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Applied Geography 31 (2011) 600e608

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Applied Geography

journal homepage: www.elsevier .com/locate/apgeog

Analyzing and modeling land use land cover change (LUCC) in the DaqingCity, China

Wanhui Yu a, Shuying Zang a,*, Changshan Wub, Wen Liu a, Xiaodong Na a

aKey Laboratory for Remote Sensing Monitoring of Geographic Environment, Harbin Normal University, Harbin, Heilongjiang 150025, ChinabDepartment of Geography, University of Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI 53201, USA

Keywords:Land use/land cover changeDriving forcesSystem dynamicsScenario simulationResources based city

* Corresponding author.E-mail address: zsy6311@163.com (S. Zang).

0143-6228/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.apgeog.2010.11.019

a b s t r a c t

During the past decades, land use land cover change (LUCC) has taken place around most Chinese citiesat unprecedented rates. During this process, many rural lands, such as forests and wetlands, havetransformed to human settlements. Taking Daqing City, Heilongjiang Province, China as the case studyarea, this paper analyzed the long-term (from 1977 to 2007) land use land cover change, and modeledthe change using a system dynamic model. In particular, land use land cover maps in 1977, 1988, 1992,1996, 2001, and 2007 were derived from Landsat multi-spectral Scanner (MSS) and Thematic Mapper(TM) imagery. Through analyzing the trend of land use land cover change, three groups of driving forces,including land use management, population growth, and economic and social policies, have beenidentified to model LUCC in the Daqing City. Finally, future land use change scenarios were simulatedunder three socio-economic policies: 1) current economic growth, 2) rapid economic growth, and 3)sustainable development. Analysis of results suggests that Daqing City has expanded significantly at thecost of forests and wetlands from 1977 to 2007. Moreover, systems dynamic modeling results suggestthat three identified groups of driving forces can effectively explain past land use change in the studyarea. Finally, simulation results indicate that 1) under current and rapid economic growth policies, built-up land in Daqing City increase significantly, while the areas of grassland and wetlands decreaseremarkably, and 2) under the sustainable development policy, the conflict between population expansionand land resource shortage can be alleviated to some extent. These results provide an important deci-sion-making reference for land use planning and sustainable development in Daqing City.

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Introduction

During the past two decades, most metropolitan areas in Chinahave experienced unprecedented expansion, mainly due to overallpopulation growth and migration from rural to urban areas. Theurban population in China grew from 302 million in 1990 to 456million in 2000, and it is projected that in 2020, approximately 900million Chinese people will reside in urban areas. Simultaneously,the percentof theurbanpopulation in the total population increasedfrom 26% in 1990 to 36% in 2000, and it is estimated that more than65% of the Chinese populationwill be urban dwellers in 2050 (Song& Ding, 2009). Associated with the process of rapid urbanization,a significant amount of natural lands, such as forests and wetlands,has been developed into agricultural lands and human settlements(Song & Ding, 2009). Such rapid land use and land cover change(LUCC) has profound influences on human and natural environ-ments. In particular, it impacts land and air resources, biodiversity,

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water quality, radiation budgets, carbon cycling, and livelihoods(Lambin et al., 2000, Turner 1994, Verburg et al., 2002a, 2002b). Asan example, agricultural growth and intensification have causeddeforestation, soil erosion, watershed degradation, reduced biodi-versity, and agrochemical pollution (World Bank, 2008). In additionto the changes related to agricultural land uses, accelerated urban-ization leads to an increase in impervious surface area, which booststhe transportation and accumulation of non-point pollutants viasurface runoff (Xian, Crane, & Su, 2007). Because of these greatimpacts, understanding and modeling LUCC has become an impor-tant topic for environmental management and land use planning.

