triggered the urban forest fragmen-tation while the significantly increased income of city residents drove the de-fragmentation trend. The

ed so

artificial forestation show

. Introduction

Urban forests are natural components of urban ecosystemsnd provide vital ecological, economical, and social benefits andervices such as the preservation of biodiversity, prevention of

∗ Corresponding author. Tel.: +86 20 3933 2980; fax: +86 20 3933 2980.E-mail addresses: chongfeng.gong@gmail.com, chongfeng.gong@usm.edu

C. Gong), lssysx@mail.sysu.edu.cn (S. Yu), Heather.Joesting@usm.edu (H. Joesting),iquan.chen@utoledo.edu (J. Chen).

1 Current address: Gulf Coast Research Laboratory, University of Southern Mis-issippi, Ocean Springs, MS 39564, USA.

169-2046/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.landurbplan.2013.04.009

me but a limited role in mitigating forest fragmentation.© 2013 Elsevier B.V. All rights reserved.

soil erosion, absorption of rain water and pollutants, mitigationof urban heat-island effects, improvement of amenity/recreationvenues, reduction of work-related stress, and maintenance of adja-cent property values (Conine, Xiang, Young, & Whitley, 2004;Gobster & Westphal, 2004; Kong, Yin, & Nakagoshi, 2007; Yuan& Bauer, 2007). Consequently, an increasing number of studieshave aimed at the development of sound strategies and poli-cies to manage the forests (Jim & Chen, 2009; Kowarik, 2011;Tratalos, Fuller, Warren, Davies, & Gaston, 2007; Zhao, Escobedo,

& Staudhammer, 2010). Escobedo, Kroeger, and Wagner (2011),for example, suggested that “the scale, contexts, heterogene-ity, management intensities and other socioeconomic co-benefits,tradeoffs, and costs affecting stakeholders and urban sustainability

5 Urba

gp

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irs(2lalsdy2

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8 C. Gong et al. / Landscape and

oals” should be considered in urban forest managementlans.

Urban forests are vulnerable to area loss and fragmentationue to the interaction of natural disturbances, urbanization, and

ntensive human disturbances that is inherent to such a tightlyoupled human and natural (CHN) urban ecosystem. Urban for-st area has been previously shown to decrease linearly andnversely with urban growth, regardless of spatial and temporalrivers (MacDonald & Rudel, 2005). However, a recent study foundhat changes in urban forest area were non-linear but stronglyelated to human demands, socioeconomic changes, and govern-ent policy (Gong, Chen, & Yu, 2011; Tian, Chen, & Yu, 2013).

onsidering the increasing trend of intensive forest fragmenta-ion within and around urban areas (Alig, Kline, & Lichtenstein,004; Kupfer, 2006), there is a pressing need to determine the rela-ionship between urban forest fragmentation and the urbanizationrocess. In this regard, a basic understanding of the drivers of urbanorest fragmentation (e.g., population increase, urban sprawl, andconomic development) is critical for developing effective manage-ent plans and predicting the future conditions of urban forests

Bürgi, Hersperger, & Schneeberger, 2004; Velazquez et al., 2003).In CHN ecosystems, socioeconomic drivers such as household

ncome, age of residents, housing tenure, ethnicity, population size,esident education level, political economy, and resident mind-ets are likely important drivers of urban forest fragmentationLiu et al., 2007a; Szantoi, Escobedo, Wagner, Rodriguez, & Smith,012; Turner & Meyer, 1991). For example, census tabulation of

ocal socioeconomic data has been used to investigate the neg-tive impacts of social, demographic, and economic activities onand cover change dynamics (Uusivuori, Lehto, & Palo, 2002). Also,everal time series of socioeconomic data have been utilized toetermine drivers in land cover change using a correlation anal-sis (Chen, Gong, Wu, & Yu, 2012), multivariate analysis (Aspinall,004), and econometric models (Veldkamp & Verburg, 2004).

Recently, many studies in China have focused on urban foresthange dynamics as a result of the interaction between rapid urban-zation and dramatic environmental deterioration (e.g., Cheng &u, 2010; Zhao et al., 2006; Zhao, Kong, Escobedo, & Gao, 2010;hou & Wang, 2011). A meta-analysis of major ecosystem servicesrovided by urban forests in China suggested that the researchcope and methods for describing ecosystem services need to bemproved (Jim & Chen, 2009). Specifically, the causal relationshipsetween urban forest fragmentation and socioeconomic factorshould to be investigated to further understand the role of anthro-ogenic forces in urban forest fragmentation.

