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Journal of Geographical Sciences    2018, Vol. 28 Issue (11) : 1611-1625     DOI: 10.1007/s11442-018-1532-7
Special Issue: Land system dynamics: Pattern and process |
Modeling the spatio-temporal changes in land uses and its impacts on ecosystem services in Northeast China over 2000-2050
XIA Tian1,2(),WU Wenbin1,*(),ZHOU Qingbo1,TAN Wenxia2,Peter H. VERBURG3,YANG Peng1,YE Liming4,5
1. Key Laboratory of Agri-informatics, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2. Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province / College of Urban & Environmental Science, Central China Normal University, Wuhan 430079, China
3. Institute for Environmental Studies, VU University Amsterdam, 1081 HV Amsterdam, The Nether-lands
4. CAAS-UGent Joint Laboratory of Global Change and Food Security / Institute of Agricultural Re-sources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
5. Department of Geology (WE13), Ghent University, 9000 Gent, Belgium
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Abstract  

Land use and its dynamics have attracted considerable scientific attention for their significant ecological and socioeconomic implications. Many studies have investigated the past changes in land use, but efforts exploring the potential changes in land use and implications under future scenarios are still lacking. Here we simulate the future land use changes and their impacts on ecosystem services in Northeast China (NEC) over the period of 2000-2050 using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model under the scenarios of ecological security (ESS), food security (FSS) and comprehensive development (CDS). The model was validated against remote sensing data in 2005. Overall, the accuracy of the CLUE-S model was evaluated at 82.5%. Obtained results show that future cropland changes mainly occur in the Songnen Plain and the Liaohe Plain, forest and grassland changes are concentrated in the southern Lesser Khingan Mountains and the western Changbai Mountains, while the Sanjiang Plain will witness major changes of the wetlands. Our results also show that even though CDS is defined based on the goals of the regional development plan, the ecological service value (ESV) under CDS is RMB 2656.18 billion in 2050. The ESV of CDS is lower compared with the other scenarios. Thus, CDS is not an optimum scenario for eco-environmental protection, especially for the wetlands, which should be given higher priority for future development. The issue of coordination is also critical in future development. The results can help to assist structural adjustments for agriculture and to guide policy interventions in NEC.
Keywords Northeast China      land use      spatio-temporal change      scenario      ecosystem service     
Fund:Agricultural Outstanding Talents Research Foundation of Ministry of Agriculture (MOA);Key Laboratory of Agri-Informatics Foundation of MOA No.2015001;Natural Science Foundation of Hubei Province No.2016CFB558;The Fundamental Research Funds for the Central Universities, No.CCNU15A05058
Corresponding Authors: WU Wenbin     E-mail: xiatian@mail.ccnu.edu.cn;wuwenbin@caas.cn
Issue Date: 21 December 2018
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XIA Tian
WU Wenbin
ZHOU Qingbo
TAN Wenxia
Peter H. VERBURG
YANG Peng
YE Liming
Cite this article:   
XIA Tian,WU Wenbin,ZHOU Qingbo, et al. Modeling the spatio-temporal changes in land uses and its impacts on ecosystem services in Northeast China over 2000-2050[J]. Journal of Geographical Sciences, 2018, 28(11): 1611-1625.
