GeospatialAnalysisofUrbanExpansionUsingRemoteSensing...

ResearchArticleGeospatial Analysis of Urban Expansion Using Remote SensingMethods and Data A Case Study of Yangtze River Delta China

Wei Sun 12 Jie Shan3 Zhiming Wang3 Lei Wang14 Dianmei Lu5 Zhifeng Jin67

and Kun Yu 3

1Nanjing Institute of Geography amp Limnology Chinese Academy of Sciences Nanjing Jiangsu 210008 China2Key Laboratory of Watershed Geographic Sciences Chinese Academy of Sciences Nanjing Jiangsu 210008 China3Institute of Agricultural Information Jiangsu Academy of Agricultural Sciences Nanjing Jiangsu 210014 China4Manchester Urban Institute School of Environment Education and Development -e University of ManchesterManchester M13 9PL UK5Geological Exploration Technology Institute of Jiangsu Province Nanjing Jiangsu 210046 China6School of Resource and Environmental Sciences Wuhan University Wuhan Hubei 430079 China7Jiangsu Research Center of Land and Resources Nanjing Jiangsu 210017 China

Correspondence should be addressed to Kun Yu yukunjaasaccn

Received 9 June 2020 Revised 13 July 2020 Accepted 23 July 2020 Published 14 August 2020

Guest Editor Jun Yang

Copyright copy 2020Wei Sun et al)is is an open access article distributed under the Creative CommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Land use in the Yangtze River Delta in 2000 and 2017 was classified by the visual interpretation of Landsat satellite images )enthese images were overlain with economic and physical geographical data to analyze the urban spatial expansion pattern and itsphysical constraints and socioeconomic influence factors by employing a combination of transition matrix analysis expansionintensity indices and equal-fan analysis )e results showed that from 2000 to 2017 there was a significant increase in built-upareas in the region with rapid expansion in the core area)e northern and southern parts of the Yangtze River Delta experienceddifferent urban spatial expansions with a higher scale and rate in the cities along the Yangtze River and the coast in JiangsuProvince in the north than in Zhejiang Province in the south Cities expanded towards megacities or hubs along the Yangtze Riveror the coast indicating that urban expansion is influenced by preferential policies and urban planning factors in addition to thespillover effects of neighboring cities and the adjacency to seas or large rivers Finally urban expansion is significantly constrainedby elevation with cities at lower elevations or in flat terrain undergoing more rapid urban expansion and development

1 Introduction

Urbanization is a socioeconomic phenomenon with far-reaching effects and is a major indicator of regional eco-nomic development and social progress Following fourdecades of reform and opening-up to globalization Chinahas experienced rapid economic growth accompanied byrapid urbanization with the urban population ratio in-creasing from 1792 in 1978 to 5958 in 2019 [1] Urbanland increased at an even higher rate by nearly two and ahalf times from the 1990s to early 2000s [2] As such urbanspatial expansion is a major indicator of urbanizationmaking it necessary to implement long-term monitoring of

urban expansion and in-depth studies of the rate directionand scale of expansion to identify the corresponding criticaldriving factors and reveal their variation patterns In doingso land resources can be analyzed in a systematic andreasonable manner to sufficiently balance urbanization andsustainable economic development

Urbanization studies date back to the early 20th centuryfocusing on the spatiotemporal patterns driving factors andimpact of urban expansion A more theoretical system ofurbanization research was established in the 1950s [3] Forexample based on an analysis of land consumption inMilanItaly Camagni et al [4] identified the following modes ofurban expansion packing extension linear expansion

HindawiComplexityVolume 2020 Article ID 3239471 12 pageshttpsdoiorg10115520203239471

(along transportation lines) sprawling and ldquosatelliterdquo typeexpansion Extensions of this research allowed for theinclusion of physical socioeconomic accessibility andneighborhood factors in the analysis of urban expansionand the spatiotemporal variation of relevant driving factors[5] Moreover new emerging technologies and method-smdashin particular wide application of remote sensingtechnologiesmdashhave enabled urban expansion research onan immense spatiotemporal scale Since the 1970s theUnited States began to use long time series remote sensingdata from Landsat to investigate the patterns and drivingforces of urban land cover expansion and landscape metricsto predict future urban development trends [6 7] Othercountries are also currently focusing on monitoring andpredicting the rapid urbanization of cities by using Landsat)ematic Mapper (TM) or Operational Land Imager (OLI)[8ndash11]

In recent years with the increasing availability of remotesensing data investigating urban development patternsthrough the integration of different sources of remotesensing information has become a popular avenue of ur-banization research For example studies of the intensityand mode of construction land expansion in developed anddeveloping countries using multiple-source remote sensingdata and the Defense Meteorological Satellites ProgramOperational Linescan System (DMSPOLS) night-time lightdata revealed the effect of policy factors at different eco-nomic development levels and of heterogeneous regionallandscapes on urbanization [12ndash14] Following the imple-mentation of the Chinese national policy of reform andopening-up in the 1980s the spatiotemporal changes ofChinese cities and their driving factors have attracted in-creasing attention from researchers [15ndash19]

In studies of the spatiotemporal differentiation andtransfer patterns of urban land expansion of large andmedium cities including Shanghai Beijing Hangzhou andNanjing remote sensing technology provided timely cost-efficient and effective data for large-scale urbanization re-search [20ndash25] As a byproduct of investing increasing ef-forts for reform and economic accessibility Chinaexperienced increasingly rapid urbanization In particularthe Yangtze River Delta region is now considered the largestregional economy of China In a regional development planpublished by the Chinese government the Yangtze RiverDelta is positioned as an economic center with the strongestcomprehensive power in China it is considered not only as amajor international gateway to the Asia-Pacific region butalso as a major base of global advanced manufacturing andthe first megalopolis of China with international implica-tions As such urbanization in this region has become apopular research topic However most remote sensing data-based studies of the urban spatiotemporal expansion in theYangtze River Delta are outdated [26ndash30] More recentstudies of urbanization in this region havemainly focused onindividual cities however a comprehensive investigation ofthe Yangtze River Delta is distinctly lacking In this studythe Yangtze River Delta was investigated to establish thecharacteristics patterns and mechanisms of urban spatio-temporal expansion since 2000 in order to provide scientific

input to urban development planning and sustainableeconomic development in the region

2 Materials and Methods

21 Study Area Located adjacent to the Yellow and EastChina Seas the Yangtze River Delta megalopolis (Figure 132deg34primendash29deg20primeN 115deg46primendash123deg25primeE) is comprised of twoprovinces as well as Shanghai municipality )e area has asubtropical monsoon climate and serves as a major interfaceof the Belt and Road economies and the Yangtze Rivereconomic belt Covering an area of approximately11times 104 km2 the core of the megalopolis includes the citiesof Shanghai Nanjing Yangzhou Zhenjiang Suzhou WuxiChangzhou Taizhou (Jiangsu) Nantong HangzhouShaoxing Huzhou Jiaxing Ningbo Zhoushan and Taizhou(Zhejiang) )is megalopolis is one of the most developedand urbanized regions in China and thus is both the mostdensely populated region in China and one of the six biggestmegalopolises in the world [31] In 2016 the region had aGDP of 18 trillion USD accounting for 167 of theChinese economy with a population of more than9800times104 [1 32 33]

