Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to 2010

doi:10.1007/s11442-015-1149-z

Abstract

Abstract:

Urban land cover has major impacts on a city’s ecosystem services and the inherent quality of its urban residential environment. The spatio-temporal distribution of impervious surface area and green areas in Chinese cities has exhibited a significantly marked difference in comparison with USA cities. This study focused on monitoring and comparing the spatio-temporal dynamics, land cover patterns and characteristics of functional regions in six Chinese (n=3) and USA (n=3) cities. The study data were collated from Landsat TM/MSS imagery during the period 1978-2010. Results indicate that Chinese cities have developed compactly over the past three decades, while development has been notably dispersed among USA cities. Mean vegetation coverage in USA cities is approximately 2.2 times that found amongst Chinese urban agglomerations. Land use types within Chinese cities are significantly more complex, with a higher density of impervious surface area. Conversely, the central business district (CBD) and residential areas within USA cities were comprised of a lower proportion of impervious surface area and a higher proportion of green land. Results may be used to contribute to future urban planning and administration efforts in both China and the USA.

Key words: urban structure, impervious surface area, vegetation, China, USA, remote sensing

Wenfeng CHI, Wenjiao SHI, Wenhui KUANG. Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to 2010[J].Journal of Geographical Sciences, 2015, 25(1): 3-18.

Figures/Tables 8

Figure 1

Figure 2

Figure 3

Figure 4

Table 1

Changes in intra-urban land cover within Chinese and USA study cities from 1978 to 2010 (km2)"

Intra-urban land cover types	1978-1990		1990-2000		2000-2010		1978-2010
Intra-urban land cover types	ΔS	ΔS′	ΔS	ΔS′	ΔS	ΔS′	ΔS	ΔS′
China
ISA	309.68	25.81	589.85	58.98	1083.98	108.40	1983.51	61.98
Vegetation	-139.53	-11.63	-87.80	-8.78	-40.10	-4.01	-267.43	-8.36
Water	-3.90	-0.33	-4.63	-0.46	-0.55	-0.05	-9.08	-0.28
Others	-166.24	-13.85	-497.42	-49.74	-1043.34	-104.33	-1707.00	-53.34
USA
ISA	447.87	37.32	76.48	7.65	106.76	10.68	631.12	19.72
Vegetation	31.45	2.62	8.91	0.89	-72.34	-7.23	-31.98	-1.00
Water	-0.73	-0.06	-0.14	-0.01	-0.43	-0.04	-1.30	-0.04
Others	-478.60	-39.88	-85.26	-8.53	-33.99	-3.40	-597.84	-18.68
Beijing
ISA	169.17	14.10	236.97	23.70	293.11	29.31	699.24	21.85
Vegetation	-60.00	-5.00	-32.36	-3.24	-5.26	-0.53	-97.62	-3.05
Water	-3.47	-0.29	-3.19	-0.32	-0.46	-0.05	-7.11	-0.22
Others	-105.70	-8.81	-201.41	-20.14	-287.39	-28.74	-594.51	-18.58
Guangzhou
ISA	37.88	3.16	132.92	13.29	307.92	30.79	478.72	14.96
Vegetation	-15.19	-1.27	-8.90	-0.89	-5.13	-0.51	-29.22	-0.91
Water	-0.42	-0.03	-0.80	-0.08	-0.05	0.00	-1.27	-0.04
Others	-22.27	-1.86	-123.22	-12.32	-302.74	-30.27	-448.23	-14.01
Shanghai
ISA	102.63	8.55	219.96	22.00	482.96	48.30	805.55	25.17
Vegetation	-64.34	-5.36	-46.54	-4.65	-29.71	-2.97	-140.58	-4.39
Water	-0.02	0.00	-0.64	-0.06	-0.04	0.00	-0.70	-0.02
Others	-38.27	-3.19	-172.78	-17.28	-453.21	-45.32	-664.26	-20.76
Chicago
ISA	359.53	29.96	61.25	6.13	28.68	2.87	449.47	14.05
Vegetation	120.08	10.01	15.81	1.58	-20.11	-2.01	115.78	3.62
Water	-1.92	-0.16	-0.01	0.00	-0.13	-0.01	-2.06	-0.06
Others	-477.69	-39.81	-77.05	-7.71	-8.44	-0.84	-563.19	-17.60
Los Angeles
ISA	31.21	2.60	6.39	0.64	19.36	1.94	56.97	1.78
Vegetation	-21.35	-1.78	-9.36	-0.94	4.80	0.48	-25.91	-0.81
Water	1.28	0.11	-0.21	-0.02	-0.22	-0.02	0.85	0.03
Others	-11.14	-0.93	3.17	0.32	-23.94	-2.39	-31.90	-1.00
New York
ISA	57.13	4.76	8.84	0.88	58.72	5.87	124.69	3.90
Vegetation	-67.28	-5.61	2.45	0.25	-57.03	-5.70	-121.85	-3.81
Water	-0.08	-0.01	0.08	0.01	-0.08	-0.01	-0.08	0.00
Others	10.24	0.85	-11.38	-1.14	-1.61	-0.16	-2.75	-0.09

