sustainability

Article Study on Urban Expansion Using the Spatial and Temporal Dynamic Changes in the Impervious Surface in

Yanping Qian 2 and Zhen Wu 1,* 1 School of Architecture, , Nanjing 210000, China 2 College of Landscape Architecture, Nanjing Forestry University, Nanjing 210000, China; [email protected] * Correspondence: [email protected]

 Received: 15 January 2019; Accepted: 8 February 2019; Published: 12 February 2019 

Abstract: Impervious surface area is a key factor affecting urbanization and urban environmental quality. It is of great significance to analysis timely and accurately the dynamic changes of impervious surface for urban development planning. In this study, we use a comprehensive method to extract the time series data on the impervious surface area (ISA) from the multi-temporal Landsat remote sensing images with a high overall accuracy of 90%. The processes and mechanisms of urban expansion at different political administration and direction level in the Nanjing metropolitan area are investigated by using the comprehensive classification method consisting of minimum noise fraction, linear spectral mixture analysis, spectral index, and decision tree classifiers. The expansion of Nanjing is examined by using various ISA indexes and concentric regression analyses. Results indicate that the overall classification accuracy of ISA is higher than 90%. The ISA in Nanjing has dramatically increased in the past three decades from 427.36 km2 to 1780.21 km2 and with a high expansion rate of 0.48 from 2000 to 2005. The city sprawls from monocentric to urban core with multiple subcenters in a concentric structure, and the geometric gravity center of construction land moves southward annually. The stages of urbanization in different levels and the dynamic changes in different direction levels are influenced by the topographic and economic factors.

Keywords: urban expansion; impervious surface area; Nanjing

1. Introduction Development of a city during rapid urbanization is often at the expense of natural landscape pervious surfaces which are replaced with an impervious surface composed of mixed clay, asphalt, stone, and metal [1]. The transformation of the earth’s surface from non-urban land to urban land becomes an irreversible process [2,3], thereby generating serious problems, such as farmland loss, heat island effect, flood hazard, and habitat fragmentation [4–13]. In recent years, many studies have focused on examining the urban land cover and its dynamic changes using the remote sensing (RS) method [14–17]. However, urban land cover extraction from RS images results in poor accuracy because an urban land is usually a mosaic of different land covers, such as plants, soils, buildings, and water [18,19]. Impervious surface area (ISA) is an essential component of urban landscapes and is the artificial surface that cannot be penetrated by water because of the composition of man-made construction materials in structures, such as road and building roofs [18]. ISA can truly reflect the expansion of urban construction land and can be extracted from RS images [13]. Ridd proposed the vegetation–impervious surface–soil conceptual model to explain urban composition [20]. He regarded urban surface as a combination of vegetation, impervious surface, and

Sustainability 2019, 11, 933; doi:10.3390/su11030933 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 933 2 of 22 soil [20]. Wu and Murray applied the conceptual model of fixed end-member to the Landsat Thematic Mapper Plus (ETM+) image, and they developed the real and operable linear spectral mixture analysis (LSMA) [21]. Various methods, such as spectral mixture analysis, multiple end-member spectral mixture analysis, and ISA index, have been used to improve the precision of ISA extraction [21–26]. RS data, such as QuickBird, DMSP-OLS, Terra MODIS, and so on, have also been used to improve accuracy [27–31]. Developing countries, especially China, have become the main driving force of global urbanization in recent years [32–34]. The number of large-sized and medium-sized cities are increasing rapidly, and the scale of urban construction is growing. China has experienced an acceleration of urban expansion from the late 1980s [33]. In addition, 70% population of Chinese will live in urban areas by 2030, and 200 million rural immigrants will be received by Chinese cities [35]. As a result, a sharp increase in ISA will occur. This study uses a large city, Nanjing, as an example. The sprawl of the city and each district is measured using impervious surface, and the consequences of city expansion are analyzed. Nanjing is the old capital of six dynasties in Chinese history and the capital of Province [36]. The urban expansion and development trajectories of Nanjing differ from those of globalizing first-tier metropolitan cities in China in recent years, such as Beijing and Shanghai [37]. Nanjing is a second-tier city with a rapidly growing global economy and is a representative site of research [38]. Some studies have been conducted on the spatiotemporal patterns of urban expansion by monitoring the dynamic changes in ISA [37,39–43]. The current research not only analyzes the status of ISA development in the entire city but also quantifies and compares spatiotemporal patterns of ISA and urban expansion details in different directions and districts. The impacts of the economy on different district development phases are also analyzed. Therefore, this study mainly aims to

(1) Use a comprehensive method to extract ISA from Landsat images with an overall accuracy of 90%; (2) Map locations, density, the geometric center of gravity, and extents of ISA in Nanjing; (3) Quantify spatiotemporal patterns of urban expansion in the entire city, and (4) Comprehensively compare similarities and differences in the development phases of urbanization under different direction and district levels.

This type of research provides a method to improve the accuracy of urban expansion research data extracted from RS images and provide new understanding on the interactions of socioeconomic systems, different development phases, expansion mechanisms, and urbanization patterns from the perspective of direction, district levels, and the entire city. The other value of this study is to better understand the details of urban expansion in Nanjing, and to provide valuable insights for improving urban planning, management, and sustainability.

2. Materials and Methods

2.1. Study Area Nanjing is a city with a 2000 years history and was an ancient Chinese capital [36]. Nanjing is the current capital of Jiangsu Province [38]. Thus, Nanjing is the center of politics, economy, and culture of the province and is the core area of the Yangtze River economic belt [44]. Its level of economic development is at the forefront in China [44]. Nanjing metropolis includes 11 districts, namely, Xuanwu, Qinhuai, Gulou, Jianye, Yuhuatai, Qixia, Pukou, Luhe, Jiangning, Lishui, and Gaochun, with a total area of 6587.02 km2 [45]. Its economic growth accelerated after the economic reform, especially in the 1990s. During this period, it was transformed from a medium-sized city to a megacity. Its Gross Domestic Product (GDP) and the municipal population has grown from RMB 16 billion and 5 million people in 1990 to RMB 9720 billion and 8 million people in 2015 [45] (Figure1). SustainabilitySustainability 20192019, ,1111, ,x 933FOR PEER REVIEW 3 3of of 22 22

Figure 1. Location of the study area. Figure 1. Location of the study area. 2.2. Data Collection and Preprocessing 2.2. Data Collection and Preprocessing RS data used in this research was mainly from the Landsat Thematic Mapper (TM), Enhanced ETM+,RS data and Operationalused in this research Land Imager was mainly from 1990, from 1995, the Landsat 2000, 2005, Thematic 2010, andMapper 2017 (TM), (Path: Enhanced 120, Row: ETM+,38) (Table and1 Operational). The digital Land elevation Imager model from (DEM) 1990, 1995 data, came2000, from2005, the2010, ASTER and 2017 Global (Path: DEM 120, (GDEM) Row: 38) in (TableNanjing. 1). The digital Environmental elevation Systems model (DEM) Research data Institute came from (ESRI) the Shapefile ASTER ofGlobal Nanjing DEM administrative (GDEM) in Nanjing.units was The used. Environmental The images wereSystems georeferenced Research Instit in theute Universal (ESRI) Shapefile Transverse of MercatorNanjing administrative system (WGS84 unitsdatum, was Zone51 used. The North), images and were Google georeferenced Earth images in the from Universal 1990, 1995,Transverse 2000, 2005,Mercator 2010, system and 2017 (WGS84 were datum,used as Zone51 the reference North), data and for Google evaluating Earth ISA images accuracy. from 1990, 1995, 2000, 2005, 2010, and 2017 were used asPopulation the reference and data GDP for data evaluating of Nanjing ISA megacity accuracy. were collected from Nanjing Statistical Yearbook, whichPopulation is published and byGDP China data Statistics of Nanjing Press. megacity Ancillary were data collected such as from urban Nanjing planning Statistical and management Yearbook, whichwas also is published collected forby examiningChina Statistics the different Press. Anci influencesllary data of urban such as development urban planning policies and on management urbanization waspatterns also andcollected rates. for examining the different influences of urban development policies on urbanization patterns and rates.

Table 1. Summary of data used in research.

Data types Datasets Time Path/row Image type Remote sensing data 1990-7-11 Landsat 5-TM 120/38 1995-10-13 Landsat 5-TM 120/38 Sustainability 2019, 11, 933 4 of 22

Table 1. Summary of data used in research.

