Incorporating synthetic aperture radar and optical images to investigate the annual dynamics of anthropogenic impervious surface at large scale

Remote Sensing of Environment

Volumn 242, Issue , 2020, Pages

  Lin, Yinyi ^a   Zhang, Hongsheng ^b   Lin, Hui ^c   Gamba, Paolo Ettore ^d   Liu, Xiaoping ^e  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

^c JIANGXI NORMAL UNIVERSITY   (China)

^d UNIVERSITY OF PAVIA   (Italy)

^e SUN YAT SEN UNIVERSITY   (China)

Author keywords

Impervious surface; Multisource data fusion; SAR; Urban land cover

Indexed keywords

BACKSCATTERING; DATA FUSION; DYNAMICS; GEOMETRICAL OPTICS; LARGE DATASET; MAPPING; POPULATION STATISTICS; RADAR IMAGING; REMOTE SENSING; TEXTURES;

BACKSCATTERING COEFFICIENTS; COMPREHENSIVE ASSESSMENT; FEATURE LEVEL FUSION; IMPERVIOUS SURFACE; MULTISOURCE DATA; SPATIAL AND TEMPORAL PATTERNS; SPECTRAL CHARACTERISTICS; URBAN LAND COVER;

SYNTHETIC APERTURE RADAR;

ANTHROPOGENIC EFFECT; CENSUS; MAPPING METHOD; OPTICAL METHOD; SATELLITE IMAGERY; WORLD WIDE WEB;

CHINA;

EID: 85081736297     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2020.111757     Document Type: Article

References (63)