22 Data

221 Satellite Data TM and OLI are key instrumentsonboard Landsat satellites which have a ground revisitperiod of 16 days In 2009 images collected by Landsatsatellites became freely available from the United StatesGeological Survey (USGS) with historical images datingback to 1982 at 15sim30m pixel resolutions A total of 7spectral bands between 450 nm and 125 μm and 9 spectralbands between 433 nm and 23 μm are available by TMimages and OLI images respectively allowing the explo-ration of the Earthrsquos resources and environments on a globalscale at maximal resolution but with minimal costs Imagesfrom the Landsat series satellites were selected for this studyowing to the consistent resolution and continuity of the dataAll data used in this study were provided by the USGSEROSData Center including 16 views of images from the Landsat-5 TM in 2000 and 15 views of images from the Landsat-8 OLIin 2017 All remote sensing images were of good quality andcovered the entire study area (Table 1)

222 DEM Data A digital elevation model (DEM) is adigital simulation of surface terrain using limited topo-graphic elevation data (or a digital representation of thesurface morphology) and is a physical model of surfaceelevation represented in matrices of ordered values )eAdvanced Spaceborne )ermal Emission and ReflectionRadiometer (ASTER) Global Digital Elevation Model(GDEM) V2 data used in this study were provided by theGeospatial Data Cloud (httpwwwgscloudcn) Comparedwith the ASTER GDEM V1 data the V2 data use moreadvanced algorithms and have an enhanced spatial resolu-tion and elevation accuracy )e V2 data have a spatialresolution of 30m and are presented in the WGS84

2 Complexity

123deg0prime0PrimeE

120deg0prime0PrimeE

120deg0prime0PrimeE

33deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

70deg0prime0PrimeE

10deg0prime0PrimeE

35deg0prime0PrimeE

95deg0prime0PrimeE 120deg0prime0PrimeE 145deg0prime0PrimeE30

deg0prime0PrimeN

0 100 200km50

Zhejiang provinceJiangsu provinceShanghai city

DiaoyusIsland

South China Sea

Yangzhou

Nanjing Zhenjiang

Taizhou Nantong

Changzhou Wuxi

Suzhou

Shanghai

JiaxingHuzhou

Hangzhou

Shaoxing Ningbo

Zhoushan

Taizhou

China

N

S

W E

Figure 1 Yangtze River Delta megalopolis Inset shows location within eastern China

Complexity 3

coordinate system using the Universal Transverse Mercator(UTM) projection

223 Ground Observation Data )e remote sensing imagesselected for this study were obtained during the period ofMarch to November for the years 2000 and 2017 To facilitateaccurate visual interpretation of the images more than 1500reference ground objects including 500 surface quadratsmeasuring 500times 500m were randomly selected in the studyarea using a Trimble GEO-XT6000 GPS with their latitudesand longitudes identified

23 Methods

231 Data Preprocessing Data preprocessing began withatmospheric correction on selected remote sensing imagesusing the FLAASH module in ENVI 51 software Secondgeometric corrections were performed on the remotesensing images from 2017 using the latitude and longitude ofreference ground objects as ground control point data withthe error held below 05 pixels Next geometric correctionswere performed on the remote sensing images from 2000using the geometrically corrected 2017 images as the ref-erence All the geometrically corrected images were thenpresented in the WGS84 coordinate system using the UTMprojection Finally all the remote sensing images were

tailored using the latest vector boundaries of the citiescovered in this study

232 Interpretation Land cover in the study area wasclassified into one of the following six categories farmlandforest grassland water bodies (including artificial waterbodies) construction land and unused land (including nativevegetation and barren land) )e classification was made byreferencing GBT21010-2017 Current Land Use Classificationissued by the General Administration of Quality SupervisionInspection and Quarantine of the Peoplersquos Republic of Chinaand noting the characteristics of land use in the study area)emean values standard deviations and correlation coefficientsof the images were quantitatively analyzed by frequency bandusing the Optimum Index Factor (OIF) method proposed byLu et al [34] Next the information content of various fre-quency band combinations was computed )e resulting OIFvalues were arranged sequentially from high to low )e rednear-infrared and short-wave infrared bands (correspondingto bands 3 4 and 5 and 4 5 and 6 of Landsat-5 TM andLandsat-8 OLI respectively) were selected as the preferredbands in correspondence to the purpose of the study theparticular situation of the study area and the characteristics ofthe spectral-reflectance of different ground objects in thefrequency bands of the images Next after repeated trials thered near-infrared and short-wave infrared bands were

Table 1 Landsat data used in this study

Collection date Sensor Orbit number Ground resolution (m)2000514 Landsat-5 TM 117039 302017817 Landsat-8 OLI 152000521 Landsat-5 TM

11803830

201742 Landsat-8 OLI 152017824200066 Landsat-5 TM

11803930

201742 Landsat-8 OLI 15201782420002152000724 Landsat-5 TM 118040 30

201731 Landsat-8 OLI 118040 152000410 Landsat-5 TM 119037 30201738 Landsat-8 OLI 152000613

Landsat-5 TM 119038 3020007222000917201738 Landsat-8 OLI 152000613 Landsat-5 TM

119039302000917

2017511 Landsat-8 OLI 1520171132000512 Landsat-5 TM 119040 302017527 Landsat-8 OLI 152000108 Landsat-5 TM 120037 302017518 Landsat-8 OLI 152000108 Landsat-5 TM 120038 302017518 Landsat-8 OLI 15200053 Landsat-5 TM 120039 302017518 Landsat-8 OLI 152000417 Landsat-5 TM 120040 302017518 Landsat-8 OLI 15

4 Complexity

rendered in blue red and green colors respectively totransform the data into quasicolor data )e resulting datashowed strong color contrast for different ground objects andhad rich color gradation thereby facilitating manual datainterpretation Finally the land uses of the study area in 2000and 2017 were classified using the visual interpretationmethod that is the interpretation of the remote sensingimages based on their spectral characteristics geosciencepatterns and expertise as well as the brightness color tonetexture spatial relationship and phase of image pixels withthe results presented in Figure 2

233 Accuracy Evaluation )e confusion matrix method isnow the most widely applied method for evaluating theclassification accuracy of remote sensing images [35] )ismethod operates by comparing classification results and fieldobservations using an ntimes nmatrix (where n is the number ofclassifications) with the elements in the matrix representingthe number of pixels to be verified [36] Research shows thatthe number of points for verifying the classification accuracyof a single class should be no less than 75 [37] In the land useclassification of this study 200 points were randomly selectedfor each class of ground objects Confusion matrices wereestablished by comparing the classification results of thesereference points at different time points with the corre-sponding field investigation results and high-resolution re-mote sensing data from Google Earth (including QuickBirdIKONOS and SPOT5 satellites) Producerrsquos accuracy userrsquosaccuracy overall accuracy and Kappa coefficient were used tomeasure the land use classification accuracy calculated as

