Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Book cover Geo-informatics in Sustainable Ecosystem and Society (GSES 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 980))

  • 890 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. AFCD: Mai Po Inner Deep Bay Ramsar site management plan (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  7. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)

    Article  Google Scholar 

  8. Harris, T: Spectral target detection for detecting and characterizing floating marine debris. American Geophysical Union, San Francisco, CA, 3–7 December 2012

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  11. Heumann, B.: An object-based classification of mangroves using a hybrid decision tree-support vector machine approach. Remote Sens. 3, 2440–2460 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Kent, M., Coker, P.: Vegetation Description and Analysis: A Practical Approach. Wiley, London (1992)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Miller, D., Kaminsky, E., Rana, S.: Neural network classification of remote-sensing data. Comput. Geosci. 21, 377–386 (1995)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Richards, J.A.: Remote Sensing Digital Image Analysis: An Introduction, 2nd edn. Springer, Heidelberg (1993). https://doi.org/10.1007/978-3-642-88087-2

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. WWF Hong Kong: Mai Po Nature Reserve habitat management, monitoring and research plan 2013–2018 (2013)

    Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, CAS, Xueyuan Avenue 1068, Shenzhen, People’s Republic of China

    Hongzhong Li, Yu Han, Jinsong Chen & Shanxin Guo

  2. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, People’s Republic of China

    Hongzhong Li

Authors
  1. Hongzhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinsong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shanxin Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongzhong Li .

Editor information

Editors and Affiliations

  1. Eastern Michigan University, Ypsilanti, MI, USA

    Yichun Xie

  2. Hebei University of Engineering, Handan, China

    Anbing Zhang

  3. Hebei University of Engineering, Handan, Hebei, China

    Haixin Liu

  4. Hebei University of Engineering, Handan, China

    Lili Feng

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, H., Han, Y., Chen, J., Guo, S. (2019). Mapping the Distribution of Exotic Mangrove Species in Shenzhen Bay Using Worldview-2 Imagery. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7025-0_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7024-3

  • Online ISBN: 978-981-13-7025-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation