基于Sentinel-2A/B的新疆典型城市不透水面提取及空间差异分析
Extraction of the impervious surface of typical cities in Xinjiang based on Sentinel-2A/B and spatial difference analysis
- 2022年26卷第7期 页码:1469-1482
纸质出版日期: 2022-07-07
DOI: 10.11834/jrs.20210174
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纸质出版日期: 2022-07-07 ,
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段潘,张飞,刘长江.2022.基于Sentinel-2A/B的新疆典型城市不透水面提取及空间差异分析.遥感学报,26(7): 1469-1482
Duan P,Zhang F abd Liu C J. 2022. Extraction of the impervious surface of typical cities in Xinjiang based on Sentinel-2A/B and spatial difference analysis. National Remote Sensing Bulletin, 26(7):1469-1482
随着城市化进程加快,自然地表物理结构及属性的变化使得城市不透水面不断增加,进而造成城市土地覆盖类型剧烈变化,极大地影响着其环境质量和生态循环。因此,探讨不透水面的空间变化规律,对建设生态、和谐、宜居城市变得极为重要。本文选取新疆维吾尔自治区(简称新疆)典型城市(乌鲁木齐、喀什、哈密及克拉玛依)主城区为研究区,通过对Sentinel-2A/B影像L2范数归一化处理,结合增强型归一化差值不透水面指数ENDISI(Enhanced Normalized Difference Impervious Surface Index),采用最大类间方差法(OTSU)自适应确定阈值,提取2017年和2019年新疆典型城市不透水面。结果表明:L2范数归一化处理与ENDISI结合能较好的突显不透水面与非不透水面的差异;OTSU自适应确定的阈值能够很好的区分不透水面,经示例验证(2019年乌鲁木齐主城区),不透水面提取结果总体精度为86.60%,Kappa系数为0.73。通过对不透水面空间差异分析可知:从剖面线角度分析得出乌鲁木齐北部、喀什中部和北部及哈密中东部和北部ENDISI指数值均显著增加,而克拉玛依北部和中西部区域ENDISI指数值增加较少;从不透水面盒维数分析中得出,新疆典型城市的盒维数值均呈增加趋势,城市结构复杂度不断增强,其中哈密盒维数值最大,乌鲁木齐盒维数值最低,且哈密的盒维数值变化幅度最大,克拉玛依的盒维数值变化最小。本文可为新疆典型城市内涵式发展提供科学指导,为干旱区城市生态环境保护提供理论依据。
As the process of urbanization accelerates
changes in the physical structure and attributes of the natural surface have increased the impervious surface of cities
resulting in drastic changes in the types of urban land cover
which greatly affect environmental quality and the ecological cycle. Therefore
exploring the spatial structure of urban impervious surfaces crucial to urban ecological environment protection and urban green space planning.
The main urban areas of typical Xinjiang cities (Urumqi
Kashgar
Hami
and Karamay) are selected as the study area in this work. First
the L2 norm of Sentinel-2A/B images is normalized to weaken the urban bare soil interference information in arid areas. Combined with the Enhanced Normalized Difference Impervious Surface Index (ENDISI)
the maximum between-class variance method (OTSU) is used to adaptively determine the threshold
and the impervious surfaces of typical cities in Xinjiang in 2017 and 2019 are extracted. Second
the spatial differences of the ENDISI values in each direction of impervious surfaces in the study area are analyzed with the profile line method
and the box dimension method is introduced to dissect the spatial structure characteristics of impervious surfaces in different time phases and reveal the law of urban spatial structure changes.
Results show that the combination of L2 norm normalization treatment and ENDISI can effectively highlight the difference between impervious and non-impervious surfaces. The threshold value determined by OTSU adaptively can distinguish impervious surfaces well
and the overall accuracy of the impervious surface extraction results is 86.60% with a kappa coefficient of 0.73 as verified by an example (2019 Urumqi main urban area). With the junction area of a city and bare soil as the research object
the classification effects of ENDISI
NDBI
and UI methods are compared
and the accuracy is verified using the visual interpretation method. The results show that ENDISI’s extraction effect is the best
and its extraction accuracy for urban contours and bare soil areas is the highest (83.18%). NDBI’s extraction effect is relatively poor (78.38%)
and UI’s extraction accuracy is the lowest (60.18%).
