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)


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.


Mangrove Exotic species Sonneratia Worldview-2 



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.


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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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