With the objective of promoting sustainable development, theJoint International Land Use/Cover Change Project of the Interna-tional Geosphere & Biosphere Program (IGBP) and the InternationalHuman Dimensions Program (IHDP) on Global EnvironmentalChange undertook the monitoring, interpreting, modeling, andprediction of LUCC extensively at global and regional scales (Nunes& Auge, 1996; Turner, David, & Liu, 1995). To monitor and under-stand LUCC, satellite remote sensing techniques have been widelyapplied to extract multi-temporal land use information (Lu, Mausel,

W. Yu et al. / Applied Geography 31 (2011) 600e608 601

Brondízio, & Moran, 2004; Shalaby & Tateishi, 2007). In addition,many change detection techniques, such as principal componentanalysis (PCA), band differencing, band ratioing, and post-classifi-cation comparison, have been developed to identify the extent andlocations of LUCCs (Lu et al., 2004; Ridd & Liu, 1998). Recently,remote sensing techniques have been integrated with geographicinformation systems (GIS) for better analyzing and monitoringLUCC (Anthony & Li, 1998, Mesev, 2007). With multi-temporal landuse information, numerous models have been proposed to explorethe process of LUCC. These models can be divided into two broadgroups: spatially explicit and aspatial models (Seto & Kaufmann,2003). Many spatially explicit models have been developed bygeographers and landscape ecologists with the purpose ofexplaining and predicting the locations of LUCCs through eitherempirical-statistical models (Irwin & Geoghegan, 2001; Lambin,Rounsevell, & Geist, 2000; Mertens & Lambin, 1997) or rule-basedsimulation models, particularly cellular automata (Clarke, Hoppen,& Gaydos, 1997; Kamusoko, Aniya, Adi, & Manjoro, 2009) andagent-based models (Evans & Kelley, 2004; Mena et al., 2011;Parker, Manson, Janssen, Hoffmann, & Deadman, 2003). Aspatialmodels have been primarily developed by economists, and themajor goal of these models is to predict the amount of LUCC inaggregated geographical regions. Studies using aspatial models canbe traced back as far as the late 1950’s, when Herbert and Stevens(1960) proposed a residential location model using the bid-renttheory introduced by Alonso (1960). In these models, distance tothe markets and accessibility were specified as the driving forces ofLUCC (Alonso,1964). Following this idea, many econometric modelshave been applied in examining LUCC (Chomitz & Gray, 1996;Huang, Xie, Tay, & Wu, 2009; Landis & Zhang, 1998).

While both spatially explicit models and aspatial models havebeen extensively applied in examining LUCC in the past decades,this report only emphasizes aspatial models for two reasons. Thefirst reason is that determining the quantity and rate of LUCC, nottheir particular locations, is essential for understanding the processof land conversion, especially in developing countries, such asChina. Second, the LUCC process in China is always very compli-cated; many land use choices may be exogenous to land users, andtheir individual preferences may not be the deciding factor for landuse conversion (Seto & Kaufmann, 2003). As a result, it is difficult todevelop a rule-based spatially explicit model (e.g., cellular autom-ata or multi-agent model) to predict the locations of future LUCC inChina. For these reasons, this study aimed to develop an aspatialmodel to understand the process of LUCC.

Among aspatial models, empirical-statistical methods, inparticular multivariate regression models and logit regressionmodels, have been typically applied to examine the possibleexogenous contributions of causal factors (Landis & Zhang, 1998,Zang and Huang 2006, Luo & Wei, 2009). Although these statis-tical models have been able to identify causal factors and to predictLUCC with some success, their abilities to accurately modeldynamic LUCC have been questioned. In many cases, the prereq-uisite assumptions of regression models cannot be satisfied (Zangand Huang, 2006, Huang et al., 2009). To address the inefficiencyof multivariate regression analysis and logistic regression analysis,Luo andWei (2009) proposed a geographically weighted regression(GWR)model to examine the urban development in Nanjing, China.Huang et al. (2009) proposed an unbalanced support vectormachines model and applied it to examining LUCC in Calgary,Canada. Thus far, although many models have been proposed, fewmodels have considered LUCC as a complex process resulting froma variety of interactions between natural environments and humanactivities. Therefore, simple empirical-statistical models are unableto capture the major driving forces in these processes and examinetheir interrelationships. However, system dynamics modeling