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http://www.geogsci.com/EN/10.1007/s11442-018-1532-7     OR     http://www.geogsci.com/EN/Y2018/V28/I11/1611
Figure 1  Map of Northeast China
Figure 2  CLUE-S model structure
Land use type Cropland Forest Grassland Water body Built-up Wetland Unused land
ELAS 0.6 0.8 0.5 0.9 0.9 0.7 0.4
Table 1  Parameter settings of ELAS
Level 1 Level 2 Cropland Forest Grassland Water body Wetland Unused land
Supply services Food production 1.00 0.33 0.43 0.53 0.36 0.02
Production of materials 0.39 2.98 0.36 0.35 0.24 0.04
Regulating services Gas release regulation 0.72 4.32 1.50 0.51 2.41 0.06
Climate regulation 0.97 4.07 1.56 2.06 13.55 0.13
Hydrological adjustment 0.77 4.09 1.52 18.77 13.44 0.07
Waste treatment 1.39 1.72 1.32 14.85 14.4 0.26
Cultural services Provide aesthetic landscape 0.17 2.08 0.87 4.44 4.69 0.24
Support services Improve soil integrity 1.47 4.02 2.24 0.41 1.99 0.17
Maintain biodiversity 1.02 4.51 1.87 3.43 3.69 0.40
Table 2  Equivalent value per unit area of ecosystem services in NEC
Cropland Forest Grassland Water body Built-up Wetland Unused land
ESS -0.30 0.25 0.30 0.25 0.25 0.30 -0.75
FSS 0.25 -0.05 -1.50 0.25 0.25 -0.85 -2.5
CDS 0.03 0.02 -0.30 0.01 0.25 -0.85 -0.75
Table 3  Annual change rate of the area of each land use type under different scenarios (%)
Data type Data name Data format Description and source
Land use Land use basic data Grid Land use resources in seven types of basic data type of land use, spatial resolution of 1 km
Biophysical DEM Grid Institute of Geographic Sciences, CAS, spatial resolution of 1 km
Years of average temperature distribution map Grid The national meteorological data compilation, spatial resolution of 500 m
Years of average rainfall distribution map Grid The national meteorological data compilation, spatial resolution of 1 km
Years of average≥0°C accumulated temperature distribution map Grid The national meteorological data compilation, spatial resolution of 500 m
Years of average of ≥10°C accumulated temperature distribution map Grid The national meteorological data compilation, spatial resolution of 500 m
Soil map Grid FAO soil classification
Level 1-3 traffic network distribution map Vector The national fundamental geographic information data
Level 1-3 river water distribution map Vector The national fundamental geographic information data
Town centers Vector The national fundamental geographic information data
Socio-economic Demographic distribution map Grid Institute of Geographic Sciences, CAS, 1 km grid population: people/km2
GDP distribution diagram Grid Institute of Geographic Sciences, CAS, 1 km grid GDP unit: million RMB/km2
Statistical Area of each land type for 2000-2010 Text China Statistical Yearbooks
Table 4  Type and source of data
Figure 3  Multiscale simulation test validation
Figure 4  The three scenarios of land use patterns of Northeast China in 2050
Figure 5  The three scenarios (ESS: Ecological security scenario; FSS: Food security scenario; CDS: Comprehensive development scenario) of cropland conversion in Northeast China in 2010-2050
Forest into cropland Cropland into forest Grassland into cropland Cropland into grassland Water body into cropland Cropland into water body Built-up into cropland Cropland into built-up Unused land into cropland Cropland into unused land Wetland into cropland Cropland into wetland
ESS 2010-2020 1005 956 452 481 141
2020-2030 9959 668 451 515 369
2030-2040 9147 1352 525 504 458
2040-2050 9044 1359 522 523 545
FSS 2010-2020 1449 4667 14 1280 612
2020-2030 1493 4363 444 688 1001
2030-2040 1626 3621 272 1511 973
2040-2050 1581 3750 161 1509 982
CDS 2010-2020 570 5 519