The analysis of the spatial differences of the impervious surfaces reveals that the ENDISI values for northern Urumqi
central–northern Kashgar
and central-eastern and northern Hami increase significantly from the perspective of profile lines
but the ENDISI values for northern Karamay and central-western regions increase only slightly. The analysis of the box dimension of the impervious surface shows that the box dimension values of typical cities in Xinjiang and the urban structure complexity are increasing. Among these cities
Hami and Urumqi have the highest and lowest box dimension values
respectively; Hami has the largest variation in box dimension values
whereas Karamay has the smallest. In this study
a new method of extracting urban impervious surfaces in arid areas is proposed. The method reveals the change law of impervious surfaces of cities in various directions
provides scientific guidance for the connotative development of typical cities in Xinjiang
and serves as a theoretical basis for the protection of urban ecological environments in arid areas.
遥感城市不透水面Sentinel-2A/BENDISIOTSU盒维数新疆维吾尔自治区
remote sensingurban impervious surfaceSentinel-2A/BENDISIOTSUthe box-counting dimensionXinjiang Uygur Autonomous Region of China
Bao Y, Chen H Z, Shen P P and Mei Y X. 2020. Analysis on temporal and spatial variation of impervious surface area in Ningbo during 1987—2015. Journal of Geomatics, 45(2): 35-40
包颖, 陈海珍, 申佩佩, 梅元勋. 2020. 1987—2015年宁波市不透水面时空变化分析. 测绘地理信息, 45(2): 35-40 [DOI: 10.14188/j.2095-6045.2018315http://dx.doi.org/10.14188/j.2095-6045.2018315]
Cheng X, Luo R, Shi G Z, Xia L G and Shen Z F. 2020. Automated detection of impervious surfaces using night-time light and Landsat images based on an iterative classification framework. Remote Sensing Letters, 11(5): 465-474 [DOI: 10.1080/2150704X.2020.1730471http://dx.doi.org/10.1080/2150704X.2020.1730471]
Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1): 35-46 [DOI: 10.1016/0034-4257(91)90048-Bhttp://dx.doi.org/10.1016/0034-4257(91)90048-B]
Creutzig F, Agoston P, Minx J C, Canadell J G, Andrew R M, Le Quéré C, Peters G P, Sharifi A, Yamagata Y and Dhakal S. 2016. Urban infrastructure choices structure climate solutions. Nature Climate Change, 6(12): 1054-1056 [DOI: 10.1038/nclimate3169http://dx.doi.org/10.1038/nclimate3169]
Deng C B and Zhu Z. 2020. Continuous subpixel monitoring of urban impervious surface using Landsat time series. Remote Sensing of Environment, 238: 110929 [DOI: 10.1016/j.rse.2018.10.011http://dx.doi.org/10.1016/j.rse.2018.10.011]
Dou Y Y and Kuang W H. 2020. A comparative analysis of urban impervious surface and green space and their dynamics among 318 different size cities in China in the past 25 years. Science of the Total Environment, 706: 135828 [DOI: 10.1016/j.scitotenv.2019.135828http://dx.doi.org/10.1016/j.scitotenv.2019.135828]
Fang C L. 2019. The basic law of the formation and expansion in urban agglomerations. Journal of Geographical Sciences, 29(10): 1699-1712 [DOI: 10.1007/s11442-019-1686-yhttp://dx.doi.org/10.1007/s11442-019-1686-y]
Fu B Z. 2017. The Temporal-Spatial Evolution and Fractal Study of Impervious Surface in Central District of Fuzhou. Fuzhou: Fujian Normal University
傅滨桢. 2017. 福州市中心城区不透水面时空演变与分形研究. 福州: 福建师范大学
Gong P, Li X C, Wang J, Bai Y Q, Chen B, Hu T Y, Liu X P, Xu B, Yang J, Zhang W and Zhou Y Y. 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236: 111510 [DOI: 10.1016/j.rse.2019.111510http://dx.doi.org/10.1016/j.rse.2019.111510]
Grimm N B, Faeth S H, Golubiewski N E, Redman C L, Wu J G, Bai X M and Briggs J M. 2008. Global change and the ecology of cities. Science, 319(5864): 756-760 [DOI: 10.1126/science.1150195http://dx.doi.org/10.1126/science.1150195]
Jensen J R and Lulla K. 1987. Introductory digital image processing: a remote sensing perspective. Geocarto International, 2(1): 65 [DOI: 10.1080/10106048709354084http://dx.doi.org/10.1080/10106048709354084]
Kawamura M, Jayamana S and Tsujiko Y. 1996. Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. The International Archives of the Photogrammetry, Remote Sensing, 31(B7): 321-326.