PPA Kjj

K+j

PUA Kii

Ki+

POA 1113936

ni1 Kii

T

K T 1113936

ni1 Kii minus 1113936

nij1 Ki+K+j1113872 1113873

T2 minus 1113936nij1 Ki+K+j1113872 1113873

(1)

where PPA is the producerrsquos accuracy PUA is the userrsquosaccuracy POA is the overall accuracy K is the Kappa co-efficient n is the number of columns in the confusion matrix(number of classifications) Kii is the number of pixels in thematrix at the intersection of the ith row and the ith column(the number of correct classification) Ki+ and K+j are thetotal number of pixels in the ith row and the ith columnrespectively and T is the total number of pixels used for theaccuracy evaluation

24 Data Analysis

241 Temporal Analysis Transition matrix-based analyticalresults can intuitively reflect transitions between differenttypes of ground objects during two adjacent periods [38 39]

To analyze temporal changes of land use in the study areaand to identify inherent driving factors of urban expansionand pattern of land use changes the transition matrixmethod was combined with the overlay analysis function inArcGIS 93 software to compute transition matrices of landuse )ese computational results were used to analyze theland use changes in the cities covered in the study during theperiod 2000ndash2017

242 Spatial Analysis To analyze the spatial differentiationand morphological evolution of urban land expansion in theYangtze River Delta over the last 20 years the dimension andintensity of land expansion in the region were analyzed bycombining DEM elevation data the expansion intensityindex and the equal-fan analysis method

)e expansion intensity index is the ratio of the area ofurban land expansion to the total land area in a spatial unitin the study period )is index normalizes the annual meanexpansion rate based on the land area in a spatial unitthereby enabling comparative analysis )e expansion in-tensity indexmdashan indicator of urban expansion status-mdashenables a comparison between different geographicalareas of a city in terms of the intensity rate and trend of landuse expansion )e index is computed using the followingequation [40 41]

Ui Ua minus Ub

T times Uc

times 100 (2)

where Ui is the expansion intensity in the ith spatial unit Uais the area of the urban land in the ith spatial unit in period aUb is the area of the urban land in the ith spatial unit inperiod bUc is the total land area of the ith spatial unit and Tis the time span from period a to period b in the unit of year

)e equal-fan analysis operates by mapping a study areainto a circle and dividing the circle into equal fans )e landuse classifications in different periods are then plotted ontoequal fans for overlay analysis and the expansion intensitiesin different aspects are computed to describe the spatialdifferentiation in land classification )e center of the circleis usually the central business district (CBD) of a city[42 43] with the radius of the circle determined based onthe criteria that the circle can cover the entire city In thisstudy each of the cities considered was divided into 16 equalfans (where the angle of each fan is 225deg) with the divisionstarting in the north Urban land expansion intensity indicesin the different fans in various periods were then computed)e computational results were then combined with theDEM elevation data to analyze the differentiation in urbanspatial expansion intensity

3 Results

31 Accuracy Evaluation )e accuracy of land use classi-fications for the Yangtze River Delta in 2000 and 2017 wasevaluated by establishing confusion matrices and computingthe producerrsquos accuracy userrsquos accuracy overall accuracyand Kappa coefficient Table 2 shows that the overall ac-curacies of the classifications in 2000 and 2017 were higher

Complexity 5

than 90 and the Kappa coefficients were greater than 090)is confirms that the land use classifications were suffi-ciently accurate for this study However all accuracy indi-cators showed that the land use classification in 2017 wasmore accurate than in 2000 which is likely because theground resolution of the Landsat-8 OLI data used for the2017 classification was higher than that of the Landsat-5 TMdata used for the 2000 classification this suggests to acertain extent that a higher spatial resolution leads to a moreaccurate visual interpretation Moreover the producerrsquosaccuracies and userrsquos accuracies for the construction landclassifications in 2000 and 2017 were higher than 93

32 Temporal Changes in Urbanization Among the sixclassifications of land use a noticeable reduction offarmland and grassland occurred from 2000 to 2017 Morespecifically farmland was reduced by approximately111times 106 hm2 accounting for 992 of the total landwhereas the grassland accounted for less than 1 of thetotal land after its reduction Forest water bodies con-struction land and unused land increased in area during

the study period with the largest increase occurring forconstruction land which expanded by approximately857 times105 hm2 equivalent to an increase of 761 of thetotal land In contrast forest water bodies and unusedland increased only slightly together accounting for ap-proximately 2 of the total land (Figure 3) )e areatransition matrix analysis showed that construction landrepresented the largest transition in the 17-year period )earea of farmland converted into construction land wasapproximately 891times 105 hm2 (Table 3) accounting for4766 of the total construction land in 2017

Regarding the increase in the ratio of the constructionland to the administrative areas (Figure 4) the cities coveredin the study experienced different rates of urban spatialexpansion More specifically Shanghai and Suzhou expe-rienced the biggest increase (higher than 13) with theincrease of Suzhou higher than that of Shanghai Jiaxing andWuxi experienced increases of approximately 10 NanjingChangzhou and Taizhou (Jiangsu) of 8ndash9 NingboHuzhou Yangzhou and Nantong of 6ndash8 and ZhenjiangShaoxing Zhoushan Hangzhou and Taizhou (Zhejiang) of4ndash6

Classification of the land use in the Yangtze River Delta in 2000

Water bodiesConstruction landUnused land

FarmlandForestGrassland

123deg0prime0PrimeE120deg0prime0PrimeE


33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(a)






33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(b)

Figure 2 Classification of land use in the Yangtze River Delta in (a) 2000 and (b) 2017

6 Complexity

Table 2 Classification accuracy evaluation results

YearClassification data

Farmland Forest Grassland Waterbodies

Constructionland

Unusedland Total

Producerrsquosaccuracy

()Verification data

2000

Farmland 181 6 9 1 3 4 204 8873Forest 5 182 5 0 2 3 197 9239

Grassland 6 6 180 2 2 4 200 9000Water bodies 1 0 1 190 4 4 200 9500

Construction land 2 3 2 4 186 2 199 9347Unused land 5 3 3 3 3 183 200 9150

Total 200 200 200 200 200 200 1200Userrsquos accuracy

() 9050 9100 9000 9500 9300 9150

Overall accuracy 9183 Kappa coefficient 090

2017

Farmland 191 2 4 1 1 3 202 9455Forest 3 191 3 0 0 1 198 9646




() 9550 9550 9450 9750 9600 9500


0

10

20

30

40

50

60

Farmland Forest Grassland Water bodies Constructionland

Perc

enta

ge o

f are

a

20002017

Unused land

Figure 3 Changes () in the different classifications of land use from 2000 to 2017

Table 3 Transition matrix of the land use transitions (hm2) in the Yangtze River Delta from 2000 to 2017

