基于Landsat影像的环胶州湾不透水面格局演变过程

doi:10.18402/resci.2018.11.12

摘要/Abstract

摘要：

不透水面是影响城市生态环境的关键因子,及时、精确地掌握不透水面的动态变化对城市的发展规划具有重要指导意义。本文以环胶州湾为例,基于1990年、1995年、2000年、2005年、2011年Landsat TM影像和2016年Landsat OLI影像,采用线性光谱混合分析和决策树相结合的方法分析了环胶州湾不透水面格局演变过程。结果表明：①本文提取的1990—2016年环胶州湾不透水面丰度与验证数据的决定系数R²均在0.70以上,均方根误差RMSE均在16.70%以下。②环胶州湾不透水面面积由1990年的105.17km²增长到2016年的620.02km²,总的扩展方向为由南向北、向东和向西移动。③1990年城阳区成为辅助市区发展的重心,现由城阳区逐渐向黄岛区、崂山区、即墨区和胶州市发展。

关键词: 发展重心, 不透水面, Landsat, 时空分布, 环胶州湾

Abstract:

Impervious surface areas (ISA) are key factor of affecting urban ecological environments. Information on ISA distribution and dynamics was useful for the planning and development of cities. This study explored a hybrid method consisting of linear spectral mixture analysis (LSMA) on the basis of vegetation-high albedo-low albedo-soil model (V-H-L-S), and regression tree (RT) for estimation of ISA in the Jiaozhou Bay. Medium-resolution remote sensing data (Landsat TM images in 1990, 1995, 2000, 2005, 2011, and Landsat OLI images in 2016) were used as basic data. Then the spatial and temporal distributions of ISA from 1990 to 2016 were identified in the Jiaozhou Bay based on the estimation of ISA. In addition, the regional development center of gravity was located by the extended index and buffer zone. The results demonstrated that the coefficients of determination (R²) of ISA in 1990, 1995, 2000, 2005, 2011, and 2016 were greater than 0.70 and their root mean square error (RMSE) were less than 16.70%. It is noted that the hybrid method consisting of LSMA and RT for estimation of ISA is feasible. The ISA increased from 105.17 km² in 1990 to 620.02 km² in 2016 in the Jiaozhou Bay. The expansion trend was from south to north, east to west. The area of ISA was increasing, but the increasing rate of ISA did show a little change. From 1990 to 1995, the expansion intensity of ISA was the largest, which was much larger than other periods. Downtown has always been the center of urban development, then Chengyang has become the focus of auxiliary urban since 1990. However, the trend was from Chengyang to Laoshan in 1993, Jiaozhou and Jimo in 2005, and Huangdao in 2011. This study will provide scientific foundation for the development and construction of sponge city in the Jiaozhou Bay.

Key words: development center, impervious surface areas, Landsat, spatio-temporal distribution, the Jiaozhou Bay

吴溪, 郭斌, 陈忠升, 史文娇. 基于Landsat影像的环胶州湾不透水面格局演变过程[J]. 资源科学, 2018, 40(11): 2260-2269.

Xi WU, Bin GUO, Zhongsheng CHEN, Wenjiao SHI. Spatio-temporal dynamic characterization of impervious surface in the Jiaozhou Bay based on Landsat imagery[J]. Resources Science, 2018, 40(11): 2260-2269.