Data Types Datasets Time Path/row Image type 1990-7-11 Landsat 5-TM 120/38 1995-10-13 Landsat 5-TM 120/38 Remote sensing data 2000-6-12 Landsat 5-TM 120/38 2005-4-7 Landsat 5-TM 120/38 2010-4-5 Landsat 7-ETM 120/38 2017-7-21 Landsat 8-OLI 120/38 DEM data ASTER GDEM with 30m spatial resolution Boundary files Nanjing administration boundary shape data GDP data GDP data in 1995, 2000, 2005, 2010, and 2015 year at district level in Nanjing Ancillary data Urban planning and policies of Nanjing

2.3. Mapping ISA Distribution The proposed comprehensive classification framework for improved LSMA consisted of minimum noise fraction (MNF), LSMA, spectral index, and decision tree classifiers. Figure2 illustrates the major steps of the framework. The major steps are explained in detail below:

(1) Basic RS image operations, such as atmospheric correction and radiometric calibration, were used. ISO unsupervised classification and spectral index of MNDWI were calculated through ENVI 5.2 [46,47]. (2) MNF transform is a tool for determining the number of bands in the image data, separating the noise in the data, and reducing the computing demand [21]. This method can effectively eliminate noise and reduce the dimension of an image. We used the MNF method to extract good-quality end-members. High albedo, low albedo, and vegetation end-members were extracted on the basis of the geometric vertices in a two-dimensional scatter graph [48]. (3) Three end-members were used as the ROIs to produce fractional images by using the LSMA approach [48]. (4) Three fractional images, namely, high-albedo, low-albedo, and green vegetation fraction images, with the Iterative Selforganizing Data Analysis Techniques Algorithm (ISODATA) were used in the decision tree classifier to produce ISA and other data. (5) Post processing was conducted, and the results of ISA accuracy were evaluated [49]. SustainabilitySustainability 20192019,, 1111,, x 933 FOR PEER REVIEW 55 of of 22 22

Figure 2. Flowchart for mapping impervious surface distribution. Figure 2. Flowchart for mapping impervious surface distribution. 2.4. Dynamic Changes in ISA 2.4. Dynamic Changes in ISA 2.4.1. Density of Dynamic Change in ISA 2.4.1. Density of Dynamic Change in ISA We mapped the density of ISA to reflect the characteristics of its expansion in time and space. ThisWe can mapped intuitively the explaindensity theof ISA time to series reflect of the ISA characteristics distribution characteristicof its expansion in in the time entire and city space. and Thiscan illustratecan intuitively the distribution explain the of time human series activities. of ISA di Westribution transformed characteristic the original in the 30 entire m-sized city ISA and raster can illustrateimages into the the distribution 1 km-sized of ISA human raster activities. images by We using transformed the aggregate the methodoriginal on 30 ArcGIS m-sized software ISA raster [50]. imagesAs a result, into the the 1 scale km-sized and direction ISA raster of images the ISA by transformation using the aggregate from themethod rural on areas ArcGIS to the software urban areas[50]. Ascould a result, be determined. the scale and direction of the ISA transformation from the rural areas to the urban areas couldThe be determined. following formula can be used to calculate the density of ISA: The following formula can be used to calculate the density of ISA: ISA Density of ISA DOI = . (1) Density of ISA 𝐷𝑂𝐼 = (1) TA where DOI is the density of ISA in the study area, ISA is the area of impervious surface in the study where DOI is the density of ISA in the study area, ISA is the area of impervious surface in the study area, and TA is the total area in the study area. area, and TA is the total area in the study area.

Sustainability 2019, 11, 933 6 of 22

2.4.2. Center of Gravity of Dynamic Change in ISA The geometric center of gravity of ISA distribution was measured by ArcGIS to reflect the overall situation of urban expansion in Nanjing. The center of gravity model is composed of 11 district units, and the center of gravity model of ISA is as follows:

n ∑i (IiQ(xi, yi)) Gravity center of ISA ISAi(x, y) = n , (2) ∑i Ii where ISAi(x,y) are the geographic coordinates of the center of gravity of ISA in Nanjing, Ii is the area of the impervious surface in different districts, and Q(xi,yi) are the geographic coordinates of the center of gravity of ISA in different districts in Nanjing.

2.4.3. Expansion Rate and Area of Dynamic Change in ISA Some indexes of ISA change were used to analyze the entire city and its districts. By analyzing the ISA change data in time and space, the expansion of a city can be described. The following variables are calculated to analyze the dynamic change in ISA [51]. The first two formulas are mainly analyzed from the area of ISA, and the latter formulas are mainly analyzed from the growth efficiency of ISA.

Overall ISA changed area OICA = ISA(t2) − ISA(t1) (3)

Annual expansion area AEA = [ISA(t2) − ISA(t1)]/(t2 − t1) (4)

Expansion rate ER = [ISA(t2) − ISA(t1)]/ISA(t1) (5) where ISA(t) is the area of ISA in different time, t1 is the prior time of t2.

2.4.4. Dynamic Change in ISA in Different Directions One urban expansion model assumes that urban expansion is a concentric circle structure [44]. This model indicates that a city takes the Central Business District (CBD) as its expansion circle center and expands from the center to suburbs in the form of concentric circles [52,53]. On the basis of this theory, we created the concentric buffer zone structure of the ISA in ArcGIS to analyze the city expansion in different directions. We defined Xinjiekou CBD as the concentric circle center of Nanjing on the basis of previous research experience [52]. This CBD is located at the intersection of Hanzhong Road and Zhongshan Road. Meanwhile, 5 km buffer zone intervals were created to cover the entire city [44]. An equal interval of 22.5 degrees for each direction was used, and the study area and the concentric circles were divided into eight parts: east, southeast, south, southwest, west, northwest, north, and northeast (Figure1). The density and expansion rate of ISA were calculated using this method to investigate the spatial expansion of the city in different directions.

2.4.5. Impacts of Topography on Dynamic Change in ISA Nanjing is in a hilly region [36]. Thus, the impact of terrain on its urban expansion should be investigated. In explaining the relationship between the impact of topography and city expansion, different interval ranges of elevation and slope were used as statistical indicators to describe the distribution and dynamic change in ISA in different topographies. In this case, the elevation was divided into six levels, and the slope was divided into four levels.

2.4.6. Relationship between ISA Dynamics and GDP in Different Districts Economic development has directly promoted the process of urbanization and determined the level of urbanization [3,54]. However, the relationship between economic and ISA changes at the district level has not been investigated. In this study, we used the regression models to explore the Sustainability 2019, 11, 933 7 of 22 relationship between the GDP data and the ISA change data from 11 districts in Nanjing. We aimed to find the different development phases of five urban core districts and six suburban districts in Nanjing and to develop effective strategies of urban planning on different urban expansion patterns in district level.

3. Results

3.1. Dynamic Change in ISA in the Entire City The accuracy assessment of the confusion matrix showed that the overall accuracy of the ISA data in Nanjing was more than 90% in 2010 and 2017 (Table2). The producer’s and user’s accuracies in 2010 were 89.0% and 94.5%, respectively. The producer’s and user’s accuracies in 2017 were 92.0% and 95.5%, respectively. According to previous researchers, the results in other time series were in similar accuracy.

Table 2. Accuracy assessment of impervious surface mapping result.

2010 2017 Producer’s User’s Producer’s User’s ISA Others ISA Others accuracy (%) accuracy (%) accuracy (%) accuracy (%) ISA 89 11 89.0 89.0 92 8 91.1 92.0 Others 11 189 94.5 94.5 9 191 96.0 95.5 Overall 92.7 92.6 accuracy (%)

As shown in Figure3, the density of ISA distribution in Nanjing has drastically changed in the past 30 years. The density of ISA increased from the urban core to the suburban, and the ISA expansion was separated into two parts by the Yangtze River. After 2000, the ISA located in the northern part of the Yangtze River accelerated and expanded. The urban expansion was marked by two axes in the east–west and in the north–south. Sustainability 2019, 11, 933 8 of 22 Sustainability 2019, 11, x FOR PEER REVIEW 8 of 22

Figure 3. Density of dynamic change in impervious surface area (ISA) in Nanjing from 1990 to 2017. Figure 3. Density of dynamic change in impervious surface area (ISA) in Nanjing from 1990 to 2017. The transitions in the geometric gravity center of ISA change can explain the overall situation of urbanThe expansion. transitions From in the 1990 geometric to 2017, thegravity geometric center centerof ISA of change gravity can of ISAexplain generally the overall moved situation southward of urban expansion. From 1990 to 2017, the geometric center of gravity of ISA generally moved annually. This transition was divided into two stages. In the first stage, from 1990 to 1995, the center southward annually. This transition was divided into two stages. In the first stage, from 1990 to 1995, of gravity of ISA gradually moved to southwest; in the second stage, from 1995 to 2017, the center of the center of gravity of ISA gradually moved to southwest; in the second stage, from 1995 to 2017, the gravity of ISA gradually shifted to the southeast. The southern expansion of Nanjing was obvious center of gravity of ISA gradually shifted to the southeast. The southern expansion of Nanjing was from 2005 to 2010. However, in the past years from 2010 to 2017, the distance between the geometric obvious from 2005 to 2010. However, in the past years from 2010 to 2017, the distance between the center of gravity of ISA of the two years became small. Therefore, the construction of ISA increased in geometric center of gravity of ISA of the two years became small. Therefore, the construction of ISA the northwest, and the expansion became balanced between the north and south directions (Figure4). increased in the northwest, and the expansion became balanced between the north and south directions. Sustainability 2019, 11, x FOR PEER REVIEW 9 of 22 Sustainability 2019, 11, 933 9 of 22

32.000 Gravity center of ISA 31.990 1990 1995 31.980

31.970 2005 2000 31.960

31.950 2010 North latitude (°) North latitude 31.940 2017 31.930 118.828 118.830 118.832 118.834 118.836 East longitude (°)

FigureFigure 4. 4.Center Center of of gravity gravity of of dynamic dynamic change change in in ISA ISA in in Nanjing Nanjing from from 1990 1990 to to 2017. 2017.