20172000 Farmland Forest Grassland Water bodies Construction land Unused land TotalFarmland 4327710 136653 3708 294997 891980 22339 5677387Forest 44518 3049272 2639 16808 37918 7299 3158454Grassland 5525 67091 48279 17019 5946 1730 145591Water bodies 82243 5721 2759 1122505 53019 6594 1272841Construction land 98675 8783 835 16196 882330 6527 1013347Unused land 1081 458 0 540 480 1894 4452Total 4559752 3267977 58220 1468066 1871672 46384 11272071

Complexity 7

33 Spatial Changes of Urbanization Expansion intensityindices for the municipalities identified above (Figure 1)were computed using an equal-fan analysis )e computa-tional results for growth between 2000 and 2017 were thenplotted as radar charts as shown in Figure 5

Shanghai experienced very rapid expansion in theSWW-W-NWW directions and rapid expansion in the NE-E-SEE and SSE directions (Figure 5(a)) )is is attributed tothe accelerated development of the Hongqiao transportationhub in the west and the Pudong New Area and the ShanghaiPort in the east

Construction of the Xianlin University Town the QilinScience and Technology Park in the east and the JiangbeiNew District in the north contributed significantly to ex-pansion in Nanjing (Figure 5(b)) )ese new constructionsresulted in a significant increase in growth in the NE-NEEdirection but less rapid development in the SEE SWW andNW directions

Zhenjiang underwent considerable growth in the NEE-Eand SWW directions together with noticeable expansion inthe SE and SSW directions (Figure 5(c)) In the ZhenjiangEconomic and Technological Development Zone located inthe eastern part of the city a high-quality coated papermanufacturing base (the largest in the world) an engi-neering plastic particle base (the most extensive in China)an automobile engine cylinder block manufacturing base(the most substantial in China) and a controllable propellermanufacturing base were recently constructed Located atthe intersection of the Shanghai and Nanjing metropolitanareas the Zhenjiang High-Tech Zone in the west of the city isincluded in the core area of the Nanjing-Zhenjiang-Yangzhou Integrated Strategy Initiative and is one of nineNational High-Tech Zones in the southern Jiangsu NationalInnovation Demonstration Zone )e Zhenjiang Scienceand Technology Town located in the southern part of thecity was constructed in 2016 therefore the expansion in-tensity in this direction was slightly lower

)e municipality of Yangzhou (Figure 5(d)) saw sig-nificant development in the SSE direction together withnotable expansion in the SE S SSW and SW directions )eNational Economic Development Zone located in thesouthern part of the city boasts the geographical advantageof being adjacent to seas and rivers an investment envi-ronment with comprehensive support facilities and an in-dustrial base with unique characteristics Following theconstruction of an integrated transportation hub in theeastern part of the city in 2016 the focus of the urban ex-pansion has gradually shifted to the SE direction

Taizhou (Jiangsu) City experienced very rapid expansionin the S-SSW and SWW directions and extensive growth tothe NNE and NEE (Figure 5(e)) Construction of theTaizhou Medical New amp High-tech Industrial DevelopmentZone the Taizhou Binjiang Industrial Park and the Riv-erside Economic Development Zone has significantly drivenurban expansion in this area

Considerable development took place in Nantong(Figure 5(f )) primarily in the SE direction with addi-tional heightened growth in the SWW Due to the strongeconomic attraction Shanghai has served as a drivingforce of the urban development of Nantong Since theTongzhou municipality was reorganized as a districtin 2009 urban development has gradually shifted fromthe south (Chongchuan and Gangzha Districts) to thesoutheast

In Changzhou marked expansion in the N-NNE andE-SEE directions and development to a lesser degree in theSSE direction were observed (Figure 5(g)) )is is mainlybecause the Changzhou National High-tech DevelopmentZone is in the north of the city and construction projects forequipment manufacturing new chemical materials and portlogistics have been implemented in the Riverside EconomicDevelopment Zone since 2006 Located in the eastern part ofthe city the Changzhou Economic Zone displays the mostrapid development

Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a

Figure 4 Increases () in construction land in the Yangtze River Delta by city

8 Complexity

Suzhou (Figure 5(h)) experienced very rapid expansionin the NEE direction and evident growth in the SSE andNWW direction Construction projects in the Suzhou In-dustrial Park the Wujiang Lakeside New Town and theSuzhou National Hi-Tech District contributed to the sig-nificant expansion in the easterly direction

Wuxi experienced noticeable expansion in the NEE-E-SEE-SE-SSE direction (Figure 5(i)) Construction of the

Xishan District in the east and the attraction of Shanghai andSuzhou contributed to significant expansion in the southeastdirection

)e city of Hangzhou (Figure 5(j)) experienced veryrapid development in the SEE direction and rapid growth inthe NNE and NNW directions )e urban development ofHangzhou to the west is constrained by the western Zhejianghilly area )e east side of Hangzhou is located on the

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

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NEE

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SEE

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SSES

SSW

SW

SWW

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NWW

NW

NNW

(e)

000

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200N

NNENE

NEE

E

SEE

SESSE

SSSW

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SWW

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NWW

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(f )

000050100150200250

NNNE

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NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

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NNE

NE

NEE

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SEE

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SSES

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(i)

000050100150200250

NNNE

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NEE

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SEE

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SSES

SSW

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NWW

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NNW

(j)

000030060090120150

NNNE

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NEE

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SEE

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SSES

SSW

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NWW

NW

NNW

(k)

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050

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NNE

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NEE

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SSES

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(l)

000050100150200250

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000050100150200250

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000050100150200250300350

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NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)

Figure 5 Spatial expansion directions of cities covered in the study during the period 2000ndash2017 )e numbers in the (a)sim(p) werecalculated by equation (2) representing the expansion intensity in the spatial direction (a) Shanghai (b) Nanjing (c) Zhenjing (d)Yangzhou (e) Taizhou (Jiangsu) (f ) Nantong (g) Changzhou (h) Suzhou (i) Wuxi (j) Hangzhou (k) Huzhou (l) Jiaxing (m) Shaoxing(n) Ningbo (o) Taizhou (p) Zhoushan

Complexity 9

Northern Zhejiang Plain and has traditionally been amanufacturing hub As such Hangzhou experienced sig-nificant expansion to the east particularly after the planningand construction of the Jiangdong New District

Huzhou experienced marked expansion in the NEE andNNW-N directions and rapid increases in growth in theNWW and S directions (Figure 5(k)) )e construction ofthe Taihu New Town contributed to significant expansion inthe north and east

In Jiaxing well-defined expansion occurred in the NEdirection (Figure 5(l)) simultaneously with minor expan-sion to the E SSW and NW Jiaxing adopts integration intoShanghaimdashin particular integration with the Jinshan andSongjiang Districts of Shanghaimdashas its core strategy of urbandevelopment)erefore Jiaxing exhibited significant growthto the east and more significantly the northeast

Shaoxing experienced very rapid growth in the N andNWW-NW directions and notable expansion in the NEEand SSW directions (Figure 5(m)) Expansion to the NW-Nis a direct result of the construction projects implemented inrecent years including a textile and small motor center anenergy-saving photoelectricity center and a biologicalbrewing industry

