Quantifying different types of urban growth in Guangzhou using multi-temporal remote sensing data

International Journal of Applied Earth Observation and Geoinformation 21(2013)409–417Contents lists available at SciVerse ScienceDirectInternational Journal of Applied Earth Observation andGeoinformationj o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /j agQuantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing dataCheng Sun a ,b ,c ,∗,Zhi-feng Wu c ,d ,Zhi-qiang Lv e ,Na Yao f ,Jian-bing Wei caGuangzhou Institute of Geochemistry,Chinese Academy of Sciences,Guangzhou 510640,China bGraduate University of Chinese Academy of Science,Beijing 100049,China cGuangdong Institute of Eco-environment and Soil Sciences,Guangzhou 510650,China dSchool of Geographical Sciences,Guangzhou University,Guangzhou 510006,China eDepartment of Land Resource Management,Chongqing Technology and Business University,Chongqing 400067,China fSchool of Remote Sensing Information Engineering,Wuhan University,Wuhan 430079,Chinaa r t i c l ei n f oArticle history:Received 21May 2011Accepted 17December 2011Keywords:Urban growth types Change dynamic Spatial metricsObject-oriented classificationa b s t r a c tThere is a widespread concern about urban sprawl.It has negative impacts on natural resources,economic health,and community character.Without a universal definition of urban sprawl,its quantification and modeling is difficult.Traditionally,urban sprawl was described using qualitative terms,and landscape patterns.Quantitative methods are required to help local,regional and state land use planners to better identify,understand and address it.In this study,an integrated approach of remote sensing and GIS was used to identify three urban growth types of infilling growth,outlying growth and edge-expansion growth at the city of Guangzhou,China.Spatial metrics were used to characterize long-term trends and patterns of urban growth.Result shows that the proposed method can identify and visualize different urban growth types.Infilling growth is the dominant expansion type.Edge-expansion is concentrated at suburban areas.Outlying growth mainly occurs relatively far from the urban core.The analysis shows that initially the urban area expands mainly as outlying growth,causing increased fragmentation and dispersion of urban areas.Next,growth filled in vacant non-urban area inwards,resulting into a more compact and aggregated urban pattern.The study shows an improved understanding of urban growth,and helps to provide an effective way for urban planning.©2012Elsevier B.V.All rights reserved.1.IntroductionUrbanization has been an important social and economic phe-nomenon taking place at an unprecedented scale and rate all over the world.The dynamic process of urbanization has led to fun-damental changes resulting in various impacts on the structure,functions,and dynamics of ecological systems at a wide range of scales (Luck and Wu,2002;Charles et al.,2005;Ma et al.,2008).Much research has addressed urban growth,and especially the concept of urban sprawl (Ewing,1997;Hasse and Lathrop,2003).Evidence of the environmental impact of sprawl has been well-documented (Ewing,1997;Hasse and Lathrop,2003).It is cited as a factor in air pollution because of the car-dependent lifestyle imposed by dispersed land development leading to increased fossil fuel consumption (Johnson,2001).Widely dispersed development requires more pavements that cause more urban run-off,thus pol-luting waterways (Tang et al.,2005).Other concerns focused on∗Corresponding author.Tel.:+862087024633;fax:+862087024123.E-mail address:sunz0412@ (C.Sun).the increasing loss of critical land resources such as agriculture (Camagni et al.,2002).Despite these concerns,there is no universally accepted defi-nition of urban sprawl (Hoffhine Wilson et al.,2003;Hasse and Lathrop,2003;Bhatta et al.,2010).Without a universal defini-tion,quantifying and modeling urban sprawl is difficult,especially from remote sensing data (Bhatta et al.,2010).Traditionally,urban sprawl has been described using qualitative terms (Ewing,1997).Several recent studies attempted to develop a means of character-izing sprawl (Hasse and Lathrop,2003).A general consensus exists that urban sprawl is characterized by unplanned and uneven pattern of growth,leading to ineffi-cient resource utilization (Ewing,1997;Hasse and Lathrop,2003;Bhatta et al.,2010).Research has further pointed out that the use of the term urban sprawl has a negative connotation,and not all urban growth is considered to be sprawl (Hoffhine Wilson et al.,2003).Recent research has started to pay