proposed by Forrester (1961, 1969) may have the potential uncoverthe complex relationships between LUCC and its driving forces(Baynes, 2009; He, Okada, Zhang, Shi, & Zhang, 2006). Systemdynamics modeling was proposed to achieve systems thinkingusing computer models, focusing on how the objects studiedinteract with other objects within a system. This type of model hasbeen applied to understand complex systems in tourism manage-ment (Georgantzas, 2003), water resource analysis (Winz, Brierley,& Trowsdale, 2009), watershed management (Guo et al., 2001,Neto et al. 2006), and regional land uses (Verburg et al., 2002a,2002b, He et al., 2006, Han, Hayashi, Cao, & Imura, 2009). Fewstudies, however, have applied this model to explore the causalfactors of LUCC, particularly for understanding the rapid urbani-zation process in developing countries, such as China (Han et al.,2009; Neto et al., 2006).

Therefore, the present study attempted to quantify the long-term LUCC in Daqing, China, a typical resource-based city, analyzethe driving forces of such rapid change, and predict future LUCCunder different socio-economic policies. The first objective of thisstudy was to quantify long-term LUCC in Daqing City, China.Second, we selected and examined major driving forces of LUCC.Finally, a land use model based on system dynamics was developedand calibrated to predict future LUCC under three socio-economicpolicies: 1) current economic growth; 2) rapid economic growth;and 3) sustainable development.

Study area and data

Daqing City, located in Helongjiang Province, China, wasselected as the study area to conduct this research (see Fig. 1).Daqing City is within the latitudes 45�460 w 46�550N and longi-tudes 124�190 w 125�120E and lies in the middle of the Ha-Da-Qiindustrial corridor, Heilongjiang Province, China (see Fig. 1). DaqingCity is characterized by a continental monsoon climate, with anextremely cold winter and frequent snowfalls, mild but rainy springand autumn seasons, and dry and pleasant summer season. It hasa short frost-free period (229 days), and ample rainfall follows thewarm season, which promotes the growth of crops and herbaceousvegetation. Daqing has an average yearly temperature of 5.6�

Celsius, and the summer average temperature is approximately 22�

Celsius. Daqing is a typical resource-based city, which developedaround petroleum fields. Since the production of petroleum beganin October, 1959, the lands used for oil field construction andrelated industries have expanded at unprecedented rates. Before1959, Daqing was covered by wetlands and grasslands, with almostno human population. Currently, the geographic area of Daqing Cityis approximately 5066 square kilometers, and the population ofDaqing City is approximately 900,000. Such significant LUCC maybe attributed to inadequate land use planning, rapid economicgrowth, and migration. Therefore, a sustainable developmentstrategy for Daqing City is essential for the revitalization andredevelopment of the Ha-Da-Qi industrial corridor, which repre-sents a major traditional industrial base in northeastern China.Because of its importance, the Northeastern China Promotion Planhas focused on Daqing City for its strategic land use planning.

Six scenes of cloud-free remote sensing imagery, including oneLandsat Multi-Spectral Scanner (MSS) and five Thematic Mapper(TM) images, were obtained from the China Remote SensingSatellite Ground Station and Heilongjiang Academy of AgriculturalScience. The MSS image, acquired in 1977, was utilized due tothe unavailability of TM data at that time. This image consists offour spectral bands with a spatial resolution of 79 m. In addition,five Landsat TM images (Path 113, Row 28), recorded on 7 July,1988,21 July, 1992, 14 June, 1996, 21 June, 2001 and 27 July, 2007,were also obtained, and these images consist of six spectral bands

Fig. 1. Daqing City, Ha-Da-Qi industrial corridor, Heilongjiang Province, China as the study area, scale bar applied to inset.