Ningbo experienced marked development to theN-NNE-NE-NEE (Figure 5(n)) and to a lesser extent in theNWW direction )e Ningbo Hangzhou Bay New ZonemdashaNational Economic and Technological DevelopmentZonemdashis in the north of Ningbo In addition large petro-leum chemical and steel enterprises are constructed in theBeilun and Zhenhai Districts in the eastern part of the city

In the city of Taizhou (Zhejiang) significant expansiontook place in the E direction owing to port construction Inaddition minor growth was noted to the south (Figure 5(o))

Zhoushanmdasha city surrounded by seamdashexperienced veryrapid development in the E and NW directions and rapidexpansion in the SSE and SWW-W directions (Figure 5(p)))is growth is attributed to the construction of theZhoushan Port in the east and the Jintang Bridge thatconnects Ningbo in the west

)e land use classification and DEM elevation data ofthe study area were overlain to perform an expansionclassification analysis in order to understand how ele-vation affects land use and expansion )e results showthat more than 90 of land use changes occurred in areaswith an elevation of less than 100m 99 of the con-struction land expansions occurred in areas with an el-evation of less than 100m and land use changesparticularly changes in construction land decreased withan increase in elevation (Figure 6)

4 Discussion

)e most drastic land use changes from 2000 to 2017resulted from farmland and construction with the overallpattern of land use change being the conversion of theformer to the latter )e assessment of spatial changes inurbanization in the 15 cities (excluding Shanghai) demon-strated an increasing rate of urbanization that is higher than6 in 7 cities which are located within Jiangsu Province)isindicates that the overall urbanization rate of JiangsuProvince exceeds that of Zhejiang Province (Figure 1) Inaddition cities in the Yangtze River Delta megalopolis withthe exclusion of Shanghai a municipality directly under thecentral government Nanjing capital of Jiangsu Provinceand Hangzhou capital of Zhejiang Province have the fol-lowing urban spatial expansion characteristics (1) expansiontowards a municipality that is directly administrated by thecentral government or the provincial capital for exampleSuzhou Wuxi Changzhou and Nantong expanding to-wards Shanghai or Huzhou and Jiaxing and Shaoxingexpanding towards Hangzhou and (2) expansion towardsmajor port hubs and industrial platforms along the YangtzeRiver or the coast for example Yangzhou Taizhou(Jiangsu) and Zhenjiang expanding along the Yangtze Riverand Ningbo Taizhou (Zhejiang) and Zhoushan expandingtowards sea ports )is indicates that urban expansion isinfluenced by geographical factors to a certain degree inaddition to the influence of policy factors

Lower than 100 100ndash500 500ndash1000 Higher than 1000Elevation (m)

Land useConstruction land

0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a

Figure 6 Land use changes () by elevation

10 Complexity

Notably the present study has certain limitations Firstit spanned a long period but higher temporal resolution dataare needed to further investigate urban expansion duringthis period In addition only the key factors influencingurban spatial expansion were considered yet the addition ofsocioeconomic indicators would help explain the demandfactor of urban land expansion For future studies multiple-source remote sensing data with a higher resolution shouldbe used to obtain a more accurate classification of land useand longer periods should be covered to carry out an in-depth investigation of the characteristics of urban expansionby stage Economic social and ecological factors in additionto the topographical geographical and policy factors shouldbe included and additional analysis methods and mea-surement indicators such as compactness fractal index andlandscape index should be employed to comprehensivelyanalyze the urban expansion characteristics and pattern ofthe study area

5 Conclusions

In this study the land use of 16 cities in the Yangtze RiverDelta in 2000 and 2017 was classified by carrying out a visualinterpretation of 31 images from the Landsat series satellitesLand use classification data were then combined with DEMelevation data to analyze the urban spatial expansion pattern ofthe cities by employing a transition matrix an expansionintensity index and an equal-fan analysis method We arrivedat the following conclusions (1) land use change in the corearea of the Yangtze River Delta from 2000 to 2017 significantlyincreased and exhibited characteristics of rapid urban ex-pansion (2) the southern and northern parts of the core areaexperienced different urban spatial expansions with the scaleand rate of urban expansion in the cities along the YangtzeRiver and the northern coast in Jiangsu Province higher thanthose of cities in Zhejiang Province to the south (3) citiesexpanded towards megacities or hubs along the Yangtze Riveror the coast indicating that urban expansion was also influ-enced by preferential policy and urban development planningfactors in addition to the size of neighboring cities and geo-graphic conditions (ie proximity to seas or large rivers) (4)urban expansion is significantly constrained by elevationwhereby cities with lower elevation or flat terrain were subjectto enhanced urban spatial expansion and development relativeto cities located in hilly or mountainous areas

)e method demonstrated is potentially applicable toother megalopolises such as the Northeastern Corridorregion on the United States coast the Great Lakes in NorthAmerica the Pacific coastal area of Japan London in the UKand northwestern Europe )e methods employed here willaid planners to understand the spatiotemporal factorsdriving urban expansion facilitate more reasonable inte-gration and utilization of the limited land resources andprovide valuable input for scientific long-term developmentplanning of megalopolises

Data Availability

Data are available upon request

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)e authors acknowledge USGS for providing Landsat dataand the ASTER GDEMV2 data were provided by GeospatialData Cloud site Computer Network Information CenterChinese Academy of Sciences )is work was supported bythe National Natural Science Foundation of China underGrant no 41871119 the Jiangsu Agricultural Science andTechnology Innovation Fund under Grant no CX-18-3044and the Natural Resource Science and Technology Foun-dation of Jiangsu Province under Grant no 2020009

References

[1] National Bureau of Statistics of the Peoplersquos Republic ofChina China Statistical Yearbook 2019 China Statistics PressBeijing China 2019

[2] L Wang C C Li Q Ying et al ldquoChinarsquos urban expansionfrom 1990 to 2010 determined with satellite remote sensingrdquoChinese Science Bulletin vol 57 pp 1388ndash1399 2012 inChinese with English abstract

[3] K T Jackson Crabgrass Frontier -e Suburbanization of theUnited States p 12 Oxford University Press New York NYUSA 1985

[4] R Camagni M C Gibelli and P Rigamonti ldquoUrban mobilityand urban form the social and environmental costs of dif-ferent patterns of urban expansionrdquo Ecological Economicsvol 40 no 2 pp 199ndash216 2002

[5] D Guanglong X Erqi and Z Hongqi ldquoUrban expansion andspatiotemporal relationships with driving factors revealed bygeographically weighted logistic regressionrdquo Journal of Re-sources and Ecology vol 8 no 3 pp 277ndash286 2017

[6] J G Masek F E Lindsay and S N Goward ldquoDynamics ofurban growth in the Washington DC metropolitan area1973ndash1996 from Landsat observationsrdquo International Journalof Remote Sensing vol 21 no 18 pp 3473ndash3486 2000

[7] W Ji J Ma R W Twibell and K Underhill ldquoCharacterizingurban sprawl using multi-stage remote sensing images andlandscape metricsrdquo Computers Environment and UrbanSystems vol 30 no 6 pp 861ndash879 2006