2 2 Overall,Overall, the the ISA ISA in in Nanjing Nanjing metropolis metropolis increased increased rapidly rapidly from from 427.36 427.36 km kmin2 in 1990 1990 to to 1780.21 1780.21 km km2 inin 2017 2017 (Table (Table3). 3).

Table 3. Impervious surface area (ISA) data in Nanjing from 1990 to 2017. Table 3. Impervious surface area (ISA) data in Nanjing from 1990 to 2017. Year 1990 1995 2000 2005 2010 2017 Year 1990 1995 2000 2005 2010 2017 ISA (km2) 427.36 545.57 668.72 987.58 1271.93 1780.21 ISA (km2) 427.36 545.57 668.72 987.58 1271.93 1780.21

WithWith regard regard to to area area change, change, the the increasing increasing amount amount of of ISA ISA and and the the annual annual expansion expansion area area reached reached thethe maximum maximum in in the the period period of of 2010 2010 to to 2017; 2017; the the period period of of 2000 2000 to to 2005 2005 was was in in second second place; place; the the area area changechange in in other other periods periods was was relatively relatively low. low. In In terms term ofs rateof rate change, change, the the expansion expansion rate rate reached reached 0.48 0.48 in thein periodthe period 2000 2000 to 2005, to 2005, respectively; respectively; on on the the contrary, contrary, the the rates rates only only reached reached 0.23 0.23 in in the the period period 1995 1995 toto 2000. 2000. The The calculation calculation results results of of urban urban expansion expansion showed showed that, that, before before 2000, 2000, the the urban urban expansion expansion of of NanjingNanjing was was relatively relatively slow; slow; after after 2000, 2000, Nanjing Nanjing rapidly rapidly developed, developed, especially especially in in the the periods periods 2000 2000 to to 20052005 and and 2010 2010 to to 2017 2017 (Table (Table4). 4).

Table 4. Analysis of dynamic change in ISA in Nanjing from 1990 to 2017. Table 4. Analysis of dynamic change in ISA in Nanjing from 1990 to 2017.

1990–19951990–1995 1995–20001995–2000 2000–20052000–2005 2005–20102005–2010 2010–20172010–2017 OverallOverall ISA ISA changed changed area area (km (km22)) 118.22118.22 123.15123.15 318.86318.86 284.34284.34 508.29508.29 AnnualAnnual expansion area (km 2/year)/year) 23.6423.64 24.6324.63 63.7763.77 56.8756.87 72.6172.61 ExpansionExpansion rate rate 0.28 0.28 0.230.23 0.480.48 0.290.29 0.400.40

3.2.3.2. Dynamic Dynamic Change Change in in ISA ISA in in District District Level Level PlannersPlanners usually usually distinguish distinguish the the 11 11 districts districts of of Nanjing Nanjing as as two two types: types: urban urban core core and and suburban suburban districtsdistricts (Figure (Figure1). 1). Urban Urban core core districts districts include includ Xuanwu,e Xuanwu, Qinhuai, Qinhuai, Gulou, Gulou, Jianye, Jianye, and Yuhuatai, and Yuhuatai, while suburbanwhile suburban districts districts include include Qixia, Pukou, Qixia, Luhe,Pukou, Jiangning, Luhe, Jiangning, Lishui, andLishui, Gaochun. and Gaochun. FigureFigure5(a1,a2) 5(a1,a2) indicates indicates obvious obvious differences differences between between the the urban urban core core and and suburban suburban districts districts in in termsterms of of ISA ISA distribution. distribution. At At the the total total tendency tendency of of ISA ISA amount, amount, the the amount amount of of ISA ISA in in all all urban urban core core districtsdistricts was was below below 60 km60 2km, and2, and the trendthe trend of ISA of growth ISA growth gradually grad slowedually slowed down because down because of the limited of the spacelimited in thespace urban in the districts. urban Thedistricts. ISAamount The ISA was amount over was 150 kmover2, and150 km the2 trend, and the in ISA trend growth in ISA steadily growth increasedsteadily increased in the suburban in the districtssuburban compared districts compared with those with in the those urban in corethe urban districts. core Although districts. theAlthough trend inthe ISA trend growth in ISA in the growth urban in core the districtsurban core slowed districts down, slowed the amount down, ofthe ISA amount in Jianye of ISA and in Yuhuatai Jianye and in theYuhuatai urban core in increasedthe urban relatively core increased more than relatively that in others.more than Among that the in suburban others. Among districts, the the suburban amount ofdistricts, ISA in Jiangning the amount and Luheof ISA was in relatively Jiangning large and owing Luhe to was the urbanrelatively development large owing and theto largethe urban area ofdevelopment their own administration. and the large area of their own administration. Sustainability 2019, 11, 933 10 of 22 Sustainability 2019, 11, x FOR PEER REVIEW 10 of 22

Xuanwu Gulou Qinhuai Xuanwu Gulou Qinhuai 70.0 Jianye Yuhua 0.9 Jianye Yuhua 60.0 0.8

) 0.7 2 50.0 0.6 40.0 0.5 30.0 0.4 0.3 Area (km 20.0 ISA density 0.2 10.0 0.1 0.0 0.0 1990 1995 2000 2005 2010 2017 1990 1995 2000 2005 2010 2017 Year Year (a1) (b1) Jiangning Pukou Qixia Jiangning Pukou Qixia Lishui Luhe Gaochun Lishui Luhe Gaochun 500.0 0.5 0.5 400.0 0.4 ) 2 0.4 300.0 0.3 0.3 200.0 0.2 0.2 Area (km 100.0 ISA density 0.1 0.1 0.0 0.0 1990 1995 2000 2005 2010 2017 1990 1995 2000 2005 2010 2017 Year Year (a2) (b2)

FigureFigure 5. 5. AmountAmount and and density density of of ISA ISA in in different different districts districts in in Nanjing Nanjing from from 1990 1990 to to 2017. 2017. (Note: (Note: (a1 (a1) ) AmountAmount of of ISA ISA in in urban urban core core districts; districts; ( (a2a2)) Amount Amount of ISA inin suburbansuburban districts;districts; (b1(b1)) Density Density of of ISA ISA in inurban urban core core districts; districts; (b2 (b2) Density) Density of of ISA ISA in in suburban suburban districts). districts).

FigureFigure 5(b1,b2)5(b1,b2) indicate indicate that that the the urban urban core core di districtsstricts presented presented higher higher ISA ISA densities densities than than the the suburbansuburban districts. districts. The The density density of of ISA ISA in in the the urba urbann core core districts districts was was more more than than 40%. 40%. Among Among them, them, thethe ISA ISA density density of of Gulou Gulou and and Qinhuai Qinhuai were were the the larg largest.est. By By contrast, contrast, Xuanwu Xuanwu exhibited exhibited a alow low density density owingowing to to the the Zijin Zijin mountains mountains and and the the Xuanwu , Lake, which which are are ecologically ecologically protected protected areas. areas. In In the the futurefuture development development of of the urbanurban corecore districts, districts, Jianye Jianye and and Yuhuatai Yuhuatai will will further further develop develop to be to the be mainthe mainareas areas of city of construction.city construction. InIn the the suburbs, suburbs, the the density density of ofISA ISA was was less less than than 40％ 40%,, except except in Qixia. in Qixia. The The development development trend trend of Qixiaof Qixia was was gradually gradually near near that that of Jianye of Jianye and and Yuhuatai Yuhuatai because because its its geographical geographical location location is isnear near the the corecore of of the the city. city. Gaochun, Gaochun, Luhe, Luhe, and and Lishui Lishui pres presentedented a alow low density density of of ISA ISA because because of of their their large large agriculturalagricultural and and mountainous mountainous areas. areas. TheThe density density of of ISA ISA in in other other suburban suburban districts districts was was around around 20%–30%. 20%–30%. However, However, the the development development trendstrends of of the the ISA ISA density density showed showed an an accelerated accelerated incr increaseease in in the the period period 2005 2005 to to 2017. 2017. The The density density will will growgrow rapidly rapidly in in the the future. future. The The results results also also coincide coincidedd with with the the actual actual situation. situation. In In particular, particular, three three suburbansuburban cores cores were were formed: formed: Xianling Xianling suburban suburban core core in in Qixia , District, Dongshan Dongshan suburban suburban core core in in JiangningJiangning District, District, and and Jiangbei Jiangbei su suburbanburban core core in in Pukou . District. Figure6 indicates that the increase of ISA in suburban districts was more than that in urban core districts. In recent years, the increase in urban area is below 1.5 km2/year, and the increase in the suburb is over 5 km2/year. The annual expansion area in Jianye and Yuhuatai increased more than that in three other districts; the annual expansion area development of the three districts in the city core was consistent. Jiangning exhibited the highest value of the annual expansion area. The construction of Jiangning and Lishui has been growing rapidly since 1990, and the construction of Qixia has slowed down since 2005. Sustainability 2019, 11, 933 11 of 22 Sustainability 2019, 11, x FOR PEER REVIEW 11 of 22