W. Yu et al. / Applied Geography 31 (2011) 600e608602

with a 30-m spatial resolution (bands 1 to 5 and band 7). Thegeometric errors of these remote sensing images were evaluatedand corrected using 66 ground control points (GCPs). Theseselected GCPs were evenly distributed throughout the study area,and most of them were placed on distinguishable objects, forinstance, the intersections of roads or fence lines. Resampling wasperformed using a nearest neighbor algorithm. The transformationhad a root mean square error (RMSE) of 0.5 pixels with half (33) ofthe GCPs employed as checking points. All images were registeredin a Gauss projection (identical to that of the digital district map)and re-sampled to a pixel size of 30 � 30 m using ERDAS IMAGINE8.7 software (ERDAS Inc., www.erdas.com).

In addition to the remote sensing imagery, social statistical dataand economic data of Daqing in different years were obtained fromfour Daqing Statistics Yearbooks (1976e1992, 1993e1995,1996e2000 and 2001e2008). As this is a new economic zone, therehave been many changes of administrative boundaries and dataformats used. As a result, for different years, the social statisticaland economic data of Daqing are inconsistent. As an example, dueto the rapid development of Daqing City, initially rural areasbecame urbanized and were subsequently included in the cityboundary. To address this problem, we applied interpolationtechniques to create spatially and temporally consistent data. Arealinterpolation and dasymetric mapping, in particular, were imple-mented to derive spatially consistent data. For economic data, thegross domestic products (GDP) for the agricultural, industrial, andservice sectors for Daqing were collected, and the annual GDPgrowth rate was also calculated. In addition to this economic data,

Table 1Accuracy assessment of the land use/cover classification (Wet ¼ Wetlands, Gra ¼ Grassla

Year User’s accuracy (%) Producer’s ac

Wet Gra Agr For Unn Bui Wet Gr

1977 88.2 81.7 83.2 91.6 84.7 91.3 67.5 911988 87.8 80.4 90.1 90.1 86.5 80.2 66.2 841992 80.1 86.4 88.8 94.1 81.3 93.2 70.1 901996 91.7 83.2 86.8 93.2 87.7 89.7 92.1 812001 86.4 87.7 87.5 89.7 83.6 82.8 84.1 862007 82.3 85.6 89.4 95.3 82.4 95.3 78.9 89

population information was obtained from the Daqing StatisticsYearbooks. Furthermore, the planning regulations related to landuse development were acquired from the land use planningguidelines of Daqing City and the Eleventh Five-year Plan of Socialand Economic Development in 2007.

Methods

Land use classification

For each of the six Landsat images, an unsupervised classifica-tion (e.g., Isodata) was applied to classify the land uses of Daqinginto six types: agricultural land; grassland; forests; wetlands(swamps, lake basins, bogs, floodplains, rivers, reservoirs, andponds); unused land (e.g., alkaline and salinized land, sand); andbuilt-up land (commercial, residential, and industrial, including oilfields, and transportation). After this step, a post-classification wasperformed to ensure the classification accuracy. For the post-clas-sification, manual correction and digitizationwere applied throughvisual comparisons with historical aerial photographs, land usemaps, and other existing ground information. For assessment of theaccuracy of these land use maps, 600 validation pixels for six landuse/cover types were identified from the following: 1) field loca-tions referenced on the ground with a global positioning system(GPS) unit; and 2) spatially referenced vegetation plots andinventory data with vegetation species records. The producer’saccuracy, user’s accuracy, overall accuracy, and Kappa coefficientswere derived and are presented in Table 1. The Kappa indices of

nd, Agr ¼ Agricultural land, For ¼ Forests, Unu ¼ Unused land, Bui ¼ Built-up land).

curacy (%) Overall accuracy (%) Kappa

a Agr For Unn Bui

.4 90.6 91.2 90.1 79.1 89.89 0.87

.2 94.2 96.3 78.6 93.7 90.46 0.86

.2 81.4 94.2 84.6 89.2 90.46 0.87

.1 74.2 90.1 95.3 81.5 90.79 0.88

.5 85.6 91.2 91.2 92.1 91.89 0.89

.7 90.7 97.5 95.5 99.5 91.01 0.88

Table 2Variables utilized in the system dynamic models.