[8] B K Terfa N Chen D Liu X Zhang and D Niyogi ldquoUrbanExpansion in Ethiopia from 1987 to 2017 characteristicsspatial patterns and driving forcesrdquo Sustainability vol 11no 10 p 2973 2019

[9] U Alganci ldquoDynamic land cover mapping of urbanized citieswith Landsat 8 multi-temporal images comparative evalua-tion of classification algorithms and dimension reductionmethodsrdquo ISPRS International Journal of Geo-Informationvol 8 no 3 p 139 2019

[10] H Moayedi A Jamali M B A Gibril L Kok Foong andM Bahiraei ldquoEvaluation of tree-base data mining algorithmsin land usedland cover mapping in a semi-arid environmentthrough Landsat 8 OLI image Shiraz Iranrdquo GeomaticsNatural Hazards and Risk vol 11 no 1 pp 724ndash741 2020

[11] J A Gomez J E Patino J C Duque and S Passos ldquoSpa-tiotemporal modeling of urban growth using machinelearningrdquo Remote Sensing vol 12 p 109 2020

Complexity 11

[12] H M Pham Y Yamaguchi and T Q Bui ldquoA case study onthe relation between city planning and urban growth usingremote sensing and spatial metricsrdquo Landscape and UrbanPlanning vol 100 no 3 pp 223ndash230 2011

[13] Q Zhang and K C Seto ldquoMapping urbanization dynamics atregional and global scales using multi-temporal DMSPOLSnighttime light datardquo Remote Sensing of Environmentvol 115 no 9 pp 2320ndash2329 2011

[14] J Yang J Sun H Zhao J Xi and X Li ldquoSpatio-temporaldifferentiation of residential land for coastal town a case studyof Dalian Jinshitanrdquo Chinese Geographical Science vol 26no 4 pp 566ndash576 2016

[15] J Peng H Lin Y Chen et al ldquoSpatiotemporal evolution ofurban agglomerations in China during 2000ndash2012 a night-time light approachrdquo Landscape Ecology vol 35 no 2pp 421ndash434 2020

[16] L Wang H Anna L Zhang et al ldquoSpatial and temporalchanges of arable land driven by urbanization and ecologicalrestoration in Chinardquo Chinese Geographical Science vol 29no 5 pp 809ndash819 2019

[17] Y Zhao K Zhong J Xu C Sun and Y Wang ldquoDirectionalanalysis of urban expansion based on sub-pixel and regionalscale a case study of main districts in Guangzhou ChinardquoChinese Geographical Science vol 29 no 4 pp 652ndash666 2019

[18] D Ben Z Cao X Liu et al ldquoChanging connectivities ofChinese cities in the world city network 2010ndash2016rdquo ChineseGeographical Science vol 28 no 2 pp 183ndash201 2018

[19] S Jin J Yang E Wang and J Liu ldquo)e influence of high-speed rail on ice-snow tourism in northeastern ChinardquoTourism Management vol 78 p 104070 2020

[20] M Anxin and L Meng ldquoSediment of Yangtze and urbani-zation of Shanghai monitoring by using remote sensingrdquoRemote Sens Informvol 2 p 12 1990 in Chinese withEnglish abstract

[21] F Zuojiang C Jicheng and L Qi ldquoUrbanization monitoringby remote sensing and geography information systemrdquo Re-mote Sensing Information vol 3 pp 12ndash16 1997 in Chinesewith English abstract

[22] X Li J Fang and S Piao ldquo)e comparison of spatialcharacteristics in urban landuse growth among the centraland sub-cities in Shanghai Regionrdquo Geographical Researchvol 22 pp 769ndash779 2003 in Chinese with English abstract

[23] R H Ma C L Gu Y X Pu X D Ma and Z Zhu ldquoUrbanspatial sprawl pattern andmetrics in south of Jiangsu provincealong the Yangtze riverrdquo Acta Geographica Sinica vol 62pp 1011ndash1022 2007 in Chinese with English abstract

[24] J L Gao J L Chen F Yuan Y D Wei and W ChenldquoPatterns functions and underlying mechanisms of urbanland expansion in Nanjingrdquo Geographical Research vol 33pp 1892ndash1907 2014 in Chinese with English abstract

[25] D-D Zhang and L Zhang ldquoLand cover change in the centralregion of the lower Yangtze River based on Landsat imageryand the Google earth engine a case study in nanjing ChinardquoSensors vol 20 no 7 p 2091 2020

[26] J Li J Xu W Li and C Liu ldquoSpatio-temporal characteristicsof urbanization area growth in the Yangtze River Deltardquo ActaGeographica Sinica vol 62 pp 437ndash447 2007 in Chinesewith English abstract

[27] L Wang and X Duan ldquo)e expansion of urbanization area inYangtze River Deltardquo Scientia Geographica Sinica vol 16pp 702ndash709 2010 in Chinese with English abstract

[28] Q Che X Duan Y Guo L Wang and Y Cao ldquoUrban spatialexpansion process pattern and mechanism in Yangtze River

Deltardquo Acta Geographica Sinica vol 66 pp 446ndash456 2011 inChinese with English abstract

[29] C P Wang H W Wang C M Li and R C Dong ldquoAnalysisof the spatial expansion characteristics of major urban ag-glomerations in China using DMSPOLS imagesrdquo ActaEcologica Sinica vol 32 pp 942ndash954 2012 in Chinese withEnglish abstract

[30] X Gao H Liu Y Zhang et al ldquoSpatio-temporal patterns ofurban expansion in the Yangtze River Delta megalopolis from1990 to 2010rdquo Journal of Beijing Normal University (NaturalScience) vol 52 pp 645ndash650 2016 in Chinese with Englishabstract

[31] X Xu and Y Zhou Urban Geography Higher EducationPress Beijing China 1997

[32] Jiangsu Provincial Bureau of Statistics Jiangsu StatisticalYearbook 2017 China Statistics Press Beijing China 2017

[33] Zhejiang Provincial Bureau of Statistics Zhejiang StatisticalYearbook 2017 China Statistics Press Beijing China 2017

[34] D Lu X You and S Cui ldquoA study on the analysis of TMimage information content and the extraction of characteristicinformationrdquo Journal of Remote Sensing vol 6 no 4pp 267ndash274 1991 in Chinese with English abstract

[35] R G Congalton and K Green Assessing the Accuracy ofRemotely Sensed Data Principles and Practices CRC PressBoca Raton FL USA 2nd edition 2009

[36] R G Congalton ldquoA review of assessing the accuracy ofclassifications of remotely sensed datardquo Remote Sensing ofEnvironment vol 37 no 1 pp 35ndash46 1991

[37] J Cohen ldquoWeighted kappa nominal scale agreement pro-vision for scaled disagreement or partial creditrdquo PsychologicalBulletin vol 70 no 4 pp 213ndash220 1968

[38] D Xiao Y Zhao Z Sun and G Zhang ldquoStudy on thevariation of landscape pattern in the west suburbs of She-nyangrdquo Chinese Journal of Applied Ecology vol 1 pp 75ndash841990 in Chinese with English abstract

