近20年京津唐地区不透水面变化的遥感监测

doi:10.12082/dqxxkx.2018.170618

摘要/Abstract

摘要：

不透水面是衡量城市化程度的重要指标之一,对京津唐城市群的不透水面进行深入研究,可以量化城市群扩张过程及其影响,对该区域多城市协调发展及规划布局具有重要意义。本文结合高分辨遥感影像、生长季及落叶季的Landsat TM遥感影像和夜间灯光数据等,采用分类和回归树（Classification and rRegression Tree, CART）算法,构建了适于京津唐地区不透水面盖度提取的技术方案,获取了京津唐地区1995-2016年共5期地表不透水面盖度专题信息,并分析了地表不透水面的时空演变规律,结论为：① 适于京津唐地区不透水面盖度提取的CART算法的最佳输入变量组合为：生长季和落叶季的Landsat TM图像以及对应的夜间灯光数据;其次为生长季Landsat TM遥感图像和夜间灯光数据组合方案。利用该组合方案,ISP估算输出结果的交叉验证精度R值可以达到约0.85,可以满足地表不透水面纵向对比分析的需要。② 从地表不透水面总面积数量值来看,1995-2016年京津唐主体城市区域整体上呈增长趋势,其中2011-2016年地表不透水面积增加愈加明显;③ 从地表不透水面盖度值的高低来看,1995-2016年京津唐中、高盖度不透水面的占比都是在不断增长的,低盖度不透水面占比存在少量下降现象,且京、津、唐3城市的主体城区各阶段变化差异较大,反映出了各城市扩张具有各自不同的时空演变特征。

关键词: 京津唐城市群, 不透水面盖度, 分类回归树, 遥感监测, 城市扩张

Abstract:

Impervious surface refers to the surface unable to allow water to percolate through, such as pavements that are covered by impenetrable materials and rooftops. Increased impervious surface area is a consequence of urbanization. Impervious surface percent (ISP) is an indicator to quantify the urbanization level. Therefore, accurate mapping and estimation of ISP in Beijing-Tianjin-Tangshan urban agglomeration are significant for multi-city coordinated development and urban layout. Based on classification and regression tree (CART) algorithm, a technical scheme of extracting ISP which is suitable for Beijing-Tianjin-Tangshan urban agglomeration was constructed in this paper. High-resolution remote sensing data (i.e. QuickBird images), medium-resolution remote sensing data (i.e. Landsat TM images in leaf-on and leaf-off seasons), and nighttime light data were used as basic data in this scheme. Five-year ISP results from 1995 to 2016 were estimated to analyze the spatial-temporal evolution patterns of ISP using this scheme. The main conclusions are as follows: (1) The optimal input variables are the Landsat TM images in leaf-on and leaf-off seasons and the corresponding nighttime light data. Since the number of Landsat TM images in leaf-off season is less in line with the quality requirements, the alternative choice is to use the Landsat TM images in leaf-on season and the corresponding nighttime light data as the input variables. After the accuracy verification, the correlation coefficient (R) is about 0.85, which can meet the need of the comparison of ISP results between different years. (2) During 1995 to 2016, the total impervious surface area increased gradually in Beijing-Tianjin-Tangshan urban agglomeration. Within the period, the most dramatic growth was between the year 2011 and 2016. (3) ISP results were divided into areas with high-, medium- and low-density impervious cover. During 1995 to 2016, the high-density and medium-density impervious cover increased gradually in Beijing-Tianjin-Tangshan urban agglomeration, while the low-density impervious cover decreased slightly. The changes of ISP results in each stage were significantly different among cities of Beijing, Tianjin and Tangshan. It shows that the spatial-temporal evolution patterns are different in the process of urban expansion of each city.

Key words: Beijing-Tianjin-Tangshan urban agglomeration, impervious surface percent, classification and regression tree (CART) algorithm, remote sensing monitoring, urban expansion

向超, 朱翔, 胡德勇, 乔琨, 陈姗姗. 近20年京津唐地区不透水面变化的遥感监测[J]. 地球信息科学学报, 2018, 20(5): 684-693.DOI:10.12082/dqxxkx.2018.170618

XIANG Chao,ZHU Xiang,HU Deyong,QIAO Kun,CHEN Shanshan. Monitoring of the Impervious Surface with Multi-resource Remote Sensing Images in Beijing-Tianjin-Tangshan Urban Agglomeration in the Past Two Decades[J]. Journal of Geo-information Science, 2018, 20(5): 684-693.DOI:10.12082/dqxxkx.2018.170618