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参考文献 41

[1]	Slonecker E T, Jennings D B, Garofalo D.Remote sensing of impervious surfaces: A review[J]. Remote Sensing Reviews, 2001, 20(3): 227-255.
[2]	任金华, 吴绍华, 周生路, 等. 城市不透水面遥感研究进展[J]. 国土资源遥感, 2012, (4): 8-15.
	[Ren J H, Wu S H, Zhou S L, et al. Advances in remote sensing research on urban impervious surface[J]. Remote Sensing for Land & Resources, 2012, (4): 8-15.]
[3]	Weng Q H.Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends[J]. Remote Sensing of Environment, 2012, 117(2): 34-49.
[4]	王静, 苏根成, 匡文慧, 等. 特大城市不透水地表时空格局分析-以北京市为例[J]. 测绘通报, 2014, (4): 90-94.
	[Wang J, Su G C, Kuang W H, et al. Spatial and temporal patterns analysis of impervious surface in megacity: a case study of Beijing[J]. Bulletin of Surveying and Mapping, 2014, (4): 90-94.]
[5]	Haustein M D.The Urban-Rural Environment: Effects of Impervious Surface Land Cover on Lake Ecosystems[D]. Minnesota: The University of Minnesota, 2010.
[6]	Chithra S V, Nair M H, Amarnath A, et al. Impacts of impervious surfaces on the environment[J]. International Journal of Engineering Science Invention, 2015, 4(5): 2319-6726.
[7]	Zhang L, Weng Q H.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 and Remote Sensing, 2016, 113(3): 86-96.
[8]	徐涵秋, 王美雅. 地表不透水面信息遥感的主要方法分析[J]. 遥感学报, 2016, 20(5): 1270-1289.
	[Xu H Q, Wang M Y.Remote sensing-based retrieval of ground impervious surfaces[J]. Journal of Remote Sensing, 2016, 20(5): 1270-1289.]
[9]	Li L W, Lu D S, Kuang W H.Examining urban impervious surface distribution and its dynamic change in Hangzhou Metropolis[J]. Remote Sensing, 2016, 8(3): 265.
[10]	王俊松, 杨逢乐, 贺彬, 等. 利用QuickBird 影像提取城市不透水率的研究[J]. 遥感信息, 2008, 23(3): 69-73.
	[Wang J S, Yang F L, He B, et al. Study on reducing shadow impact in the extraction impervious surface coverage of urban based on Quick Bird image[J]. Remote Sensing Information, 2008, (3): 69-73.]
[11]	Zhang Y S, Odeh I O A, Han C F. Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis[J]. International Journal of Applied Earth Observation & Geoinformation, 2009, 11(4): 256-264.
[12]	胡实, 熊黑钢, 朱忠鹏. 北京市各区县不透水面动态变化特征研究[J]. 安徽农业科学, 2015, 43(7): 237-240.
	[Hu S, Xiong H G, Zhu Z P.Dynamic characteristics of the impervious surface changing in different districts in Beijing[J]. Journal of Anhui Agricultural Sciences, 2015, 43(7): 237-240.]
[13]	李德仁, 罗晖, 邵振峰. 遥感技术在不透水层提取中的应用与展望[J]. 武汉大学学报·信息科学版, 2016, 41(5): 569-703.
	[Li D R, Luo H, Shao Z F.Review of impervious surface mapping using remote sensing technology and its application[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 569-703.]
[14]	穆亚超, 颉耀文, 张玲玲, 等. 一种新的增强型不透水面指数[J]. 测绘科学, 2018, 43(2): 84-87.
	[Mu Y C, Xie Y W, Zhang L L, et al. An enhanced normalized difference impervious surface index[J]. Science of Surveying and Mapping, 2018, 43(2): 84-87.]
[15]	王浩, 卢善龙, 吴炳方, 等. 不透水面遥感提取及应用研究进展[J]. 地球科学进展, 2013, 28(3): 327-336.
	[Wang H, Lu S L, Wu B F, et al. Advances in remote sensing of impervious surfaces extraction and its applications[J]. Advances in Earth Science, 2013, 28(3): 327-336.]
[16]	Yue W Z.Improvement of urban impervious surface estimation in shanghai using Landsat 7 ETM+ data[J]. Chinese Geographical Science, 2009, 19(3): 283-290.
[17]	Hu X F, Weng Q H.Estimation of impervious surfaces of Beijing, China, with spectral normalized images using linear spectral mixture analysis and artificial neural network[J]. Geocarto International, 2010, 25(3): 231-253.
[18]	Weng Q.Remote Sensing of Impervious Surfaces[M]. Boca Raton: CRC Press, 2007.
[19]	马立杰, 杨曦光, 祁雅莉, 等. 胶州湾海域面积变化及原因探讨[J]. 地理科学, 2014, 34(3): 365-369.
	[Ma L J, Yang X G, Qi Y L, et al. Oceanic area change and contributing factor of Jiaozhou Bay[J]. Scientia Geographica Sinica, 2014, 34(3): 365-369.]
[20]	中国科学院资源环境科学数据中心. 中国土地利用现状遥感监测数据库[EB/OL]. (2016-02-01)[2018-06-12]. .
	[Resource and Environment Data Cloud Platform. Remote Sensing Monitoring Database of Land Use Status in China[EB/OL]. (2016-02-01) [2018-06-12]. .]
[21]	United States Geological Survey. Landsat 5 TM and Landsat 8 OLI[EB/OL]. (2016-06-16)[2018-06-12].
[22]	翟珂, 吴晓青, 秦元伟, 等. 基于OLI影像的环渤海湾不透水面提取及空间格局分析[J]. 资源科学, 2015, 37(10): 1920-1928.
