Examining urban land-cover characteristics and ecological regulation during the construction of Xiong’an New District, Hebei Province, China

doi:10.1007/s11442-018-1462-4

Abstract

Abstract:

Development of Xiong'an New District (XND) is integral to the implementation of the Beijing-Tianjin-Hebei (BTH) Integration Initiative. It is intended to ease the non-capital functions of Beijing, optimize regional spatial patterns, and enhance ecosystem services and living environment in this urban agglomeration. Applying multi-stage remote sensing (RS) images, land use/cover change (LUCC) data, ecosystem services assessment data, and high-precision urban land-cover information, we reveal the regional land-cover characteristics of this new district as well as across the planned area of the entire BTH urban agglomeration. Corresponding ecological protection and management strategies are also proposed. Results indicated that built-up areas were rapidly expanding, leading to a continuous impervious surface at high density. Urban and impervious surface areas (ISAs) grew at rates 1.27 and 1.43 times higher than that in the 2000s, respectively, seriously affecting about 15% area of the sub-basins. Construction of XND mainly encompasses Xiongxian, Rongcheng, and Anxin counties, areas which predominantly comprise farmland, townships and rural settlements, water, and wetland ecosystems. The development and construction of XND should ease the non-capital functions of Beijing, as well as moderately control population and industrial growth. Thus, this development should be included within the national ‘sponge city’ construction pilot area in early planning stages, and reference should be made to international low-impact development modes in order to strengthen urban green infrastructural construction. Early stage planning based on the existing characteristics of the underlying surface should consider the construction of green ecological patches and ecological corridors between XND and the cities of Baoding, Beijing, and Tianjin. The proportion of impervious surfaces should not exceed 60%, while that of the core area should not exceed 70%. The development of XND needs to initiate the concept of ‘planning a city according to water resource amount’ and incorporate rainwater collection and recycling.

Key words: Xiong’an New District;, urban land use, urban impervious surface, Beijing-Tianjin-Hebei urban agglomeration, ecological protection strategies

Wenhui KUANG, Tianrong YANG, Fengqin YAN. Examining urban land-cover characteristics and ecological regulation during the construction of Xiong’an New District, Hebei Province, China[J].Journal of Geographical Sciences, 2018, 28(1): 109-123.

Figures/Tables 9

Table 1

Figure 1

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Figure 3

Table 2

Figure 4

Table 3

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Table 4

Potential eco-environmental impacts on the construction of XND and proposed control measures"

Expected impact and regulation index	Initial stage	Medium-term		Long-term
Expected impact and regulation index	Expected by 2020	Expected by 2025	Expected by 2030	Expected by 2050
Population size	Between half a million and one million people	Between one million and two million people	Between two million and five million people	Greater than five million people
Built-up area	Between 60 km² and 120 km²	Between 120 km² and 240 km²	Between 240 km²and 600 km²	Greater than 600 km²
Urban land use, industrial regulation, and control model	Ease Beijing city non-capital functions via moderate control		Strengthen the construction of the ecological zone in the fringe area, and strictly control continuous agglomerated sprawl growth
Impervious surface control in built-up areas	Control the ratio of ISAs to less than 60%		Control the ratio of ISAs to between 50% and 60%, and maintain the urban greening rate at a level higher than 40%
Land use	Predominantly include cultivated land and township rural residential areas. Build an ecological protection zone between urban areas and Baiyangdian Lake		Strengthen the control of green areas and an ecological corridor between the cities of Baoding, Beijing, and Tianjin to mitigate continuous development along the traffic axis
Urban heat island	Although the space occupied by urban heat island will expand, this can be controlled via urban ecological structures to a variation of 1oC		The space occupied by urban heat island will continue to expand, but via scientific planning this can be limited to variation between 1oC and 1.5oC
Potential ecological impacts and proposed control strategies	This region is low-lying and vulnerable to storm impacts. Construction, population, industry, and other urban agglomeration-related factors will reduce ecosystem water conservation and other service functions		Expansion of impervious surfaces in urban areas, coupled with the low-lying terrain, will increase the risk of floods and other disasters. It will therefore be necessary to consider ecological protection and the development of a corridor mosaic as part of eco-city planning and design
Potential environmental impacts and proposed control strategies	Low-lying flat, high-rise buildings will affect the diffusion capacity of the local atmosphere, leading to increases in haze and other pollution. In addition to being a source of pollution, impervious surfaces will enable other pollution sources and damage the freshwater quality of Baiyangdian Lake. It will be necessary to strictly develop systems to control industrial access as well as high standards to deal with waste		The impervious surface proportion within the sub-basin will rise to between 15% and 25%, will significantly affect the health of the river basin ecosystem, and will have a serious impact on the water quality of Baiyangdian Lake. It will be necessary to draw an itinerary for urban green development, and guide construction according to these guidelines