The objective of this study was to investigate the changesn forest fragmentation rates during the urbanization process1973–2005) of China’s first special economic zone (SEZ) – Shen-hen – to examine the potential anthropogenic drivers as well asheir interactions on urban forest fragmentation. Shenzhen SEZ waselected as the study area for the following reasons: (1) after initialstablishment in 1979, the area experienced complete urbaniza-ion, which converted land-use from natural and/or agriculturalandscapes to a developed urban setting, (2) reliable, historical landover records were available via remote sensing technology (e.g.,he Earth Resources Technology Satellite launched in July 1972)rior to SEZ establishment, and (3) Shenzhen SEZ is the economiceform pilot area of mainland China with well-preserved seriesf socioeconomic details for achieving our study objective. Land-cape metrics and corresponding change dynamics for temporalhanges in forest fragmentation were measured using the urbanand and forest coverage maps that were derived from nine sets

f Landsat MSS/TM/ETM imagery (Gong et al., 2011). Step-wiseultiple regression models were used to elucidate causal relation-

hips between forest fragmentation processes and socioeconomicrivers. We hypothesize that a non-linear change in urban forest

n Planning 117 (2013) 57– 65

fragmentation will be apparent throughout the urbanization pro-cess and there will be a variety of significant diversity components(e.g., urbanization, economic development, government policy, andpopulation growth) that are responsible for the changes in forests.

2. Study area and methods

2.1. Study area

Located in southern coastal China (113◦51′ E 22◦27′ N and114◦21′ E 22◦39′ N), Shenzhen SEZ covers 395.8 km2 (Fig. 1) andis characterized by open plains, rolling hills, and mountains. Theclimate is southern subtropical with a mean annual temperature of22.4 ◦C and mean annual rainfall of 1933.3 mm.

As the first SEZ after the reformation of mainland China in 1979,Shenzhen served as the regional hub connecting the mainland toHong Kong, resulting in a change from a rural setting to a moderncity and a dramatic increase in economic development and popu-lation size. The gross domestic product (GDP) from 1979 to 2011increased from 0.20 billion RMB to 1150.20 billion RMB, and thepopulation increased from 0.31 million to 10.47 million (ShenzhenBureau of Statistics, 2012).

2.2. Land-cover change and spatial pattern detection

Nine sets of Landsat images covering the study area(Path130/Row44 and Path131/Row44 for MSS, Path121/Row44 andPath122/Row44 for TM and ETM+) from 1973, 1979, 1986, 1991,1995, 1998, 2000, 2003, and 2005 were used to derive the urbanland and forest coverage maps (Fig. 1, Appendix A). Detailed imageprocessing techniques, including radiometric calibration, geomet-ric correction, image classification, and accuracy assessment, wereperformed in Gong et al. (2011).

To further quantify changes in the urban forests of ShenzhenSEZ, landscape metrics (McGarigal, Cushman, Neel, & Ene, 2002)from three categories (i.e., patch density, patch distribution, andpatch shape) were calculated following Kong and Nakagoshi (2006),Li, Li, Wang, Ma, and Wang (2004), Matsushita, Xu, and Fukushima(2006), and Weng (2007). Because these landscape metrics arehighly correlated (Riitters et al., 1995), uncorrelated metrics haveto be carefully selected to maximize the required amount of data.Based on normality tests and correlation analyses with a signifi-cance level of 0.01, three uncorrelated metrics – patch density (PD,number/ha), Euclidian mean nearest neighbor distance (MNN, m),and area-weighted mean patch fractal dimension (AWPFD, no unit)– were used as proxies for forest patch density, distribution, andshape, respectively (Table 1). From classified land cover maps, PD,MNN, and AWPFD metrics were computed as:

PD = n

A(1)

where A is the total area of forest coverage (ha) and n is the totalnumber of forest patches.

MNN =∑n

i=1hi

n(2)

where hi is the distance (m) from forest patch i to its nearest neigh-boring forest patch, based on patch edge-to-edge distance, and iscomputed from cell center to cell center.

AWPFD =n∑[

FRACi

(ai∑

)]and FRAC = 2ln(0.25pi)

i=1 i=1

(3)

where pi is the perimeter (m) of forest patch i and ai is the area offorest patch i.

