applied sciences

Article An Analysis of Land-Use and Land-Cover Change in the Zhujiang–Xijiang Economic Belt, China, from 1990 to 2017

Yunfeng Hu 1,* ID and Batunacun 1,2,3,* ID

1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 2 Department of Geography, Humboldt–Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany 3 Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374 Müncheberg, Germany * Correspondence: [email protected] (Y.H.); [email protected] (B.); Tel.: +86-10-6488-8020 (Y.H.); +49-1577-714-0786 (B.)


Received: 16 July 2018; Accepted: 24 August 2018; Published: 1 September 2018


Abstract: Land-use and land-cover change (LUCC) are currently contested topics in the research of global environment change and sustainable change. Identifying the historic land-use change process is important for the new economic development belt (the Zhujiang–Xijiang Economic Belt, ZXEB). During this research, based on long-time-series land-use and land-cover data, while using a combination of a transition matrix method and Markov chain model, the authors derive the patterns, processes, and spatial autocorrelations of land-use and land-cover changes in the ZXEB for the periods 1990–2000 and 2000–2017. Additionally, the authors discuss the spatial autocorrelation of land-use in the ZXEB and the major drivers of urbanization. The results indicate the following: (1) The area of cropland reduces during the two periods, and woodland decreases after the year 2000. The woodland is the most stable land-use type in both periods. (2) Built-up land expansion is the most important land-use conversion process; the major drivers of built-up land expansion are policy intervention, GDP (gross domestic product), population growth, and rural population migration. (3) Transition possibilities indicate that after 2000, most land-use activities become stronger, the global and local Moran’s I of all land-use types show that the spatial autocorrelations have become more closely related, and the spatial autocorrelation of built-up land has become stronger. Policies focus on migration from rural to urban, and peri-urban development is crucial for future sustainable urbanization.

Keywords: land-use and land-cover; urbanization; spatial-temporal characteristics; Zhujiang–Xijiang River Basin

1. Introduction Land-use and land-cover change (LUCC), as a present feature of global environmental change, are directly driven by human activities [1,2] and have become a contested topic in research on global environmental change and sustainable change [3–5]. LUCC specifically refers to changes in the biophysical attributes of the Earth’s surface and the application of these attributes for human purposes [6]. Previous studies have provided examples showing that LUCC is closely related to land degradation [7,8], soil erosion [9], and biodiversity losses [5], as well as threatening food security [5] and contributing to climate change [10].

Appl. Sci. 2018, 8, 1524; doi:10.3390/app8091524 www.mdpi.com/journal/applsci Appl. Sci. 2018, 8, 1524 2 of 18

China is an important part of the global environment. The LUCC and land management in China have experienced significant changes since the 1980s, as the country launched market reforms and actively intersected with globalization [3,11,12]. Different land-use conversion patterns have occurred in different sub-regions of China. Examples include grassland degradation in the northern region [13]; deforestation in the southern region [14]; water body loss in the northern region [15]; the loss of cropland, particularly in coastal regions [16]; and built-up land expansion throughout China [17]. Built-up land expansion is a major land-use conversion process in China and has been shown to have direct and indirect effects on land-use transformation [18]. Specifically, experts and scholars have revealed the patterns, drivers, and ecological issues related to this expansion [12,19,20]. Urban land expansion is occurring rapidly all over the world, causing various problems and relationships with other land-use types, not only in China. Timon et al. [21] indicate that rapid urbanization in North America has challenged the conservation biodiversity and regional ecosystem services, and urbanization has also led to a series of conversions from other land-use types to built-up land. Deriving the historical urbanization patterns and processes is crucial for the sustainable development of urbanization in the future [21,22]. Since 1978, economic reform in China and built-up land expansion, especially urban land expansion, have mainly been a result of economic development, extensive population growth, and policy changes [23,24]. Built-up land expansion includes urban land expansion, rural settlement expansion, and infrastructure area expansion [25–27]. Urban land is defined as the built-up land used for urban settlements, and urban land expansion is defined as the urban land containing new built-up areas that are built within a certain time period [23]. The term urban land expansion in this research refers to urbanization. Urban land in China has experienced a significant increase, especially since the 1980s. The number of cities increased from 69 in 1947 to 223 in 1990. Until 2012, there were 670 cities, hosting about 44% of the total population of China [12]. The count of prefecture-level cities rose from 188 in 1990 to 287 in 2000. The percentage of urban population (the population that lives on urban land) reached 49.9% in 2010 and exceeded 50% by the end of 2012. China has become a predominantly urbanized country [17]. The rapid urbanization process has had significant effects on climate change [28], soil protection [29,30], and water conservation [31], and the urban island has led to air pollution, threatening human health [29]. Additionally, urbanization has also affected rural land via the “ecological footprint” through the consumption of cropland in peri-urban residential areas, as well as through infrastructure and amenity uses [5]. Above all, determining detailed land-use change information over a long time period and determining urban land expansion patterns and processes is strategically important for understanding land-use patterns and driving forces in China, as well as for sustainable land-use planning. The Zhujiang–Xijiang Economic Belt (ZXEB) developed a national level strategy in 2014. The strategy aims to develop the ZXEB’s economy, to promote the development in the east and minority areas of China, to strengthen ecological construction and environmental protection, and to narrow regional development differences [32]. The ZXEB is chosen as a case study because of its economic status and urgent land needs. The ZXEB, as a new economic belt in China, faces both huge development opportunities and huge challenges. Thus, the balance between economic development and environmental protection is an important topic for ecological researchers. Determining historical LUCC information and major land-use conversion processes, as well as the urban explosion over the past four decades, is a prerequisite for the sustainable development in the ZXEB. Analyzing the urban dynamics at the regional scale and identifying the characteristics over space and time is important for sustainable urban development in the ZXEB. Urbanization is always accompanied by a population boom and growing gross domestic product (GDP) [12,33]. Growth in population size and economics are a result of urbanization, but also accelerate the process [12,24,34]. A combination of a land-use matrix, Markov chain, and Maron’s I is used to project land-use patterns, processes, and spatial autocorrelation features in this study. Both a land-use matrix and Markov chain methods are used widely to evaluate LUCC conversion patterns. These methods Appl. Sci. 2018, 8, x FOR PEER REVIEW 3 of 18 Appl. Sci. 2018, 8, 1524 3 of 18 generally adopted to understand the dynamic change in land use and land cover at different scales can[35,36], provide including processes urban and areas historic and transition non-urban directions areas [37]. of land-use It focuses dynamics. on initial The proportion Markov chain and wastransition generally possibilities adopted toin understanddifferent state, the dynamic and predicting change in future land use state and based land coveron the at differentcurrent development trend [38]. Many previous researchers have tested and validated these methods scales [35,36], including urban areas and non-urban areas [37]. It focuses on initial proportion and [39,40]. Moran’s I is used explore internal differences and spatial correlation characteristics of transition possibilities in different state, and predicting future state based on the current development land-use in ZXEB. trend [38]. Many previous researchers have tested and validated these methods [39,40]. Moran’s I is Using LUCC data from China’s land-use/land-cover datasets (CLUDs) [41], the objectives of used explore internal differences and spatial correlation characteristics of land-use in ZXEB. this study are as follows: (1) to evaluate the LUCC patterns and transition process in the ZXEB from Using LUCC data from China’s land-use/land-cover datasets (CLUDs) [41], the objectives of 1990–2017; (2) to identify the urbanization process that has occurred based on LUCC in this region; this study are as follows: (1) to evaluate the LUCC patterns and transition process in the ZXEB from and (3) to discuss the general drivers of built-up land expansion. 1990–2017; (2) to identify the urbanization process that has occurred based on LUCC in this region; and (3) to discuss the general drivers of built-up land expansion. 2. Materials and Methods 2. Materials and Methods 2.1. Study Area 2.1. Study Area The Zhujiang–Xijiang Economic Belt (ZXEB), located in Southern China, covers an area of 16.45 × 104 kmThe2 and Zhujiang–Xijiang is located at 104°3 Economic′–114°3′ E Belt and(ZXEB), 22°04′–26°05 located′ N. inThe Southern area has China,an elevation covers of an50–2057 area ofm 16.45(Figure× 101).4 Thekm2 totaland population is located atwas 104 4923◦30–114 × 104◦30 asE andof 2015; 22◦ 04at0 –26this◦ 05time,0 N. the The gross area domestic has an elevation product of(GDP) 50–2057 was m 1110 (Figure × 1041). The billion total yuan. population The ZXEB was 4923 is a× typical104 as ofkarst 2015; landscape, at this time, and the soil gross from domestic thick productlimestone (GDP) material was 1110is widely× 104 distributed billion yuan. in Thethe ZXEBarea [4 is2]. a typicalThe ZXEB karst includes landscape, four and prefecture-level soil from thick limestonecities, namely, material Zhaoqing, is widely Yunfu, distributed Foshan, in and the areaGuangzhou, [42]. The in ZXEB Guangdong includes fourprovince, prefecture-level and seven cities,cities, namely, Zhaoqing,Baise, Chongzuo, Yunfu, Foshan, Nanning, and Laibin, Guangzhou, Liuzhou, in GuangdongGuigang, and province, Wuzhou, and in seven Guangxi cities, province. namely, Baise,The ZXEB Chongzuo, was created Nanning, to connect Laibin, Liuzhou,the developed Guigang, area and in Wuzhou,the East to in Guangxithe undeveloped province. area The in ZXEB the wasWest, created thus touniting connect Guangdong the developed and area Guangxi, in the East which to the are undeveloped connected to area Yunnan in the West,and thusGuizhou uniting in Guangdongnorthern China and Guangxi,and Hong which Kong are and connected Macao to in Yunnan southern and GuizhouChina [32]. in northern The ZXEB—as China and a Hongnew, Kongdeveloping and Macao zone, in was southern created China in 2014 [32]. Theand ZXEB—asaims to improve a new, developing the economic zone, development was created inin 2014 the andmajority aims toof improveChina. It the is economiccritical that development the historic in theland-use majority conversion of China. Itprocess is critical and that urbanization the historic land-usepatterns conversionin this special process region and urbanizationare studied patternsto provide in this reference special regioninformation are studied for sustainable to provide referencedevelopment information in the ZXEB. for sustainable development in the ZXEB.