Xuanwu Gulou Qinhuai Jiangning Pukou Qixia Jianye Yuhua Lishui Luhe Gaochun 3.5 25.0 3.0 20.0 ）

） 2.5

2.0 15.0 /year /year 2 2 1.5

km 10.0 km

1.0 （ （ 5.0 0.5 Annual growth amount Annual growth amount 0.0 0.0 1990-1995 1995-2000 2000-2005 2005-2010 2010-2017 1990-1995 1995-2000 2000-2005 2005-2010 2010-2017 Year Year (a1) (b1) Xuanwu Gulou Qinhuai Jiangning Pukou Qixia Jianye Yuhua Lishui Luhe Gaochun 14% 14%

12% 12%

10% 10%

8% 8%

6% 6%

4% 4%

2% 2% Expansion rate of ISA

Expansion rate of ISA 0% 0% 1990-1995 1995-2000 2000-2005 2005-2010 2010-2017 1990-1995 1995-2000 2000-2005 2005-2010 2010-2017 Year Year (a2) (b2)

FigureFigure 6. 6. AnnualAnnual growth growth amount amount and and rates rates of of ISA ISA in in di differentfferent districts districts in in Nanjing Nanjing from from 1990 1990 to to 2017. 2017. (Note:(Note: (a1 (a1) )Annual Annual growth growth amount amount of of ISA in urban corecore districts;districts; ((a2a2)) AnnualAnnual growth growth amount amount of of ISA ISA in insuburban suburban districts; districts; (b1 (b1) Annual) Annual growth growth rate rate of of ISA ISA in in urban urban core core districts; districts; (b2 (b2) Annual) Annual growth growth rate rate of ofISA ISA in in suburban suburban districts). districts).

FigureAfter 2005,6 indicates obvious that differences the increase were of ISA found in subu in therban development districts was of more the urban than corethat in and urban suburban core districts.districts. In The recent growth years, point the of increase ISA shifted in urban from the area urban is below area to1.5 the km suburban,2/year, and and the the increase suburb enteredin the suburbthe main is over period 5 km of development.2/year. The annual expansion area in Jianye and Yuhuatai increased more than that inFigure three6 other(b2) shows districts; that, the in annual the period expansion 1990 to area 2005, development Jianye and Yuhuatai of the three were districts rapidly developed.in the city coreHowever, was consistent. the annual Jiangning expansion exhibited growth rate the of hi allghest the urbanvalue coreof the districts annual slowed expansion down area. after 2005,The constructionand the trend of Jiangning of the urban and core Lishui districts has been consistently growing rapidly stabilized. since In 1990, the periodand the 2000 construction to 2005, theof Qixiaannual has expansion slowed down rate ofsince all the2005. suburban areas grew fast, whereas the development of Qixia region becameAfter slow. 2005, obvious differences were found in the development of the urban core and suburban districts. The growth point of ISA shifted from the urban area to the suburban, and the suburb entered 3.3. Dynamic Change in ISA in Different Directions the main period of development. FigureFigure 6(b2)7 shows shows that that, Nanjing in the expansion period 1990 showed to 2005, a concentric Jianye and circle Yuhuatai characteristic. were rapidly Compared developed. with However,that in other the cities,annual the expansion overall density growth of rate ISA of in all Nanjing the urban was core lower districts in the slowed same period. down after The reason2005, andwas the that trend Nanjing of the is urban a landscape core districts city. The consistently Yangtze River stabilized. and Zijin In the Mountain period 2000 are also to 2005, in the the middle annual of expansionthe city, thereby rate of reducingall the suburban the total areas density grew of ISA.fast, whereas From 1990 the to development 2017 within theof Qixia distance region of 0 became to 5 km, slow.the density of ISA was the highest in each period. The total ISA density in this area increased from 0.63 to 0.83. However, the density of ISA over 40 km was less than 0.3 in all the periods. At around 15 km, 3.3.the Dynamic ISA density Change drastically in ISA in changed Different and Directions increased from 0.10 in 1990 to 0.55 in 2017. This result also reflectsFigure that, 7 shows before that 2000, Nanjing the main expansion expansion showed mostly a occurredconcentric in circle the city characteristic. center in the Compared range of 10 with km. thatAfter in 2000,other especially cities, the afteroverall 2005, density the city of expansionISA in Nanjing concentrated was lower on thein the space same between period. 15 The and reason 20 km. was that Nanjing is a landscape city. The Yangtze River and Zijin Mountain are also in the middle of the city, thereby reducing the total density of ISA. From 1990 to 2017 within the distance of 0 to 5 km, Sustainability 2019, 11, x FOR PEER REVIEW 12 of 22 the density of ISA was the highest in each period. The total ISA density in this area increased from 0.63 to 0.83. However, the density of ISA over 40 km was less than 0.3 in all the periods. At around 15 km, the ISA density drastically changed and increased from 0.10 in 1990 to 0.55 in 2017. This result also reflects that, before 2000, the main expansion mostly occurred in the city center in the range of 10 km. After 2000, especially after 2005, the city expansion concentrated on the space between 15 and Sustainability 2019, 11, 933 12 of 22 20 km. Different direction figures show that a slight “U” appeared in the east, north, and northwest directionsDifferent because direction of the figurestopographic show features, that a slight such “U”as the appeared Yangtze inRiver the east,and Zijin north, Mountain, and northwest which blockdirections the city because expansion. of the topographic However, another features, wave such aspeak the Yangtzeappeared River after and the Zijin U shape. Mountain, This whichpeak indicatesblock the that city the expansion. urban subcenter However, was another built wavearound peak the appeared15 km area after in each the Udirection. shape. This The peak figure indicates shows thatthat the the west urban side subcenter was not waschanging built arounddramatically the 15 ou kmtside area the in 10 each km direction.area. The reason The figure is that shows the west that areathe west belongs side to was the not hilly changing area of dramatically Jianghuai and outside the urban the 10 construction km area. The is reason greatly is influenced that the west by areathe terrain.belongs Figure to the hilly7 shows area ofthat Jianghuai the development and the urban of ISA construction in southwest is greatly Nanjing influenced increased by theobviously terrain. aroundFigure7 the shows 10 km that area. the This development phenomenon of ISA is attributed in southwest to the Nanjing formation increased of the obviouslynew city center around called the Hexi.10 km After area. 2000, This phenomenonthe area between is attributed 10 and 25 to thekm formationwas influenced of the newby the city formation center called of the Hexi. new After city center.2000, the As area the economy between 10developed, and 25 km the was density influenced of the by45 km the formationarea in southwest of the new increased city center. beyond As the cityeconomy concentric developed, structure. the The density main of reason the 45 is km that area the in area southwest between increased the two cities beyond of the cityYangtze concentric River formedstructure. the The economic main reason zone. is The that Banqiao the area betweencity belt theis the two economic cities of the belt Yangtze between River Nanjing formed and the Maanshan.economic zone. Three The peaks Banqiao in the citysouth belt direction is the economic at 20, 55, beltand between80 km formed Nanjing the andsouthern Maanshan. New Town, Three Lukoupeaks inAirport, the south and direction construction at 20, center 55, and of 80 Gaochun km formed District, the southern respectively. New Town, Lukou Airport, and constructionFifteen and center 50 ofkm Gaochun areas of District, the southeast respectively. formed the subcenter of Dongshan City and the constructionFifteen center and 50 of km Lishui areas District. of the southeast formed the subcenter of Dongshan City and the construction center of .

0.9 1990 0.8 1995 0.7 2000 2005 0.6 2010 0.5 2017

0.4

0.3 ISA density 0.2

0.1

0.0 0 102030405060708090100 Distance to CBD (km) (a1) 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 ISA density ISA density 0.1 0.1 0.0 0.0 0 10203040 0 20406080 Distance to CBD (km) Distance to CBD (km) (a2) (a3)

Figure 7. Cont. Sustainability 2019, 11, 933 13 of 22 Sustainability 2019, 11, x FOR PEER REVIEW 13 of 22

0.8 1.0 0.7 0.8 0.6

0.5 0.6 0.4 0.3 0.4 ISA density ISA density 0.2 0.2 0.1 0.0 0.0 0 20406080 0 10203040 Distance to CBD (km) Distance to CBD (km) (a4) (a5) 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 ISA density

ISA density 0.2 0.2

0.0 0.0 0 1020304050 0 1020304050 Distance to CBD (km) Distance to CBD (km) (a6) (a7) 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 ISA density ISA density 0.2 0.2

0.0 0.0 0 20406080100 0 20406080100 Distance to CBD (km) Distance to CBD (km) (a8) (a9)