C. Gong et al. / Landscape and Urban Planning 117 (2013) 57– 65 59

over m

2

og

TS

TS

Fig. 1. Location of Shenzhen SEZ (a and b) and the time series of land-c

.3. Analysis of drivers

Based on available data and potential drivers, four categoriesf socioeconomic drivers (i.e., economic development, populationrowth, government policy, and urbanization) were used in this

able 1elected metrics on forest fragmentation in this study for understanding the changes of S

Abbreviation Name Description (unit)a

PD Patch density The number of patches of

area (number per hectaresMNN Euclidian mean nearest

neighbor distanceThe mean distance to the ntype, based on shortest ed

AWPFD Area-weighted mean patchfractal dimension

The sum of patch fractal dcorresponding patch type,patch (none)

a Details can be found in McGarigal et al. (2002).b Categories were adapted from Riitters et al. (1995).

able 2elected urban growth and socioeconomic factors in this study as explanatory factors in t

Abbreviation Description (Unit)

TGDPd Total gross domestic product (MCYa)

GDPA Gross domestic product of agriculture (MCY)

GDPS Gross domestic product of industry (MCY)

GDPT Gross domestic product of other professions (MCY)

LGR Local government revenue (MCY)

RIPCd Resident income per capita (TCYb)

TRP Total resident population (THPc)

TRH Total resident household (THP)

NIRP Natural increase of resident population, i.e., birth population mMIRPd Migrant increase of resident population, i.e., immigration popuTCAGd Total construction area of urban greenland (hectare)

AWMSI ULd Area-weighted mean shape index of urban patches (none)

MNN ULd Euclidian mean nearest neighbor distance of urban patches (mIJI ULd Interspersion and juxtaposition index between urban patches

a Million Chinese Yuan.b Thousand Chinese Yuan.c Thousand people.d Explanatory variables used in multiple regression models based on the results of corr

aps (c) derived from nine sets of Landsat imagery from 1973 to 2005.

study (Table 2). Urbanization (i.e., changes of urban patches) was

considered the most apparent and direct factor driving the changesin urban forest distribution. Other potential socioeconomic driversinclude total gross domestic product (TGDP), resident income percapita (RIPC), and migrant increase of resident population (MIRP).

henzhen forests through multiple regression analyses.

Categoryb

corresponding patch type divided by total landscape)

Patch size

earest neighboring patch of the same landscapege-to-edge distance (meter)

Patch distribution

imension indexes, across all patches of multiplied by the proportional abundance of the

Patch shape

he multiple regression models.

Category

Economic developmentEconomic developmentEconomic developmentEconomic developmentEconomic developmentEconomic developmentPopulation growthPopulation growth

inus dead population (THP) Population growthlation minus outmigration population (THP) Population growth

Government policyUrbanization

eter) Urbanizationand forest patches (percent) Urbanization

elation analyses (Table 3) and PCA analysis (Table 4).

60 C. Gong et al. / Landscape and Urban Planning 117 (2013) 57– 65

Table 3Results of correlation analyses (R2) among the socioeconomic drivers for exploring the changes in Shenzhen forests and landscapes.

TGDP GDPA GDPS GDPT LGR RIPC TRP TRH NIRP MIRP TCAG AWMSI UL MNN UL IJI UL

TGDP 1 0.781* 0.958** 0.868** 0.909** 0.616 0.597 0.221 −0.340 0.369 0.122 0.103 −0.525 0.464GDPA 1 0.602 0.906** 0.691 0.798* 0.871** −0.323 −0.170 0.536 0.322 −0.373 −0.241 0.318GDPS 1 0.717* 0.878** 0.424 0.353 0.399 −0.435 0.290 0.120 0.188 −0.675 0.338GDPT 1 0.879** 0.701 0.828* −0.260 −0.116 0.336 0.137 −0.068 −0.136 0.459LGR 1 0.509 0.514 0.014 −0.321 0.163 0.075 0.148 −0.388 0.407RIPC 1 0.796* −0.139 −0.283 0.159 −0.103 −0.351 −0.220 0.425TRP 1 −0.374 0.242 0.497 −0.029 −0.227 0.147 0.501TRH 1 −0.245 −0.016 −0.264 0.546 −0.534 0.235NIRP 1 0.327 −0.329 0.211 0.732* 0.153MIRP 1 0.460 −0.100 −0.133 0.225TCAG 1 −0.227 −0.201 −0.125AWMSI UL 1 0.204 0.634MNN UL 1 0.219IJI UL 1

ns.