Figure 1. Location of the study area and its eleven municipalities.

2.2. Data Collection During this research, research, LUCC LUCC data data from from 1990, 1990, 2000 2000 (based (based on on Landsat Landsat MSS/TM/ETM+/OLI), MSS/TM/ETM+/OLI), and and2017 2017 from from Gaofen-1 Gaofen-1 (GF-1) (GF-1) (http://www.cresda.com/CN/Satellite/3076.shtml) (http://www.cresda.com/CN/Satellite/3076.shtml were) wereused usedas the as key the information sources (Figure 2). The spatial resolution of the data was 30 m for 1990 and 2000 and 8 m Appl. Sci. 2018, 8, 1524 4 of 18 Appl. Sci. 2018, 8, x FOR PEER REVIEW 4 of 18 keyfor 2017, information which was sources sufficient (Figure for2). land-use The spatial analysis resolution [1,2,6]. of the The data land-use was 30 and mfor land-cover 1990 and data 2000 were and 8produced m for 2017, by whichthe Chinese was sufficient Academy for of land-use Science (CAS). analysis During [1,2,6]. the The 1990s, land-use the CAS and land-covercreated the data National were producedResource and by the Environments Chinese Academy Database of Science and has (CAS). since Duringaccumulated the 1990s, massive the CAS amounts created of the LUCC National data Resource[43]. The andoriginal Environments data source Database for these and hasLUCC since data accumulated was remote massive images. amounts More ofthan LUCC 500 data remote [43]. Thesensing original images data from source Landsat for theseTM/ETM+/OLI LUCC data (https: was//www.usgs.gov/) remote images. More have thanbeen 500used remote to map sensing LUCC imagesdata for from China Landsat [6,43]. TM/ETM+/OLI Most of the images (https://www.usgs.gov/ were obtained from )similar have been seasons used (July–October) to map LUCC dataand forunder China cloud-free [6,43]. Mostconditions. of the imagesHuman–computer were obtained interactive from similar interpretation, seasons (July–October) renowned for and its under high cloud-freeaccuracy, conditions.was used to Human–computer digitize the LUCC interactive data at interpretation, a spatial scale renowned of 1:100,000 for its high [16,41]. accuracy, The wasinterpretation used to digitize was approached the LUCC based data aton a 25 spatial subclasses scale of of land-use 1:100,000 types [16,41 that]. The were interpretation defined by Liu was et approachedal. [16]. The baseddata were on 25 then subclasses classified of land-useinto six typesclasses—including that were defined cropland, by Liu woodland, et al. [16]. grassland, The data werewater then body, classified unused intoland, six and classes—including built-up land—and cropland, 25 subclasses woodland, (Table grassland, 1, Liu et water al., and body, Dent unused et al. land,[16,41]). and built-up land—and 25 subclasses (Table1, Liu et al., and Dent et al. [16,41]).

Figure 2. Land use maps in 1990, 2000, and 2017.

Table 1.1. The land-use classificationclassification system.system. Classes Code Description Classes Code Description Cropland 1 Land for growing crops. WoodlandCropland 2 Land 1 for Land growing for growing trees, arbo crops.r, shrubs, bamboo, and forestry use. GrasslandWoodland 3 Land 2 for Land herbaceous for growing plants. trees, arbor, shrubs, bamboo, and forestry use. GrasslandNatural 3 Landwater for areas, herbaceous including plants. constructed reservoirs as well as water Water bodies 4 reservationsNatural and water irrigation areas, includingfacilities. constructed reservoirs as well as water Water bodies 4 Unused land 6 Land thatreservations is not put andinto irrigation practical facilities. use or that is difficult to use. Unused landHuman 6 settlements Land that is notin urban put into and practical rural useareas or thatas well is difficult as factories to use. and Built-up land 5 transportationHuman facilities. settlements in urban and rural areas as well as factories and Built-up land 5 Urban land 51 Built-up transportationareas used for facilities. urban settlements Rural Urban land52 51Land used Built-up for small areas towns used for or urbanvillage settlements settlements. settlementsRural settlements 52 Land used for small towns or village settlements. Infrastructure Land used for factories, quarries, mining, and oil fields outside cities, as 53 Land used for factories, quarries, mining, and oil fields outside cities, as Infrastructure areas 53 areas well as landwell for as land roads for and roads other and othertransportation transportation infrastructure infrastructure

The accuracy of the LUCC data was validated by field surveys. The results indicated that the overall accuracy of the first-level land use types was 94.0%, and the accuracy for the 25 sub-classes was 91.2% [41]. These data have been used previously to evaluate land degradation [13], to identify Appl. Sci. 2018, 8, 1524 5 of 18

The accuracy of the LUCC data was validated by field surveys. The results indicated that the overall accuracy of the first-level land use types was 94.0%, and the accuracy for the 25 sub-classes was 91.2% [41]. These data have been used previously to evaluate land degradation [13], to identify the major driver of LUCC change [2,41], to predict future land-use change [44], and to model optimal land-use strategies [45]. The classification system is important for the production of LUCC data. The classification used in this study was based on six land-use types defined by Liu et al. [16]. Twenty-five subclasses were grouped into the following six classes: cropland, woodland, grassland, water bodies, built-up land, and unused land. To measure urban expansion in the ZXEB, the authors also analyzed the following sub-classes: urban land, rural settlements, and infrastructure areas, all of which belong to the general class of built-up land (Table1). To analyze the socio-economic drivers of built-up land expansion, the authors also collected gross domestic product (GDP) (for ZXEB, except Chongzuo), urban and rural net income (for ZXEB, except Chongzuo), and total population (for Guangdong and Guangxi province); data were collected from 1994 to 2015. Some of the statistical data in Chongzuo were not available because of an administrative level change. The rural and employment data in Guangxi and Guangdong provinces were collected from 1980 to 2010. All the data were collected from the National Bureau of Statistics of China [46], in order to keep the statistical data consistent.