FigureFigure 7. 7. DensityDensity of ISA in differentdifferentdirections directions in in Nanjing Nanjing from from 1990 1990 to to 2017. 2017. (Note: (Note: (a1 ()a1 Entire) Entire area; area; (a2 ) (East;a2) East; (a3) Northeast;(a3) Northeast; (a4) North; (a4) North; (a5) Northwest; (a5) Northwest; (a6) West; (a6 ()a7 West;) Southwest; (a7) Southwest; (a8) South; (a8 (a9) )South; Southeast). (a9) Southeast). Analysis of the entire change of ISA density at different distances in Nanjing can reflect the focus areaAnalysis of city construction of the entire in change different of ISA periods. density Figure at different8 indicates distances that, atin theNanjing center can of reflect the city the around focus area5 km, of thecity change construction of ISA in density different was periods. the largest Figure in the 8 indicates period 1990 that, to at 1995. the center Then, of the the rate city gradually around 5reduced km, the tochange approximately of ISA density 0.01 inwas 2010 the to largest 2017. in This the finding period shows1990 to that 1995. the Then, city corethe rate formed gradually in the reducedperiod 1990 to approximately to 1995. After 1995,0.01 in the 2010 focus to of2017. its constructionThis finding shifted.shows that Around the city the core 10 km formed area, the in the ISA periodpresented 1990 the to 1995. greatest After change 1995, ratethe focus in the of period its construction 2000 to 2005. shifted. After Around this period, the 10 the km change area, the rate ISA of presentedISA decreased. the greatest Therefore, change the rate region in the focused period on 2000 its construction to 2005. After before this period, 2005. Around the change the rate 15 km of area,ISA decreased.the change Therefore, rate of ISA the increased region focused from 1990 on its to 2005construction and reached before the 2005. peak Around stage in the the 15 period km area, 2000 the to change2005. In rate the of past ISA years increased from from 2010 1990 to 2017, to 2005 the changeand reached in ISA the density peak stage was overin the 0.05 period in the 2000 distance to 2005. of In35 the km past area. years Therefore, from 2010 this trendto 2017, is the focuschange of in development ISA density andwas constructionover 0.05 in the in recent distance years. of 35 km area. Therefore,From Figure this8, thetrend focus is the area focus of different of development directions and can construction be analyzed. in Afterrecent 2000, years. the strength of constructionFrom Figure increased 8, the infocus the area 15 km of areadifferent of the direct east.ions The can high be change analyzed. value Afte of ther 2000, 30 tothe 35 strength km area of in construction increased in the 15 km area of the east. The high change value of the 30 to 35 km area in 2005 was due to the rapid construction of Tangshan according to the actual situation. In the northeast, Sustainability 2019, 11, 933 14 of 22

Sustainability 2019, 11, x FOR PEER REVIEW 14 of 22 2005 was due to the rapid construction of Tangshan according to the actual situation. In the northeast, thethe construction construction accelerated accelerated in in the the 15 15 km km area area in in the the period period 1995 1995 to to 2005. 2005. At At present, present, the the construction construction focusesfocuses on on the the 25 25 km km area. area. In In the the north, north, northwest, northwest, and and west west directions, directions, the the city city grew grew very very fast fast from from 20102010 to 2017. The mainmain reasonreason is is the the construction construction of of Jiangbei Jiangbei national national development development zone zone and and Hexi Hexi city citycore. core. After After 2000, 2000, the change the change rate of rate ISA increasedof ISA increased in the south, in the southeast, south, southeast, and southwest and southwest directions. directions.The change The rate change of ISA hasrate beenof ISA the has highest been inthe recent highest years. in recent Therefore, years. the Therefore, south area the is thesouth main area area is theofurban main area expansion of urban after expansion 2000. after 2000.

0.16 1990-1995 1995-2000 0.14 2000-2005 0.12 2005-2010 2010-2017 0.10

0.08

0.06

0.04 Change of ISA density Change 0.02

0.00 0 102030405060708090100 Distance to CBD (km)

(a1) 0.16 0.25 0.14 0.20 0.12 0.10 0.15 0.08 0.06 0.10 0.04 0.05 0.02 Change of ISA density Change Change of ISA density Change 0.00 0.00 0 10203040 0 20406080 Distance to CBD (km) Distance to CBD (km) (a2) (a3) 0.12 0.14

0.10 0.12 0.10 0.08 0.08 0.06 0.06 0.04 0.04 0.02 0.02

0.00 of ISA density Change 0.00 Change of ISA density Change 0 20406080 0 10203040 Distance to CBD (km) Distance to CBD (km) (a4) (a5)

Figure 8. Cont. Sustainability 2019, 11, 933 15 of 22 Sustainability 2019, 11, x FOR PEER REVIEW 15 of 22

0.30 0.60

0.50 0.25

0.40 0.20

0.30 0.15

0.20 0.10

0.10 0.05

0.00 of ISA density Change Change of ISA density Change 0.00 0 1020304050 0 1020304050 Distance to CBD (km) Distance to CBD (km) (a6) (a7) 0.35 0.25 0.30 0.20 0.25

0.20 0.15

0.15 0.10 0.10 0.05 0.05 Change of ISA density Change Change of ISA density Change 0.00 0.00 0 20406080100 0 20406080100 Distance to CBD (km) Distance to CBD (km) (a8) (a9)

FigureFigure 8. 8. DensityDensity of of change change in in ISA ISA in in different different dire directionsctions in in Nanjing Nanjing from from 1990 1990 to to 2017. 2017. (Note: (Note: (a1 (a1) ) EntireEntire area; area; (a2 (a2) )East; East; (a3 (a3) )Northeast; Northeast; (a4 (a4) )North; North; (a5 (a5) )Northwest; Northwest; (a6 (a6) )West; West; (a7 (a7) )Southwest; Southwest; (a8 (a8) )South; South; (a9(a9) )Southeast). Southeast).

3.4. Impacts of Topography on Dynamic Change in ISA 3.4. Impacts of Topography on Dynamic Change in ISA Table5 shows the ISA at different altitude levels from 1990 to 2017. The results show that most of Table 5 shows the ISA at different altitude levels from 1990 to 2017. The results show that most the distribution of ISA was less than 40 m in height. In the 0 to 20 m and 20 to 40 m levels, the total of the distribution of ISA was less than 40 m in height. In the 0 to 20 m and 20 to 40 m levels, the total amount increased from 198.71 (176.45 km2) to 987.86 (629.37 km2) from 1990 to 2017 as the altitude amount increased from 198.71 (176.45 km2) to 987.86 (629.37 km2) from 1990 to 2017 as the altitude increased. The increase of ISA in the same period was small. That is, ISA increased in different increased. The increase of ISA in the same period was small. That is, ISA increased in different altitudes. However, as altitude increased, the expansion rate became small. This result indicates that altitudes. However, as altitude increased, the expansion rate became small. This result indicates that urban expansion occurs mainly in relatively low-altitude areas and that elevation is a constraint on the urban expansion occurs mainly in relatively low-altitude areas and that elevation is a constraint on urbanization of Nanjing. A similar situation is observed for the slope groups. Table6 shows that the the urbanization of Nanjing. A similar situation is observed for the slope groups. Table 6 shows that majority of ISA was located in areas of slope values less than 5 degrees, and these areas were suitable the majority of ISA was located in areas of slope values less than 5 degrees, and these areas were for construction. This result indicates that the relationship between Nanjing expansion and terrain was suitable for construction. This result indicates that the relationship between Nanjing expansion and obvious. The expansion of Nanjing was mainly distributed in the flat terrain, and the elevation and terrain was obvious. The expansion of Nanjing was mainly distributed in the flat terrain, and the slope were important constraints for the expansion of the city (Figure9). elevation and slope were important constraints for the expansion of the city (Figure 9).

Table 5. Impacts of elevation on dynamic change in ISA in Nanjing from 1990 to 2017.

0–20 20–40 40–60 60–80 80–100 100–150 150–200 >200 km2 % km2 % km2 % km2 % km2 % km2 % km2 % km2 % 1990 198.71 45.40 176.45 40.31 44.12 10.08 11.13 2.54 3.80 0.87 2.98 0.68 0.46 0.11 0.05 0.01 1995 251.78 45.05 227.93 40.78 54.59 9.77 14.67 2.62 5.27 0.94 3.88 0.69 0.66 0.12 0.08 0.01 2000 329.59 48.14 268.42 39.21 61.05 8.92 15.08 2.20 5.42 0.79 4.03 0.59 0.92 0.13 0.14 0.02 2005 518.78 51.32 371.43 36.74 83.84 8.29 20.61 2.04 7.77 0.77 6.35 0.63 1.79 0.18 0.40 0.04 2010 689.58 52.96 463.18 35.57 104.94 8.06 24.42 1.88 9.12 0.70 7.85 0.60 2.34 0.18 0.59 0.05 2017 987.86 54.19 629.37 34.53 147.60 8.10 32.44 1.78 11.62 0.64 9.86 0.54 3.13 0.17 1.02 0.06 Sustainability 2019, 11, 933 16 of 22

Table 6. Impacts of slope on dynamic change in ISA in Nanjing from 1990 to 2017.