attention to differ-ent types of urban growth.This identifies both how much urban growth has occurred,and also its ing results of urban growth types,users could decide what is to be considered as sprawl according to the study objective (Hoffhine Wilson et al.,2003).0303-2434/$–see front matter ©2012Elsevier B.V.All rights reserved.doi:10.1016/j.jag.2011.12.012410 C.Sun et al./International Journal of Applied Earth Observation and Geoinformation21(2013)409–417Developing a quantitative method to identify different types of urban growth is useful and meaningful to help local,regional and state land use planners better understand and address the issues attributed to sprawl.In many of the previous researches,urban growth analyses were conducted either at the metropolitan scale as a whole(Weng,2002;Yang and Lo,2002;Serra et al.,2003; Xian and Crane,2005;Huang et al.,2007;Jat et al.,2008a,b),or within natural landscape boundaries such as those of a watershed (Clapham,2003).Technology about quantifying different urban growth types from remote sensing data has not been adequately investigated.Hoffhine Wilson et al.(2003)first quantified three urban growth types of infilling,edge-expansion,and outlying from Land-sat classification imagery.Each type represents a special urban growth pattern.Infilling means the non-urban area surrounded by urban being converted to urban.Edge-expansion refers to the newly developed urban area spreading out from the fringe of existing urban patches.The outlying growth tends to be distributed at a larger distance from existing developed areas. It can be further classified into isolated,linear and clustered growth.Hoffhine Wilson et al.(2003)quantified three urban growth types according to the percentage of land cover types by a moving window method.The results,however,may vary with the win-dow size.Xu et al.(2007)observed that the ratio between the length of common edge and patch perimeter can be used to dis-tinguish between infilling,edge-expansion,and outlying growth, but no spatial visualization of different urban growth types has been provided.Pham and Yamaguchi(2011)used a percentage of a like adjacency metric to generate the three urban growth types from Landsat TM/MSS imagery.The results are likely to have a relationship with the window size.In this study,we developed a quantitative method in order to identify and visualize different types of urban growth.Research on different urban growth types has been mainly rmation derived solely from maps of urban growth types is incapable of describing the spatial temporal details of urban pattern dynamics.Each of the three urban growth types is generated by a specific urban growth process,which may lead to different environmental impact.Therefore,characterizing the changing pattern and trend of urban growth types over time is essential for monitoring and alleviating ecological consequences caused by urban growth and urban sprawl(Luck and Wu,2002).There has been an increasing interest in applying spatial met-rics in analyzing land use dynamics and urban growth processes (Charles et al.,2005;Jat et al.,2008a,b;Deng et al.,2009).Remote sensing provides a spatially consistent coverage of large areas with a high spatial detail and a high temporal frequency.It has advan-tages in characterizing the spatial temporal trends of urban growth using multi-stage images(Weng,2002;Yang and Lo,2002;Serra et al.,2003;Xian and Crane,2005;Huang et al.,2007;Jat et al., 2008a,b).Combined with spatial metrics,remote sensing data offer the potential to characterize the change of urban growth dynamics (Herold et al.,2003).This paper explores how using remote sens-ing technology in combination with spatial metrics can improve the understanding of urban growth processes.Urbanization has caused significant impacts on the environ-ment.Therefore,quantifying the urban growth pattern and its change is essential for monitoring and assessing urbanization and its ecological consequences.The primary objective of this research is to quantify different urban growth types.The change dynamics have been explored in Guangzhou,a fast growing region in south China.To address these objectives,multi-temporal remote sensing data and GIS are used to quantify infilling,edge-expansion,outly-ing urban growth types,and to characterize the long-term trends and patterns of urban growth.2.Study area and data pre-processing2.1.Study areaGuangzhou is located in south China between22◦26 N–23◦56 N and112◦57 E–114◦3 E,with an area of approximately7434.4km2. It is located in the piedmont and coastal plain regions at the conflu-ence of