Subsystemtypes

Indicators Units

Populationsubsystem

Birthrate &Mortality rate &Immigration rate &Migration rate &

Economic andsocial policysubsystem

The primary industry population personThe secondary industry population personThe tertiary industry population personThe investment of primary industry Ten thousand yuanThe investment of secondary industry Ten thousand yuanIndustrial pollution investment Ten thousand yuanEnvironmental protection investment Ten thousand yuanEducational investment Ten thousand yuanScience and technological investment Ten thousand yuan

land usesubsystem

Forests decrease to the other landcover types

km2

Forests increase from the other landcover types

km2

Agricultural land decrease to the otherland cover types

km2

Agricultural land increase from theother land cover types

km2

Wetlands decrease to the other landcover types

km2

Wetlands increase from the other landcover types

km2

Unused land decrease to the other landcover types

km2

Unused land increase from the otherland cover types

km2

Build-up land decrease to the otherland cover types

km2

Build-up land increase from the otherland cover types

km2

W. Yu et al. / Applied Geography 31 (2011) 600e608 603

these land use maps are above 0.85, indicating acceptable accura-cies for further LUCC analysis.

Driving force identification

Natural environments, land use management, and social/economic factors have been proposed as the three main drivingforces of land use/cover change by the IGBP and IHDP (Nunes & Auge,

farm la

fue

gra

built- incre

built-up land increase

water decrease

farm land

forests decrease

grassland decrease

unused land decrease

demand of crop and vegetation

difference between demand and supply

population

yield

irrigation water

irrigation area

farm land decrease farm land increase

forests increase

grassland increase

production of crop and vegetation

water decrease

agricultural land

grassland

grassland decrease grassland increase

built-up land increase

forests increase

crop land increase

water increase

Grassland water storage

difference between supply and demand for livestock

water demand

artificial grassland planting

unused land decrease

Forests decrease

Fig. 2. Systems dynamic flow cha

1996; Turner et al., 1995; Vellinge, 1998). In particular, natural envi-ronments include air temperature, precipitation, and topography;land use management refers to land development policies, such aszoning and smart growth; and socio-economic factors include pop-ulation information, education levels, social trends, economy, andtechnology development. Because natural environments have littleimpact on LUCC for a small geographical region such as Daqing, inthis study, we only considered the land use management and socio-economic factors, and divided them into three groups: 1) land usemanagement; 2) population; and 3) economic and social policy.

System dynamics model framework

With the identified major driving forces, a system dynamicmodel was created with the help of the Vensim program developedby Ventana Systems, Inc. (http://www.vensim.com/). Inputs to thissystems dynamic model were multi-temporal socio-economicstatistical data and land use information, and the outputs from thismodel were the areal changes of all land use types under differentdevelopment scenarios. In theory, all of the components related toland use management and petroleum field development in Daqingshould be considered. However, based on the availability of dataand the classification of driving forces, this study simplified themajor components of the system and grouped these componentsinto three subsystems: 1) a land use subsystem; 2) a populationsubsystem; and 3) an economic and social policy subsystem. Thesethree subsystems are consistent with the three groups of causalfactors that affect LUCCs in Daqing (see Table 2). The internalstructures and the interactions of these three subsystems areshown in Figs. 2e4. In particular, Fig. 2 describes the majorcomponents and their interactions involved in the land usesubsystem. The major components are the six land use types, i.e.,agricultural land, grassland, forests, wetlands, unused land, andbuilt-up land. The changes of each land use type are influencedby other land uses, as well as population information and socio-economic factors (e.g., population, water demand, gross domesticproduct), which are obtained from the population subsystem andeconomic subsystem respectively. Fig. 3 displays the structure ofthe population subsystem. The total population change depends onfactors such as the birth rate, mortality rate, immigration andemigration rates, and family planning policy. As a result, population

unused land decrease

Water for live

Water for industry

forests

forests decrease

nd increase

forests increase

farm land decrease

grassland decrease

water increase

Forests water storage

water decrease

forests water demand

forests fuel

difference between supply and demand

l demand

ssland increase

up land ase

d

precipitation

wetlands

Water storage for grassland

Water storage for forests

Water for irrigation

Water for grassland

Water for forests

Contamination quantity

Wetlands decrease Wetlands increase

rt of the land use subsystem.