Table 1

Table 2

Table 3

Figure 5

References 33

1	Alberti M, 2010. Maintaining ecological integrity and sustaining ecosystem function in urban areas.Current Opinion in Environmental Sustainability, 2(3): 178-184.
2	Arnold C L, Gibbons C J, 1996. Impervious surface coverage: The emergence of a key environmental indicator.Journal of the American Planning Association, 62(2): 243-258.
3	Ashie Y, Ca VT, Asaeda T, 1999. Development of a numerical model for the evaluation of the urban thermal environment.Journal of Wind Engineering and Industrial Aerodynamics, 81: 181-196.
4	Byomkesh T, Nakagoshi N. DewanA M, 2012. Urbanization and green space dynamics in greater Dhaka, Bangladesh.Landscape and Ecological Engineering, 8(1): 45-58.
5	Cao X, Onishi A, Chen Jet al., 2010. Quantifying the cool island intensity of urban parks using ASTER and IKONOS data.Landscape and Urban Planning, 96: 224-231.
6	Goldbach A, Kuttler W, 2012. Quantification of turbulent heat fluxes for adaptation strategies within urban planning.International Journal of Climatology, 33(1): 143-159.
7	Grimm N B, Faeth S H, Golubiewski N Bet al., 2008.Global change and the ecology of cities.Science, 319(5864): 756-760.
8	Grimmond C, Blackett M, Best Met al., 2010. The international urban energy balance models comparison project: First results from phase 1.Journal of Applied Meteorology and Climatology, 49(6): 1268-1292.
9	Kuang W H, 2011. Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area.Journal of Geographical Sciences, 21(2): 317-330.
10	Kuang W H, 2012a. Evaluating impervious surface growth and its impacts on water environment in Beijing-Tianjin-Tangshan Metropolitan Area.Journal of Geographical Sciences, 22(3): 535-547.
11	Kuang W H, 2012b. Spatio-temporal patterns of intra-urban land use change in Beijing, China between 1984 and 2008.Chinese Geographical Science, 22(2): 210-220.
12	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.
13	Kuang W H, Zhang S W, Liu J Yet al., 2010. Methodology for classifying and detecting intra-urban land use change: A case study of Changchun city during the last 100 years.Journal of Remote Sensing, 14(2): 345-355. (in Chinese)
14	Laaidi K, Zeghnoun A, Dousset Bet al., 2012. The impact of heat islands on mortality in Paris during the August 2003 heat wave.Environment Health Perspectives, 120(2): 254-259.
15	Lu D, Moran E, Hetrick S, 2011. Detection of impervious surface change with multi-temporal Landsat images in an urban-rural frontier.ISPRS Journal of Photogramm. Remote Sensing, 66(3): 298-306.
16	McGranahan G, Balk D, Anderson B, 2007. The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones.Environment and Urbanization, 19(1): 17-37.