0–5 5–10 10–15 15–20 >20 km2 % km2 % km2 % km2 % km2 % 1985 121.72 41.67 119.44 40.89 38.11 13.05 9.16 3.14 3.68 1.26 1990 184.09 43.01 173.68 40.57 52.39 12.24 12.45 2.91 5.44 1.27 1995 235.1 43.03 222.24 40.67 66.75 12.22 15.59 2.85 6.74 1.23 2000 294.18 43.93 270.89 40.45 79.18 11.82 18.03 2.69 7.43 1.11 2005 446.13 45.10 395.2 39.95 111.48 11.27 25.11 2.54 11.21 1.13 2010 585.97 45.98 504.69 39.61 138.13 10.84 30.95 2.43 14.55 1.14 Sustainability 2019, 11, x FOR PEER REVIEW 16 of 22 2017 836.25 46.88 703.1 39.41 185.66 10.41 40.27 2.26 18.65 1.05

(a) (b)

FigureFigure 9. 9. ImpactsImpacts of of Topography Topography in in ISA. ISA. (Note: (Note: ( (aa)) Elevation; Elevation; ( (bb)) Slope). Slope).

3.5. RelationshipTable between5. Impacts ISA ofDynamics elevation on and dynamic GDP in chan Differentge in ISA Districts in Nanjing from 1990 to 2017.

Economic0–20 condition 20–40 is an important 40–60 driver60–80 of urbanization. 80–100 In100–150 this study, 150–200 we examined >200 the relationship km2 between% km GDP2 and% ISA bykm2 exploring % regressionkm2 % modelskm2 ％ of 11km districts2 % inkm Nanjing.2 % km The2 result% shows1990 198.71 a strong 45.40 positive 176.45 relation 40.31 44.12 between 10.08 ISA 11.13 and 2.54 GDP 3.80 at different 0.87 2.98 periods;0.68 0.46 the 0.11 coefficients 0.05 0.01 of determination1995 251.78 45.05 (R2 ) reached227.93 40.78 more than 54.59 0.88 9.77 for all 14.67 districts 2.62 (Table 5.27 7 0.94). 3.88 0.69 0.66 0.12 0.08 0.01 2000 Figure 329.59 10 48.14shows 268.42 that GDP 39.21 was 61.05 more sensitive 8.92 15.08 to ISA2.20 in 5.42 the suburban 0.79 4.03 districts0.59 0.92 than 0.13 the urban0.14 0.02 core 2005 518.78 51.32 371.43 36.74 83.84 8.29 20.61 2.04 7.77 0.77 6.35 0.63 1.79 0.18 0.40 0.04 districts2010 689.58 in Nanjing. 52.96 463.18 In the 35.57% urban core104.94 districts, 8.06 24.42 the changes1.88 9.12 in ISA 0.70 in7.85 the Jianye0.60 2.34 and Yuhuatai0.18 0.59 areas0.05 were2017 sensitive 987.86 54.19 to the 629.37 changes 34.53 in GDP. 147.60 Compared 8.10 32.44 with other1.78 11.62 mature 0.64 urban 9.86 core0.54 districts, 3.13 0.17 the changes1.02 0.06 in ISA in Jianye and Yuhuatai presented a great development space in the next few years. In the suburb districts, theTable driving 6. Impacts force of of Qixia’s slope on economic dynamic development change in ISA toin ISANanjing was from the smallest,1990 to 2017. followed by that of Jiangning. The ISA of Pukou, Luhe, and Gaochun was significantly affected by GDP, and the state 0–5 5–10 10–15 15–20 >20 of the totalkm ISA2 increased% significantly.km2 % km2 % km2 % km2 % 1985 121.72 41.67% 119.44 40.89% 38.11 13.05% 9.16 3.14% 3.68 1.26% 1990 184.09 43.01% 173.68 40.57% 52.39 12.24% 12.45 2.91% 5.44 1.27% 1995 235.1 43.03% 222.24 40.67% 66.75 12.22% 15.59 2.85% 6.74 1.23% 2000 294.18 43.93% 270.89 40.45% 79.18 11.82% 18.03 2.69% 7.43 1.11% 2005 446.13 45.10% 395.2 39.95% 111.48 11.27% 25.11 2.54% 11.21 1.13% 2010 585.97 45.98% 504.69 39.61% 138.13 10.84% 30.95 2.43% 14.55 1.14% 2017 836.25 46.88% 703.1 39.41% 185.66 10.41% 40.27 2.26% 18.65 1.05%

3.5. Relationship between ISA Dynamics and GDP in Different Districts Economic condition is an important driver of urbanization. In this study, we examined the relationship between GDP and ISA by exploring regression models of 11 districts in Nanjing. The result shows a strong positive relation between ISA and GDP at different periods; the coefficients of determination (R2) reached more than 0.88 for all districts (Table 7). Figure 10 shows that GDP was more sensitive to ISA in the suburban districts than the urban core districts in Nanjing. In the urban core districts, the changes in ISA in the Jianye and Yuhuatai areas were sensitive to the changes in GDP. Compared with other mature urban core districts, the changes in ISA in Jianye and Yuhuatai presented a great development space in the next few years. In the suburb districts, the driving force of Qixia’s economic development to ISA was the smallest, Sustainability 2019, 11, x FOR PEER REVIEW 17 of 22 followed by that of Jiangning. The ISA of Pukou, Luhe, and Gaochun was significantly affected by GDP, and the state of the total ISA increased significantly. Sustainability 2019, 11, 933 17 of 22 Table 7. Relationship between ISA dynamics and GDP in different districts in Nanjing.

2 DistrictTable 7. Relationship between ISA dynamicsFormula and GDP in different districts in Nanjing. R Xuanwu y = 2.9239 ln(x) + 16.24 0.9672 2 y = 2.8946 Formula ln(x) + 20.198 R 0.9747 Gulou Xuanwu y = y 0.9895 = 2.9239 ln(x) ln(x) + + 37.588 16.24 0.9672 0.9711 Yuhuatai Qinhuai y y= =10.965 2.8946 ln(x) ln(x) +- 10.446 20.198 0.9747 0.9864 Gulou y = 0.9895 ln(x) + 37.588 0.9711 Jianye y = 8.5131 ln(x) + 11.01 0.9683 Yuhuatai y = 10.965 ln(x) - 10.446 0.9864 Qixia Jianye y = y 26.873 = 8.5131 ln(x) ln(x) − + 16.089 11.01 0.9683 0.9636 Gaochun Qixia y y= =38.893 26.873 ln(x) ln(x) − 82.05116.089 0.9636 0.9259 Pukou Gaochun y y = = 44.196 38.893 ln(x) − − 82.05192.63 0.9259 0.8891 − Luhe Pukou y y= =54.399 44.196 ln(x) ln(x) − 91.67992.63 0.8891 0.9044 Luhe y = 54.399 ln(x) − 91.679 0.9044 Lishui Lishui y y= =48.067 48.067 ln(x) ln(x) − 119.11119.11 0.9529 0.9529 Jiangning Jiangning y y= =104.21 104.21 ln(x) ln(x) − 312.02312.02 0.9828 0.9828

Xuanwu Gulou Qinhuai Jiangning Pukou Qixia Jianye Yuhua Xuanwu Luhe Gaochun Lishui Gulou Qinhuai Jianye Jiangning Pukou Qixia Yuhua Luhe Gaochun Lishui 70 500 450 60 400 ) )

50 2 2 350

40 300 250 30 200 20 150 ISA area (km ISA area ISA area (km ISA area 100 10 50 0 0 0 200 400 600 800 1000 0 500 1000 1500 GDP (Million ¥) GDP (Million ¥) (a) (b)

FigureFigure 10. 10. RelationshipsRelationships between between ISA ISA and and GDP GDP in in differ differentent directions directions in in Nanjing Nanjing from from 1990 1990 to to 2017. 2017. (Note:(Note: (a (a) )Relationships Relationships between between ISA ISA and and GDP GDP in in urban urban core core districts; districts; (b (b) )Relationships Relationships between between ISA ISA andand GDP GDP in in suburban suburban districts). districts).

4.4. Discussion Discussion 4.1. Improving the Accuracy of Urban Expansion Research Data 4.1. Improving the Accuracy of Urban Expansion Research Data Urban expansion analysis has been usually conducted by extracting the data of urban construction Urban expansion analysis has been usually conducted by extracting the data of urban land use [14–17]. However, the relatively coarse spatial resolution of Landsat images, mixed pixel, construction land use [14–17]. However, the relatively coarse spatial resolution of Landsat images, and complex urban land cover composition reduces the accuracy of urban construction land mixed pixel, and complex urban land cover composition reduces the accuracy of urban construction classification [19]. In this study, the comprehensive method of a pixel-based classification was used land classification [19]. In this study, the comprehensive method of a pixel-based classification was to extract ISA. This method is more accurate than the construction land that is directly extracted used to extract ISA. This method is more accurate than the construction land that is directly extracted from Landsat files. We improved LSMA, which has been validated by scholars as having a valid ISA from Landsat files. We improved LSMA, which has been validated by scholars as having a valid ISA extraction accuracy, through a variety of methods, such as Modified Normalized Difference Water extraction accuracy, through a variety of methods, such as Modified Normalized Difference Water Index (MNDWI) and decision tree. The improved method can effectively extract ISA with an overall Index (MNDWI) and decision tree. The improved method can effectively extract ISA with an overall accuracy of 90%. accuracy of 90%. This method is effective in producing accurate distributions of ISA, which is especially necessary for testing urban expansion. Given that this study explores the expansion of land use in each district and different directions in Nanjing, the improvement in the accuracy of ISA plays a very crucial role in Sustainability 2019, 11, 933 18 of 22 the entire study. Accurate extraction of ISA allows a clear analysis of ISA changes in each area and direction of the urban expansion.