Pearl River(Fig.1).Guangzhou consists of10administrative districts and two county-level cities.Huadu and Panyu,formerly county-level cities,were amalgamated to Guangzhou as urban districts by the State Council in June2000.This study focused on the core area of Guangzhou,which is composed of eight dis-tricts,including Liwan,Yuexiu,Dongshan,Haizhu,Fangcun,Tianhe, Baiyun and Huangpu district.Guangzhou is the largest city in south China.It is also the bus-iest port in China after Shanghai.In2008,Forbes assessed the Top Ten Cities that had experienced the largest changes in China. Guangzhou,which was an agricultural city30years ago,was now on the top of the list of international big cities.As the capital of the Guangdong province,it is an important political,economic, cultural,and scientific center in southern China.Guangzhou has experienced different economic stages since1970s.Rapid urban development started in the1970s,when the central government stimulated economic growth by embarking on a series of ambi-tious reforms.Since the implementation of the economic reform and opening-up policy at the late of1970s,Guangzhou has expe-rienced large economic growth and rapid urbanization.After the introduction of the land market reform,the economic value of land has been fully recognized in nd became a main source of revenue for the government(Zhang,2000),thus providingfinan-cial resources for the construction of new roads,resulting in a fast urban growth in period1990–1995.Affected by the Asianfinancial crisis,the economic growth of China declined slowly during the first half of1998.Exports,imports,and foreign direct investments fell sharply,resulting into a decrease of urban growth(GAR-ON YEH and Li,1999).In2000,the Panyu District was incorporated into the city scope of Guangzhou.After that urban areas expanded in a gradually accelerating rate under the guidance of a new urban development planning strategy.2.2.Data pre-processingAs urban growth rate in Guangzhou is not even,we selected four periods to characterize the long-term trends and patterns of urban growth.Five cloud-free Landsat MSS/TM/ETM+images(path 122,row44)of Guangzhou were acquired on October19,1979 (MSS),October13,1990(TM),December30,1995(TM),September 14,2000(ETM+)and December1,2008(TM).These images were rectified to the WGS-84datum and Universal Transverse Merca-tor zone49N coordinate system.The geometric rectification was based on ground control points(GCPs)that were evenly spread over the study area.The root mean square error(RMSE)was less than0.4pixel.The nearest-neighbor algorithm was used to resam-ple all the reflectance bands of MSS/TM/ETM+images to a spatial resolution of30m.Object-oriented classification was used to get land use maps for thefive years.In addition to the spectral signature,the tone,tex-ture,and shape of image objects were evaluated when performing the object-oriented classification(Geneletti and Gorte,2003;Benz et al.,2004;Durieux et al.,2008).In addition,the Normalized Differ-ence Water Index(NDWI)was combined with other images bands for image segmentation and classification as it reportedly has the potential to highlight the information of water bodies(Xu,2006;C.Sun et al./International Journal of Applied Earth Observation and Geoinformation21(2013)409–417411Fig.1.Location of the study area,Guangzhou city,in the Southern part of China. Jackson et al.,2004;Chen et al.,2009).The NDWI is expressed byEq.(1):NDWI= G− NIRG+ NIR(1)where G and NIR are the near-infrared band and red band reflectances of TM images,respectively(Gao,1996).This index pro-duced values in the range from−1to1,in which positive values indicate water areas and negative values signify non-water regions.Six land use types were identified:urban area,cropland,shrub,fishery,bare land,forest,and water.Urban area,as used in this study,is synonymous with developed land,and includes residential as well as commercial and industrial land uses.These have proven to be key indicators for monitoring the spatial extent and intensity of urbanization(Clapham,2003).The overall accuracy was89.11%,86.52%,88.16%,89.24%,and 89.94%and the Kappa coefficient was0.8503,0.8412,0.8317, 0.8615,and0.8741for the years1979,1990,1995,2000and2008, respectively.For the purpose of this study,the classification result is satisfactory.Since the focus of this study was urban growth, the classified land use types were further converted into the two classes:urban and non-urban.3.Methods3.1.Identify three urban growth types of infilling,edge-expansion,and outlyingHoffhine Wilson et al.