Kuang W H. 2019. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences, 62(10): 1591-1606
匡文慧. 2019. 全球城市人居环境不透水面与绿地空间特征制图. 中国科学(地球科学), 49(7): 1151-1168 [DOI: 10.1007/s11430-018-9342-3http://dx.doi.org/10.1007/s11430-018-9342-3]
Li H, Li L, Zhang T and Chen L Q. 2019. Mapping and characterizing the spatio-temporal heterogeneity of impervious surface in Xuzhou urban area. Resources and Environment in the Yangtze Basin, 28(3): 668-680
李涵, 李龙, 张婷, 陈龙乾. 2019. 徐州市中心城区不透水面时空异质性分析. 长江流域资源与环境, 28(3): 668-680 [DOI: 10.11870/cjlyzyyhj201903018http://dx.doi.org/10.11870/cjlyzyyhj201903018]
Li J, Du Q and Sun C X. 2009. An improved box-counting method for image fractal dimension estimation. Pattern Recognition, 42(11): 2460-2469 [DOI: 10.1016/j.patcog.2009.03.001http://dx.doi.org/10.1016/j.patcog.2009.03.001]
Li L L. 2012. A Study on Auto-Thresholding Selection Methods for Image Segmentation. Lanzhou: Lanzhou University
李琳琳. 2012. 遥感图像分割中阈值的自动选取技术研究. 兰州: 兰州大学
Lin S, Yu X J, Zhuang X B, Li Y L, Zhang Y and Zhao Z Q. 2020. Fractal characteristics evolution of coastline of the Xiamen Island. Advances in Marine Science, 38(1): 121-129
林松, 俞晓牮, 庄小冰, 李焱龙, 张宇, 赵志庆. 2020. 厦门岛海岸线分形特性演变规律的研究. 海洋科学进展, 38(1): 121-129 [DOI: 10.3969/j.issn.1671-6647.2020.01.013http://dx.doi.org/10.3969/j.issn.1671-6647.2020.01.013]
Lin Y S, Xu H Q and Zhou R. 2007. A study on urban impervious surface area and its relation with urban heat island: Quanzhou City, China. Remote Sensing Technology and Application, 22(1): 14-19
林云杉, 徐涵秋, 周榕. 2007. 城市不透水面及其与城市热岛的关系研究——以泉州市区为例. 遥感技术与应用, 22(1): 14-19 [DOI: 10.3969/j.issn.1004-0323.2007.01.003http://dx.doi.org/10.3969/j.issn.1004-0323.2007.01.003]
Liu B, Zhang Y, Cheng T and Song Y. 2017. Urban impervious surface extraction based on the GF-2 satellite imagery. Geomatics World, 24(2): 103-107
刘波, 张源, 程涛, 宋杨. 2017. 基于高分二号卫星影像的城市不透水面提取. 地理信息世界, 24(2): 103-107 [DOI: 10.3969/j.issn.1672-1586.2017.02.020http://dx.doi.org/10.3969/j.issn.1672-1586.2017.02.020]
McFeeters S K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Miyata T and Watanabe T. 2003. Approximate resolutions and box-counting dimension. Topology and its Applications, 132(1): 49-69 [DOI: 10.1016/S0166-8641(02)00362-0http://dx.doi.org/10.1016/S0166-8641(02)00362-0]
Mu Y C, Xie Y W, Zhang L L and Chen Y H. 2018. An enhanced normalized difference impervious surface index. Science of Surveying and Mapping, 43(2): 83-87
穆亚超, 颉耀文, 张玲玲, 陈云海. 2018. 一种新的增强型不透水面指数. 测绘科学, 43(2): 83-87 [DOI: 10.16251/j.cnki.1009-2307.2018.02.015http://dx.doi.org/10.16251/j.cnki.1009-2307.2018.02.015]
Nie F P, Huang H, Cai X and Ding C. 2010. Efficient and robust feature selection via joint ℓ2,1-norms minimization//Proceedings of the 23rd International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc.: 1813-1821.
