Modeling the spatio-temporal changes in land uses and its impacts on ecosystem services in Northeast China over 2000-2050

doi:10.1007/s11442-018-1532-7

Abstract

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.

Key words: Northeast China, land use, spatio-temporal change, scenario, ecosystem service

Tian XIA, Wenbin WU, Qingbo ZHOU, Wenxia TAN, H. VERBURG Peter, Peng YANG, Liming YE. 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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References 41

[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, Maranhão state, Brazil.Journal of Environmental Management, 115: 270-277. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. doi: 10.1007/s11434-009-0733-y>
[19]	Parry M, Arnell N, Hulme M et al., 1998. Adapting to the inevitable.Nature, 395: 741. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. doi: 10.1016/j.rse.2014.04.004

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
Supply services	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
Support services	Maintain biodiversity	1.02	4.51	1.87	3.43	3.69	0.40

	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

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
Socio-economic	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

		Forest into cropland	Cropland into forest	Grassland into cropland	Cropland into grassland	Water body into cropland	Cropland into water body	Cropland into built-up	Unused land into cropland	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