(2003)developed a quantitative method to characterize three urban growth categories:infilling,edge-expansion,and outlying.Xu et al.(2007)and Pham and Yamaguchi (2011)further developed Hoffhine Wilson’s method,maintaining the definition of the urban growth categories.Other patterns can be regarded as variants or hybrids of these three basic forms(Hoffhine Wilson et al.,2003;Xu et al.,2007).This study also adopts Hoffhine Wilson’s definition of the urban growth categories.The ratio between the length of common edge and patch perimeter of patches was used to distinguish these urban growth types(Xu et al.,2007).A patch means a relatively homo-geneous area that differs from its surroundings.It is the basic unit of landscape change.In previous research,GIS along with remote sensing data can help in analyzing the growth,pattern and extent of sprawl(Sudhira et al.,2004).In this study,we use GIS to visualize the three urban growth types.To get the three urban growth types,wefirst converted the clas-sified images into vector format.For each period,the urban patches412C.Sun et al./International Journal of Applied Earth Observation and Geoinformation 21(2013)409–417Table 1Definitions of spatial metrics for the urban growth change analysis.Metrics (abbreviation)EquationVariablesUnitPatch Density (PD)n i A(10,000).(100)n i =number of patches in the landscape of patch type (class)i ;A =total landscape area (m 2)Number per 100haLandscape Shape Index (LSI)e i min e ie i =total length of the edge of class i ;mine i =minimum total length of edge (or perimeter)of class iNoneEuclidean Nearest-Neighbor Distance (ENN)h ijh ij =distance (m)patch distance of the same typeMetersMean Patch Size (MPS)ni =1a iNa i =area (m 2)of patch of class i ;n i =number of patchesha 2were divided into two groups,i.e.newly developed urban patches and old urban patches.The urban patch which existed in the two adjacent study years was defined as old urban areas,wheras the patch that did not exist in the previous year and appeared in the lat-ter year was labeled as newly developed urban area.Fig.2illustrates the three types of urban growth.We define a metric R for calculating the ratio between the length of common edge between newly developed urban patches and existing urban patch and the total perimeter of the newly developed urban patch as:R =l c l(2)where l c is the length of the common edge between a newly devel-oped urban patch and an existing urban patch and l is the perimeter of the newly developed urban patch.The value of R ranges from 0to 1.The infilling type has R >0.5,and refers to the development of newly developed urban patch surrounded by at least 50%old urban area (Fig.2a).Edge-expansion has 0<R <0.5as it is characterized by the newly developed urban area spreading out from the edge of an existing urban area and surrounded by less than 50%exist-ing urban area (Fig.2b).Outlying growth is newly developed urban area without spatial connection to the existing urban area (Fig.2c)and hence has R =0.For each newly developed urban patch,the value of R can be quantified and calculated according to Eq.(2).After convertingpolygon features into line features,the common edge was extracted according to the feature attribute.3.2.Spatial metricsTo distinguish the pattern of the urban growth types,this paper used spatial metrics.Several spatial metrics are highly correlated,although no single metric can capture the complex patterns of urban change (Luck and Wu,2002;Herold et al.,2003;Huang et al.,2007;Hung et al.,2010).We combined four landscape-level spa-tial metrics selected to characterize the urban landscape pattern,i.e.the Landscape Shape Index,Patch Density,Euclidean Nearest-Neighbor Distance (ENN)and mean Patch Size (MPS)(Table 1).These metrics describe four key aspects of the landscape:edge,density,isolation,and size metric and have proven to be useful in quantify unique spatial urban characteristics (Neel et al.,2004;Weng,2007;Xu et al.,2007;Ji et al.,2006).The Landscape Shape Index (LSI)is a measure of irregularity of the landscape and should increase with the emergence of new urban nuclei,but may decline as urban areas fuse.Patch Density (PD)is a measure of landscape heterogeneity and is lowest when the urban landscape is dis-persed and fragmented.The Euclidean Nearest-Neighbor distance (ENN)equals the distance over all patches of a class to the nearest neighboring patch based on shortest patch edge-to-edge distance,computed from cell center to cell center.The Mean Patch Size (MPS)is a measure of the average area of all patches in the landscape.Calculations of spatial metrics were performed using thesoftwareFig.2.Three types of