population

The level of urbanization

The policy of family planning population increase population decrease

birth rate immigration rate mortality rate emigration rate

contamination quantity

the primary industry population

the secondary industry population

the tertiary industry population

difference between supply and demand for crop and vegetation

demand for crop and vegetation

difference between supply and demand for habitat area

demand for habitat area

rural population

difference between supply and demand for livestock

demand for livestock

demand for fuel

Fig. 3. Systems dynamic flow chart of the population subsystem.

W. Yu et al. / Applied Geography 31 (2011) 600e608604

changes affect economic activities and lead to changes in built-upland and agricultural land. In addition to the land use and pop-ulation subsystems, the economic and social policy subsystem (seeFig. 4) examines the influences of economic and social investmentson the output of the primary (agricultural), secondary (industrial),and tertiary (services) industries and evaluates their influences onLUCC. In particular, the gross domestic product (GDP) is considereda major economic factor resulting from the output of primary,secondary, and tertiary industries, and it affects other factors, suchas social product investment, industrial pollution, and investmentin environmental protection. In addition to economic components,national policies, such as educational investment and science andtechnological investment, have a profound influence on the level ofGDP, which feeds back to other subsystems. This systems dynamicmodel was calibrated using historical data for the six periods, andfuture land use scenarios were simulated under different socio-economic policies.

Results

Land use classification

Through applying unsupervised classification and post-classifi-cation techniques to the Landsat MSS and TM images, we derived sixland use maps from 1977 to 2007 for the study area (see Fig. 5).Analysis of these land usemaps indicated that the geographical areasof built-up land, agricultural land, and unused land increasedsignificantly, together with a great decline in wetlands and forests(see Table 3). In fact, the geographic area of built-up land increasedfrom577km2 in1977 to1077km2 in2007, and the area of agriculturalland increased from 1632 km2 in 1977 to 1971 km2 in 2007. Theseincreases were mainly due to rapid industrialization and the culti-vation of grassland and forests in Daqing City. Similarly, the

so

scientific and technical level

the primary industry population

the secondary industry population

the tertiary industry population science and technolo

industrial pollution contamination quantity per million yuan

the output of primary industry

the output of secondary industry

the output of tertiary indus

The investment of p

The i

GDP

Fig. 4. Systems dynamic flow chart of the e

geographical area of unused land, including alkaline and salinizedland and sand, doubled from 1977 (264 km2) to 2007 (524 km2).Together with the great increase in built-up land, agricultural land,and unused land, wetlands and forests declined significantly duringthese thirty years. In particular, the geographic area of wetlandsdecreased from 973 km2 in 1977 to 372 km2 in 2007. Similarly, thetotal area of forests decreased from 1385 km2 in 1977 to 846 km2 in2007. The significant increase in unused land and the decrease inwetlands and forests indicatedeteriorated environmental conditions.

Driving force identification

Three groups of driving forces have been identified forexplaining the process of LUCC in Daqing City. For the land usemanagement group, the driving forces are the factors impacting thetransformation of a particular land use type. For instance, thedriving forces of wetland declines include increased water use forirrigation, grasslands, forests, industry, and residents (see Fig. 2).Accordingly, the driving forces of wetlands restoration includeprecipitation and water storage by grasslands and forests. In thepopulation group, factors such as birth and mortality rates, immi-gration and emigration rates, family planning policy, and thedegree of urbanization directly impact LUCC (see Fig. 3). For thesocio-economic group, the driving forces of LUCC include the grossdomestic product in the agricultural, industrial, and service sectors,scientific and technical capabilities, and educational levels. Thevalues for all of these factors were obtained from the land use mapsand Daqing Statistics Yearbooks.

