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Land Cover Change Analysis in Wuhan, China Using Google Earth Engine Platform and Ancillary Knowledge

  • Yahya Ali KhanEmail author
  • Yuwei Wang
  • Zongyao Sha
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

The land type cover information has significantly important role to understand land type change and earth activities. Despite the availability of numerous high-resolution satellite data, very minimal number of land type change researches are available up till now due to the computational limitations. This study provides land type change mapping based on high-resolution (30 m) using high computing cloud base platform (google earth engine). The supervised classification training technique is being used in our study. We gathered the land cover type of two year (2016–2017) and addressed the transformations among them during these years. The study is based on four land cover types specifically water, urban land, forest and crop land. This method is provided to overcome the limitation of high computing platform and lack of the availability of high-resolution land cover change data.

Keywords

LCC: land cover change GEE: google earth engine NDVI: normal difference vegetation index 

Notes

Acknowledgement

The National Natural Science Foundation of China (Nos. 41371371 and 41871296), Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education) and East China Normal University (No. KLGIS2017A05).

References

  1. 1.
    Chen, J., Liao, A.P., Cao, X.: Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS J. Photogramm. Remote. Sens. 103, 7–27 (2015)CrossRefGoogle Scholar
  2. 2.
    Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land use classification in remote sensing images by convolutional neural networks. http://arxiv.org/abs/1508.00092
  3. 3.
    Midekisa, A., et al.: Mapping land cover change over continental Africa using landsat and google earth engine cloud computing. PLoS ONE 12(9), e0184926 (2017).  https://doi.org/10.1371/journal.pone.0184926CrossRefGoogle Scholar
  4. 4.
    Samaniego, L., Schulz, K.: Supervised classification of agricultural land cover using a modified k-NN technique (MNN) and landsat remote sensing imagery. Remote Sens. 1, 875–895 (2009).  https://doi.org/10.3390/rs1040875CrossRefGoogle Scholar
  5. 5.
    Yan, L., Roy, D.P.: Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction. Remote Sens. Environ. 158, 478–491 (2015)CrossRefGoogle Scholar
  6. 6.
    Sidhu, N., Pebesma, E., Câmara, G.: Using Google earth engine to detect land cover change: Singapore as a use case. Eur. J. Remote Sens. 51(1), 486–500 (2018).  https://doi.org/10.1080/22797254.2018.1451782CrossRefGoogle Scholar
  7. 7.
    Gounaridis, D., Symeonakis, E.: Incorporating density in spatiotemporal land use/cover change patterns: the case of Attica. Greece. Remote Sens. 10, 1034 (2018).  https://doi.org/10.3390/rs10071034CrossRefGoogle Scholar
  8. 8.
    Donchyts, G., Baart, F., Winsemius, H., Gorelick, N., Kwadijk, J., van de Giesen, N.: Earth’s surface water change over the past 30 years. Nat. Clim. Change 6(9), 810–813 (2016)CrossRefGoogle Scholar
  9. 9.
    Patel, N.N., et al.: Multitemporal settlement and population mapping from Landsat using Google Earth Engine. Int. J. Appl. Earth Obs. 35, 199–208 (2015)CrossRefGoogle Scholar
  10. 10.
    Xiong, J., et al.: Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. 126, 225–244 (2017)CrossRefGoogle Scholar
  11. 11.
    Hansen, M.C., et al.: High-resolution global maps of 21st-century forest cover change. Science 342(6160), 850–853 (2013)CrossRefGoogle Scholar
  12. 12.
    Liss, B., Howland, M.D.: Testing Google Earth Engine for the automatic identification and vectorization of archaeological features: a case study from Faynan. Jordan J. Archaeol. Sci. Rep. 15, 299–304 (2017)Google Scholar
  13. 13.
    Huang, H., et al.: Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. 202, 166–176 (2017)CrossRefGoogle Scholar
  14. 14.
    Mellor, A., Haywood, A.: The performance of random forests in an operational setting for large area sclerophyll forest classification Andrew. Remote Sens. 5, 2838–2856 (2013).  https://doi.org/10.3390/rs5062838CrossRefGoogle Scholar
  15. 15.
    Gómez-Chova, L., Amorós-López, J., Mateo-García, G., Muñoz-Marí, J., Camps-Valls, G.: Cloud masking and removal in remote sensing image time series. J. Appl. Remote Sens. 11(1), 015005 (2017).  https://doi.org/10.1117/1.JRS.11.015005CrossRefGoogle Scholar
  16. 16.
    Lu, H., Zhang, C., Liu, G., Ye, X., Miao, C.: Mapping China’s ghost cities through the combination of nighttime satellite data and daytime satellite data. Remote Sens. (2018).  https://doi.org/10.3390/rs10071037CrossRefGoogle Scholar
  17. 17.
    Tsai, Y.H., Stow, D., Chen, H.L., Lewison, R., An, L., Shi, L.: Mapping vegetation and land use types in Fanjingshan National Nature Reserve using Google Earth Engine. Remote Sens. 10(6), 927 (2018)CrossRefGoogle Scholar
  18. 18.
    Zhao, H., Zhang, H., Miao, C., Ye, X., Min, M.: Linking heat source–sink landscape patterns with analysis of urban heat islands: study on the fast-growing Zhengzhou City in Central China. Remote Sens. (2018).  https://doi.org/10.3390/rs10081268CrossRefGoogle Scholar
  19. 19.
    Yan, Y., Zhou, R.: Suitability evaluation of urban construction land based on an approach of vertical-horizontal processes. ISPRS Int. J. Geo-Inf. 7, 198 (2018).  https://doi.org/10.3390/ijgi7050198CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Remote Sensing and EngineeringWuhan UniversityWuhanChina

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