1. Ban, Y.F., Jacob, A., Gamba, P., Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor. ISPRS J. Photogramm. Remote Sens. 103 (2015), 28–37.
2. Brabec, E., Schulte, S., Richards, P.L., Impervious surfaces and water quality: a review of current literature and its implications for watershed planning. J. Plan. Lit. 16 (2002), 499–514.
3. Branstetter, L., Lardy, N., China's Embrace of Globalization. 2006, National Bureau of Economic Research.
4. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
5. Cao, S.S., Hu, D.Y., Zhao, W.J., Mo, Y., Chen, S.S., Monitoring spatial patterns and changes of ecology, production, and living land in Chinese urban agglomerations: 35 years after reform and opening up, where, how and why?. Sustainability, 9, 2017.
6. Conners, R.W., Trivedi, M.M., Harlow, C.A., Segmentation of a high-resolution urban scene using texture operators. Computer Vision Graphics and Image Processing 25 (1984), 273–310.
7. Deng, C.B., Wu, C.S., The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques. ISPRS J. Photogramm. Remote Sens. 86 (2013), 100–110.
8. Deng, C.B., Li, C.J., Zhu, Z., Lin, W.Y., Xi, L., Subpixel urban impervious surface mapping: the impact of input Landsat images. ISPRS J. Photogramm. Remote Sens. 133 (2017), 89–103.
9. Elvidge, C.D., Tuttle, B.T., Sutton, P.S., Baugh, K.E., Howard, A.T., Milesi, C., Bhaduri, B.L., Nemani, R., Global distribution and density of constructed impervious surfaces. Sensors 7 (2007), 1962–1979.
10. Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., Schaaf, C., Global land cover mapping from MODIS: algorithms and early results. Remote Sens. Environ. 83 (2002), 287–302.
11. Gamba, P., Herold, M., Global Mapping of Human Settlement: Experiences, Datasets, and Prospects. 2009, CRC Press.
12. Gao, F., De Colstoun, E.B., Ma, R.H., Weng, Q.H., Masek, J.G., Chen, J., Pan, Y.Z., Song, C.H., Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta, China. Int. J. Remote Sens. 33 (2012), 7609–7628.
13. Geurts, P., Ernst, D., Wehenkel, L., Extremely randomized trees. Mach. Learn. 63 (2006), 3–42.
14. Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C.Y., Serrano-Candela, F., Khandelwal, A.K., Cheng, W.H., Balling, R.C., Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205 (2018), 253–275.
15. Gong, P., Li, X.C., Zhang, W., 40-year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Science Bulletin 64 (2019), 756–763.
16. Gong, P., Li, X.C., Wang, J., Bai, Y.Q., Cheng, B., Hu, T.Y., Liu, X.P., Xu, B., Yang, J., Zhang, W., Zhou, Y.Y., Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens. Environ., 2020, 236.
17. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202 (2017), 18–27.
18. Gu, C., Wu, L., Cook, I., Progress in research on Chinese urbanization. Frontiers of Architectural Research 1 (2012), 101–149.
19. Guo, H.D., Yang, H.N., Sun, Z.C., Li, X.W., Wang, C.Z., Synergistic use of optical and polSAR imagery for urban impervious surface estimation. Photogramm. Eng. Remote. Sens. 80 (2014), 91–102.
20. Guo, W., Li, G.Y., Ni, W.J., Zhang, Y.H., Lu, D.S., Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data. Giscience & Remote Sensing 55 (2018), 699–717.
21. Guyon, I., Weston, J., Barnhill, S., Vapnik, V., Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002), 389–422.
22. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R., High-resolution global maps of 21st-century forest cover change. Science 342 (2013), 850–853.
23. Haralick, R.M., Shanmugam, K., Dinstein, I., Textural features for image classification. IEEE Transactions on Systems Man and Cybernetics, 1973, 610–621 Smc3.
24. Herman, T., A geography of China. Geogr. J., 132, 1966 101-&.
25. Homer, C., Dewitz, J., Yang, L.M., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N., Wickham, J., Megown, K., Completion of the 2011 National Land Cover Database for the conterminous United States - representing a decade of land cover change information. Photogramm. Eng. Remote. Sens. 81 (2015), 345–354.
26. Imhoff, M.L., Lawrence, W.T., Stutzer, D.C., Elvidge, C.D., A technique for using composite DMSP/OLS “city lights” satellite data to map urban area. Remote Sens. Environ. 61 (1997), 361–370.
27. Jiang, L.M., Liao, M.S., Lin, H., Yang, L.M., Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong. Int. J. Remote Sens. 30 (2009), 2781–2796.
28. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M.R., Kuemmerle, T., Meyfroidt, P., Mitchard, E.T.A., Reiche, J., Ryan, C.M., Waske, B., A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens., 8, 2016.
29. Ju, J.C., Roy, D.P., The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally. Remote Sens. Environ. 112 (2008), 1196–1211.
30. Jun, C., Ban, Y.F., Li, S.N., Open access to Earth land-cover map. Nature, 514, 2014, 434.
31. Labib, S.M., Harris, A., The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method. European Journal of Remote Sensing 51 (2018), 231–240.
32. Li, X.C., Gong, P., Liang, L., A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sens. Environ. 166 (2015), 78–90.
33. Liu, X.P., Hu, G.H., Ai, B., Li, X., Shi, Q., A normalized urban areas composite index (NUACI) based on combination of DMSP-OLS and MODIS for mapping impervious surface area. Remote Sens. 7 (2015), 17168–17189.