2.3. Methods

2.3.1. Transition Matrix To determine the land-use type conversion processes based on Markov model, a land-use transition matrix (primary matrix) was used to describe the net land-use change or lack of change in two time periods: 1990–2000 and 2000–2017 [2]. Additionally, a further calculation using the matrix table was made to calculate gains (increases) and losses (decreases). Gains in one land-use type represented the degree to which a land-use type increased between the study periods, while losses measured the extent to which a land-use type was reduced [39].

2.3.2. Markov Modeling of Land-Use and Land-Cover Change The primary occupation and transition probabilities (TPs) of different periods were used to determine the trend of development and predict the state of the future [40]. Markov chains have been widely used to evaluate changes in land-use and land-cover at different scales, covering different land-use types [35,40,47]. The applicability and feasibility of Markov chains have been testified by many previous studies and the results approximately conform to observed results [36,48]. Moreover, the combination of a geographic information system (GIS), remote sensing data, and Markov chains was promoted because of the nature of GIS and its integration with remote sensing [35,40] Markov chains have many assumptions. One basic assumption of land-use and land-cover change is that they are a stochastic process, and different categories are the states of a chain [35]. A chain is defined as a stochastic process with the property value of the process at time t, where Xt only depends on the value at previous time t–1, Xt–1, and is not related to the sequence of values Xt–2, Xt–2 ...... X0. [40,49]. A chain is a transition process from one state to another state. P is transition possibility, standing for the possibility of change from current state to next state, and can be expressed as follows:   P11 P12 ··· P1n  ···   P21 P22 P2n  P = Pij =   (1)  ············  Pn1 Pn2 ··· Pnn where P stands for probability from state i to state j. Appl. Sci. 2018, 8, 1524 6 of 18

Equation (1) must satisfy the following two conditions:

n ∑ Pij = 1 (2) j=1

0 ≤ Pij ≤ 1 (3) During this study, Markov chain analysis was used as a descriptive tool to understand and quantify the land-use transition processes in ZXEB.

2.3.3. Detection of Spatial Autocorrelation Spatial autocorrelation, including global spatial autocorrelation and local spatial autocorrelation, can be used to describe and compare the spatial structure of the data. Generally, global spatial autocorrelation was used to present the average degree of spatial difference between a region and its surroundings, while local spatial autocorrelation was used for further analysis as to whether there was a high or low value of local space and the aggregation of the observed value [50]. Here, the authors introduced the principle of Moran’s I regarding spatial autocorrelation as follows [50]: n n n ∑i ∑j Wij(xi − x) I = (4) S0 n 2 ∑i (xi − x)(xi − x) where, for i 6= j, n was the total amount of the space unit and Xi was the observation value in the space unit i; x was the average value of Xi; and Wij was the spatial weight matrix, which represented the neighborhood relation of area i and area j and can be measured by distance criterion of adjacency criterion. The authors calculated the global spatial autocorrelation and local spatial autocorrelation via Geoda [51]. The value of Moran’s I, which is normally between [−1, 1]—although when the value was larger than 0, it showed a positive autocorrelation, and vice versa when the value was 0—implied no autocorrelation between the data [52].

3. Results

3.1. The LUCC Pattern in 1990, 2000, and 2017 Woodland was the dominant land-use type (63.4% of the total area) in the ZXEB in 2017, followed by cropland and grassland (22.9% and 7.1%, respectively). Total built-up land, water bodies, and unused land comprised less than 10% of this area. During the period from 1990 to 2000, cropland experienced the largest decrease, and built-up land saw the largest increase in total area (−0.49% and 0.39%, respectively) (Figure2 and Figure S1). Grassland decreased by 404 km 2, while woodland and water bodies increased by 238 km2 and 321 km2, respectively. Considering the time frame from 2000 to 2017, the areas of cropland, woodland, grassland, and water bodies decreased. Cropland experienced the largest decrease in total area (1481 km2 or 0.9%), followed by woodland and water bodies (0.33% and 0.09%, respectively). Built-up land increased by 2176 km2 (1.32%) (Figure3 and Figure S1). Above all, built-up land always increased, and cropland decreased between 1990–2017. Alternatively, woodland and water body areas first increased and then decreased, and grassland areas experienced slight decreases in both periods. Appl. Sci. 2018, 8, x FOR PEER REVIEW 6 of 18

During this study, Markov chain analysis was used as a descriptive tool to understand and quantify the land-use transition processes in ZXEB.

2.3.3. Detection of Spatial Autocorrelation Spatial autocorrelation, including global spatial autocorrelation and local spatial autocorrelation, can be used to describe and compare the spatial structure of the data. Generally, global spatial autocorrelation was used to present the average degree of spatial difference between a region and its surroundings, while local spatial autocorrelation was used for further analysis as to whether there was a high or low value of local space and the aggregation of the observed value [50]. Here, the authors introduced the principle of Moran’s I regarding spatial autocorrelation as follows [50]: n ∑∑ W (x − x) = (4) S0 ∑( x − x) (x − x) where, for i ≠ j, n was the total amount of the space unit and Xi was the observation value in the space unit i; x was the average value of Xi; and Wij was the spatial weight matrix, which represented the neighborhood relation of area i and area j and can be measured by distance criterion of adjacency criterion. The authors calculated the global spatial autocorrelation and local spatial autocorrelation via Geoda [51]. The value of Moran’s I, which is normally between [−1, 1]—although when the value was larger than 0, it showed a positive autocorrelation, and vice versa when the value was 0—implied no autocorrelation between the data [52].

3. Results

3.1. The LUCC Pattern in 1990, 2000, and 2017 Woodland was the dominant land-use type (63.4% of the total area) in the ZXEB in 2017, followed by cropland and grassland (22.9% and 7.1%, respectively). Total built-up land, water bodies, and unused land comprised less than 10% of this area. During the period from 1990 to 2000, cropland experienced the largest decrease, and built-up land saw the largest increase in total area (−0.49% and 0.39%, respectively) (Figure 2 and Figure S1). Grassland decreased by 404 km2, while woodland and water bodies increased by 238 km2 and 321 km2, respectively. Considering the time frame from 2000 to 2017, the areas of cropland, woodland, grassland, and water bodies decreased. Cropland experienced the largest decrease in total area (1481 km2 or 0.9%), followed by woodland and water bodies (0.33% and 0.09%, respectively). Built-up land increased by 2176 km2 (1.32%) (Figure 3 and Figure S1). Above all, built-up land always increased, and cropland decreased between 1990–2017. Alternatively,Appl. Sci. 2018, 8, woodland 1524 and water body areas first increased and then decreased, and grassland 7areas of 18 experienced slight decreases in both periods.