2020-2030 501 5 346 110
2030-2040 495 230 313
2040-2050 469 32 82 351
Table 5  Cropland and other land use conversion types in the three scenarios (km2)
Figure 6  Different scenarios of ESV in 2010-2050
[1] Barreto L, Schoorl J M, Kok K et al., 2013. Modelling potential landscape sediment delivery due to projected soybean expansion: A scenario study of the Balsas sub-basin, Cerrado, Maranh?o state, Brazil.Journal of Environmental Management, 115: 270-277.http://www.ncbi.nlm.nih.gov/pubmed/23276733
doi: 10.1016/j.jenvman.2012.11.017 pmid: 23276733
[2] Bonilla-Moheno M, Aide T M, Clark M, 2012. The influence of socioeconomic, environmental, and demographic factors on municipality-scale land-cover change in Mexico.Regional Environmental Change, 12(3): 543-557.http://link.springer.com/10.1007/s10113-011-0268-z
doi: 10.1007/s10113-011-0268-z
[3] Carpenter S R, Mooney H A, Agard J et al., 2009. Science for managing ecosystem services: Beyond the millennium ecosystem assessment.Proceedings of the National Academy of Sciences, 106(5): 1305-1312.http://www.pnas.org/cgi/doi/10.1073/pnas.0808772106
doi: 10.1073/pnas.0808772106
[4] Chen C, Qian C, Deng A et al., 2012. Progressive and active adaptations of cropping system to climate change in Northeast China.European Journal of Agronomy, 38: 94-103.https://linkinghub.elsevier.com/retrieve/pii/S1161030111000827
doi: 10.1016/j.eja.2011.07.003
[5] Chen L, Wang J, Fu B et al., 2001. Land-use change in a small catchment of northern Loess Plateau, China.Agriculture, Ecosystems & Environment, 86(2): 163-172.http://europepmc.org/abstract/AGR/IND23261775
doi: 10.1016/S0167-8809(00)00271-1
[6] Costanza R, 1989. Model goodness of fit: A multiple resolution procedure.Ecological Modelling, 47(3/4): 199-215.http://linkinghub.elsevier.com/retrieve/pii/030438008990001X
doi: 10.1016/0304-3800(89)90001-X
[7] Costanza R, D’Arge R, de Groot R et al., 1997. The value of the world’s ecosystem services and natural capital.Nature, 387(6630): 253-260.http://www.nature.com/doifinder/10.1038/387253a0
doi: 10.1038/387253a0
[8] Costanza R, de Groot R, Sutton P et al., 2014. Changes in the global value of ecosystem services.Global Environmental Change, 26: 152-158.https://linkinghub.elsevier.com/retrieve/pii/S0959378014000685
doi: 10.1016/j.gloenvcha.2014.04.002
[9] David B L A C, 2007. Global scale climate-crop yield relationships and the impacts of recent warming.Environmental Research Letters, 2(1): 14002.http://stacks.iop.org/1748-9326/2/i=1/a=014002?key=crossref.26752daa27a9c7dccd75abb5e87fd088
doi: 10.1088/1748-9326/2/1/014002
[10] Duan P, Qin L, Wang Y et al., 2016. Spatial pattern characteristics of water footprint for maize production in Northeast China.Journal of the Science of Food and Agriculture, 96(2): 561-568.http://doi.wiley.com/10.1002/jsfa.2016.96.issue-2
doi: 10.1002/jsfa.7124 pmid: 25654998
[11] Eitelberg D A, Vliet J, Verburg P H, 2015. A review of global potentially available cropland estimates and their consequences for model-based assessments.Global Change Biology, 21(3): 1236-1248.http://doi.wiley.com/10.1111/gcb.2015.21.issue-3
doi: 10.1111/gcb.12733 pmid: 25205590
[12] Fu B, Zhang L, Xu Z et al., 2015. Ecosystem services in changing land use.Journal of Soils and Sediments, 15(4): 833-843.http://link.springer.com/10.1007/s11368-015-1082-x
doi: 10.1007/s11368-015-1082-x
[13] Geoghegan J, Villar S C, Klepeis P et al., 2001. Modeling tropical deforestation in the southern Yucatán peninsular region: Comparing survey and satellite data.Agriculture, Ecosystems & Environment, 85(1-3): 25-46.http://europepmc.org/abstract/AGR/IND23223624
doi: 10.1016/S0167-8809(01)00201-8