urban growth.The grey area represents the newly developed urban areas and the dark area represents the old urban areas.C.Sun et al./International Journal of Applied Earth Observation and Geoinformation21(2013)409–417413Fig.3.Urban areas as observed atfive years between1979and2008. Table2Urban area of Guangzhou infive different years.Year19791990199520002008Urban area(km2)63.35146.94251.19310.14487.66FRAGSTATS version3.3.The four spatial metrics were calculated forurban areas offive years respectively.4.Results4.1.Detection of urban area from1979to2008Fig.3shows the urban area in Guangzhou from1979to2008.Asshown in Table2,the urban area in Guangzhou was63.3km2,cov-ering4.6%of the study area.In2008,the urban area in Guangzhouequaled487.7km2,covering35.6%of the study area.Apparently,the urban area expanded by more than seven times during30years.Table3shows the spatial distribution of different urban growthtypes in four periods.The increasing rate of urban area wasdiffered in each of the four periods:1979–1990,1990–1995,1995–2000and2000–2008.During the period1979–1990,theurban area of Guangzhou increased by83.6km2,at an averageannual rate7.59km2yr−1.The urban area increased to251.2km2during the period1990–1995,growing at an average annual rateTable3Increasing rate and annual change rate for urban area in four different periods1979–1990,1990–1995,1995–2000and2000–2008.1979–19901990–19951995–20002000–2008Increasing area(km2)83.59104.2658.94177.52Annual change rate (km2/year)7.6020.8511.7922.1920.85km2yr−1.During the third period(1995–2000),the urbanarea increased to310.13km2at an average annual growth rate of11.79km2yr−1,which was a little slower than before.In the fourthperiod(2000–2008),the mean annual growth rate was equal to22.19km2yr−1.4.2.Map three urban growth typesThe output for the three urban growth types according to Eq.(2)is a series of maps that illustrate the changes in the urban areasof Guangzhou during the four periods:1979–1990,1990–1995,1995–2000and2000–2008(Fig.4).Edge-expansion growth was mainly taken place along the majortransportation networks.Infilling growth mainly occurred withinthe free spaces among urban core areas.Outlying growth wasmainly scattered far from the urban core.As can been seen from Fig.4,urban growth shows distinctgrowth patterns during the different periods in study area.In thefirst period1979–1990,urban growth was dominated by the out-lying growth and infilling growth was less(Fig.4a).During theperiod of1990–1995,outlying growth decreased,while the edge-expansion growth became the dominant type(Fig.4b)and infillinggrowth increased as well.In this way,a multi nuclei urban patternwas formed in Guangzhou.During the period of1995–2000,theoutlying-type growth was still decreasing,and the edge-expansiontype and the infilling growth became dominant(Fig.4c).Duringthe last period2000–2008,edge-expansion and infilling growthremained dominant(Fig.4d).To obtain a more detailed temporal characteristic of urbangrowth in Guangzhou,the area and proportion of the three growthtypes in the four periods was calculated.Fig.5shows that edge-expansion growth was the dominant type of urban growth duringthe whole study period.As the majority urban growth type,outly-ing growth reached a peak value during the period1990–1995and414C.Sun et al./International Journal of Applied Earth Observation and Geoinformation 21(2013)409–417Fig.4.Spatial distribution of three urban growth types in study area during the four periods 1979–1990,1990–1995,1995–2000and 2000–2008.then decreased,whereas outlying and infilling growth increased.During 2000–2008,infilling growth exceeded outlying growth and became the main type of urban growth.There was little evidence of infilling growth before 2000.In the first period 1979–1990,the infilling type of the entire study area was 5.04km 2,covering 6.03%of the total newly developed urban areas.Outlying growth reached the peak value during this period,with the proportion of 82.37%.The edge-expansion type accounted for a considerable proportion of 16.5%.In the period of 1990–1995,the proportion of the outlying type reduced to 60.5%.In contrast,there was a tremendous increase of the infilling type and the edge-expansion type,with the proportion of 25.2%and 63.7%respectively.During the period 1995–2000,the area of the infilling type increased to 14.37km 2,covering 24.37%of the totalnewly developed urban areas.Meanwhile the edge-expansion type accounted for 51.43%,whereas the outlying type decreased to 24.2%in this period.During the period 2000–2008,the outlying type became the smallest type (5.02%),whereas the