(a) (b)

Appl. Sci.Figure 2018, 8 ,3. x NetFOR change PEER REVIEW in land-use and land-cover from 1990 to 2017. ( a) 1990–2000. ( b) 2000–2017. 7 of 18

Land-use change was unbalanced in all cities in both periods (Figure 4). During 1990–2000, a Land-use change was unbalanced in all cities in both periods (Figure4). During 1990–2000, total of 1906 km2 (1.16% of the total area) of six land-use classes changed in the ZXEB. Regarding a total of 1906 km2 (1.16% of the total area) of six land-use classes changed in the ZXEB. Regarding 1990–2000, the most obvious change occurred in the east (Foshan and Guangzhou) and middle areas 1990–2000, the most obvious change occurred in the east (Foshan and Guangzhou) and middle areas (Guigang, Nanning, and Laibin) of the ZXEB, followed by the eastern cities, Zhaoqing and Wuzhou. (Guigang, Nanning, and Laibin) of the ZXEB, followed by the eastern cities, Zhaoqing and Wuzhou. Concerning the western cities, Baise and Chongzuo experienced less change in LUCC. During the Concerning the western cities, Baise and Chongzuo experienced less change in LUCC. During the period from 2000–2017, a total of 3256 km2 (1.98% of the total area) of all land-use types experienced period from 2000–2017, a total of 3256 km2 (1.98% of the total area) of all land-use types experienced greater changes than in the earlier period. Similar to the first period, the most obvious change greater changes than in the earlier period. Similar to the first period, the most obvious change occurred occurred in the eastern cities that belong to Guangdong province (Guangzhou, Foshan, and in the eastern cities that belong to Guangdong province (Guangzhou, Foshan, and Zhaoqing), followed Zhaoqing), followed by the middle area cities, Nanning and Wuzhou. The western city, Baise, by the middle area cities, Nanning and Wuzhou. The western city, Baise, experienced more LUCC experienced more LUCC during this period. Concurrently, Laibin and Guigang experienced less during this period. Concurrently, Laibin and Guigang experienced less change compared with the change compared with the previous period. previous period.

FigureFigure 4. MapMap of of the the net net change change in in six land-use types from 1990 to 2000 and 2000 to 2017.

3.2. The The Temporal Temporal and Spatial VariationVariation Trends ofof LUCCLUCC SinceSince 19901990 The spatiotemporal characteristics of LUCC were presented using a conversion matrix by areas. The TPs in the ZXEB are shown in Tables 2 2–5,–5, thethe mapmap ofof thisthis conversionconversion processprocess isis projectedprojected inin Figure 5. During both periods, unused land was the smallest land-use type (less than 0.01% of total area), so this class was not analyzed. During 1990–2000, the TPs of all the land-use types were kept relatively stable and were larger than 0.95. However, TPs indicated that woodland was the most stable land-use type with 0.9973 TPs. Woodland was also the dominant land-use type (63% of total area) having experienced both gain and loss processes; a total of 522 km2 of woodland was increased and then 286 km2 decreased (Table 2). The most obvious woodland increase occurred in the middle cities Guigang, Laibin, and Nanning (Figure 5a). The most dynamic classes were grassland with TPs of 0.9564, followed by cropland and water bodies with TPs of 0.9672 and 0.9741 (Table 3), respectively. Approximately 527 km2 of grassland was converted into other land-use types, mostly woodland (470 km2). Meanwhile, a total of 123 km2 of grassland was converted from other land-use types. The most obvious losses in grassland occurred in Guiang, Laibin, Wuzhou, and Nanning. Cropland decreased by about 992 km2. Most of it turned into water bodies and built-up land, and the most obvious cropland losses occurred in Guangdong province (Guangzhou, Foshan, and Zhaoqing). The size of water bodies increased dramatically during this period, predominantly as a result of conversion from cropland, and mainly in the eastern cities (Zhaoqing, Foshan, and Guangzhou). Appl. Sci. 2018, 8, 1524 8 of 18

Figure5. During both periods, unused land was the smallest land-use type (less than 0.01% of total area), so this class was not analyzed. During 1990–2000, the TPs of all the land-use types were kept relatively stable and were larger than 0.95. However, TPs indicated that woodland was the most stable land-use type with 0.9973 TPs. Woodland was also the dominant land-use type (63% of total area) having experienced both gain and loss processes; a total of 522 km2 of woodland was increased and then 286 km2 decreased (Table2). The most obvious woodland increase occurred in the middle cities Guigang, Laibin, and Nanning (Figure5a). The most dynamic classes were grassland with TPs of 0.9564, followed by cropland and water bodies with TPs of 0.9672 and 0.9741 (Table3), respectively. Approximately 527 km 2 of grassland was converted into other land-use types, mostly woodland (470 km2). Meanwhile, a total of 123 km2 of grassland was converted from other land-use types. The most obvious losses in grassland occurred in Guiang, Laibin, Wuzhou, and Nanning. Cropland decreased by about 992 km2. Most of it turned into water bodies and built-up land, and the most obvious cropland losses occurred in Guangdong province (Guangzhou, Foshan, and Zhaoqing). The size of water bodies increased dramatically during this period, predominantly as a result of conversion from cropland, and mainly in the eastern cities (Zhaoqing, Foshan, and Guangzhou). Built-up land experienced the most obvious increase (648 km2) during this period, most significantly in Guangdong province (Guanghzhou and Foshan) (Figure5b). There was no loss of built-up land during this period.

Table 2. The land-use and land-cover change (LUCC) matrix in the Zhujiang–Xijiang Economic Belt (ZXEB) from 1990 to 2000 (unit: km2).

Cropland Woodland Grassland Water Body Built-Up Land Unused Land Losses Cropland 38,936.19 49.77 3.27 396.47 542.02 0.01 991.54 Woodland 92.23 104,364.36 119.25 20.78 53.29 0.00 285.55 Grassland 42.73 470.11 11,577.32 5.15 9.19 0.00 527.18 1990–2000 Water body 55.69 2.00 0.47 3824.41 43.59 0.09 101.85 Built-up land 0.00 0.00 0.00 0.00 3930.47 0.00 0.00 Unused land 0.00 0.00 0.00 0.03 0.00 24.48 0.03 Gains 190.66 521.88 123.00 422.42 648.09 0.10 1906.16

Table 3. Land-use transition possibilities, 1990–2000.

Cropland Woodland Grassland Water Body Built-Up Land Unused Land Cropland 0.9752 0.0012 0.0001 0.0099 0.0136 0.0000 Woodland 0.0009 0.9973 0.0011 0.0002 0.0005 0.0000 Grassland 0.0035 0.0388 0.9564 0.0004 0.0008 0.0000 1990–2000 Water body 0.0142 0.0005 0.0001 0.9741 0.0111 0.0000 Built-up land 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 Unused land 0.0000 0.0000 0.0000 0.0012 0.0000 0.9988 Note: The built-up land generally did not change into other land-use types, so the transition possibility was “0.0000”.

Table 4. The LUCC matrix in the ZXEB from 2000 to 2017 (units: km2).

Cropland Woodland Grassland Water Body Built-Up Land Unused Land Losses Cropland 37,526.41 157.99 12.86 131.98 1296.82 0.82 1600.46 Woodland 81.47 103,935.64 293.33 48.21 526.45 3.01 952.48 Grassland 4.26 248.41 11,345.65 17.23 84.78 0.04 354.73 2000–2017 Water body 33.94 6.95 36.22 3901.20 266.68 1.89 345.68 Built-up land 0.00 0.00 0.00 0.00 4578.57 0.00 0.00 Unused land 0.02 0.42 0.02 0.42 1.55 22.16 2.43 Gains 119.70 413.77 342.42 197.85 2176.28 5.77 3255.78 Appl. Sci. 2018, 8, 1524 9 of 18

Table 5. Land-use transition possibilities, 2000–2017.

Cropland Woodland Grassland Water Body Built-Up Land Unused Land Cropland 0.9591 0.0040 0.0003 0.0034 0.0331 0.0000 Woodland 0.0008 0.9909 0.0028 0.0005 0.0050 0.0000 Grassland 0.0004 0.0212 0.9697 0.0015 0.0072 0.0000 2000–2017 Water body 0.0080 0.0016 0.0085 0.9186 0.0628 0.0004 Built-up land 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 Appl. Sci. 2018, 8, Unusedx FOR PEER land REVIEW 0.0008 0.0171 0.0008 0.0171 0.0630 0.9012 9 of 18

FigureFigure 5.5. TheThe spatialspatial patternpattern ofof land-useland-use andand land-coverland-cover changechange (LUCC)(LUCC) inin thethe Zhujiang–XijiangZhujiang–Xijiang EconomicEconomic BeltBelt (ZXEB).(ZXEB). ((toptop):): 1990–2000,1990–2000, ((bottombottom):): 2000–2017.2000–2017.

Compared with last period, the TPs and transition area of most land-use types decreased; the Compared with last period, the TPs and transition area of most land-use types decreased; land-use activities in this region became stronger in this period (Tables 4 and 5). the land-use activities in this region became stronger in this period (Tables4 and5). During 2000–2017, woodland was still the most stable class with TPs of 0.9909. Woodland During 2000–2017, woodland was still the most stable class with TPs of 0.9909. Woodland areas areas experienced deforestation (woodland lost) in the eastern cities (Wuzhou, Guangzhou, and experienced deforestation (woodland lost) in the eastern cities (Wuzhou, Guangzhou, and Zhaoqing) Zhaoqing) during this period. Most of the lost woodland was converted into built-up land and during this period. Most of the lost woodland was converted into built-up land and grassland. grassland. Water bodies were the most dynamic land-use types with TPs of 0.9186, followed by cropland Water bodies were the most dynamic land-use types with TPs of 0.9186, followed by cropland and grassland (Table5). Water bodies continued to increase during this period (346 km 2). Cropland and grassland (Table 5). Water bodies continued to increase during this period (346 km2). Cropland continued to decrease by a total of 1600 km2 (Table4). The most obvious loss of cropland occurred in continued to decrease by a total of 1600 km2 (Table 4). The most obvious loss of cropland occurred the eastern cities and supported the built-up land expansion (Figure5c,d). Grassland changed slightly in the eastern cities and supported the built-up land expansion (Figure 5c,d). Grassland changed (Table4). The most obvious loss in grassland was in the western and middle areas of the ZXEB, while slightly (Table 4). The most obvious loss in grassland was in the western and middle areas of the most of the grassland increases occurred in the middle and eastern areas (Wuzhou and Zhaoqing) ZXEB, while most of the grassland increases occurred in the middle and eastern areas (Wuzhou and (Figure5d). Zhaoqing) (Figure 5d). Built-up land dramatically increased in Guangzhou and Foshan, followed by Nanning and Built-up land dramatically increased in Guangzhou and Foshan, followed by Nanning and Zhaoqing. Other cities experienced different levels of built-up land expansion (Table3 and Figure5d). Zhaoqing. Other cities experienced different levels of built-up land expansion (Table 3 and Figure To summarize, four land-use conversion processes occurred in this region; namely, built-up land 5d). To summarize, four land-use conversion processes occurred in this region; namely, built-up expansion, woodland loss, water body increases, and cropland decreases. land expansion, woodland loss, water body increases, and cropland decreases. 3.3. Built-up Land Expansion Since 1990 3.3. Built-up Land Expansion Since 1990 Built-up land expansion was the major land-use conversion process in the ZXEB from 1990 to 2017.Built-up The total land area ofexpansion built-up was land the increased major significantly,land-use conversion from 3930 process km2 in in 1990 the toZXEB 6755 from km2 in1990 2017, to 2 2 of2017. which The urban total landarea increasedof built-up by land 1431 increased km2, the infrastructuresignificantly, from area increased3930 km byin 11811990 kmto 67552, and km rural in 2 2 settlements2017, of which increased urban byland a minimum increased ofby 212 1431 km km2 (Figure, the infrastructure6). Regarding area the two increased periods, by 1990–2000 1181 km , and and rural settlements increased by a minimum of 212 km2 (Figure 6). Regarding the two periods, 1990–2000 and 2000–2017, the most obvious built-up land expansion occurred in Guangzhou and Foshan, followed by Nanning and Zhaoqing (Figure 7). Yunfu, Chongzuo, and Laibin experienced the least amount of expansion. During both periods, all sub-classes of built-up land (urban land, rural settlements, and infrastructure area) continued to increase. Rural settlements constituted the most significant sub-class of built-up land in the ZXEB, showing a steady increase in size (Tables 3 and 4). To determine the regional differences in urban expansion, the authors analyzed the built-land expansion and mapped the urban land expansion (Class Code 51, Table 1, Figure 8). Urban land increased in size significantly, from 739 km2 in 1990 to 2169 km2 in 2017. Guangdong, Foshan, and Nanning experienced the largest increase in urban land in both periods, followed by Nanning and Appl. Sci. 2018, 8, 1524 10 of 18

2000–2017, the most obvious built-up land expansion occurred in Guangzhou and Foshan, followed by Nanning and Zhaoqing (Figure7). Yunfu, Chongzuo, and Laibin experienced the least amount of expansion. During both periods, all sub-classes of built-up land (urban land, rural settlements, and infrastructure area) continued to increase. Rural settlements constituted the most significant sub-class of built-up land in the ZXEB, showing a steady increase in size (Tables3 and4). To determine the regional differences in urban expansion, the authors analyzed the built-land expansion and mapped the urban land expansion (Class Code 51, Table1, Figure8). Urban land 2 2 Appl.increasedAppl. Sci. Sci. 2018 2018 in, ,8 8, ,sizex x FOR FOR significantly, PEER PEER REVIEW REVIEW from 739 km in 1990 to 2169 km in 2017. Guangdong, Foshan,1010 of of 18 18 and Nanning experienced the largest increase in urban land in both periods, followed by Nanning Zhaoqing;andZhaoqing; Zhaoqing; generally,generally, generally, thethe thecitiescities cities inin Guangdong inGuangdong Guangdong provprov provinceinceince evolvedevolved evolved thethe the fastestfastest fastest (Figure(Figure (Figure 8),88),), and and suchsuchsuch urbanurbanurban expansionexpansionexpansion was waswas at at theat thethe expense expenseexpense of croplands. ofof croplandcropland Guangxis.s. GuangxiGuangxi province, province,province, Nanning, Nanning,Nanning, followed followedfollowed by Liuzhou byby LiuzhouandLiuzhou Baise, andand was Baise,Baise, found waswas to havefoundfound experienced toto havehave experiencedexperienced the greatest thth increaseee greatestgreatest in increaseincrease urban land inin urbanurban from 1990 landland to fromfrom 2017, 19901990 while toto 2017,Wuzhou,2017, whilewhile Chongzuo, Wuzhou,Wuzhou, and Chongzuo,Chongzuo, Yunfu (Guangdong) andand YunfuYunfu (Guangdong)(Guangdong) saw the least sawsaw growth. thethe leastleast growth.growth.

FigureFigure 6. 6.6. Built-up Built-up land land expansion expansion in in the the ZXEB ZXEB from fromfrom 1990 19901990 to to 2017. 2017.2017.