Orzechowski M E. 1998. A lower bound on the box-counting dimension of crossings in fractal percolation. Stochastic Processes and their Applications, 74(1): 53-65 [DOI: 10.1016/S0304-4149(97)00117-8http://dx.doi.org/10.1016/S0304-4149(97)00117-8]
Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]
Peng J, Liu Y X, Shen H, Xie P, Hu X X and Wang Y L. 2016. Using impervious surfaces to detect urban expansion in Beijing of China in 2000s. Chinese Geographical Science, 26(2): 229-243 [DOI: 10.1007/s11769-016-0802-5http://dx.doi.org/10.1007/s11769-016-0802-5]
Ren C X, Dai D Q and Yan H. 2012. Robust classification using ℓ2, 1-norm based regression model. Pattern Recognition, 45(7): 2708-2718 [DOI: 10.1016/j.patcog.2012.01.003http://dx.doi.org/10.1016/j.patcog.2012.01.003]
Ridd M K. 1995. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16(12): 2165-2185 [DOI: 10.1080/01431169508954549http://dx.doi.org/10.1080/01431169508954549]
Sarkar N and Chaudhuri B B. 1994. An efficient differential box-counting approach to compute fractal dimension of image. IEEE Transactions on Systems, Man, and Cybernetics, 24(1): 115-120 [DOI: 10.1109/21.259692http://dx.doi.org/10.1109/21.259692]
Shao Z F, Zhang Y, Zhou W Q and Song Y. 2016. Extraction of urban impervious surface based on high resolution remote sensing image. Geospatial Information, 14(7): 1-5
邵振峰, 张源, 周伟琪, 宋杨. 2016. 基于测绘卫星影像的城市不透水面提取. 地理空间信息, 14(7): 1-5 [DOI: 10.3969/j.issn.1672-4623.2016.07.001http://dx.doi.org/10.3969/j.issn.1672-4623.2016.07.001]
Slonecker E T, Jennings D B and Garofalo D. 2001. Remote sensing of impervious surfaces: a review. Remote Sensing Reviews, 20(3): 227-255 [DOI: 10.1080/02757250109532436http://dx.doi.org/10.1080/02757250109532436]
Wang Z H, Yang S W, Zhang S and Liu L L. 2019. Study on seepage model of loess landslide in Lanzhou based on impervious layer. Journal of Lanzhou Jiaotong University, 38(6): 97-102
王兆华, 杨树文, 张珊, 刘龙龙. 2019. 基于不透水层的兰州市黄土滑坡渗水模型研究. 兰州交通大学学报, 38(6): 97-102 [DOI: 10.3969/j.issn.1001-4373.2019.06.015http://dx.doi.org/10.3969/j.issn.1001-4373.2019.06.015]
Wu C S and Murray A T. 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84(4): 493-505 [DOI:10.1016/S0034-4257 (02)00136-0http://dx.doi.org/10.1016/S0034-4257(02)00136-0]
Xia Y, Sun Z C, Gao J and Du W J. 2019. Rapid extraction algorithm of impervious surface from diffcult region. Geospatial Information, 17(9): 95-98, 102
夏宇, 孙中昶, 高建, 杜文杰. 2019. 一种困难地区不透水面快速提取算法. 地理空间信息, 17(9): 95-98, 102 [DOI:10.3969/j.issn.1672-4623.2019.09.029http://dx.doi.org/10.3969/j.issn.1672-4623.2019.09.029]
Xu H Q. 2008. A new remote sensing index for fastly extracting impervious surface information. Geomatics and Information Science of Wuhan University, 33(11): 1150-1153
徐涵秋. 2008. 一种快速提取不透水面的新型遥感指数. 武汉大学学报(信息科学版), 33(11): 1150-1153
Xu H Q. 2009. Quantitative analysis on the relationship of urban impervious surface with other components of the urban ecosystem. Acta Ecologica Sinica, 29(5): 2456-2462
徐涵秋. 2009. 城市不透水面与相关城市生态要素关系的定量分析. 生态学报, 29(5): 2456-2462 [DOI: 10.3321/j.issn:1000-0933.2009.05.032http://dx.doi.org/10.3321/j.issn:1000-0933.2009.05.032]