infilling and edge-expansion types dominated,increasing to 47.47%and 47.5%,respectively.4.3.Changing patterns of urban areaAn analysis of spatial patterns of urban growth can further strengthen the understanding of urban growth dynamics.The met-ric signatures shown in Fig.6reflect the dynamic patterns of the urban growth.C.Sun et al./International Journal of Applied Earth Observation and Geoinformation 21(2013)409–417415Fig.5.Area of three urban growth types during the four periods 1979–1990,1990–1995,1995–2000and 2000–2008.Fig.6a shows the changes of Patch Density (PD).Patch den-sity increased exponentially with accelerating urbanization in Guangzhou,reaching its peak in 2000.The Euclidean Nearest-Neighbor Distance (ENN)reached its highest value in 1979.With the emergence of new urban cen-ters,urban patches fused together and boundaries of urban areas became dissolved.The value of ENN decreased from 374m to 181m in 1995.Fig.6c shows the changes of the Landscape Shape Index (LSI).The LSI value increased steadily in the early stage of urbanization,and it decreased during the period 2000–2008.The Mean Patch Size (MPS)decreased continuously before 2000,in contrast to the LSI.The lowest value of Mean patch size was 19ha 2which occurred in the year 2000.Thereafter it increased,reaching its highest value of 36ha 2in the year 2008.5.Discussion5.1.Urban growth typesThis paper describes an approach to identify different urban growth types.Input data of this method are land cover maps which were converted to two classes:urban and non-urban.This method can be easy to calculate for widespreadapplication.Fig.6.Spatial metrics of Guangzhou during the period 1979–2008:(a)Patch density (PD);(b)Euclidean Nearest-Neighbor Distance (ENN);(c)Landscape Shape Index (LSI);and (d)Mean Patch Size (MPS).416 C.Sun et al./International Journal of Applied Earth Observation and Geoinformation21(2013)409–417Ewing(1997)states that,whatever is used to separate sprawl development and non-sprawl development must be quantifiable and related to impacts.This paper describes an approach to quantify and visualize different urban growth types.Although our method is not directly related to impact,users of our method could decide what to consider as sprawl according to the study objective.5.2.Change dynamic of urban growth types along with urban landscape patternIn this study,the combined analysis of three urban growth types and spatial metrics demonstrated two distinct phases of urban growth in Guangzhou from1979to2008.At the initial period of urban growth,urban expansion mainly occurred as outlying,caus-ing an increased fragmentation and dispersion of urban areas.As urban growth continues,newly grown urban area started tofill in the vacant non-urban area inwards.With the increase of infilling growth,the urban pattern became more aggregated and compact. This process is reflected in the spatial metrics.Urban growth in Guangzhou started with the expansion of the urban seed or urban core.The density metric(PD)is a gen-eral measure of landscape heterogeneity and is lowest when the urban landscape is more dispersed and fragmented.The lowest PD,LSI value and the largest MPS,ENN values in1979indicated the small urban core at the initial stage of urban growth.At the initial period of urban growth,urban area expanded mainly as out-lying,and there was little evidence of infilling growth in this period (Figs.4and5).With the increase of outlying growth,more indi-vidual urban patches are formed,causing a multiple nuclei urban growth.This process caused an increased fragmentation and com-plexity of the urban area,as reflected in the spatial metrics(Fig.6). It is also reflected by the decreasing isolation metric and size metric in the initial period.Urban expansion along both sides of the roads was particularly apparent from1990onwards when urban growth occurred around the periphery of the initial urban area,leading to edge-expansion growth.Hence the urban area became less dispersed,and frag-mentation decreased.This is reflected by a peak in the four spatial metrics(Fig.6).After1995,urban area became more spread out from the fringe of existing area and vacant non-developed urban area inwards werefilled in and the urban pattern become more aggregated and compact(Fig.4).The increase of individual urban patches and expansion into open spaces continued toward the later stage of urban growth.As the infilling type became the predominant growth type,the urban core started to form a homogeneous urban patch as is reflected by the decreasing patch density and edge metric in later periods as well as by the