предыдущий|следующий

Extraction of the impervious surface of typical cities in Xinjiang based on Sentinel-2A/B and spatial difference analysis

  • role： First author第一作者
  • Affiliation：

    College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China

  • Email：13808270530@163.com
  • Introduction：段潘，研究方向为干旱区生态环境遥感。E-mail： 13808270530@163.com
DUAN Pan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China

    China Key Laboratory of Smart City and Environmental Modeling, Xinjiang University, Urumqi 830046, China

  • Email：zhangfei3s@163.com
  • Introduction：张飞，研究方向为干旱区生态环境遥感应用。E-mail： zhangfei3s@163.com
ZHANG Fei123*,  
  • Affiliation：

    College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China

LIU Changjiang12

реферат

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.

ключеви́че слова́

remote sensing;urban impervious surface;Sentinel-2A/B;ENDISI;OTSU;The Box-counting Dimension;Xinjiang Uygur Autonomous Region of China

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    Congalton R G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1): 35-46
  4. 4.
    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
  5. 5.
    Deng C B and Zhu Z. 2020. Continuous subpixel monitoring of urban impervious surface using Landsat time series. Remote Sensing of Environment, 238: 110929
  6. 6.
    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
  7. 7.
    Fang C L. 2019. The basic law of the formation and expansion in urban agglomerations. Journal of Geographical Sciences, 29(10): 1699-1712
  8. 8.
    Fu B Z. 2017. The Temporal-Spatial Evolution and Fractal Study of Impervious Surface in Central District of Fuzhou. Fuzhou: Fujian Normal University
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Jensen J R and Lulla K. 1987. Introductory digital image processing: a remote sensing perspective. Geocarto International, 2(1): 65
  12. 12.
    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.
  13. 13.
    Kuang W H. 2019. Mapping global impervious surface area and green space within urban environments. Science China Earth Sciences, 62(10): 1591-1606
  14. 14.
    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
  15. 15.
    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
  16. 16.
    Li L L. 2012. A Study on Auto-Thresholding Selection Methods for Image Segmentation. Lanzhou: Lanzhou University
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    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
  21. 21.
    Miyata T and Watanabe T. 2003. Approximate resolutions and box-counting dimension. Topology and its Applications, 132(1): 49-69
  22. 22.
    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
  23. 23.
    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.
  24. 24.
    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
  25. 25.
    Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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
  31. 31.
    Slonecker E T, Jennings D B and Garofalo D. 2001. Remote sensing of impervious surfaces: a review. Remote Sensing Reviews, 20(3): 227-255
  32. 32.
    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
  33. 33.
    Wu C S and Murray A T. 2003. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84(4): 493-505
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    Xu H Q and Wang M Y. 2016. Remote sensing-based retrieval of ground impervious surfaces. Journal of Remote Sensing, 20(5): 1270-1289
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    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
  44. 44.
    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
  45. 45.
    Zhao Y S. 2003. Principles and Methods of Remote Sensing Application Analysis. Beijing: Science Press: 203-208
  46. 46.
    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
  47. 47.
    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

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website