34. Liu, X.P., Hu, G.H., Chen, Y.M., Li, X., Xu, X.C., Li, S.Y., Pei, F.S., Wang, S.J., High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine platform. Remote Sens. Environ. 209 (2018), 227–239.
35. Liu, C., Shao, Z., Chen, M., Luo, H., MNDISI: a multi-source composition index for impervious surface area estimation at the individual city scale. Remote Sensing Letters 4 (2013), 803–812.
36. Marceau, D.J., Howarth, P.J., Dubois, J.M.M., Gratton, D.J., Evaluation of the grey-level co-occurrence matrix method for land-cover classification using spot imagery. IEEE Trans. Geosci. Remote Sens. 28 (1990), 513–519.
37. McDonald, R.I., Weber, K., Padowski, J., Florke, M., Schneider, C., Green, P.A., Gleeson, T., Eckman, S., Lehner, B., Balk, D., Boucher, T., Grill, G., Montgomery, M., Water on an urban planet: urbanization and the reach of urban water infrastructure. Global Environmental Change-Human and Policy Dimensions 27 (2014), 96–105.
38. Nghiem, S.V., Balk, D., Rodriguez, E., Neumann, G., Sorichetta, A., Small, C., Elvidge, C.D., Observations of urban and suburban environments with global satellite scatterometer data. ISPRS J. Photogramm. Remote Sens. 64 (2009), 367–380.
39. Puissant, A., Hirsch, J., Weber, C., The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery. Int. J. Remote Sens. 26 (2005), 733–745.
40. Quin, G., Pinel-Puyssegur, B., Nicolas, J.M., Loreaux, P., MIMOSA: an automatic change detection method for SAR time series. IEEE Trans. Geosci. Remote Sens. 52 (2014), 5349–5363.
41. Roy, D.P., Ju, J.C., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., Zhang, C.S., Web-enabled Landsat Data (WELD): Landsat ETM plus composited mosaics of the conterminous United States. Remote Sens. Environ. 114 (2010), 35–49.
42. Schneider, A., Friedl, M.A., Potere, D., A new map of global urban extent from MODIS satellite data. Environ. Res. Lett., 4, 2009.
43. Schneider, A., Friedl, M.A., Potere, D., Mapping global urban areas using MODIS 500-m data: new methods and datasets based on ‘urban ecoregions’. Remote Sens. Environ. 114 (2010), 1733–1746.
44. Shao, Z.F., Fu, H.Y., Fu, P., Yin, L., Mapping urban impervious surface by fusing optical and SAR data at the decision level. Remote Sens., 8, 2016.
45. Shi, K.F., Huang, C., Yu, B.L., Yin, B., Huang, Y.X., Wu, J.P., Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas. Remote Sensing Letters 5 (2014), 358–366.
46. Smits, P.C., Dellepiane, S.G., Schowengerdt, R.A., Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. Int. J. Remote Sens. 20 (1999), 1461–1486.
47. Stehman, S.V., Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62 (1997), 77–89.
48. Stehman, S.V., Foody, G.M., Key issues in rigorous accuracy assessment of land cover products. Remote Sens. Environ., 2019, 231.
49. Sun, Z.C., Xu, R., Du, W.J., Wang, L., Lu, D.S., High-resolution urban land mapping in China from Sentinel 1A/2 Imagery based on Google Earth Engine. Remote Sens., 11, 2019.
50. Weng, Q., Quattrochi, D., Gamba, P.E., Urban Remote Sensing. 2018, CRC press.
51. Weng, Q.H., Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sens. Environ. 117 (2012), 34–49.
52. Xian, G., Homer, C., Updating the 2001 national land cover database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sens. Environ. 114 (2010), 1676–1686.
53. Xie, Y.H., Weng, Q.H., Spatiotemporally enhancing time-series DMSP/OLS nighttime light imagery for assessing large-scale urban dynamics. ISPRS J. Photogramm. Remote Sens. 128 (2017), 1–15.
54. Xu, H., A new index for delineating built-up land features in satellite imagery. Int. J. Remote Sens. 29 (2008), 4269–4276.
55. Xu, H.Q., Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI). Photogramm. Eng. Remote. Sens. 76 (2010), 557–565.
56. Yuan, F., Bauer, M.E., Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens. Environ. 106 (2007), 375–386.
57. Zhang, C., Li, W., Travis, D., Gaps-fill of SLC-off Landsat ETM plus satellite image using a geostatistical approach. Int. J. Remote Sens. 28 (2007), 5103–5122.
58. Zhang, H.S., Xu, R., Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta. Int. J. Appl. Earth Obs. Geoinf. 64 (2018), 87–95.
59. Zhang, H.S., Wang, T., Zhang, Y.H., Dai, Y.R., Jia, J.J., Yu, C., Li, G., Lin, Y.Y., Lin, H., Cao, Y., Quantifying short-term urban land cover change with time series Landsat data: a comparison of four different cities. Sensors, 18, 2018.
60. Zhang, L., Weng, Q.H., Shao, Z.F., An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015. Remote Sens. Environ. 201 (2017), 99–114.
61. Zhang, Y.Z., Zhang, H.S., Lin, H., Improving the impervious surface estimation with combined use of optical and SAR remote sensing images. Remote Sens. Environ. 141 (2014), 155–167.
62. Zhou, T., Zhao, M.F., Sun, C.L., Pan, J.J., Exploring the impact of seasonality on urban land-cover mapping using multi-season sentinel-1A and GF-1 WFV images in a subtropical monsoon-climate region. ISPRS Int. J. Geo Inf., 2018, 7.
63. Zhou, Y., Dong, J.W., Liu, J.Y., Metternicht, G., Shen, W., You, N.S., Zhao, G.S., Xiao, X.M., Are there sufficient Landsat observations for retrospective and continuous monitoring of land cover changes in China?. Remote Sens., 11, 2019.