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参考文献 28

[1]	Arnold Jr C L,Gibbons C J. Impervious surface coverage: The emergence of a key environmental indicator[J]. Journal of the American Planning Association, 1996,62(2):243-258. doi: 10.1080/01944369608975688
[2]	Xiao R B, Ouyang Z Y, Cai Y N.Urban landscape pattern study based on sub-pixel estimation of impervious surface[J]. Acta Ecologica Sinica, 2007,27:3189-3197.
[3]	Li J, Song C, Cao L, et al.Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China[J]. Remote Sensing of Environment, 2011,115(12):3249-3263. doi: 10.1016/j.rse.2011.07.008
[4]	Zhang L, Weng Q.Annual dynamics of impervious surface in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2016,113(3):86-96. doi: 10.1016/j.isprsjprs.2016.01.003
[5]	Weng Q.Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends[J]. Remote Sensing of Environment, 2012,117(2):34-49. doi: 10.1016/j.rse.2011.02.030
[6]	Yang X.Estimating landscape imperviousness index from satellite imagery[J]. IEEE Geoscience & Remote Sensing Letters, 2006,3(1):6-9. doi: 10.1109/LGRS.2005.853929
[7]	Jin H, Mountrakis G.Integration of urban growth modelling products with image-based urban change analysis[J]. International Journal of Remote Sensing, 2013,34(15):5468-5486. doi: 10.1080/01431161.2013.791760
[8]	SanbumL,Lathrop R G. Subpixel Analysis of Landsat ETM/sup+/Using Self-organizing Map(SOM) Neural Networks for Urban Land Cover Characterization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006,44(6):1642-1654. doi: 10.1109/TGRS.2006.869984
[9]	曹丽琴,李平湘,张良培,等. Fuzzy ARTMAP算法在城市不透水面估算中的应用研究[J].武汉大学学报·信息科学版,2012,37(10):1236-1239.
	[ Cao L Q, Li P X, Zhang L P, et al.Estimating impervious surfaces using the fuzzy artmap[J]. Geomatics and Information Science of Wuhan University, 2012,37(10):1236-1239. ]
[10]	Patel N, Mukherjee R.Extraction of impervious features from spectral indices using artificial neural network[J]. Arabian Journal of Geosciences, 2014,8(6):3729-3741. doi: 10.1007/s12517-014-1492-x
[11]	Yang L, Huang C, Homer C G, et al.An approach for mapping large-area impervious surfaces: Synergistic use of Landsat 7 ETM+ and high spatial resolution imagery[J]. Canadian Journal of Remote Sensing, 2003,29(2):230-240. doi: 10.5589/m02-098
[12]	Xian G, Crane M.Assessments of urban growth in the tampa bay watershed using remote sensing data[J]. Remote Sensing of Environment, 2005,97(22):203-215. doi: 10.1016/j.rse.2005.04.017
[13]	Wu C, Murray A T.Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment, 2003,84(23):493-505. doi: 10.1016/S0034-4257(02)00136-0
[14]	周纪,陈云浩,张锦水,等.北京城市不透水层覆盖度遥感估算[J].国土资源遥感,2007,19(3):13-17. doi: 10.3969/j.issn.1001-070X.2007.03.003
	[ Zhou J, Chen Y H, Zhang J S, et al.Estimating impervious surfaces using the fuzzy artmap[J]. Remote Sensing for Land and Resources, 2007,19(3):13-17. ] doi: 10.3969/j.issn.1001-070X.2007.03.003
[15]	Hao W, Wu B F, Li X S.Extraction of impervious surface in Hai Basin using remote sensing[J]. Journal of Remote Sensing, 2011,15(2):388-400. doi: 10.3724/SP.J.1011.2011.00403
[16]	Breiman L, Friedman J, Olshen R.Classification and Regression Tree[M]. New York: Chapman and Hall, 1984.
[17]	Friedl M A, Brodley C E, Strahler A H.Maximizing land cover classification accuracies produced by decision trees at continental to global scales[J]. IEEE Trans on Geoscience and Remote Sensing, 1999,37(2):969-977. doi: 10.1109/36.752215
[18]	Lawrence R, Bunn A, Powell S.Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis[J]. Remote Sensing of Environment, 2004,90(3):331-336. doi: 10.1016/j.rse.2004.01.007
[19]	Homer C, Dewitz J, Yang L, et al.Completion of the 2011 national land cover database for the conterminous united states representing a decade of land cover change information[J]. Photogrammetric Engineering & Remote Sensing, 2015,81(5):346-354. doi: 10.1016/S0099-1112(15)30100-2
[20]	Xian G, Homer C.Updating the 2001 national land cover database impervious surface products to 2006 using landsat imagery change detection methods[J]. Remote Sensing of Environment, 2010,114(8):1676-1686. doi: 10.1016/j.rse.2010.02.018
[21]	Xian G, Homer C, Fry J.Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods.[J]. Remote Sensing of Environment, 2009,113(6):1133-1147. doi: 10.1016/j.rse.2009.02.004
[22]	张佳华. 城市热环境遥感[M].北京:气象出版社,2010.
	[ Zhang J H.Urban thermal environment remote sensing[M]. Beijing: China Meteorological Press, 2010. ]
[23]	Huang C,Townshend J R G. A stepwise regression tree for nonlinear approximation: Application to estimating subpixel land cover[J]. International Journal of Remote Sensing, 2003,24(1):75-90. doi: 10.1080/01431160305001
[24]	Liu Y H, Niu Z, Wang C Y.Research and application of the decision tree classification using MODIS data[J]. Journal of Remote Sensing, 2005,9(4):405-411.
[25]	Liu Y H, Niu Z, Wang C Y.Research and application of the decision tree classification using MODIS data[J]. Journal of Remote Sensing, 2005,9(4):405-411.
[26]	Lu D, Hetrick S, Moran E, et al.Detection of urban expansion in an urban-rural landscape with multitemporalQuickBird images[J]. Journal of Applied Remote Sensing, 2010,4(1):201-210.
[27]	Li X, Gong P, Liang L.A 30-year (1984-2013) record of annual urban dynamics of Beijing City derived from Landsat data[J]. Remote Sensing of Environment, 2015,166(1):78-90. doi: 10.1016/j.rse.2015.06.007
[28]	Wang J, Li C, Hu L, et al.Seasonal land cover dynamics in beijing derived from Landsat 8 data using a spatio-temporal contextual approach[J]. Remote Sensing, 2015,7(1):865-881. doi: 10.3390/rs70100865