FigureFigure 7. 7.7. Map Map of of built-up built-up land land expansion expansion from fromfrom 1990 19901990 to toto 2017. 2017. Appl. Sci. 2018, 8, 1524 11 of 18 Appl. Sci. 2018, 8, x FOR PEER REVIEW 11 of 18

Figure 8. Urban expansion in eleven prefecture‐level cities in the ZXEB (1990, 2000, and 2017). Note: Figure 8. Urban expansion in eleven prefecture-level cities in the ZXEB (1990, 2000, and 2017). Guangzhou and Foshan are connected. Note: Guangzhou and Foshan are connected. 4. Discussion 4. Discussion During this research, the authors investigated the LUCC and urbanization processes in the DuringZXEB from this research,1990 to 2017 the authorsand revealed investigated the characteristics the LUCC of and LUCC urbanization over space processesand time. inSpatial the ZXEB from 1990autocorrelation to 2017 and of revealed land‐use the types characteristics were also related of LUCC closely over to space economic and time. and Spatialpolicy conditions. autocorrelation of land-useSocioeconomic types were and policy also related factors were closely also to the economic dominant anddriving policy forces conditions. of Chinese urbanization Socioeconomic and and industrialization [17]. Here, the authors analyze the most important drivers in these processes; namely, policy factors were also the dominant driving forces of Chinese urbanization and industrialization [17]. population, GDP, policy, and rural–urban migration. Population growth and economic development are Here, thethe most authors reported analyze possible the drivers most of important urbanization drivers [5,23,53]. in theseEconomic processes; reform was namely, launched population, in China in GDP, policy,the and 1980s rural–urban and rural–urban migration. migration Populationaccelerated the growth urbanization and and economic industrialization development processes are [24]. the most reported possible drivers of urbanization [5,23,53]. Economic reform was launched in China in the 1980s and4.1. The rural–urban Spatial Autocorrelation migration of accelerated Land Use and the Drivers urbanization Analysis in and ZXEB industrialization processes [24]. The Moran’s I for all global land‐use types that presented spatial autocorrelation in 1990, 2000, 4.1. Theand Spatial 2017 Autocorrelationhas been summarized of Land in UseFigure and 9. Drivers Figure 9a Analysis indicates in ZXEBthat, before 2000, the Moran’s I of Thecropland, Moran’s woodland,I for all globaland grassland land-use increased. types that This presented was mainly spatial a result autocorrelation of the three land in 1990,‐use 2000, conversions being relatively stable during this period; the TPs were larger than 0.95, generally, even and 2017 has been summarized in Figure9. Figure9a indicates that, before 2000, the Moran’s I when slightly increased (woodland) or decreased (cropland, grassland), the aggregation still of cropland,increased. woodland, The Moran’s and I of grassland water bodies increased. decreased, This mainly was because mainly of aa resultwater‐use of theincrease three in land-usethe conversionsarea during being this relatively period, which stable increased during thisthe aggregation period; the of TPs water were bodies. larger Following than 0.95, 2000, generally, the even whenMoran’s slightly I showed increased an opposite (woodland) trend; the Moran’s or decreased I of cropland, (cropland, woodland, grassland), and grassland the aggregation decreased, still increased.and Thethat Moran’sfor waterI bodiesof water and bodies built‐ decreased,up land increased. mainly becauseThis was of mainly a water-use a result increase of the rapid in the area during this period, which increased the aggregation of water bodies. Following 2000, the Moran’s I showed an opposite trend; the Moran’s I of cropland, woodland, and grassland decreased, and that for water bodies and built-up land increased. This was mainly a result of the rapid urbanization process, causing large areas of cropland, woodland, and grassland to change into built-up land. Appl. Sci. 2018, 8, x FOR PEER REVIEW 12 of 18 urbanization process, causing large areas of cropland, woodland, and grassland to change into built-up land. Figure 9b indicates that the Moran’s I of built-up land, as well the urban land and rural settlements, increased first and then decreased, while Moran’s I of infrastructure areas always decreased. This was mainly a result of the development of built-up land like “make a Pisa”; the gap Appl. Sci. 2018, 8, x FOR PEER REVIEW 12 of 18 between peri-urban and urban land decreased quickly [54]. The Moran’s I indicated that all land-use types in the ZXEB area were related highly and, after 2000, the mode of land-use Appl.urbanization Sci. 2018, 8, 1524process, causing large areas of cropland, woodland, and grassland to change12 ofinto 18 conversion process changed. built-up land. Figure 9b indicates that the Moran’s I of built-up land, as well the urban land and rural settlements, increased first and then decreased, while Moran’s I of infrastructure areas always decreased. This was mainly a result of the development of built-up land like “make a Pisa”; the gap between peri-urban and urban land decreased quickly [54]. The Moran’s I indicated that all land-use types in the ZXEB area were related highly and, after 2000, the mode of land-use conversion process changed.

(a) (b)

Figure 9.9. Moran’s II ofof land-use/coverland-use/cover of study area duringduring 1990 and 2005.2005. Note: unused land was ignored asas a a result result of of it beingit being the smallestthe smallest area (lessarea than(less 0.01%than of0.01% total of area), total (a )area), Moran’s (a) IMoran'sof cropland, I of woodland,cropland, woodland, grassland andgrassland water body,and water (b) Moran’s body, (Ibof) Moran’s urban land, I of rural urban settlements, land, rural infrastructure settlements, areasinfrastructure and built-up areas land. and built-up land.

4.2. The Socioeconomic Driving Forces of Urbanization Figure9b indicates that(a) the Moran’s I of built-up land, as well the urban(b land) and rural settlements, increasedSocialFigure first and9. Moran’s and economic then I decreased,of land-use/coverbehavior while dominates of Moran’s study urban areaI of du infrastructure expansionring 1990 andprocesses areas 2005. always Note: [27]. unused decreased.Population land Thiswas growth was mainlyand economicignored a result as development ofa result the development of it havebeing beenthe of smallest built-upthe major area land driv (less likeers than “makeof urbanization 0.01% a Pisa”; of total the in area), China gap between(a [12,23].) Moran's peri-urbanGenerally, I of andurbanization urbancropland, land iswoodland, decreaseddefined asgrassland quicklya rise inand [54 the]. water Theratio Moran’sbody, of the (b urban)I indicatedMoran’s population I thatof urban all to land-use land,the totalrural types population, settlements, in the ZXEB and areaeconomic wereinfrastructure relateddevelopment highlyareas and and,is built-updefined after 2000, land.as the the shift mode in ofa land-usenation’s conversionpopulation processfrom rural changed. to urban [23]. Found in Guangdong and Guangxi province, the total population experienced dramatic growth, 4.2.increasing4.2. The Socioeconomic from 11,182 DrivingDriving × 104 in ForcesForces 1994 ofofto UrbanizationUrbanization 15,645 × 104 in 2015 (Figure 10a). The GDP increased 20-fold (from 2008.5 billion yuan in 1994 to 41,057.9 billion yuan in 2016) (Figure 10b). During 1990–2017, SocialSocial andand economiceconomic behavior behavior dominates dominates urban urban expansion expansion processes processes [27 ].[27]. Population Population growth growth and urban land increased from 784 km2 in 1990 to 2169 km2 in 2017 (Figure 6). Urbanization has a close economicand economic development development have have been been the the major major drivers drivers of of urbanization urbanization in in China China [ 12[12,23].,23]. Generally, causal effect on economic growth [34]. Kuang et al. [17] indicated that GDP growth was correlated urbanizationurbanization is defined defined as as a arise rise in in the the ratio ratio of ofthe the urban urban population population to the to thetotal total population, population, and closely with urbanization, especially during 2000–2005. Deng et al. suggested [23,26] that urban land andeconomic economic development development is defined is defined as the as the shift shift in ina anation’s nation’s population population fr fromom rural rural to to urban urban [23]. [23]. must increase by 3% and that GDP must grow by 10% if China wants to maintain high economic FoundFound inin GuangdongGuangdong andand GuangxiGuangxi province,province, thethe totaltotal populationpopulation experiencedexperienced dramatic growth, growth. Thus, urban land expansion4 might have to4 continue. Urbanization is strongly affected by increasingincreasing fromfrom 11,18211,182× × 104 in 1994 toto 15,64515,645 ×× 10104 inin 2015 2015 (Figure (Figure 10a).10a). The GDP increased 20-fold20-fold population increases [2,12]. (from(from 2008.52008.5 billionbillion yuanyuan inin 19941994 toto 41,057.941,057.9 billionbillion yuanyuan inin 2016)2016) (Figure(Figure 1010b).b). DuringDuring 1990–2017, 2 2 urbanurban landland increasedincreased fromfrom 784784 kmkm2 inin 19901990 toto 21692169 kmkm2 inin 20172017 (Figure(Figure6 6).). Urbanization Urbanization hashas a a close close causalcausal effecteffect onon economiceconomic growthgrowth [[34].34]. KuangKuang etet al.al. [[17]17] indicatedindicated thatthat GDPGDP growthgrowth waswas correlatedcorrelated closelyclosely withwith urbanization,urbanization, especiallyespecially duringduring 2000–2005.2000–2005. Deng et al.al. suggested [[23,26]23,26] thatthat urbanurban landland mustmust increaseincrease by 3%3% andand thatthat GDPGDP mustmust growgrow byby 10%10% ifif ChinaChina wantswants toto maintainmaintain highhigh economiceconomic growth.growth. Thus, urban land expansionexpansion might havehave toto continue.continue. UrbanizationUrbanization is stronglystrongly affectedaffected byby populationpopulation increases [[2,12].2,12].