Tsr

f2dp

f

wl2

i(c

A

wmao

M

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I

wpnu

dX

R

wvt

mapping, landscape pattern analyses, and statistical analyses wereconducted using Erdas Imagine 9, FRAGSTATS 3.3, and R 2.15,respectively.

Table 4Results of a principal component analysis (PCA) on the socioeconomic factors, show-ing the principal components with eigenvalue > 1.

Comp1 Comp2 Comp3 Comp4

TGDP 0.953 −0.250 0.097 0.034GDPA 0.923 0.309 −0.216 0.037GDPS 0.838 −0.491 0.072 0.085GDPT 0.939 0.210 0.060 −0.083LGR 0.873 −0.195 0.109 −0.098RIPC 0.769 0.211 −0.082 −0.431TRP 0.771 0.588 0.132 −0.089TRH −0.017 −0.814 0.382 0.119NIRP −0.283 0.653 0.521 0.246MIRP 0.443 0.281 −0.017 0.763TCAG 0.189 0.023 −0.528 0.673AWMSI UL −0.080 −0.364 0.863 0.201MNN UL −0.403 0.744 0.436 −0.059

* Correlation is significant at the 0.05 level.** Correlation is significant at the 0.01 level. See Table 2 for the variable descriptio

otal construction area of green spaces (TCAG) was also used as aocioeconomic factor that reflects government policy since it rep-esents government plans for urban forestry.

Annual socioeconomic data from 1979 to 2005 was retrievedrom published statistical yearbooks (Shenzhen Bureau of Statistics,006). Based on these data, socioeconomic data for 1973 were pre-icted using logistic regression models (Birnbaum, 1968) based onost-1979 data (all R2 > 0.97, P < 0.001):

(x, (b, d, e)) = d

1 + exp{b(log(x) − log(e))} (4)

here e is the dose producing a halfway response, d is the upperimit, and b denotes the relative slope around e (Ritz & Streibig,005).

The three urbanization factors – area-weighted mean shapendex (AWMSI UL), Euclidian mean nearest neighbor distanceMNN UL), and interspersion/juxtaposition index (IJI UL) – wereomputed as:

WMSI UL =n∑

i=1

[SHAPEi

(ai∑ni=1ai

)]and SHAPE = pi

min pi(5)

here pi is the perimeter (m) of urban patch i, min pi is the mini-um perimeter of patch i in terms of the number of cell surfaces,

i is the area of urban patch i, and AWMSI UL indicates the shapef patches.

NN UL =∑n

i=1hi

n(6)

here hi is the distance (m) from urban patch i to the nearest neigh-oring urban patch and MNN UL reflects the proximity among therban land patches.

JI UL =−∑m

k=1

[(eik/

∑mk=1eik) ln(eik/

∑mk=1eik)

]ln(m − 1)

× 100 (7)

here eik is the total length of edges (m) in the landscape betweenatch types i and k (i.e., urban and forest, respectively), m is theumber of patch types (m = 2 in this study), and IJI UL reveals therban interspersion between urban land and forest patches.

The change rates of urban or forest patterns and socioeconomicata at eight urbanization time stages were calculated followingie, Mei, Tian, & Xing (2005):

i =(

V2)1/n

− 1 (8)

V1

here Ri is the rate of change, V1 is the value at year t1, V2 is thealue at year t2, n is the difference in years (i.e., t2 − t1), and t1 and2 are the neighboring years.

Correlation analyses were used to examine the relationshipsamong all available socioeconomic factors (Table 2). If significantcorrelations were found among factors in the same category, a PCAanalysis was performed to determine which factor was the mostimportant in that category. For example, significant correlationswere found for TGDP, GDPA, GDPS, GDPT, and LGR (Table 3), andbased on its highest score in the PCA analysis (Table 4), TGDP wasselected from the economic development category while the otherfactors (e.g., GDPA, GDPS, GDPT, and LGR) were excluded. On theother hand, correlation analyses did not find any significant cor-relation among factors in the urbanization category, suggestingthat these factors reflected different dimensions of urban struc-ture change (see Eqs. (5)–(7)). Consequently, all factors from thiscategory were used in the final models. Based on the above criteria,two economic development factors (TGDP and RIPC), one popula-tion growth factors (MIRP), one government policy factor (TCAG),and three urbanization factors (AWMSI UL, MNN UL, and IJI UL)were selected as the explanatory variables in the step-wise multipleregression models.