System dynamics model development

With the required inputs obtained from the land use maps andDaqing Statistics Yearbooks (1976e2007), the system dynamics

Contamination quantity per capital

cial productive investment

environmental protection investment

gy investment

educational level

educational investment

medical level

medical investment

the tertiary industry investment

domestic pollution contamination quantity

try

rimary industry

nvestment of secondary industry

conomic and social policy subsystem.

Fig. 5. Land use classification maps for Daqing in a) 1977, b) 1988, c) 1992, d) 1996, e) 2001, and f) 2007.

1050000

1100000

1150000

ion

W. Yu et al. / Applied Geography 31 (2011) 600e608 605

model was constructed using Vensim 4.0 software. In particular, themodel was constructed and calibrated based on the inputs from1976 to 1990, and its accuracy was examined by comparing thepredicted population counts and land use areas with their actualvalues for the periods from 1991 to 2007. The comparisons betweenthe predicted and the actual population counts are shown in Fig. 6,and Fig. 7 illustrate the comparisons between the simulated andactual geographic areas of each land use type. For illustrationpurposes, only the results for the years 1992 and 2007 are reportedin Fig. 7 because similar results were obtained for other years.

Table 3Geographic areas of each land use type in Daqing in different periods (km2).

Land use type 1977 1988 1992 1996 2001 2007

Agriculturalland

1631.81 1603.11 1857.08 1868.72 1903.42 1971.17

Grassland 235.48 235.11 276.00 274.75 270.61 269.21Forests 1385.15 1311.17 1164.52 1134.82 1093.24 845.95Wetlands 973.26 1029.68 646.67 668.05 627.73 371.57Unused land 263.78 265.48 290.03 296.21 301.55 531.35Built-up land 577.04 621.97 832.22 823.97 869.97 1077.27

Furthermore, a quantitative analysis showed that for the pop-ulation count and the geographic area of each land use type, therelative errors (REs) were lower than 10%, indicating a relativelyhigh (i.e., greater than 90%)modeling accuracy.With this model, we

850000

900000

950000

1000000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Popu

lat

predicted value actual value

Fig. 6. Comparisons between predicted and actual population count.

Fig. 7. Comparisons between the predicted and actual land areas for different land use/cover types in 1992 and 2007.

Fig. 8. Comparisons of simulation results for (a) agricultural land, (b) f

W. Yu et al. / Applied Geography 31 (2011) 600e608606

projected future LUCC scenarios under three socio-economic poli-cies: 1) current economic growth; 2) rapid economic growth; and3) sustainable development.

Current economic growth policy

Under the current economic growth policy, the GDP growth ratewas set to 8%, which is the average value for the past twenty years.Assuming that all other parameters in the model were constant, wesimulated the population, land use land cover information, andtotal GDP for each year from 2008 to 2048. The results (see Fig. 8)indicate that, under the current economic growth rate, thegeographic area of agricultural land and built-up land will continueto increase, while the extent of grassland and forests will decreasesignificantly. Moreover, the total population Fig. 8e) in Daqing willincrease dramatically under this development mode, mainly due torapid urbanization. The total population in Daqing will reach 1.61million in 2020 and 2.70 million in 2048, which greatly exceeds thepopulation carrying capacity according to the general land useplanning guidelines of the Daqing City.

orests, (c) grassland, (d) built-up land, (e) population, and (f) GDP.

W. Yu et al. / Applied Geography 31 (2011) 600e608 607

Rapid economic growth

Under the rapid economic growth policy, the GDP growth ratewas set to 10.3%, which is the average GDP growth rate since 2000.With other parameters set as constant, the simulation results (seeFig. 8) indicated that the extent of agricultural and built-up landwill increase significantly, and the total population will increaserapidly (e.g., reaching 2.6 million in 2020 and 3.5 million in 2050).In concert with continuous economic acceleration, the process ofurbanization will speed up, and a majority of the agricultural landand grassland will be developed, thus aggravating the existingconflicts between the human and natural environments.