[14] Jiang W, Chen Z, Lei X et al., 2016. Simulation of urban agglomeration ecosystem spatial distributions under different scenarios: A case study of the Changsha-Zhuzhou-Xiangtan urban agglomeration.Ecological Engineering, 88: 112-121.https://linkinghub.elsevier.com/retrieve/pii/S0925857415303281
doi: 10.1016/j.ecoleng.2015.12.014
[15] Lambin E F, Meyfroidt P, 2011. Global land use change, economic globalization, and the looming land scarcity.Proceedings of the National Academy of Sciences, 108(9): 3465-3472.http://www.pnas.org/lookup/doi/10.1073/pnas.1100480108
doi: 10.1073/pnas.1100480108
[16] Letourneau A, Verburg P H, Stehfest E, 2012. A land-use systems approach to represent land-use dynamics at continental and global scales.Environmental Modelling & Software, 33: 61-79.http://www.sciencedirect.com/science/article/pii/S136481521200014X
doi: 10.1016/j.envsoft.2012.01.007
[17] Li Z, Tang H, Yang P et al., 2012. Spatio-temporal responses of cropland phenophases to climate change in Northeast China.Journal of Geographical Sciences, 22(1): 29-45.http://link.springer.com/10.1007/s11442-012-0909-2
doi: 10.1007/s11442-012-0909-2
[18] Liu J, Deng X, 2010. Progress of the research methodologies on the temporal and spatial process of LUCC.Chinese Science Bulletin, 55(14): 1354-1362.http://link.springer.com/10.1007/s11434-009-0733-y
doi: 10.1007/s11434-009-0733-y>
[19] Parry M, Arnell N, Hulme M et al., 1998. Adapting to the inevitable.Nature, 395: 741.http://www.nature.com/articles/27316
doi: 10.1038/27316
[20] Roy P, Roy A, Joshi P et al., 2015. Development of decadal (1985-1995-2005) land use and land cover database for India.Remote Sensing, 7(3): 2401-2430.http://www.mdpi.com/2072-4292/7/3/2401
doi: 10.3390/rs70302401
[21] Serneels S, Lambin E F, 2001. Proximate causes of land-use change in Narok District, Kenya: A spatial statistical model.Agriculture, Ecosystems & Environment, 85(1-3): 65-81.http://www.sciencedirect.com/science/article/pii/S0167880901001888
doi: 10.1016/S0167-8809(01)00188-8
[22] Shearer A W, 2005. Approaching scenario-based studies: Three perceptions about the future and considerations for landscape planning.Environment and Planning B: Planning and Design, 32: 67-87.
[23] Song W, Deng X, Yuan Y et al., 2015. Impacts of land-use change on valued ecosystem service in rapidly urbanized North China Plain.Ecological Modelling, 318: 245-253.https://linkinghub.elsevier.com/retrieve/pii/S0304380015000484
doi: 10.1016/j.ecolmodel.2015.01.029
[24] Stürck J, Schulp C J E, Verburg P H, 2015. Spatio-temporal dynamics of regulating ecosystem services in Europe: The role of past and future land use change.Applied Geography, 63: 121-135.https://linkinghub.elsevier.com/retrieve/pii/S0143622815001538
doi: 10.1016/j.apgeog.2015.06.009
[25] Tian L, 2015. Land use dynamics driven by rural industrialization and land finance in the peri-urban areas of China: The examples of Jiangyin and Shunde.Land Use Policy, 45: 117-127.https://linkinghub.elsevier.com/retrieve/pii/S0264837715000095
doi: 10.1016/j.landusepol.2015.01.006
[26] van Vliet J, Bregt A K, Hagen-Zanker A, 2011. Revisiting kappa to account for change in the accuracy assessment of land-use change models.Ecological Modelling, 222(8): 1367-1375.https://linkinghub.elsevier.com/retrieve/pii/S0304380011000494
doi: 10.1016/j.ecolmodel.2011.01.017
[27] van Vliet J, Hagen-Zanker A, Hurkens J et al., 2013. A fuzzy set approach to assess the predictive accuracy of land use simulations.Ecological Modelling, 261/262: 32-42.https://linkinghub.elsevier.com/retrieve/pii/S0304380013001762
doi: 10.1016/j.ecolmodel.2013.03.019