[39] P Shi J Chen and Y Pan ldquoLanduse change mechanism inShenzhen cityrdquo Acta Geographica Sinica vol 55 pp 151ndash1602000 in Chinese with English abstract

[40] WWang J Jin Z Xiao and T Shi ldquoUrban expansion and itsdriving forces based on remote sensed data and GIS a casestudy of Hangzhou city from 1991 to 2008rdquo GeographicalResearch vol 28 pp 687ndash695 2009 in Chinese with Englishabstract

[41] S H Liu C J WU and H Q Shen ldquoA GIS based model ofurban land use growth in beijingrdquo Acta Geographica Sinicavol 55 pp 407ndash416 2000 in Chinese with English abstract

[42] Z Shang X Zhang and X Zhou ldquoStudy on urban spatialexpansion and external morphology evolution based on RSGIS a case of Huairsquoan cityrdquo Economic Geography vol 32pp 64ndash70 2012 in Chinese with English abstract

[43] S Jiang and Y Zhou ldquo)e fractal urban form of Beijing and itspractical significancerdquo Geographical Research vol 25pp 204ndash212 2006 in Chinese with English abstract

12 Complexity

(along transportation lines) sprawling and ldquosatelliterdquo typeexpansion Extensions of this research allowed for theinclusion of physical socioeconomic accessibility andneighborhood factors in the analysis of urban expansionand the spatiotemporal variation of relevant driving factors[5] Moreover new emerging technologies and method-smdashin particular wide application of remote sensingtechnologiesmdashhave enabled urban expansion research onan immense spatiotemporal scale Since the 1970s theUnited States began to use long time series remote sensingdata from Landsat to investigate the patterns and drivingforces of urban land cover expansion and landscape metricsto predict future urban development trends [6 7] Othercountries are also currently focusing on monitoring andpredicting the rapid urbanization of cities by using Landsat)ematic Mapper (TM) or Operational Land Imager (OLI)[8ndash11]

In recent years with the increasing availability of remotesensing data investigating urban development patternsthrough the integration of different sources of remotesensing information has become a popular avenue of ur-banization research For example studies of the intensityand mode of construction land expansion in developed anddeveloping countries using multiple-source remote sensingdata and the Defense Meteorological Satellites ProgramOperational Linescan System (DMSPOLS) night-time lightdata revealed the effect of policy factors at different eco-nomic development levels and of heterogeneous regionallandscapes on urbanization [12ndash14] Following the imple-mentation of the Chinese national policy of reform andopening-up in the 1980s the spatiotemporal changes ofChinese cities and their driving factors have attracted in-creasing attention from researchers [15ndash19]

In studies of the spatiotemporal differentiation andtransfer patterns of urban land expansion of large andmedium cities including Shanghai Beijing Hangzhou andNanjing remote sensing technology provided timely cost-efficient and effective data for large-scale urbanization re-search [20ndash25] As a byproduct of investing increasing ef-forts for reform and economic accessibility Chinaexperienced increasingly rapid urbanization In particularthe Yangtze River Delta region is now considered the largestregional economy of China In a regional development planpublished by the Chinese government the Yangtze RiverDelta is positioned as an economic center with the strongestcomprehensive power in China it is considered not only as amajor international gateway to the Asia-Pacific region butalso as a major base of global advanced manufacturing andthe first megalopolis of China with international implica-tions As such urbanization in this region has become apopular research topic However most remote sensing data-based studies of the urban spatiotemporal expansion in theYangtze River Delta are outdated [26ndash30] More recentstudies of urbanization in this region havemainly focused onindividual cities however a comprehensive investigation ofthe Yangtze River Delta is distinctly lacking In this studythe Yangtze River Delta was investigated to establish thecharacteristics patterns and mechanisms of urban spatio-temporal expansion since 2000 in order to provide scientific

input to urban development planning and sustainableeconomic development in the region

2 Materials and Methods

21 Study Area Located adjacent to the Yellow and EastChina Seas the Yangtze River Delta megalopolis (Figure 132deg34primendash29deg20primeN 115deg46primendash123deg25primeE) is comprised of twoprovinces as well as Shanghai municipality )e area has asubtropical monsoon climate and serves as a major interfaceof the Belt and Road economies and the Yangtze Rivereconomic belt Covering an area of approximately11times 104 km2 the core of the megalopolis includes the citiesof Shanghai Nanjing Yangzhou Zhenjiang Suzhou WuxiChangzhou Taizhou (Jiangsu) Nantong HangzhouShaoxing Huzhou Jiaxing Ningbo Zhoushan and Taizhou(Zhejiang) )is megalopolis is one of the most developedand urbanized regions in China and thus is both the mostdensely populated region in China and one of the six biggestmegalopolises in the world [31] In 2016 the region had aGDP of 18 trillion USD accounting for 167 of theChinese economy with a population of more than9800times104 [1 32 33]

22 Data

221 Satellite Data TM and OLI are key instrumentsonboard Landsat satellites which have a ground revisitperiod of 16 days In 2009 images collected by Landsatsatellites became freely available from the United StatesGeological Survey (USGS) with historical images datingback to 1982 at 15sim30m pixel resolutions A total of 7spectral bands between 450 nm and 125 μm and 9 spectralbands between 433 nm and 23 μm are available by TMimages and OLI images respectively allowing the explo-ration of the Earthrsquos resources and environments on a globalscale at maximal resolution but with minimal costs Imagesfrom the Landsat series satellites were selected for this studyowing to the consistent resolution and continuity of the dataAll data used in this study were provided by the USGSEROSData Center including 16 views of images from the Landsat-5 TM in 2000 and 15 views of images from the Landsat-8 OLIin 2017 All remote sensing images were of good quality andcovered the entire study area (Table 1)

222 DEM Data A digital elevation model (DEM) is adigital simulation of surface terrain using limited topo-graphic elevation data (or a digital representation of thesurface morphology) and is a physical model of surfaceelevation represented in matrices of ordered values )eAdvanced Spaceborne )ermal Emission and ReflectionRadiometer (ASTER) Global Digital Elevation Model(GDEM) V2 data used in this study were provided by theGeospatial Data Cloud (httpwwwgscloudcn) Comparedwith the ASTER GDEM V1 data the V2 data use moreadvanced algorithms and have an enhanced spatial resolu-tion and elevation accuracy )e V2 data have a spatialresolution of 30m and are presented in the WGS84

2 Complexity

123deg0prime0PrimeE

120deg0prime0PrimeE

120deg0prime0PrimeE

33deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

70deg0prime0PrimeE

10deg0prime0PrimeE

35deg0prime0PrimeE


deg0prime0PrimeN

0 100 200km50


DiaoyusIsland

South China Sea

Yangzhou

Nanjing Zhenjiang

Taizhou Nantong

Changzhou Wuxi

Suzhou

Shanghai

JiaxingHuzhou

Hangzhou

Shaoxing Ningbo

Zhoushan

Taizhou

China

N

S

W E


Complexity 3



23 Methods






11803830


11803930




119039302000917


4 Complexity



PPA Kjj

K+j

PUA Kii

Ki+

POA 1113936

ni1 Kii

T

K T 1113936


nij1 Ki+K+j1113872 1113873

T2 minus 1113936nij1 Ki+K+j1113872 1113873

(1)