Table 4

References 44

[1]	Bierwagen B G, Theobald D T, Pyke C Ret al., 2010. National housing and impervious surface scenarios for integrated climate impact assessments.PNAS, 107(49): 20887-20892. doi: 10.1073/pnas.1002096107
[2]	Carpenter S, Walker B, Anderies J M, et al.2001. From metaphor to measurement: Resilience of what to what?Ecosystems, 4(8): 765-781. doi: 10.1007/s10021-001-0045-9
[3]	Chen J Q, Yan H M, Wang S Qet al., 2014. Estimation of gross primary productivity in Chinese terrestrial ecosystems by using VPM Model.Quaternary Sciences, 34(4): 732-742. doi: 10.3969/j.issn.1001-7410.2014.04.05
[4]	Elvidge C D, Tuttle B T, Sutton P Cet al., 2007. Global distribution and density of constructed impervious surfaces.Sensor, 7(9): 1962-1979. doi: 10.3390/s7091962 pmid: 3841857
[5]	Fang Chuanglin, 2014. Progress and the future direction of research into urban agglomeration in China.Acta Geographica Sinica, 69(8): 1130-1144. (in Chinese) doi: 10.11821/dlxb201408009
[6]	Fang Chuanglin, Zhou Chenghu, Gu Chaolinet al., 2016. Theoretical analysis of interactive coupled effects between urbanization and eco-environment in mega-urban agglomerations.Acta Geographica Sinica, 71(4): 531-550. (in Chinese) doi: 10.11821/dlxb201604001
[7]	Griffin D M, Grizzard T J, Randall C Wet al., 1980. Analysis of non-point pollution export from small catchments.Journal (Water Pollution Control Federation), 52(4): 780-790. doi: 10.2118/7561-PA
[8]	Grimm N B, Faeth S H, Golubiewski N Eet al., 2008. Global change and the ecology of cities.Science, 319(5864): 756-760. doi: 10.1126/science.1150195 pmid: 18258902
[9]	Gu Chaolin, 2011. Study on urban agglomeration: Progress and prospects. Geographical Research, 16(4): 82-88. (in Chinese) doi: 10.11820/dlkxjz.1997.04.012
[10]	Guo Rongchao, Miao Changhong, Xia Baolinet al., 2010. Research on the model of optimization and reorganization of eco-spatial structure in urban agglomeration region and its application: A case study of the urban agglomeration in Central Plains Region.Progress in Geography, 29(3): 363-369. (in Chinese)
[11]	Guo W, Lu D S, Wu Y Let al., 2015. Mapping impervious surface distribution with integration of SNNP VIIRS-DNB and MODIS NDVI Data.Remote Sensing, 7(9): 12459-12477. doi: 10.3390/rs70912459
[12]	Holling C S, 1973. Resilience and stability of ecological systems.Annual Review of Ecology and Systematics, 4(1): 1-23. doi: 10.1146/annurev.es.04.110173.000245
[13]	Holling C S, Gunderson L H, 2002.Panarchy:Understanding Transformations in Systems of Humans and Nature. Resilience and Adaptive Cycles. Washington: Island Press, 25-62.
[14]	Jha A, Lamond J, Proverbs Det al., 2012. Cities and flooding: A guide to integrated urban flood risk management for the 21st century.General Information, 52(5): 885-887. doi: 10.1111/jors.12006_6
[15]	Jones B, O’Neill B C, McDaniel Let al., 2015. Future population exposure to US heat extremes.Nature Climate Change, 5(7): 652-655. doi: 10.1038/nclimate2631
[16]	Jones H P, Hole D G, Zavaleta E S, 2012. Harnessing nature to help people adapt to climate change.Nature Climate Change, 2(7): 504-509. doi: 10.1038/nclimate1463
[17]	Kuang W H, Chen L J, Liu J Yet al., 2016a. Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis.Science China Earth Sciences, 59(9): 1720-1737. doi: 10.1007/s11430-016-5295-7
[18]	Kuang W H, Chi W F, Lu D Set al., 2014. A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces.Landscape and Urban Planning, 132: 121-135. doi: 10.1016/j.landurbplan.2014.08.015
[19]	Kuang W H, Dou Y Y, Zhang Cet al., 2015a. Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement.Journal of Geophysical Research: Atmospheres, 120(1): 113-130. doi: 10.1002/2014JD022249
[20]	Kuang Wenhui, Chi Wenfeng, Lu Dengsheng et al., 2015b. Remote Sensing Analysis and Ecological Control of Urban Surface Thermal Environment. Beijing: Science Press, 109. (in Chinese)
[21]	Kuang W H, Liu J Y, Dong J Wet al., 2016b. The rapid and massive urban and industrial land expansions in China between 1990 and 2010: A CLUD-based analysis of their trajectories, patterns, and drivers.Landscape and Urban Planning, 145: 21-33. doi: 10.1016/j.landurbplan.2015.10.001
[22]	Kuang Wenhui, Liu Jiyuan, Lu Dengsheng, 2011. Pattern of impervious surface change and its effect on water environment in the Beijing-Tianjin-Tangshan Metropolitan Area.Acta Geographica Sinica, 66(11): 1486-1496. (in Chinese) doi: 10.1631/jzus.A1010009