数据集		影像获取日期
		年份	行列号（列/行）
		年份	122/32	122/33	123/32	123/33
遥感数据	Landsat 5 TM/ Landsat 7 ETM+(*)	1995	1995-04-02	1995-04-18	1995-09-16	1995-09-16
		2001	2001-09-17	2001-09-01	2001-05-19(*)	2001-05-27(*)
		2001	2001-09-17	2001-09-01	2001-08-31	2001-08-31
		2005	2005-08-19	2005-08-19	2005-05-06	2005-05-06
		2005	2005-08-19	2005-08-19	2005-11-14	2005-11-14
		2011	2010-04-27	2010-04-27	2011-06-08	2011-06-08
	Landsat 8 OLI	2015	2015-03-24	2015-03-24	2014-09-04	2014-09-04
	Landsat 8 OLI	2015	2016-05-13	2016-05-13	2015-02-11	2015-02-11
	QuickBird	2005年
	DMSP/OLS	1 km空间分辨率的夜间灯光数据（1995-2011年）
	Suomi NPP/VIIRS	500 m空间分辨率的夜间灯光数据（2016年）
地形数据	ASTER GDEM （30 m空间分辨率）
其他	县区级的行政区划数据

方案	1	2	3	4	5	6	7	8	9	10	11	12	13	14
onb1-7	√	√	√	√	√
offb1-7	√	√	√	√		√
onb145							√	√
offb145							√
onb1456									√	√	√	√	√	√
offb1456									√		√		√
onNDVI											√	√
offNDVI											√
onTC		√	√
offTC		√	√
light		√	√	√	√	√	√	√			√	√	√	√
DEM		√
slope		√

精度评价指标	1	2	3	4	5	6	7	8	9	10	11	12	13	14
AE/%	13.5	13.6	12.7	12.8	13.6	15.2	13.4	14.2	13.4	14.6	12.9	13.7	13.2	14
RE/%	0.41	0.42	0.39	0.39	0.41	0.46	0.41	0.43	0.41	0.45	0.4	0.42	0.4	0.43
R	0.84	0.84	0.86	0.86	0.84	0.81	0.85	0.83	0.85	0.81	0.86	0.84	0.85	0.83

精度评价指标	1995年	2001年	2005年	2011年	2016年
AE/%	9.7	8.9	12.8	8.6	14.9
RE	0.41	0.36	0.39	0.44	0.43
R	0.75	0.8	0.86	0.76	0.76

	1995年	2001年	2005年	2011年	2016年
均值/%	49.72	50.47	53.87	52.03	57.17
标准差/%	31.98	30.47	29.73	27.84	27.86