17	Millennium Ecosystem Assessment (MEA), 2003. Ecosystems and Human Wellbeing: A Framework for Assessment. Washington, DC: Island Press.
18	Nowak D J, Dwyer J F, 2007. Understanding the benefits and costs of urban forest ecosystems. In: Kuser J (ed.), Urban and Community Forestry in the Northeast. New York: Springer Science and Business Media, 25-46.
19	Nowak D J, Greenfield E J, 2012. Tree and impervious cover in the United States.Landscape and Urban Planning, 107(1): 21-30.
20	Oke T R, 1982. The energetic basis of the urban heat island.Quarterly Journal of the Royal Meteorological Society, 108(455): 1-24.
21	Potchter O, Cohen P, Bitanet al., 2006. Climatic behavior of various urban parks during hot and humid summer in the Mediterranean city of Tel Aviv, Israel.International Journal of Climatology, 26(12): 1695-1711.
22	Seto K C, Guneralp B, Hutyra L R, 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools.PNAS, 109(40): 16083-16088.
23	Sexton J O, Song X-P, Huang Cet al., 2013. Urban growth of the Washington, D.C.-Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover.Remote Sensing of Environment, 129: 42-53.
24	Steve M Racitia, Lucy R Hutyra, Adrien C Finzi, 2012. Depleted soil carbon and nitrogen pools beneath impervious surfaces.Environmental Pollution, 164: 248-251.
25	Tong X, Lin C, Lu Wet al., 2005. Environmental impacts of surface mining on mined lands, affected streams and agricultural lands in the Dabaoshan Mine region, southern china.Journal of Land Degradation & Development, 16(5): 463-474.
26	UNDESA (United Nations Department of Economic and Social Affairs), 2012. Population Ageing and Development.
27	Van De Voorde T, Jacquet W, Canters F, 2011. Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data.Landscape and Urban Planning, 102(3): 143-155.
28	Wang X X, Zhu Q J, Chen S Het al., 2007. Analysis of water, heat and CO2 fluxes on urban green space.Acta Ecologica Sinica, 27(8): 3232-3239. (in Chinese)
29	Wolf K M, 2003. Public response to the urban forest in inner-city business districts.Journal of Arboriculture, 29(3): 117-126.
30	Ying T Y, Li M, Fan W Yet al., 2010. Analysis of urban forests and heat island effect based on GIS.Journal of Northeast Forestry University, 38(8): 63-67. (in Chinese)
31	Yuan F, Bauer M E, 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery.Remote Sensing of Environment, 106(3): 375-386.
32	Yue W Z, Xu J H, Xu L H, 2006. An analysis on eco-environment effect of urban land use based on remote sensing images.Acta Ecologica Sinica, 26(5): 1450-1460. (in Chinese)
33	Zhang R H, 2009. Quantity Infrared Remote Sensing Model and the Ground Experiment Base. Beijing: Science Press. (in Chinese)