[23]	Kuang W H, Liu J Y, Zhang Z Xet al., 2013. Spatiotemporal dynamics of impervious surface areas across China during the early 21st century.Chinese Science Bulletin, 58(14): 1691-1701. doi: 10.1007/s11434-012-5568-2
[24]	Kuang W H, Yang T R, Liu A Let al., 2017. An ecocity model for regulating urban land cover structure and thermal environment: Taking Beijing as an example.Science China Earth Sciences, 60(6): 1098-1109. doi: 10.1007/s11430-016-9032-9
[25]	Leichenko R., 2011 Climate change and urban resilience.Current Opinion in Environmental Sustainability, 3(3): 164-168. doi: 10.1016/j.cosust.2010.12.014
[26]	Lelieveld J, Evans J S, Fnais Met al., 2015. The contribution of outdoor air pollution sources to premature mortality on a global scale.Nature, 525(7569): 367-371. doi: 10.1038/nature15371 pmid: 26381985
[27]	Liu J Y, Liu M L, Zhuang D Fet al., 2002. Study on spatial pattern of land-use change in China during 1995-2000.Science in China: Series D, 32(12): 1031-1040.
[28]	Liu J Y, Zhang Z X, Xu X Let al., 2010. Spatial patterns and driving forces of land use change in China during the early 21st century.Journal of Geographical Sciences, 20(4): 483-494. doi: 10.1007/s11442-010-0483-4
[29]	Liu Zhenhuan, Wang Yanglin, Peng Jian, 2012. Quantifying spatiotemporal patterns dynamics of impervious surface in Shenzhen.Geographical Research, 31(8): 1535-1545. (in Chinese) doi: 10.1007/s11783-011-0280-z
[30]	Lu Dadao, 2008. The Regional Developing strategy, tendency and the development of Jing-Jin-Ji.Social Science of Beijing, (6): 4-7. (in Chinese)
[31]	Lu D S, Tian H Q, Zhou G Met al., 2008. Regional mapping of human settlements in southeastern China with multi-sensor remotely sensed data.Remote Sensing of Environment, 112(9): 3668-3679. doi: 10.1016/j.rse.2008.05.009
[32]	Ma T, Zhou C H, Pei Tet al., 2014. Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities.Remote Sensing Letters, 5(2): 165-174. doi: 10.1080/2150704X.2014.890758
[33]	Meerow S, Newell J P, Stults M, 2016. Defining urban resilience: A review.Landscape and Urban Planning, 147: 38-49. doi: 10.1016/j.landurbplan.2015.11.011
[34]	Ouyang Z Y, Zheng H, Xiao Yet al., 2016. Improvements in ecosystem services from investments in natural capital.Science, 352(6292): 1455-1459. doi: 10.1126/science.aaf2295 pmid: 27313045
[35]	Peng J, Liu Y X, Shen Het al., 2016. Using impervious surfaces to detect urban expansion in Beijing of China in 2000s.Chinese Geographical Science, 26(2): 229-243. doi: 10.1007/s11769-016-0802-5
[36]	Peng J, Liu Y X, Wu J Set al., 2015. Linking ecosystem services and landscape patterns to assess urban ecosystem health: A case study in Shenzhen City, China.Landscape and Urban Planning, 143: 56-68. doi: 10.1016/j.landurbplan.2015.06.007
[37]	Pickett S T A, McGrath B, Cadenasso M Let al., 2014. Ecological resilience and resilient cities.Building Research & Information, 42(2): 143-157. doi: 10.1080/09613218.2014.850600
[38]	Schueler T K, 1987. Controlling Urban Runoff: A Practical Manual for Planning and Designing Urban BMPs. Washington: MWCOG, 1-10.
[39]	Schueler T K, 1994. The importance of imperviousness.Watershed Protection Techniques, 1: 100-101. doi: 10.9774/GLEAF.978-1-909493-38-4_2
[40]	Weng Q H, Lu D S, 2009. Landscape as a continuum: an examination of the urban landscape structures and dynamics of Indianapolis City, 1991-2000, by using satellite images.International Journal of Remote Sensing, 30(10): 2547-2577. doi: 10.1080/01431160802552777
[41]	Wu J G, Jenerette G D, Buyantuyev Aet al., 2011. Quantifying spatiotemporal patterns of urbanization: The case of the two fastest growing metropolitan regions in the United States.Ecological Complexity, 8(1): 1-8. doi: 10.1016/j.ecocom.2010.03.002
[42]	Xiao X M, Zhang Q Y, Braswell Bet al., 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data.Remote Sensing of Environment, 91(2): 256-270. doi: 10.1016/j.rse.2004.03.010
[43]	Yan Huimin, Liu Jiyuan, Cao Mingkui, 2007. Spatial pattern and topographic control of China’s agricultural productivity variability.Acta Geographica Sinica, 62(2): 171-180. (in Chinese) doi: 10.1590/S1806-37132010000400012
[44]	Zhang Zengxiang, Zhao Xiaoli, Wang Xiao et al., 2014. Remote Sensing Monitoring of Soil Erosion in China. Beijing: Planet Map Press, 23-30.