	[Zhai K, Wu X Q, Qin Y W, et al. Extraction and spatial analysis of impervious surfaces in the Bohai Bay region based on OLI imagery[J]. Resources Science, 2015, 37(10): 1920-1928.]
[23]	Wang W, Yao X F, Zai J P, et al. A tetrahedron-based endmember selection approach for urban impervious surface mapping[J]. Plos One, 2014, 9(6): e93479.
[24]	崔天翔, 宫兆宁, 赵文吉, 等. 不同端元模型下湿地植被覆盖度的提:以北京市野鸭湖湿地自然保护区为例[J]. 生态学报, 2013, 33(4): 1161-1171.
	[Cui T X, Gong Z N, Zhao W J, et al. Research on estimating wetland vegetation abundance based on spectral mixture analysis with different endmember model: a case study in Wild Duck Lake wetland, Beijing[J]. Acta Ecologica Sinica, 2013, 33(4): 1160-1171.]
[25]	Smith M O, Johnson P E, Adams J B.Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis[J]. Journal of Geophysical Research, 1985, 90(S2): 797-804.
[26]	Van Der Meer F, De Jong S M. Improving the results of spectral unmixing of Landsat Thematic Mapper imagery by enhancing the orthogonality of end-members[J]. International Journal of Remote Sensing, 2000, 21(15): 2781-2797.
[27]	Lu D S, Weng Q H.Use of impervious surface in urban land use classification[J]. Remote Sensing of Environment, 2006, 102(1): 146-160.
[28]	Roberts D A, Smith M O, Adams J B.Green vegetation, nonphotosynthetic vegetation, and soil in AVIRIS data[J]. Remote Sensing of Environment, 1993, 44(2-3): 255-269.
[29]	谭春阳, 匡文慧, 徐天蜀, 等. 近20年上海市不透水地表时空格局分析[J]. 测绘地理信息, 2014, 39(3): 71-74.
	[Tan C Y, Kuang W H, Xu T S, et al. Analyzing spatio-temporal pattern of impervious surface for recent two decades in Shanghai[J]. Journal of Geomatics, 2014, 39(3): 71-74.]
[30]	Chang C I, Ji B H.Weighted abundance-constrained linear spectral mixture analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(2): 378-388.
[31]	姜宛贝, 孙强强, 曲葳, 等. 基于多季相光谱混合分解和决策树的干旱区土地利用分类[J]. 农业工程学报, 2016, 32(19): 1-8.
	[Jiang W B, Sun Q Q, Qu W, et al. Land use classification in arid region based on multi-seasonal linear spectral mixture analysis and decision tree method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(19): 1-8.]
[32]	Zhang C, Chen Y L, Lu D S.Mapping the land-cover distribution in arid and semiarid urban landscapes with Landsat thematic mapper imagery[J]. International Journal of Remote Sensing, 2015, 36(17): 4483-4500.
[33]	Lu D S, Weng Q H.Extraction of urban impervious surfaces from an IKONOS image[J]. International Journal of Remote Sensing, 2009, 30(5): 1297-1311.
[34]	吴晓青, 胡远满, 贺红士, 等. 沈阳市城镇扩展时空格局及其驱动力[J]. 应用生态学报, 2007, 18(10): 2282-2288.
	[Wu X Q, Hu Y M, He H S, et al. Spatiotemporal pattern and its driving forces of urban growth in Shenyang City[J]. Chinese Journal of Applied Ecology, 2007, 18(10): 2282-2288.]
[35]	王厚军, 李小玉, 张祖陆, 等. 1979-2006年沈阳市城市空间扩展过程分析[J]. 应用生态学报, 2008, 19(12): 2673-2679.
	[Wang H J, Li X Y, Zhang Z L, et al. Analysis on urban spatial expansion process in Shenyang City[J]. Chinese Journal of Applied Ecology, 2008, 19(12): 2673-2679.]
[36]	杨超. 1990s以来青岛城市空间形态演变研究[D]. 济南: 山东建筑大学, 2010.
	[Yang C.Research on Evolution of Urban Form of Qingdao Since the 1990s[D]. Jinan: Shandong Jianzhu University, 2010.]
[37]	邓蕾, 赵小锋, 王慧娜, 等. 城市混合像元分解中土壤与不透水面纯像元选取方法的对比研究-以厦门为例[J]. 遥感技术与应用, 2013, 28(6): 1039-1045.
	[Deng L, Zhao X F, Wang H N, et al. Pure pixel collection methods for soil and impervious surface in urban spectral mixture comparative analysis[J]. Remote Sensing Technology and Application, 2013, 28(6): 1039-1045.]
[38]	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.
[39]	Wu C, Murray A T.Estimating impervious surface distribution by spectral mixture analysis[J]. Remote Sensing of Environment, 2003, 84(4): 493-505.
[40]	徐涵秋, 王美雅. 地表不透水面信息遥感的主要方法分析[J]. 遥感学报, 2016, 20(5): 1270-1289.
	[Xu H Q, Wang M Y.Remote sensing-based retrieval of ground impervious surfaces[J]. Journal of Remote Sensing, 2016, 20(5): 1270-1289.]
[41]	李霞, 徐涵秋, 李晶, 等.基于NDSI和NDISI指数的SPOT-5影像裸土信息提取[J]. 地球信息科学学报, 2016, 18(1):117-123.
	[Li X, Xu H Q, Li J, et al. Extraction of bare soil features from SPOT-5 imagery based on NDSI and NDISI[J]. Journal of Geo-information Science, 2016, 18(1):117-123.]

	传感器名称	影像获得时间 /年—月—日
遥感数据	Landsat 5 TM	1990—10—15
		1995—09—27
		2000—09—08
		2005—10—05
		2011—09—23
	Landsat 8 OLI	2016—06—16

指标	1990年	1995年	2000年	2005年	2011年	2016年
R²	0.74	0.70	0.73	0.75	0.71	0.78
RMSE /%	12.60	11.00	12.20	16.70	15.60	14.50