Step-wise multiple regression models were used to examinethe causal relationships between the urban forest fragmentationmetrics and selected socioeconomic factors to determine the rel-ative importance, statistical significance, and interactions for eachexplanatory variable (see working flowchart in Fig. 2). Land-cover

IJI UL 0.481 0.069 0.753 0.049Eigenvalues 6.005 2.729 2.300 1.377Proportion 42.896 19.496 16.426 9.833Cumulative 42.896 62.392 78.817 88.650

C. Gong et al. / Landscape and Urban Planning 117 (2013) 57– 65 61

namic

3

3

fhrr

s

Fig. 2. Working flowchart for quantifying the landscape dy

. Results

.1. Changes in forest fragmentation metrics

When Shenzhen was established in 1979 as the first SEZ in China,orest covered 22.69% of the total area. By 1986, forest coveragead declined to a minimum of 14.03%. Through conservation and

econstruction efforts by the local government, forest coverage wasestored to 19.48% by 2005.

As urban forest coverage changed, there was also a shift in itspatial pattern. PD was 1.49/ha in 1973 and decreased to 1.31/ha in

s and empirical explorations of the socioeconomic causes.

1979 when the city was founded. As the city grew, PD increased to2.05/ha until 1991. Although there was a temporary decline after1991, PD reached 2.06/ha by 2005. Simultaneously, MNN decreasedfrom 146.76 m in 1973 to 111.31 m in 2005 while AWPFD increasedfrom 1.22 to 1.28 during the same period. Although forest coveragewas restored to ∼85% of the pre-urbanization level in 2005, therewas an overall trend of forest fragmentation with smaller, more

interspersed, and more complex forest patches in the Shenzhenlandscape.

The forest fragmentation rate had a non-linear trend over thedistinct time stages (Fig. 3). The change rate of PD was negative

62 C. Gong et al. / Landscape and Urban Planning 117 (2013) 57– 65

F mead 1973

b1ilsow

3

emafifAitTmepta

ig. 3. Annual changes in rates of (a) patch density (PD; number/ha), (b) Euclidianimension (AWPFD) of urban forests within the study area at eight time stages from

efore 1979, positive between 1979 and 1991, negative between991 and 2000, and positive again after 2000. MNN showed an

nverse trend to PD, but the final negative rate started at an ear-ier time stage (i.e., 1998–2000 instead of 2000–2003). There was aimilar trend in AWPFD to MNN, with the exceptional time stagesf the 1986 to 1991 and 2003 to 2005, when the same trend as PDas detected.

.2. The drivers of forest fragmentation

The step-wise multiple regression models provided furtherxplanation for the nonlinear trend in PD, MNN, and AWPFD, withodeled R2 of 0.837, 0.759, and 0.985 and P-values of 0.011, 0.035,

nd 0.004, respectively (Table 5). Additionally, the models identi-ed the relative importance of each explanatory variable selected

rom Table 2. Changes in PD were primarily due to changes inWMSI UL, while changes in MNN were primarily influenced by an

ncrease in RIPC. The model for AWPFD indicated that two urbaniza-ion factors, i.e., MNN UL and IJI UL, were the dominating drivers.he positive and negative influence of the drivers on forest frag-entation provided further insights on the variation over time. For

xample, RIPC had a negative influence on PD while AWMSI ULlayed a positive role. Hence, the de-fragmentation trends in PD athe fourth, fifth, and sixth time stages were likely due to the neg-tive influence of RIPC. However, due to the positive but primary

n nearest neighbor distance (MNN; m), and (c) area-weighted mean patch fractal through 2005.

driving influence of AWMSI UL, PD showed an overall increasedtrend of fragmentation.

4. Discussions

4.1. Urban forest area loss and fragmentation

The urbanization of Shenzhen SEZ over the past 30 years hasdrastically changed the local landscape structure, resulting in lossof forest area and fragmentation. In this study, we hypothesizedthat forest loss would be observed throughout the entire urban-ization process and this was supported by our results. The analysisof changes in urban forest area showed that 38.16% of forest wasremoved between 1979 and 1986. However, by 2005, the combi-nation of tree re-growth via old-field secondary succession andreforestation efforts by city residents and the local government(e.g., the Natural Forest Conservation Program launched in 1998;Zhang et al., 2000) restored the area to ∼85% of the forest in 1979.However, studies reveal that these young forests (plantation ornaturally regenerated) may not provide the equivalent ecosys-

tem services as the original mature forest (Escobedo et al., 2011;Kowarik, 2011), suggesting that future urban forest managementplans should incorporate strategies to maintain mature forests andold trees.

