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Mapping the Distribution of Exotic Mangrove Species in Shenzhen Bay Using Worldview-2 Imagery

  • Hongzhong LiEmail author
  • Yu Han
  • Jinsong Chen
  • Shanxin Guo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 980)

Abstract

Shenzhen Bay is an important distribution area of mangrove, with Futian Reserve and Mai Po Reserve. Sonneratia (including Sonneratia caseolaris and Sonneratia apetala) are exotic mangrove species in Shenzhen Bay. Study on the Sonneratia potential impact on native mangrove species requires accurate and repeatable mapping of Sonneratia distribution. Several previous studies have mapped mangrove extent and species, but Sonneratia have been largely ignored. In this study, high resolution Worldview-2 (WV2) imagery was used in Shenzhen Bay Sonneratia mapping. Separability analyses with spectral and textural features were conducted based on Jeffries-Matusita (JM) distance. Results showed that Sonneratia caseolaris and Sonneratia apetala were inseparable in WV2 imagery, and therefore the two species were merged into a single species in the study. Maximum likelihood (ML) classifier, neural net (NN) classifier and support vector machine (SVM) classifier were applied to spectral and textural features, and six mangrove species classification results were obtained. Considering the six classification results together, the distribution of Sonneratia was mapped based on the criteria that, for each polygon, it was categorized as Sonneratia if and only if it was classified as Sonneratia in at least four classification results. The distribution results of Sonneratia showed an agreement with distribution characteristic based on field survey and past literatures. The producer’s accuracy was 79.24% and the user’s accuracy was 92.14%, which indicated great potential of using high-resolution multi-spectral data for distinguishing and mapping Sonneratia.

Keywords

Mangrove Exotic species Sonneratia Worldview-2 

Notes

Acknowledgments

The work was funded by the Fundamental Research Foundation of Shenzhen Technology and Innovation Council (JCYJ20170818155853672, JCYJ20160429191127529), Natural science foundation of China project 41771403, research project from the Chinese Academy of Sciences (XDA05050107-03, XDA19030301), and the Open Fund of Key laboratory of Urban land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2016-02-019). We wish to take this opportunity to express their sincere acknowledgment to them.