4.2. Analyzing City Expansions at Different Spatial Scales and Direction Levels Rational control of the pace and direction of the expansion of construction land in metropolitan areas has been the focus of Chinese urbanization and land management policies. Research on the expansion mechanism of construction land through different directions and spatial scales can provide a scientific basis for urban development management. This study established a relatively comprehensive analysis system of urban expansion. From the macroscopic view of the overall urban expansion, the density changes, and center of gravity of ISA were judged, and, thus, the overall trend of urban expansion was analyzed. From the perspective of micro-regional and directional levels, we studied the expansion process and change trend of ISA in every district and direction of Nanjing and analyzed the key areas and directions of urban expansion in the future. The date base of multi-center development in Nanjing can be provided for urban planning and construction needs. On the basis of this systematic research model and institutional analysis of the expansion of construction land use in Nanjing, additional research experience and resources can be provided. The results can be used to summarize a set of characteristics suitable for Chinese urbanization and land expansion construction.

4.3. Effects of Topographic and GDP Factors on Urban Expansion Topography and GDP are two important factors affecting urban ISA expansion in Nanjing. This research confirmed this fact. Examining the relationships between topography and ISA at different periods shows that topography is still an important constraint factor affecting the expansion of the city even with developed engineering technology. Using regression analysis parsing the relationship between ISA and GDP in different districts in Nanjing indicates that ISA is positively related to GDP, especially in districts at the development stage. GDP in most of these districts significantly influences urban expansion. Those districts are usually located on the urban fringe area. By contrast, GDP in other types of districts insignificantly influences the ISA expansion. On the basis of the analysis of topography and economy, the future development of different districts in Nanjing can be analyzed and the current and future development focuses of different areas can be summarized. Effective strategies for urban planning, policy and improved service for urban citizens can be provided.

4.4. Social and Political Factors on Urban Expansion Social and political factors are also important factors affecting ISA expansion in Nanjing. The household registration system and the reform and opening up policies are the main factors affecting the expansion of Chinese cities in the 21st century. In 1958, China launched the household registration system, which divided the identity of urban residents and rural residents, imposed strict restrictions on the free movement of people [55]. Since the 1990s, with the reform of the household registration system, the urban and rural residents have flowed with each other, but the inequality of resources between urban and rural areas has caused the population concentrated in the central urban area which can provide the relatively-perfected educational and medical conditions. Many rural residents have flocked to the cities, and it indirectly led to the emergence of a basic pattern of city expansion in China, and Nanjing is no exception. In recent years, to attract talents and improve the competitiveness of Nanjing, the local government has formulated a new policy that people with the bachelor’s degree or above can be directly settled down, the possibility of sustainable expansion of the city’s scale has been provided. Sustainability 2019, 11, 933 19 of 22

Since the reform and opening up, Chinese society has entered a new stage of transformation and development [56]. Under the guidance of market economic policy, Chinese enterprises have begun to change from government-led enterprises to market-oriented enterprises. Meanwhile, the hot spots of foreign investment in China are the Yangtze River Delta and the Pearl River Delta, and Nanjing, as a hot investment city in the Yangtze River Delta, has accelerated its urban expansion due to the influx of large quantities of local and foreign capital, and it is embodied in the agglomeration development mode with industrial parks. Those industrial parks have brought the agglomeration of industry and population, accelerated the development of cities, and promoted the change of urban spatial structure.

5. Conclusions Compared with other European and American megacities, Chinese megacities exhibit different urbanization processes, patterns, and mechanisms. This study used Nanjing as a case study to assess and analyze urban expansion by using a comprehensive method to extract and analyze ISA. From the results, the following conclusions were drawn: (1) The proposed comprehensive method can effectively improve the accuracy of ISA extraction. In this case study, the overall accuracy of ISA in Nanjing was over 90%. The ISA of the city was comprehensively analyzed by macro analysis of urban expansion and microcosmic analysis of ISA in different districts and directions. (2) Urban development of Nanjing has been expanding rapidly, slowing down, and expanding rapidly again. The annual expansion area from 23.64 km2/year in 1990 to 1995 increased to 63.77 km2/year in 2000 to 2005, slowed down in 2005 to 2010, and then increased again in 2010 to 2017 with the annual expansion area of 72.61 km2/year. The region of high ISA density continues to grow. The center of gravity of ISA obviously moves southeast, and the overall direction of expansion is relatively balanced in recent years. (3) Types and trends of ISA changes in urban core and suburban districts obviously differ. In recent years, the increase in the area and density of ISA in the urban core districts is relatively slow, and the change in ISA in the suburban districts is the opposite. At the same time, the different areas and rates of ISA change from the different directions of urban CBD and are greatly influenced by the urban development policy and geographical factors. (4) The case of Nanjing indicates that topography and economy are important influencing factors of urbanization. In different districts at dissimilar stages of development, the changes and trends of ISA differ. The increase of the ISA in the districts at the stage of development is positively proportional to GDP. Based on the regression analysis of the economy and ISA, the future expansion trends of each district can be approximately estimated. (5) Urban expansion in Nanjing represents a common phenomenon in the development of the second-tier cities in China. The ISA analysis of Nanjing’s expansion indicates that Nanjing is still in a period of rapid urban expansion, the ISA density of the original urban central district continues to increase, and the overall ISA area of the city continues to expand. These are also the basic characteristics of the expansion of Nanjing and the other second-tier cities in China. Meanwhile, Nanjing’s urban expansion can provide some development experience for the same type of second-tier cities and the third-tier cities in China, and the government can formulate corresponding planning strategies according to the development of the ISA distribution and the future ISA expansion trends, analyze the urban spatial form, improve the level of urban management, and make rational use of urban space.

Author Contributions: Z.W. conceived and designed the experiments; Y.Q. made contributions to the design, data processing; Z.W. wrote the paper; All authors had read and approved the manuscript. Acknowledgments: The present study is supported by the National Natural Science Foundation of China (Grant No. 31570703). The authors are grateful for the technical support of Professor Shiguang Shen. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2019, 11, 933 20 of 22

References

1. Strahler, A.H.; Woodcock, C.E.; Smith, J.A. On the nature of models in remote sensing. Remote Sens. Environ. 1986, 20, 121–139. [CrossRef] 2. Carrion-Flores, C.; Irwin, E.G. Determinants of residential land-use conversion and sprawl at the rural-urban fringe. Am. J. Agric. Econ. 2004, 86, 889–904. [CrossRef] 3. Seto, K.C.; Fragkias, M.; Güneralp, B.; Reilly, M.K. A meta-analysis of global urban land expansion. PLoS ONE 2011, 6, e23777. [CrossRef] 4. Bhatta, B.; Saraswati, S.; Bandyopadhyay, D. Urban sprawl measurement from remote sensing data. Appl. Geogr. 2010, 30, 731–740. [CrossRef] 5. Alberti, M. Maintaining ecological integrity and sustaining ecosystem function in urban areas. Curr. Opin. Environ. Sustain. 2010, 2, 178–184. [CrossRef] 6. Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.; Wu, J.; Bai, X.; Briggs, J.M. Global change and the ecology of cities. Science 2008, 319, 756–760. [CrossRef] 7. Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K. Global consequences of land use. Science 2005, 309, 570–574. [CrossRef] 8. Dupras, J.; Marull, J.; Parcerisas, L.; Coll, F.; Gonzalez, A.; Girard, M.; Tello, E. The impacts of urban sprawl on ecological connectivity in the montreal metropolitan region. Environ. Sci. Policy 2016, 58, 61–73. [CrossRef] 9. Chan, K.M.; Vu, T.T. A landscape ecological perspective of the impacts of urbanization on urban green spaces in the klang valley. Appl. Geogr. 2017, 85, 89–100. 10. Dupras, J.; Alam, M. Urban sprawl and ecosystem services: A half century perspective in the montreal area (quebec, canada). J. Environ. Policy Plan. 2015, 17, 180–200. [CrossRef] 11. Arnold Jr, C.L.; Gibbons, C.J. Impervious surface coverage: The emergence of a key environmental indicator. J. Am. Plan. Assoc. 1996, 62, 243–258. [CrossRef] 12. Myronidis, D.; Loannou, K. Forecasting the urban expansion effects on the design storm hydrograph and sediment yield using artificial neural networks. Water 2019, 11, 31. [CrossRef] 13. Myronidis, D.; Stathis, D.; Sapountzis, M. Post-Evaluation of flood hazards induced by former artificial interventions along a coastal mediterranean settlement. J. Hydrol. Eng. 2016, 21, 05016022. [CrossRef] 14. El Garouani, A.; Mulla, D.J.; El Garouani, S.; Knight, J. Analysis of urban growth and sprawl from remote sensing data: Case of fez, morocco. Int. J. Sustain. Built Environ. 2017, 6, 160–169. [CrossRef] 15. Cabral, A.I.; Costa, F.L. Land cover changes and landscape pattern dynamics in senegal and guinea bissau borderland. Appl. Geogr. 2017, 82, 115–128. 16. Hassan, M.M. Monitoring land use/land cover change, urban growth dynamics and landscape pattern analysis in five fastest urbanized cities in bangladesh. Remote Sens. Appl. Soc. Environ. 2017, 7, 69–83. [CrossRef] 17. Dahal, K.R.; Benner, S.; Lindquist, E. Urban hypotheses and spatiotemporal characterization of urban growth in the treasure valley of idaho, USA. Appl. Geogr. 2017, 79, 11–25. 18. Lu, D.; Weng, Q. Spectral mixture analysis of the urban landscape in indianapolis with landsat etm+ imagery. Photogram. Eng. Remote Sens. 2004, 70, 1053–1062. [CrossRef] 19. Schneider, A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of landsat satellite data and a data mining approach. Remote Sens. Environ. 2012, 124, 689–704. [CrossRef] 20. Ridd, M.K. Exploring a vis (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. Int. J. Remote Sens. 1995, 16, 2165–2185. [CrossRef] 21. Wu, C.; Murray, A.T. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens. Environ. 2003, 84, 493–505. [CrossRef] 22. Lu, D.; Moran, E.; Hetrick, S. Detection of impervious surface change with multitemporal landsat images in an urban–rural frontier. ISPRS J. Photogram. Remote Sens. 2011, 66, 298–306. [CrossRef] 23. Xu, H. Rule-based impervious surface mapping using high spatial resolution imagery. Int. J. Remote Sens. 2013, 34, 27–44. [CrossRef] 24. Okujeni, A.; van der Linden, S.; Hostert, P. Extending the vegetation–impervious–soil model using simulated enmap data and machine learning. Remote Sens. Environ. 2015, 158, 69–80. [CrossRef] 25. Carlson, T.N.; Arthur, S.T. The impact of land use—land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective. Glob. Planet. Chang. 2000, 25, 49–65. [CrossRef] Sustainability 2019, 11, 933 21 of 22