C. Gong et al. / Landscape and Urban Planning 117 (2013) 57– 65 63

Table 5Summary of the multiple regression models between forest fragmentation metrics (response variables) and socioeconomic drivers (explanatory variables).

Response variables Explanatoryvariables

Number ofvariables

Partial R2 Model R2 Parameterestimate

Standarderror

Pr > F (Sig.)

PDIntercept 0.102 0.043 0.065AWMSI UL 1 0.572 0.572 0.239 0.082 0.033RIPC 2 0.265 0.837 −0.727 0.256 0.036

MNNIntercept −0.071 0.025 0.035RIPC 1 0.612 0.612 0.703 0.181 0.012TGDP 2 0.147 0.759 −0.153 0.088 0.142

AWPFD

Intercept 0.001 0.0003 0.016MNN UL 1 0.585 0.585 0.005 0.001 0.003IJI UL 2 0.319 0.904 0.003 0.001 0.009MIRP 3 0.048 0.952 0.052 0.006 0.029TCAG 4 0.033 0.985 −0.039 0.006 0.084

S factors *TGD

fdAfoaZifpsPtta2smRbtisiMcfitsisc

Mfeaauom

4

p

ee Tables 1 and 2 for the variable descriptions. Required P-values for socioeconomicion equations: PD = 0.239*AWMSI UL-0.727*RIPC + 0.102; MNN = 0.703*RIPC-0.153

Human activities normally result in direct loss of forests, whichurther impact forest structure and composition and cause degra-ation of their ecological functions (Zhao, Escobedo, et al., 2010).n initial effort to quantify forest loss is necessary but not sufficient

or assessing the impact of urbanization on the forests. The studyn change in urban forest area and distribution will provide valu-ble information for future urban forest management. For example,hou and Wang (2011) used integrated approaches to character-ze the changes and intensities of green space in Kunming, Chinarom 1992 to 2009, and derived spatial variations of green spaceattern via concentric and directional landscape metrics analy-es. In the present study, three forest fragmentation indices (i.e.,D, MNN, and AWPFD) were used to quantify forest fragmenta-ion: patch size, patch distribution, and patch shape. PD indicatedhe change in the number of forest patches, and would reveal themount of forest area and its change over time (Boentje & Blinnikov,007). In practice, conserving large contiguous forests rather thanmall, isolated patches is needed to maintain a true forest environ-ent without edge effects (Chen, Franklin, & Spies, 1992; Essen &

enhorn, 1998). However, the forest distribution in Shenzhen SEZecame increasingly fragmented, as indicated by increased PD overhe study period. MNN estimated the extent to which patches werenterspersed as a “salt and pepper” mixture in the landscape. Themall MNN values found in this study indicated that the landscapes characterized with small and isolated patches (i.e., fragmented,

cGarigal et al., 2002). AWPFD was used as an indicator of shapeomplexity (Öhman & Lamas, 2005) and varied with changes inorest coverage in Shenzhen SEZ. An increase in AWPFD (i.e., morerregular forest patches) correlated well to an increase in fragmen-ation and a decrease in forest patch size. Altogether, these resultsuggest that there was an overall increase in forest fragmentationn Shenzhen SEZ after 30 years of urbanization, with a higher den-ity of patches, more interspersed patch distribution, and moreomplex patch shapes.

Contrary to a previous study on forest fragmentation trends inoscow, Russia (Boentje & Blinnikov, 2007), we found that the

ragmentation rates in Shenzhen varied over time. We originallyxpected that, as urban areas grow, the forest area would decreasend fragmentation would increase. Yet, we found that this is notlways the case over the study period, indicating the necessity ofnderstanding the underlying mechanisms following the conceptf the coupled human and natural ecosystem for future manage-ent.