(a) (b)

(a) (b)

Figure 10. Population and gross domestic product (GDP) change from 1994 to 2015. (a) Total population in ZXEB from 1994–2015. (b) GDP in the Zhujiang–Xijiang Economic Belt (ZXEB) from 1994–2015. Appl. Sci. 2018, 8, x FOR PEER REVIEW 13 of 18

Figure 10. Population and gross domestic product (GDP) change from 1994 to 2015. (a) Total population in ZXEB from 1994–2015. (b) GDP in the Zhujiang–Xijiang Economic Belt (ZXEB) from 1994–2015.

4.3. Rural–Urban Migration and Peri-Urban Development Previous studies presented that rural–urban migration was the dominant driver of urbanization. Figure 11 shows that with the built-up land expansion, the proportion of rural settlements dramatically decreased from 76% of built-up land in 1990 to 47% in 2017, while the proportion of urban built-up land significantly increased over this entire time period. Previous research indicated that, before 2000, urbanization occupied less cropland compared with rural settlements (village areas), whereas, after 2000, this conversion trend reversed [25]. Rural–urban migration was the dominant factor for urbanization in China [55]. Liu et al. [27] indicated that urban land in China will continue to expand at a rather rapid rate until 2030, so more than 50% of the population will be living in urban areas. Zhang et al. [55] stated that the income gap between rural and urban was the root cause for this migration. Figure 12 indicated that income gap between rural and urban has widened from 1994 to 2016 in ZXEB. Additionally, because of modernization and the shortage of agriculture in China, the surplus labor in the agricultural area moved to cities to seek better employment and higher income, resulting in migrant laborers (broadly defined) comprising 46% of the urban work force [56]. Concurrently, the relaxation of rural controls caused citizens to be active Appl.not only Sci. 2018 in ,traditional8, 1524 home industries, but in all types of commercial undertakings. The increase13 of in 18 urban employment reflected more quickly than that in rural employment, especially in Guangxi (Supplementary Figure S1B). All this activity accelerated rural–urban migration and urban land 4.3. Rural–Urban Migration and Peri-Urban Development expansion [24,57]. PreviousLarge migration studies presented has accelerated that rural–urban the urbaniza migrationtion wasprocess; the dominant most of driver the ofmigration urbanization. was Figureconcentrated 11 shows in the that peri-urban with the built-up areas (in land China, expansion, also called the proportion “Chengzhongcun”) of rural settlements because of dramatically low-rental decreasedhousing and from a 76%lack ofof built-up space within land in Chinese 1990 to 47%cities in [57–59]. 2017, while The the urban proportion expansion of urban has invaded built-up landcropland, significantly especially increased in peri-urban over this areas. entire The time farmers period. lost Previous their land research resources, indicated but that, remain before in 2000,housing urbanization plots [60,61] occupied in a bid less to meet cropland the huge compared demands with of rural housing settlements construction (village for areas), migrants whereas, from afterrural 2000,areas. this Previous conversion studies trend show reversed that urban [25]. Rural–urbanexpansion has migration led to more was than the dominant 40 million factor farmers for urbanizationlosing their important in China [land55]. Liuresources, et al. [27 with] indicated this number that urban increasing land inat Chinaa rate willof 2 continuemillion per to expandyear in atChina a rather [61]. rapidPeri-urban rate until development 2030, so moreresulted than from 50% the of rapid the population urbanization will and be livingmigration in urban from rural, areas. Zhangwith its et feedback al. [55] stateddriving that urbanization the income and gap urban-rural between rural migration and urban [59,62]. was However, the root the cause peri-urban for this migration.area has been Figure facing 12 indicated many thatproblems, income gaplike betweenland-use rural efficiency, and urban industrial has widened structure, from 1994and toenvironmental 2016 in ZXEB. protection, Additionally, for example because [62,63], of modernization and peri-urban and theareas shortage also have of agriculture a mix of urban in China, and therural surplus functions labor and in are the related agricultural closely area to food moved provision to cities for to the seek urban better lands employment [62,64]. Additionally, and higher income,the major resulting effect of in urbanization migrant laborers is the (broadly loss of cultivated defined) comprising land, especially 46% of in the peri-urban urban work areas force [25,62]. [56]. Concurrently,Most of this cropland the relaxation was of of high rural quality, controls and caused its loss citizens threatens to be food active security not only [17,65]. in traditional Based on home this, industries,peri-urban butdevelopment in all types is of crucial commercial for the undertakings. urbanization The process. increase More in urban specific-area employment polices reflected for moresustainable quickly development than that in inrural the peri-urban employment, area, especially along with in urban Guangxi development, (Supplementary should Figurebe followed S1B). Allin the this future. activity accelerated rural–urban migration and urban land expansion [24,57].

Figure 11. Proportion of sub-class of built-up land change in 1990, 2000, and 2017. Appl. Sci. 2018, 8,Figure x FOR PEER 11. Proportion REVIEW of sub-class of built-up land change in 1990, 2000, and 2017. 14 of 18

Figure 12. Urban and rural per capita net income.income.

4.4. Policy Driving Force of Urbanization Large migration has accelerated the urbanization process; most of the migration was concentrated in theSocioeconomics peri-urban areas and (in rural–urban China, also calledmigration “Chengzhongcun”) were the direct becausedriving factors of low-rental for urbanization housing and in aChina. lack of Policies, space within as the Chineseindirect citiesdriver, [57 have–59]. been The urbanpresented expansion as the hasdominant invaded driver cropland, of urbanization especially inin peri-urbanChina [12,17], areas. with The the farmersrate and lostpattern their of land urbanization resources, often but guided remain by in housingnational policies plots [60 [12,65].,61] in aFigure bid to 13 meet shows the that huge socioeconomics demands of housing and migration construction were the for dominant migrants fromfactors rural of urbanization areas. Previous and studiespolicy has show influenced that urban the expansion socioeconomic has led todevelopment more than 40 and million migration farmers from losing rural their areas important to urban land resources,directly. The with socioeconomic this number factors increasing (especially at a rate the of gap 2 millionbetween per rural year and in Chinaurban) [have61]. Peri-urbancaused the rural–urban migration. Since 1978, economic reform and open policies have been carried out, and the urban area has increased by 4–5-fold [66]. A set of regional economic development policies were enacted—the CWDP (China Western Development Plan, 2000), the NARP (Northeast Area Revitalization Plan, 2004), and the RCCP (Rise of Central China Plan, 2006). Thus, investment in this region increased, accelerating urban land expansion [12,17]. However, not only the developed policies, but also the spatial planning related to peri-urban development, cropland protection, and so on, should be enforced in this region in the future [62,67]. More details about whether a specific policy adopt a reactive role on the land use change process, for example, evaluating which one policy is more closely related to urbanization processes, population structure and the net income over space and time, will be a contested topic in the future to provide a further suggestion for urban development. The frontier research focus on the regulations and fires on forest have been presented by Paulo Mourao et al. [68].