24 Data Analysis





Ui Ua minus Ub

T times Uc

times 100 (2)



3 Results


Complexity 5










33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(a)






33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(b)


6 Complexity




Constructionland

Unusedland Total


()Verification data

2000

Farmland 181 6 9 1 3 4 204 8873Forest 5 182 5 0 2 3 197 9239




() 9050 9100 9000 9500 9300 9150


2017

Farmland 191 2 4 1 1 3 202 9455Forest 3 191 3 0 0 1 198 9646




() 9550 9550 9450 9750 9600 9500


0

10

20

30

40

50

60


Perc

enta

ge o

f are

a

20002017

Unused land




Complexity 7









Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a


8 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity

123deg0prime0PrimeE

120deg0prime0PrimeE

120deg0prime0PrimeE

33deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

70deg0prime0PrimeE

10deg0prime0PrimeE

35deg0prime0PrimeE


deg0prime0PrimeN

0 100 200km50


DiaoyusIsland

South China Sea

Yangzhou

Nanjing Zhenjiang

Taizhou Nantong

Changzhou Wuxi

Suzhou

Shanghai

JiaxingHuzhou

Hangzhou

Shaoxing Ningbo

Zhoushan

Taizhou

China

N

S

W E


Complexity 3



23 Methods






11803830


11803930




119039302000917


4 Complexity



PPA Kjj

K+j

PUA Kii

Ki+

POA 1113936

ni1 Kii

T

K T 1113936


nij1 Ki+K+j1113872 1113873

T2 minus 1113936nij1 Ki+K+j1113872 1113873

(1)


24 Data Analysis





Ui Ua minus Ub

T times Uc

times 100 (2)



3 Results


Complexity 5










33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(a)






33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(b)


6 Complexity




Constructionland

Unusedland Total


()Verification data

2000

Farmland 181 6 9 1 3 4 204 8873Forest 5 182 5 0 2 3 197 9239




() 9050 9100 9000 9500 9300 9150


2017

Farmland 191 2 4 1 1 3 202 9455Forest 3 191 3 0 0 1 198 9646




() 9550 9550 9450 9750 9600 9500


0

10

20

30

40

50

60


Perc

enta

ge o

f are

a

20002017

Unused land




Complexity 7









Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a


8 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity



23 Methods






11803830


11803930




119039302000917


4 Complexity



PPA Kjj

K+j

PUA Kii

Ki+

POA 1113936

ni1 Kii

T

K T 1113936


nij1 Ki+K+j1113872 1113873

T2 minus 1113936nij1 Ki+K+j1113872 1113873

(1)


24 Data Analysis





Ui Ua minus Ub

T times Uc

times 100 (2)



3 Results


Complexity 5










33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(a)






33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(b)


6 Complexity




Constructionland

Unusedland Total


()Verification data

2000

Farmland 181 6 9 1 3 4 204 8873Forest 5 182 5 0 2 3 197 9239




() 9050 9100 9000 9500 9300 9150


2017

Farmland 191 2 4 1 1 3 202 9455Forest 3 191 3 0 0 1 198 9646




() 9550 9550 9450 9750 9600 9500


0

10

20

30

40

50

60


Perc

enta

ge o

f are

a

20002017

Unused land




Complexity 7









Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a


8 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity



PPA Kjj

K+j

PUA Kii

Ki+

POA 1113936

ni1 Kii

T

K T 1113936


nij1 Ki+K+j1113872 1113873

T2 minus 1113936nij1 Ki+K+j1113872 1113873

(1)


24 Data Analysis





Ui Ua minus Ub

T times Uc

times 100 (2)



3 Results


Complexity 5










33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(a)






33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(b)


6 Complexity




Constructionland

Unusedland Total


()Verification data

2000

Farmland 181 6 9 1 3 4 204 8873Forest 5 182 5 0 2 3 197 9239




() 9050 9100 9000 9500 9300 9150


2017

Farmland 191 2 4 1 1 3 202 9455Forest 3 191 3 0 0 1 198 9646




() 9550 9550 9450 9750 9600 9500


0

10

20

30

40

50

60


Perc

enta

ge o

f are

a

20002017

Unused land




Complexity 7









Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a


8 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity










33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(a)






33deg0prime0PrimeN

30deg0prime0PrimeN

33deg0prime0PrimeN

30deg0prime0PrimeN

0 100 200km50

N

S

EW

(b)


6 Complexity




Constructionland

Unusedland Total


()Verification data

2000

Farmland 181 6 9 1 3 4 204 8873Forest 5 182 5 0 2 3 197 9239




() 9050 9100 9000 9500 9300 9150


2017

Farmland 191 2 4 1 1 3 202 9455Forest 3 191 3 0 0 1 198 9646




() 9550 9550 9450 9750 9600 9500


0

10

20

30

40

50

60


Perc

enta

ge o

f are

a

20002017

Unused land




Complexity 7









Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a


8 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity




Constructionland

Unusedland Total


()Verification data

2000

Farmland 181 6 9 1 3 4 204 8873Forest 5 182 5 0 2 3 197 9239




() 9050 9100 9000 9500 9300 9150


2017

Farmland 191 2 4 1 1 3 202 9455Forest 3 191 3 0 0 1 198 9646




() 9550 9550 9450 9750 9600 9500


0

10

20

30

40

50

60


Perc

enta

ge o

f are

a

20002017

Unused land




Complexity 7









Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a


8 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity









Shan

ghai

Chan

gzho

u

Nan

jing

Nan

tong

Suzh

ou

Taiz

hou

Wux

i

Yang

zhou

Zhen

jiang

Han

gzho

u

Huz

hou

Jiaxi

ng

Nin

gbo

Shao

xing

Taiz

hou

Zhou

shan

0

2

4

6

8

10

12

14

Perc

enta

ge o

f are

a


8 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity





000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(a)

000

040

080

120

160N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(b)

000

020

040

060

080N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(c)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(d)

000050100150200250300

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(e)

000

050

100

150

200N

NNENE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(f )

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(g)

000050100150200250

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(h)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NW

NW

NNW

(i)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(j)

000030060090120150

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(k)

000

050

100

150

200N

NNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(l)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(m)

000050100150200250

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(n)

000050100150200250300350

NNNE

NE

NEE

E

SEE

SE

SSES

SSW

SW

SWW

W

NWW

NW

NNW

(o)

000020040060080100

NNNE

NE

NEE

E

SEE

SESSE

SSSW

SW

SWW

W

NWW

NWNNW

(p)


Complexity 9









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity









4 Discussion




0

10

20

30

40

50

60

70

80

90

100

Perc

enta

ge o

f are

a


10 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity


5 Conclusions



Data Availability




Acknowledgments


References












Complexity 11


































12 Complexity


































12 Complexity

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