.2. Socioeconomic drivers of urban forest fragmentation

Rapid urbanization, industrialization, economic reforms, andopulation growth have been described as the four major factors

s to enter and leave the step model were both 0.15. Summary of the multiple regres-P-0.071; AWPFD = 0.005*MNN UL + 0.003*IJI UL + 0.052*MIRP-0.039*TCAG + 0.001.

related to landscape change in China (Long, Tang, Li, & Heilig, 2007).Verburg, de Koning, Kok, Veldkamp, and Bouma (1999) suggestedthat, as urbanization and industrialization increases, the primarylandscape change would be driven by the increasing demand fornon-agricultural lands. This appears to be a concern for Shen-zhen, as illustrated by the primary contribution of urbanization tochanges in PD and AWPFD. In reality, the direct result of urbaniza-tion is the expansion of urban land area, as indicated by the changesin the shape of urban land patches (i.e., AWMSI UL) that triggeredthe increase in forest patch density. Meanwhile, urban expansionalso led to the distribution changes of urban land patches as shownby MNN UL and IJI UL, resulting in a more complex pattern of forestpatches. Tratalos et al. (2007) explored the relationships betweenurban structure, patch characteristics, and availability of tree coverin five United Kingdom cities and found that ecosystem perfor-mance declined with increased urban density. We expect a similarconsequence in Shenzhen.

The expansion of urban land in Shenzhen SEZ was primar-ily a result of industrialization, changing the internal structureof land-use in this region (Lin & Ho, 2003). Agriculture, indus-try, and other services or trades contributed 37.0%, 20.5%, and42.5% in 1979 and 0.2%, 53.2%, and 46.6% in 2005, respectively(Shenzhen Bureau of Statistics, 2006). Apparently, the dramaticchange in economic structure was mainly due to the increase inindustry, which was tightly coupled with the rapid urbanizationin Shenzhen. In addition, as the first SEZ, the central governmentgranted Shenzhen beneficial tax policies that attracted foreigninvestment to promote its economic growth. According to theShenzhen Bureau of Statistics (2006), foreign direct investment(FDI) increased from $5.48 million in 1979 to $2.97 billion in 2005.Consequently, the total volume of imports and exports increasedsharply from $16.76 million in 1979 to $182.82 billion in 2005(Shenzhen Bureau of Statistics, 2006). The advantage of beinga port city connected to Hong Kong enabled the city to attractlarge amounts of direct foreign investment (Liu, Heilig, Chen, &Heino, 2007). As a result, the booming economic developmentsupported a nearly 3975- and 56-fold increase in TGDP and RIPCfrom 1979 to 2005, respectively (Shenzhen Bureau of Statistics,2006).

Although previous research has shown that fast economic devel-opment would facilitate deforestation (Hobbs, Schimel, Owensby,& Ojima, 1991), our empirical models confirmed that economicdevelopment produced variable effects on forest fragmentation.For example, significantly increased RIPC explained 26.5% of the

variation in forest PD and 61.2% of the variation in forest MNN.However, increased TDGP was responsible for only 14.7% of thevariation in forest MNN. Furthermore, the increased RIPC had aneffect on forest de-fragmentation, reflecting the importance of city

6 Urba

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iTtd(TbKfaeoitcatH

5

rede

4 C. Gong et al. / Landscape and

esident income on urban forest coverage as the economy developsSzantoi et al., 2012).

The investment of the local government and the public on con-truction of artificial urban forest (TCAG) can alter the effects oforest fragmentation on patch shape. No correlation was foundetween TCAG and TGDP or RIPC, demonstrating that TCAG wasrobably not influenced by socioeconomic development. More

mportantly, the multiple regression model for AWPFD addressedhe growing concern on the limited role of artificial forestation (i.e.,CAG) in mitigating forest fragmentation. Therefore, more effec-ive planning and policy decisions should be sought for the future

anagement of urban forests for Shenzhen SEZ.Population growth has long been considered a major factor

nfluencing changes in land-use (Hobbs et al., 1991; Lin & Ho, 2003;anrivermis, 2003). As population increases, there is an increase inhe demand for immediate life-sustaining resources, such as resi-ence space and food, and a corresponding decrease in forest areaDöös, 2002; Uusivuori et al., 2002). For example, oak forests in thewin Cities metropolitans of Minnesota, USA, decreased by 5.8%etween 1991 and 1998, while population grew by ∼15% (Ward,romroy, & Juzwik, 2007). However, a similar study in New Jersey

ound only a 0.8% decrease in urban forests between 1986 and 1995s the population increased by 5% (MacDonald & Rudel, 2005). Inastern China, urban green space in Shanghai was found to increasever the past 30 years as urban population and area dramaticallyncreased (Zhao et al., 2006). We found a small influence of popula-ion growth in promoting complex forest patch shapes only whenombined with changes in urban structure. This is likely due to thessociated demands of immigration on population growth that ledo rapid increases in the need for living space (Livingston, Shaw, &arris, 2003).