References

  1. 1.
    AFCD: Mai Po Inner Deep Bay Ramsar site management plan (2011)Google Scholar
  2. 2.
    Baraldi, A., Panniggiani, F.: An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sens. 33, 293–304 (1995)CrossRefGoogle Scholar
  3. 3.
    Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote. Sens. 28, 45–62 (2002)CrossRefGoogle Scholar
  4. 4.
    Cornforth, W., Fatoyinbo, T., Freemantle, T., Pettorelli, N.: Advanced Land Observing Satellite Phased Array Type L-Band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remote Sens. 5, 224–237 (2013)CrossRefGoogle Scholar
  5. 5.
    Everitt, J., Yang, C., Sriharan, S., Judd, F.: Using high resolution satellite imagery to map black mangrove on the Texas Gulf Coast. J. Coast. Res. 6, 1582–1586 (2008)CrossRefGoogle Scholar
  6. 6.
    Gao, J.: A hybrid method toward accurate mapping of mangroves in a marginal habitat from SPOT multispectral data. Int. J. Remote Sens. 19, 1887–1899 (1998)CrossRefGoogle Scholar
  7. 7.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  8. 8.
    Harris, T: Spectral target detection for detecting and characterizing floating marine debris. American Geophysical Union, San Francisco, CA, 3–7 December 2012Google Scholar
  9. 9.
    Heenkenda, M.K., Joyce, K.E., Maier, S.W., Bartolo, R.: Mangrove species identification: comparing worldview-2 with aerial photographs. Remote Sens. 6, 6064–6088 (2014)CrossRefGoogle Scholar
  10. 10.
    Heenkenda, M.K., Joyce, K.E., Maier, S.W., Bruin, S.: Quantifying mangrove chlorophyll from high spatial resolution imagery. ISPRS J. Photogrammetry Remote Sens. 108, 234–244 (2015)CrossRefGoogle Scholar
  11. 11.
    Heumann, B.: An object-based classification of mangroves using a hybrid decision tree-support vector machine approach. Remote Sens. 3, 2440–2460 (2011)CrossRefGoogle Scholar
  12. 12.
    Huang, X., Liu, X., Zhang, L.: A multichannel gray level co-occurrence matrix for multi/hyperspectral image texture representation. Remote Sens. 6, 8424–8445 (2014)CrossRefGoogle Scholar
  13. 13.
    Jia, M., Zhang, Y., Wang, Z., Song, K., Ren, C.: Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data. Int. J. Appl. Earth Obs. Geoinf. 33, 226–231 (2014)CrossRefGoogle Scholar
  14. 14.
    Kanniah, K., Sheikhi, A., Cracknell, A.: Satellite images for monitoring mangrove cover changes in a fast growing economic region in Southern Peninsular Malaysia. Remote Sens. 7, 14360–14385 (2015)CrossRefGoogle Scholar
  15. 15.
    Kent, M., Coker, P.: Vegetation Description and Analysis: A Practical Approach. Wiley, London (1992)Google Scholar
  16. 16.
    Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T.V., Dech, S.: Remote sensing of mangrove ecosystems: a review. Remote Sens. 3, 878–928 (2011)CrossRefGoogle Scholar
  17. 17.
    Kwok, W., Tang, W., Kwok, B.: An introduction to two exotic mangrove species in Hong Kong: Sonneratia caseolaris and S. apetala. Hong Kong Biodivers. 10, 9–12 (2005)Google Scholar
  18. 18.
    Liu, Z., Li, J., Lim, B., Seng, C., Inbaraj, S.: Object-based classification for mangrove with VHR remotely sensed image. In: Proceedings of SPIE the International Society for Optical Engineering, vol. 6752, pp. 83–87 (2007)Google Scholar
  19. 19.
    Miller, D., Kaminsky, E., Rana, S.: Neural network classification of remote-sensing data. Comput. Geosci. 21, 377–386 (1995)CrossRefGoogle Scholar
  20. 20.
    Mustapha, M., Lim, H., Mat Jafri, M.: Comparison of neural network and maximum likelihood approaches in image classification. J. Appl. Sci. 10, 2847–2854 (2010)CrossRefGoogle Scholar
  21. 21.
    Nussbaum, S., Niemeyer, I., Canty, M.: SEaTH-a new tool for automated feature extraction in the context of object-based image analysis. In: 1st International Conference on Object-based Image Analysis (2006)Google Scholar
  22. 22.
    Xiao, H., Zeng, H., Zan, Q., Bai, Y., Cheng, H.: Decision tree model in extraction of mangrove community information using hyperspectral image data. J. Remote Sens. 11, 531–537 (2007)Google Scholar
  23. 23.
    Richards, J.A.: Remote Sensing Digital Image Analysis: An Introduction, 2nd edn. Springer, Heidelberg (1993).  https://doi.org/10.1007/978-3-642-88087-2CrossRefGoogle Scholar
  24. 24.
    Sun, Y., Zhao, D., Guo, W.: A review on the application of remote sensing in mangrove ecosystem monitoring. Acta Ecol. Sin. 33, 4523–4538 (2013)CrossRefGoogle Scholar
  25. 25.
    Valentyn, A., Tolpekin, Alfred S.: Quantification of the effects of land-cover-class spectral separability on accuracy of markov-random-field-based superresolution mapping. IEEE Trans. Geosci. Remote Sens. 47, 3283–3296 (2009)CrossRefGoogle Scholar
  26. 26.
    Wang, L., Sousa, W.P., Gong, P., Biging, G.: Comparison of IKONOS and Quickbird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sens. Environ. 91, 432–440 (2004)CrossRefGoogle Scholar
  27. 27.
    Wang, T., Zhang, H., Lin, H., Fang, C.: Textural-spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery. Remote Sens. 8(1), 24 (2016)CrossRefGoogle Scholar
  28. 28.
    Wong, F., Fung, T.: Combining EO-1 Hyperion and Envisat ASAR data for mangrove species classification in Mai Po Ramsar Site, Hong Kong. Int. J. Remote Sens. 35, 7828–7856 (2014)CrossRefGoogle Scholar
  29. 29.
    Wu, Y.: The study on the spatial pattern of mangrove community based on contourlet transformation-taking Shenzhen Futian Mangrove Nature Reserve as example. Guangzhou University (2012)Google Scholar
  30. 30.
    WWF Hong Kong: Mai Po Nature Reserve habitat management, monitoring and research plan 2013–2018 (2013)Google Scholar
  31. 31.
    Xin, K., Zhou, Q., Arndt, S., Yang, X.: Invasive capacity of the mangrove Sonneratia apetala in Hainan Island, China. J. Trop. For. Sci. 25, 70–78 (2013)Google Scholar
  32. 32.
    Zan, Q., Wang, B., Wang, Y., Li, M.: Ecological assessment on the introduced Sonneratia caseolaris and S. apetala at the mangrove forest of Shenzhen Bay, China. Acta Bot. Sin. 45, 544–551 (2003)Google Scholar
  33. 33.
    Zheng, D., Li, M., Zheng, S., Liao, B.: Headway of study on mangrove recovery and development in China. Guangdong For. Sci. Technol. 19, 10–14 (2003)Google Scholar
  34. 34.
    Zhou, T., Liu, S., Feng, Z., Liu, G., Gan, Q., Peng, S.: Use of exotic plants to control Spartina alterniflora invasion and promote mangrove restoration. Sci. Rep. 5, 12980 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hongzhong Li
    • 1
    • 2
    Email author
  • Yu Han
    • 1
  • Jinsong Chen
    • 1
  • Shanxin Guo
    • 1
  1. 1.Shenzhen Institute of Advanced Technology, CASShenzhenPeople’s Republic of China
  2. 2.Key Laboratory of Urban Land Resources Monitoring and SimulationMinistry of Land and ResourcesShenzhenPeople’s Republic of China

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