Function areas	City	Central coordinate (LAT/LON, degree)	District
Business	Beijing	116.462/39.909	China World Trade Center Area
	Shanghai	121.438/31.195	Xujiahui region
	Guangzhou	113.321/23.120	Zhujiang New City
	New York	-73.984/40.746	Manhattan
	Chicago	-87.636/41.879	Sears Tower
	Los Angeles	-118.149/34.147	Old Town Pasadena
Residential	Beijing	116.328/40.061	Huilongguan
	Shanghai	121.379/31.112	Minhang District
	Guangzhou	113.322/22.968	Qifuxin Country
	Los Angeles	-118.106/34.094	San Gabriel
	New York	-73.951/40.626	Centralized living area
	Chicago	-87.754/41.930	Greenwich Village
Industrial	Beijing	116.461/39.810	Yizhuang Star Light Industrial Park
	Shanghai	121.620/31.262	Jinlingjinqiao Industrial Park
	Guangzhou	113.522/23.137	Luogangyunbu industrial zone
	Chicago	-87.630/41.884	Chicago Loop Area
	New York	-73.970/40.697	Brooklyn
	Los Angeles	-118.165/34.045	Seattle South Industrial Zone

[1]	HUANG Huiping, CHEN Wei, ZHANG Yuan, QIAO Lin, DU Yunyan. Analysis of ecological quality in Lhasa Metropolitan Area during 1990-2017 based on remote sensing and Google Earth Engine platform [J]. Journal of Geographical Sciences, 2021, 31(2): 265-280.
[2]	WEI Wei, GUO Zecheng, SHI Peiji, ZHOU Liang, WANG Xufeng, LI Zhenya, PANG Sufei, XIE Binbin. Spatiotemporal changes of land desertification sensitivity in northwest China from 2000 to 2017 [J]. Journal of Geographical Sciences, 2021, 31(1): 46-68.
[3]	MA Bin, ZHANG Bo, JIA Lige. Spatio-temporal variation in China’s climatic seasons from 1951 to 2017 [J]. Journal of Geographical Sciences, 2020, 30(9): 1387-1400.
[4]	LIU Xiaojing, LIU Dianfeng, ZHAO Hongzhuo, HE Jianhua, LIU Yaolin. Exploring the spatio-temporal impacts of farmland reforestation on ecological connectivity using circuit theory: A case study in the agro-pastoral ecotone of North China [J]. Journal of Geographical Sciences, 2020, 30(9): 1419-1435.
[5]	WU Li, SUN Xiaoling, SUN Wei, ZHU Cheng, ZHU Tongxin, LU Shuguang, ZHOU Hui, GUO Qingchun, GUAN Houchun, XIE Wei, KE Rui, LIN Guiping. Evolution of Neolithic site distribution (9.0-4.0 ka BP) in Anhui, East China [J]. Journal of Geographical Sciences, 2020, 30(9): 1451-1466.
[6]	XU Chenchen, LIAO Xiaohan, YE Huping, YUE Huanyin. Iterative construction of low-altitude UAV air route network in urban areas: Case planning and assessment [J]. Journal of Geographical Sciences, 2020, 30(9): 1534-1552.
[7]	YE Chao, LI Simeng, ZHANG Zhao, ZHU Xiaodan. A comparison and case analysis between domestic and overseas industrial parks of China since the Belt and Road Initiative [J]. Journal of Geographical Sciences, 2020, 30(8): 1266-1282.
[8]	WANG Xueqin, LIU Shenghe, QI Wei. Mega-towns in China: Their spatial distribution features and growth mechanisms [J]. Journal of Geographical Sciences, 2020, 30(7): 1060-1082.
[9]	YANG Fan, HE Fanneng, LI Meijiao, LI Shicheng. Evaluating the reliability of global historical land use scenarios for forest data in China [J]. Journal of Geographical Sciences, 2020, 30(7): 1083-1094.
[10]	LIU Ruiqing, XU Hao, LI Jialin, PU Ruiliang, SUN Chao, CAO Luodan, JIANG Yimei, TIAN Peng, WANG Lijia, GONG Hongbo. Ecosystem service valuation of bays in East China Sea and its response to sea reclamation activities [J]. Journal of Geographical Sciences, 2020, 30(7): 1095-1116.
[11]	ZHAO Guining, ZHANG Zhengyong, LIU Lin, LI Zhongqin, WANG Puyu, XU Liping. Simulation and construction of the glacier mass balance in the Manas River Basin, Tianshan, China from 2000 to 2016 [J]. Journal of Geographical Sciences, 2020, 30(6): 988-1004.
[12]	FANG Chuanglin, WANG Zhenbo, LIU Haimeng. Beautiful China Initiative: Human-nature harmony theory, evaluation index system and application [J]. Journal of Geographical Sciences, 2020, 30(5): 691-704.
[13]	CHEN Mingxing, LIANG Longwu, WANG Zhenbo, ZHANG Wenzhong, YU Jianhui, LIANG Yi. Geographical thoughts on the relationship between ‘Beautiful China’ and land spatial planning [J]. Journal of Geographical Sciences, 2020, 30(5): 705-723.
[14]	TANG Zhipeng, MEI Ziao, LIU Weidong, XIA Yan. Identification of the key factors affecting Chinese carbon intensity and their historical trends using random forest algorithm [J]. Journal of Geographical Sciences, 2020, 30(5): 743-756.
[15]	WANG Shaojian, GAO Shuang, HUANG Yongyuan, SHI Chenyi. Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends [J]. Journal of Geographical Sciences, 2020, 30(5): 757-774.