increase in isolation metric,and size metric(Fig.6).The analysis in this paper from the observation of urban growth in Guangzhou in a29-year period might contribute to urban model-ing.The impact of driving socioeconomic factors on urban dynamics might better explain the mechanism of periodicity of urban growth pattern.With a spatial and temporal analysis along with sufficient information provided by socioeconomic data,a guide for poten-tially more accurate representations of dynamic spatial processes might be given.Further,this research has resulted in an improved understand-ing of urban growth,and may have implications for urban planning. Rapid urban growth in Guangzhou has led to severe environmen-tal issues as urban development encroached upon cultivated land. The loss of agricultural land is alarming in the Pearl River Delta (Li and Yeh,2004).In Guangzhou,most regions surrounding the urban areas are used for agriculture.After the implementation of economic reform and opening-up policy in1979,several new development districts of various types such as Special economic development zones,and High-tech industry zones were established to attract foreign investment(Seto and Fragkias,2005;Yu and Ng, 2007).These new development industrial zones were constructed in rural areas,usually on agricultural land because of a low devel-oping cost.The rapid urban expansion resulted in agricultural land loss(Li and Yeh,2004).As the rapid urban expansion between1979 and1995encroached on a large amount of cropland,the central government started to implement stricter land use management measures.The‘Ordinance for the Protection of Primary Agricultural Land’was implemented in1994(Li and Yeh,2004).With the imple-mentation of the cropland protection strategy and the change of administrative boundaries in2000,most future growth types may turn out to be infilling,a trend which is already evident.When the growing space has been compressed and urban form has become more compact,however,the main form of urban growth may return to outlying growth.Apparently,the loss of agricultural land is inevitable in fast growing regions,but planning may focus on min-imizing this amount in the future.Considering that urbanization in Guangzhou is rapid,it is necessary to pay attention to land policies and the role of urban planning in Guangzhou.While agricultural and economic policy reforms have raised economic development, they have also caused problems in agriculture land loss.Therefore, a key question facing policymakers now is how to manage urban growth and its direct and indirect consequences.Therefore,a sus-tainable land development strategy guiding future directions and patterns of urban growth needs to be developed to minimize the amount of cropland loss caused by urban expansion in cities like Guangzhou.6.ConclusionIn this study,an integrated approach of remote sensing,and GIS was used to identify different urban growth types in Guangzhou. The dynamic pattern and change trend of three urban growth types was investigated using multi-stage remote sensing data and spatial metrics.The study revealed that the spatial relationship between urban patches is capable of identifying different urban growth types. Newly developed urban areas were grouped into infilling,edge expansion,and outlying growth.The method visualized the dif-ferent types of urban growth.Infilling growth was the dominant expansion type in urban core,whereas outlying growth was mainly located relatively far from the urban core and edge-expansion occurred in suburban areas.Edge-expansion growth was taking place at suburban areas.Examining the spatial temporal dynamic patterns of urban land-scape we concluded that this pattern is consistent with urban growth phases.At the initial period of urban growth,urban area expanded mainly as outlying growth,causing increased fragmen-tation and dispersion of urban areas.This was followed by infilling growth and later by edge-expansion growth.With the increases of infilling growth,the urban pattern becomes more aggregated and compact.The study showed that the combination of remote sensing and spatial metrics is valuable for monitoring urban growth.This research did not only identify how much growth has occurred,but also its type.It thus improved traditional research on urban growth. The results in this study may have important implications for future city design and planning in Guangzhou.AcknowledgmentThis research was in part supported by the National Natural Sci-ence Foundation of China(Nos.41171446;41171399;31170445; 41101155),and Natural Science Foundation of Guangdong province。