26. Liu, S.; Gu, G. Improving the impervious surface estimation from hyperspectral images using a spectral-spatial feature sparse representation and post-processing approach. Remote Sens. 2017, 9, 456. 27. Yang, L.; Huang, C.; Homer, C.G.; Wylie, B.K.; Coan, M.J. An approach for mapping large-area impervious surfaces: Synergistic use of landsat-7 etm+ and high spatial resolution imagery. Can. J. Remote Sens. 2003, 29, 230–240. [CrossRef] 28. Alahmadi, M.; Atkinson, P.; Martin, D. Fine spatial resolution residential land-use data for small-area population mapping: A case study in riyadh, saudi arabia. Int. J. Remote Sens. 2015, 36, 4315–4331. [CrossRef] 29. Friedl, M.A.; McIver, D.K.; Hodges, J.C.; Zhang, X.; Muchoney, D.; Strahler, A.H.; Woodcock, C.E.; Gopal, S.; Schneider, A.; Cooper, A. Global land cover mapping from modis: Algorithms and early results. Remote Sens. Environ. 2002, 83, 287–302. [CrossRef] 30. Guo, W.; Lu, D.; Wu, Y.; Zhang, J. Mapping impervious surface distribution with integration of snnp viirs-dnb and modis ndvi data. Remote Sens. 2015, 7, 12459–12477. [CrossRef] 31. Weng, Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens. Environ. 2012, 117, 34–49. [CrossRef] 32. Li, H.; Wei, Y.D.; Liao, F.H.; Huang, Z. Administrative hierarchy and urban land expansion in transitional china. Applied Geography 2015, 56, 177–186. [CrossRef] 33. Schneider, A.; Mertes, C. Expansion and growth in chinese cities, 1978–2010. Environ. Res. Lett. 2014, 9, 024008. [CrossRef] 34. Liu, Z.; He, C.; Zhang, Q.; Huang, Q.; Yang, Y. Extracting the dynamics of urban expansion in china using dmsp-ols nighttime light data from 1992 to 2008. Landsc. Urban Plan. 2012, 106, 62–72. [CrossRef] 35. Chinese national population development plan (2016–2030). Available online: http://www.gov.cn/zhengce/ content/2017-01/25/content_5163309.htm (accessed on 28 November 2017). 36. Yuan, F.; Gao, J.; Wu, J. Nanjing-an ancient city rising in transitional china. Cities 2016, 50, 82–92. [CrossRef] 37. Yin, J.; Yin, Z.; Zhong, H.; Xu, S.; Hu, X.; Wang, J.; Wu, J. Monitoring urban expansion and land use/land cover changes of shanghai metropolitan area during the transitional economy (1979–2009) in china. Environ. Monit. Assess. 2011, 177, 609–621. [CrossRef] 38. Xu, C.; Liu, M.; Zhang, C.; An, S.; Yu, W.; Chen, J.M. The spatiotemporal dynamics of rapid urban growth in the nanjing metropolitan region of china. Landsc. Ecol. 2007, 22, 925–937. [CrossRef] 39. Shi, L.; Ling, F.; Ge, Y.; Foody, G.M.; Li, X.; Wang, L.; Zhang, Y.; Du, Y. Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: A case study in wuhan city using landsat time-series datasets from 1987 to 2016. Remote Sens. 2017, 9, 1148. [CrossRef] 40. Gao, F.; de Colstoun, E.B.; Ma, R.; Weng, Q.; Masek, J.G.; Chen, J.; Pan, Y.; Song, C. Mapping impervious surface expansion using medium-resolution satellite image time series: A case study in the yangtze river delta, china. International Journal of Remote Sensing 2012, 33, 7609–7628. [CrossRef] 41. Zhang, L.; Weng, Q. Annual dynamics of impervious surface in the pearl river delta, china, from 1988 to 2013, using time series landsat imagery. ISPRS J. Photogram. Remote Sens. 2016, 113, 86–96. [CrossRef] 42. Van de Voorde, T.; Jacquet, W.; Canters, F. Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data. Landsc. Urban Plan. 2011, 102, 143–155. [CrossRef] 43. Scott, D.; Petropoulos, G.P.; Moxley, J.; Malcolm, H. Quantifying the physical composition of urban morphology throughout wales based on the time series (1989–2011) analysis of landsat tm/etm+ images and supporting gis data. Remote Sens. 2014, 6, 11731–11752. [CrossRef] 44. Tian, G.; Jiang, J.; Yang, Z.; Zhang, Y. The urban growth, size distribution and spatio-temporal dynamic pattern of the yangtze river delta megalopolitan region, china. Ecol. Model. 2011, 222, 865–878. [CrossRef] 45. Nanjing Statistical Bureau. Nanjing Statistical Yearbook; China Statistics Press: Beijing, China, 2017. 46. Han-Qiu, X. A study on information extraction of water body with the modified normalized difference water index (mndwi). J. Remote Sens. 2005, 5, 589–595. 47. Xu, X.; Zhou, Y.; Ning, Y. Urban Geography; High Education Publisher: Beijing, China, 2009. 48. Lu, D.; Weng, Q. Use of impervious surface in urban land-use classification. Remote Sens. Environ. 2006, 102, 146–160. [CrossRef] 49. Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; CRC Press: Boca Raton, FL, USA, 2008. 50. Li, L.; Lu, D.; Kuang, W. Examining urban impervious surface distribution and its dynamic change in hangzhou metropolis. Remote Sens. 2016, 8, 265. [CrossRef] Sustainability 2019, 11, 933 22 of 22

51. Kuang, W.; Chi, W.; Lu, D.; Dou, Y. A comparative analysis of megacity expansions in china and the us: Patterns, rates and driving forces. Landsc. Urban Plan. 2014, 132, 121–135. [CrossRef] 52. Chen, J.; Gao, J.; Chen, W. Urban land expansion and the transitional mechanisms in Nanjing, China. Habitat Int. 2016, 53, 274–283. [CrossRef] 53. Wei, Y.D.; Ye, X. Urbanization, urban land expansion and environmental change in china. Stoch. Environ. Res. Risk Assess. 2014, 28, 757–765. [CrossRef] 54. Bai, X.; Chen, J.; Shi, P. Landscape urbanization and economic growth in china: Positive feedbacks and sustainability dilemmas. Environ. Sci. Technol. 2011, 46, 132–139. [CrossRef] 55. Wu, X.G.; Zheng, B.D. Household registration, urban status attainment, and social stratification in China. Res. Soc. Stratific. Mobil. 2018, 53, 40–49. [CrossRef] 56. Lv, J.; Yang, B.D.; Yang, Y.J.; Zhang, Z.H.; Liu, G.J. Spatial patterns of China’s major cities and their evolution mechanisms during the past decades of Reform and Opening Up. Procedia Eng. 2017, 198, 915–925. [CrossRef]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).