. Conclusions

Although urban forest coverage in Shenzhen SEZ had beenestored to ∼85% of the pre-urbanization level, the urban for-

st was fragmented with a high patch density, more interspersedistribution, and complex shapes. The fragmentation rates, how-ver, were non-linear over time as measured by patch density,

Year WRS path/row,sensor andacquisition date

Geometric regis

1973 p131r44 MSS 19731225p130r44 MSS 19731031

±0.290

1979 p131r44 MSS 19791019p130r44 MSS 19790930

±0.297

1986 p121r44 TM5 19861224p122r44 TM5 19870106

±0.278

1991 p121r44 TM5 19911009p122r44 TM5 19901013

±0.262

1995 p122r44 TM5 19951230 ±0.241

1998 p122r44 TM5 19981222 ±0.236

2000 p122r44 TM7 20001101 Master Image

2003 p122r44 TM7 20030110 ±0.235

2005 p122r44 TM5 20051123 ±0.238

n Planning 117 (2013) 57– 65

distribution, and shape. Furthermore, socioeconomic factors,specifically, changes in urban structure, economic development,and government policy, were the major drivers of the fragmen-tation in Shenzhen. With changes in urban structure and economicdevelopment as the primary independent variables, our step-wisemultiple regression models explained 83.7%, 75.9%, and 98.5%of the variation in the fragmentation rates of forest patch den-sity, distribution, and shape, respectively. Urban structure change,industry-related economic booming, and the increase of migrantresident population triggered the urban forest fragmentation in thiscity while the significantly increased income of city residents drovethe de-fragmentation trend. The artificial forestation showed somebut a limited role in mitigating forest fragmentation.

We conclude that the dynamics of urban forest landscapehad become increasingly complex as the urbanization proceeded,which resulted from various influences of socioeconomic changes.The non-linear changes in Shenzhen SEZ were likely due to theeffect of interacting positive and negative drivers. These findingswill be beneficial in future urban forest management strate-gies and policy decisions in this region. While the model andapproaches developed in the present study could be appliedelsewhere to aid in studying and managing urban forests, thesocioeconomic influences used in this study (e.g., governmentpolicy) may need to be modified. Finally, lessons learned fromthis study are on the causes of forest fragmentation in ShenzhenSEZ. Future efforts should be also made on the consequences offragmentation, such as their effects on ecosystem services (e.g.,biodiversity; productivity; regulations of urban heating, Tian et al.,2013).

Acknowledgments

This study was supported by the City Management Departmentof Shenzhen, the Zhang-Hongda Science Foundation of Sun Yat-senUniversity and the China Scholarship Council.

Appendix A.

The summary of the Landsat images used in this study (revisedfrom Gong et al., 2011).

tration RMSE Overall accuracy Kappa

75.19% 0.71

83.76% 0.80

87.64% 0.85

77.12% 0.74

86.50% 0.82

90.14% 0.8889.67% 0.8690.82% 0.8991.00% 0.89

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Chongfeng Gong is currently a postdoctoral research associate of Gulf CoastResearch Laboratory, University of Southern Mississippi, USA. He received the PhDin Landscape Ecology from Sun Yat-sen University (China). He was also a visiting PhDstudent at the University of Toledo (USA). His areas of professional interests includelandscape ecology, urban ecology, remote sensing, GIS and ecological modeling.

Shixiao Yu is a professor of the Department of Ecology, School of Life Sciences,Sun Yat-sen University, China. He received his doctorate from Sun Yat-sen Univer-sity (China). His areas of professional interests are plant biodiversity and landscapeecology.

Heather Joesting is currently a postdoctoral research associate of Gulf CoastResearch Laboratory, University of Southern Mississippi, USA. She received thePhD in Plant Physiological Ecology from Wake Forest University (USA). Her currentresearch interests focus around three themes: plant physiology, landscape stressors,and restoration.

Jiquan Chen is a professor of the Department of Environmental Sciences, Universityof Toledo, USA. He received his doctorate from University of Washington (USA).His research and teaching interests including carbon and water cycle, ecosystemscience, ecological modeling and conservation biology.