Xu H Q and Wang M Y. 2016. Remote sensing-based retrieval of ground impervious surfaces. Journal of Remote Sensing, 20(5): 1270-1289
徐涵秋, 王美雅. 2016. 地表不透水面信息遥感的主要方法分析. 遥感学报, 20(5): 1270-1289 [DOI: 10.11834/jrs.20166210http://dx.doi.org/10.11834/jrs.20166210]
Yang S S and Shao L Y. 2006. Estimation of fractal dimensions of images based on MATLAB. Journal of China University of Mining and Technology, 35(4): 478-482
杨书申, 邵龙义. 2006. MATLAB环境下图像分形维数的计算. 中国矿业大学学报, 35(4): 478-482 [DOI:10.3321/j.issn:1000-1964.2006. 04.011http://dx.doi.org/10.3321/j.issn:1000-1964.2006.04.011]
Yang X J and Liu Z. 2005. Use of satellite-derived landscape impervious-ness index to characterize urban spatial growth. Computers Environment and Urban Systems, 29(5): 524-540 [DOI: 10.1016/j.compenvurbsys.2005.01.005http://dx.doi.org/10.1016/j.compenvurbsys.2005.01.005]
Yu H J. 2018. Analysis of Remote Sensing Estimation of Urban Impervious Surface and Its Climatic Effect in Arid Area——Take Urumqi as an Example. Urumqi: Xinjiang University
余红娇. 2018. 干旱区城市不透水面的遥感估算及其气候效应分析——以乌鲁木齐市主城区为例. 乌鲁木齐: 新疆大学
Yu X J, Zhao Z Q and Yu L. 2019. Study on the complex features of Xiamen boundaries based on fractal theory. Journal of Natural Science of Heilongjiang University, 36(6): 738-744
俞晓牮, 赵志庆, 余丽. 2019. 基于分形理论的厦门城市边界复杂特性研究. 黑龙江大学自然科学学报, 36(6): 738-744 [DOI: 10.13482/j.issn1001-7011.2019.10.019http://dx.doi.org/10.13482/j.issn1001-7011.2019.10.019]
Yuan F and 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 [DOI: 10.1016/j.rse.2006.09.003http://dx.doi.org/10.1016/j.rse.2006.09.003]
Zha Y, Gao J and Ni S. 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3): 583-594 [DOI: 10.1080/01431160304987http://dx.doi.org/10.1080/01431160304987]
Zhang J L, Pan Z H, Kuang W H, Pan Y Y, Han G L, Wang J L, Huang N, Zhang Z Y and Yin W J. 2020. Spatial-temporal change features of impervious surface and its effect on land surface temperature from 1984 to 2014 in Beijing. Climate Change Research, 16(3): 296-305
张稼乐, 潘志华, 匡文慧, 潘宇鹰, 韩国琳, 王佳琳, 黄娜, 张子源, 尹文娟. 2020. 1984年—2014年北京地区不透水地表的时空变化及其温度效应研究. 气候变化研究进展, 16(3): 296-305 [DOI: 10.12006/j.issn.1673-1719.2019.084http://dx.doi.org/10.12006/j.issn.1673-1719.2019.084]
Zhao Y S. 2003. Principles and Methods of Remote Sensing Application Analysis. Beijing: Science Press: 203-208
赵英时. 2003. 遥感应用分析原理与方法. 北京: 科学出版社: 203-208
Zhou C L and Xu H Q. 2007. A spectral mixture analysis and mapping of impervious surfaces in built-up land of Fuzhou City. Journal of Image and Graphics, 12(5): 875-881
周存林, 徐涵秋. 2007. 福州城区不透水面的光谱混合分析与识别制图. 中国图象图形学报, 12(5): 875-881 [DOI:10.3969/j.issn.1006- 8961.2007.05.018http://dx.doi.org/10.3969/j.issn.1006-8961.2007.05.018]
Zhou X D, Guo H D and Zibibula·S. 2018. Spatial pattern evolution of impervious surfaces and its influence on surface temperature in the process of urban expansion: a case study of Urumqi. Acta Ecologica Sinica, 38(20): 7336-7347
周玄德, 郭华东, 孜比布拉·司马义. 2018. 城市扩张过程中不透水面空间格局演变及其对地表温度的影响——以乌鲁木齐市为例. 生态学报, 38(20): 7336-7347 [DOI: 10.5846/stxb201711122022http://dx.doi.org/10.5846/stxb201711122022]
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