[28] Verburg P, Berkel D, Doorn A et al., 2010. Trajectories of land use change in Europe: A model-based exploration of rural futures.Landscape Ecology, 25(2): 217-232.http://link.springer.com/10.1007/s10980-009-9347-7
doi: 10.1007/s10980-009-9347-7
[29] Verburg P H, Overmars K P, 2009. Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the dyna-CLUE model.Landscape Ecology, 24(9): 1167-1181.http://link.springer.com/10.1007/s10980-009-9355-7
doi: 10.1007/s10980-009-9355-7
[30] Verburg P H, Rounsevell M D A, Veldkamp A, 2006. Scenario-based studies of future land use in Europe.Agriculture, Ecosystems & Environment, 114(1): 1-6.http://europepmc.org/abstract/AGR/IND43843670
doi: 10.1016/j.agee.2005.11.023
[31] Verburg P H, Soepboer W, Veldkamp A et al., 2002. Modeling the spatial dynamics of regional land use: The CLUE-S model.Environmental Management, 30(3): 391-405.http://link.springer.com/10.1007/s00267-002-2630-x
doi: 10.1007/s00267-002-2630-x pmid: 12148073
[32] Wang M, Xiong Z, Yan X, 2015. Modeling the climatic effects of the land use/cover change in eastern China.Physics and Chemistry of the Earth, Parts A/B/C, 87/88: 97-107.http://www.sciencedirect.com/science/article/pii/S1474706515000820
doi: 10.1016/j.pce.2015.07.009
[33] Wang Z, Zhang B, Zhang S et al., 2006. Changes of land use and of ecosystem service values in Sanjiang Plain, Northeast China.Environmental Monitoring and Assessment, 112(1-3): 69-91.http://link.springer.com/10.1007/s10661-006-0312-5
doi: 10.1007/s10661-006-0312-5
[34] Xia T, Wu W, Zhou Q et al., 2014. Spatio-temporal changes in the rice planting area and their relationship to climate change in Northeast China: A model-based analysis.Journal of Integrative Agriculture, 13(7): 1575-1585.https://linkinghub.elsevier.com/retrieve/pii/S2095311914608029
doi: 10.1016/S2095-3119(14)60802-9
[35] Xia T, Wu W, Zhou Q et al., 2016. Model-based analysis of spatio-temporal changes in land use in Northeast China.Journal of Geographical Sciences, 26(2): 171-187.http://link.springer.com/10.1007/s11442-016-1261-8
doi: 10.1007/s11442-016-1261-8
[36] Xu G, Huang X, Zhong T et al., 2015. Assessment on the effect of city arable land protection under the implementation of China’s national general land use plan (2006-2020).Habitat International, 49: 466-473.https://linkinghub.elsevier.com/retrieve/pii/S0197397515001344
doi: 10.1016/j.habitatint.2015.06.017
[37] Ye L, Tang H, Zhu J et al., 2008. Spatial patterns and effects of soil organic carbon on grain productivity assessment in China.Soil Use and Management, 24(1): 80-91.http://www.blackwell-synergy.com/toc/sum/24/1
doi: 10.1111/j.1475-2743.2007.00136.x
[38] Ye L, Xiong W, Li Z et al., 2013. Climate change impact on China food security in 2050.Agronomy for Sustainable Development, 33(2): 363-374.http://link.springer.com/10.1007/s13593-012-0102-0
doi: 10.1007/s13593-012-0102-0
[39] Zhang F, Tiyip T, Feng Z D et al., 2015. Spatio-temporal patterns of land use/cover changes over the past 20?years in the middle reaches of the Tarim River, Xinjiang, China.Land Degradation & Development, 26(3): 284-299.
[40] Zhang X, Xiong Z, Zhang X et al., 2016. Using multi-model ensembles to improve the simulated effects of land use/cover change on temperature: A case study over Northeast China.Climate Dynamics, 46(3/4): 765-778.http://link.springer.com/10.1007/s00382-015-2611-4
doi: 10.1007/s00382-015-2611-4
[41] Zhang Z, Wang X, Zhao X et al., 2014. A 2010 update of national land use/cover database of China at 1:100000 scale using medium spatial resolution satellite images.Remote Sensing of Environment, 149(149): 142-154.https://linkinghub.elsevier.com/retrieve/pii/S0034425714001424
doi: 10.1016/j.rse.2014.04.004
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