Name	Area (km²)	Population (10,000)			Economic status (billion yuan)
Name	Area (km²)	Total	Agricultural	Non-agricultural	GDP	Primary industry	Secondary industry	Tertiary industry
Rongcheng	314	27.31	13.73	13.58	5.775	0.971	3.415	1.389
Anxin	729	46.30	32.35	13.95	6.256	0.885	3.604	1.767
Xiongxian	514	39.41	26.12	13.29	9.075	1.013	6.356	1.706
Total	1557	113.02	72.20	40.82	21.106	2.869	13.375	4.862

Level	Proportion (%)	Sub-basin
Level	Proportion (%)	Number	Proportion (%)	Area (km²)	Area proportion (%)
No effect	0-1.0	36	39.13	47,049.57	21.85
Slight effect	1-5	16	17.39	71,577.27	33.25
Moderate effect	5-10	14	15.22	51,203.34	23.78
Serious effect	10-25	12	13.04	34,548.59	16.05
Severe effect	25-100	14	15.22	10,920.65	5.07
Total		92	100.00	215,299.4	100.00

Name	Cropland		Forest	Water and wetland	Urban and rural settlements
Name	Paddy	Dry farmland	Forest	Water and wetland	Built- up area	Township and rural settlements	Independent industrial and mining land
Rongcheng	0.00	224.72	1.59	5.70	9.22	67.29	5.22
Anxin	51.56	391.61	1.02	163.90	7.56	99.79	10.55
Xiongxian	0.00	399.90	6.51	5.48	17.58	79.74	6.42
Total	51.56	1016.23	9.12	175.08	34.37	246.82	22.20