Figure 13. The linkage between policy, socioeconomic, rural–urban migration, and urbanization.

5. Conclusions Using a combination of long-time-series land-use and land-cover data and statistical data, based on the land-use transition matrix, Markov chain, and Moran’s I methods, this research identified the pattern and process of all land-use types in the ZXEB during the 1990–2000 and 2000–2017 periods. The authors identified the urbanization process and discussed the possible drivers of urban land expansion. The following was found: (1) Woodland was the dominant land-use type in ZXEB, cropland increased, and woodland decreased dramatically in both periods. In the two periods, 1990–2000 and 2000–2017, Appl. Sci. 2018, 8, x FOR PEER REVIEW 14 of 18 Appl. Sci. 2018, 8, 1524 14 of 18 development resulted from the rapid urbanization and migration from rural, with its feedback driving urbanization and urban-rural migration [59,62]. However, the peri-urban area has been facing many problems, like land-use efficiency, industrial structure, and environmental protection, for example [62,63], and peri-urban areas also have a mix of urban and rural functions and are related closely to food provision for the urban lands [62,64]. Additionally, the major effect of urbanization is the loss of cultivated land, especially in peri-urban areas [25,62]. Most of this cropland was of high quality, and its loss threatens food security [17,65]. Based on this, peri-urban development is crucial for the urbanization process. More specific-area polices for sustainable development in the peri-urban area, along with urban development,Figure 12. shouldUrban and be rural followed per capita in the net future. income.

4.4. Policy Driving Force of Urbanization Socioeconomics and rural–urban migration were thethe direct driving factors for urbanizationurbanization in China. Policies, as the indirect driver, have been presentedpresented as thethe dominantdominant driverdriver ofof urbanizationurbanization inin ChinaChina [[12,17],12,17], withwith thethe raterate andand patternpattern of urbanization often guided by national policies [12,65]. [12,65]. Figure 13 showsshows thatthat socioeconomicssocioeconomics and migration were thethe dominantdominant factorsfactors ofof urbanizationurbanization and policypolicy hashas influenced influenced the the socioeconomic socioeconomic development development and migrationand migration from ruralfrom areasrural toareas urban to directly. urban Thedirectly. socioeconomic The socioeconomic factors (especially factors (especially the gap between the gap rural between and urban) rural and have urban) caused have the rural–urban caused the migration.rural–urban Since migration. 1978, economic Since 1978, reform economic and open reform policies and haveopen beenpolicies carried have out, been and carried the urban out, areaand hasthe urban increased area by has 4–5-fold increased [66 ].by A 4–5-fold set of regional [66]. A se economict of regional development economic policiesdevelopment were enacted—thepolicies were CWDPenacted—the (China CWDP Western (China Development Western Plan, Developmen 2000), thet NARPPlan, (Northeast2000), the AreaNARP Revitalization (Northeast Plan,Area 2004),Revitalization and the RCCPPlan, 2004), (Rise ofand Central the RCCP China (Rise Plan, of 2006). Central Thus, China investment Plan, 2006). in this Thus, region investment increased, in acceleratingthis region increased, urban land accelerating expansion urban [12,17 ].land However, expansion not [12,17]. only the However, developed not policies, only the but developed also the spatialpolicies, planning but also related the spatial to peri-urban planning development, related to peri-urban cropland protection,development, and cropland so on, should protection, be enforced and inso thison, should region inbe the enforced future in [62 this,67]. region in the future [62,67]. More detailsdetails about about whether whether a specifica specific policy policy adopt adopt a reactive a reactive role on role the on land the use land change use process,change forprocess, example, for example, evaluating evaluating which one which policy one is more policy closely is more related closely tourbanization related to urbanization processes, population processes, structurepopulation and structure the net incomeand the overnet income space and over time, space will and be time, a contested will be topica contested in the futuretopic in to the provide future a furtherto provide suggestion a further for urbansuggestion development. for urban The frontierdevelopment. research The focus frontier on the regulationsresearch focus and fireson the on forestregulations have beenand fires presented on forest by Paulohave been Mourao presented et al. [68 by]. Paulo Mourao et al. [68].

Figure 13. The linkage between policy, socioeconomic, rural–urban migration, and urbanization. Figure 13. The linkage between policy, socioeconomic, rural–urban migration, and urbanization. 5. Conclusions 5. Conclusions Using a combination of long-time-series land-use and land-cover data and statistical data, Using a combination of long-time-series land-use and land-cover data and statistical data, based on the land-use transition matrix, Markov chain, and Moran’s I methods, this research based on the land-use transition matrix, Markov chain, and Moran’s I methods, this research identified identified the pattern and process of all land-use types in the ZXEB during the 1990–2000 and the pattern and process of all land-use types in the ZXEB during the 1990–2000 and 2000–2017 periods. 2000–2017 periods. The authors identified the urbanization process and discussed the possible The authors identified the urbanization process and discussed the possible drivers of urban land drivers of urban land expansion. The following was found: expansion. The following was found: (1) Woodland was the dominant land-use type in ZXEB, cropland increased, and woodland (1) decreasedWoodland wasdramatically the dominant in land-useboth periods. type in In ZXEB, the croplandtwo periods, increased, 1990–2000 and woodland and 2000–2017, decreased dramatically in both periods. In the two periods, 1990–2000 and 2000–2017, woodland was found Appl. Sci. 2018, 8, 1524 15 of 18

to be the most stable land-use type, and land-use activities have become stronger compared with the past. (2) Built-up land expansion was the major land-use conversion process in the two periods and most built-up land increases came from cropland and woodland. Both the urban land and rural settlements have increased in area; however, the proportion of rural settlements in built-up land has decreased since 2000. (3) The spatial autocorrelation of all land-use types in ZXEB was related highly and the modes of land-use change have changed mainly because of the change in socioeconomics, rural–urban migration, and policies. (4) Both GDP and total population have increased dramatically from 1994 to 2015, and directly caused urbanization; while rural–urban migration has led to urbanization and its process, as well as accelerated the peri-urban development.

Policy, as an indirect but dominant driver, not only influences the economic development and leads to urbanization, but also should be enforced to reduce the gap between rural–urban incomes and promote peri-urban sustainable development.

Supplementary Materials: The following are available online at http://www.mdpi.com/2076-3417/8/9/1524/ s1. Author Contributions: Y.H. designed and supervised the research. B.(Batunacun) completed the data analysis and drafting work. Y.H. and B.(Batunacun) together completed the revision of the paper. Funding: This study was supported by the National Key Research and Development Plan (2016YFC0503701, 2016YFB0501502), the Strategic Priority Research Program of CAS (XDA19040301, XDA20010202), and the Key Project of High-Resolution Earth Observation (No. 00-Y30B14-9001-14/16). Acknowledgments: We would like to give thanks to the two anonymous referees and all editors who provided great suggestions for this study. Conflicts of Interest: The authors declare no conflict of interest.

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