Sustainable development mode

To achieve the objective of sustainable development, an optimalland use management strategy needs to be adopted, in whichhuman activities, land resources, water resources and air resourcesshould be considered simultaneously. To simulate LUCC undera sustainable development scenario, the inputs of the systemdynamics were specified as follows. First, for the economicsubsystem, the growth rate of GDPwas set to 8%. Achieving this ratehas been identified as one of the major goals in Chinese economicdevelopment guidelines. Second, for the population subsystem, thenatural population growth rate should be below 1%. Finally, forthe land use subsystem, a zoning policy was applied to control thechanges of agricultural land and the total amount of developedland. Human activities, such as the conversion of agricultural landto forests and grasslands and artificial afforestation were alsoemployed to restore natural ecosystems. The transformation ratiosfrom cultivated land to forests and grasslands were set to 0.1336%and 0.3007%, respectively. The incremental rate of artificial affor-estationwas set to 1.5%. Using all of the above parameters as inputs,the simulation results (Fig. 8) indicate that under the sustainabledevelopment policy, the increase in built-up land area and thedecrease in grassland area will be effectively controlled (see Fig. 8cand d). Moreover, the total population will increase slightly(1.2 million in 2020 and 1.5 million in 2048), while the economicgrowth rate (e.g., annual GDP growth) will maintain the same levelcompared to that under the current economic growth policy.

Conclusions and discussion

Using Daqing City, Heilongjiang Province, China as the casestudy area, this study analyzed long-term (from 1977 to 2007) LUCCand modeled the change using a system dynamic model. Moreover,future LUCC scenarios were simulated under three socio-economicpolicies: 1) current economic growth; 2) rapid economic growth;and 3) sustainable development. An analysis of the results leads toseveral conclusions.

First, the analysis of the multi-temporal land use maps indicatesthat the geographical areas of built-up land, agricultural land, andunused land increased significantly, together with a great decline inwetlands and forests from 1977 to 2007. The geographical area ofbuilt-up land and unused land doubled during these thirty years,and during the same period, the extent of wetlands decreased byapproximately 60%. The significant increase in built-up, agricul-tural, and unused land and the decrease in wetlands and forestsindicate deteriorated environmental conditions.

Second, with the systems dynamic model, the three groups ofdriving forces, including land management, population, andeconomic and social policy, effectively explained the LUCC inDaqing. The results of our accuracy assessment indicate that thesethree groups of driving forces explain the past population and LUCCreasonably well.

Finally, the simulation results indicate that under differentsocio-economic policies, future LUCC will vary significantly. Inparticular, under the current economic growth policy, the economyand population in Daqing will continue to grow. However, thisgrowth is associated with the degradation of natural land resourcesbecause forest and grassland areas will decrease sharply. Thisdevelopment mode is associated with a high cost and low benefitand is unsustainable. Similarly, rapid economic growth policyresults in short term rapid economic development at the cost ofnatural resources. The exhaustion of resources will result in seriousproblems, such as conflicts between the population and agricul-tural land, and may cause an agricultural crisis. This expansionrepresents a development mode with high commitment and highthroughput, but also with high risk. Finally, economic developmentunder the sustainable development mode is relatively slow,together with a low population growth rate and relatively stableland uses. This sustainable development model could be used toalleviate the conflicts between sustainable land use and societalneeds during the process of economic development. Thus, thesustainable development mode provides an important basis forland use planning to realize the objectives of economic reformingand sustainable development.

Acknowledgements

This research is supported by the National Natural ScienceFoundation of China (Grant No. 40771195 and 40871082), theExcellent Youth Foundation of Heilongjiang Province (China) (GrantNo. JC200714), and the Scientific and Technological Projects, Hei-longjiang (China) Department of Education (Grant No. 11531252,and 11551133). We would also like to thank the anonymousreviewers for their constructive comments on earlier versions ofthe manuscript.

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