Biodiv Sci ›› 2018, Vol. 26 ›› Issue (8): 838-849.DOI: 10.17520/biods.2018067

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Researches on mangrove forest monitoring methods based on multi-source remote sensing

Le Wang1,3,*(), Chen Shi1, Jinyan Tian1,2, Xiaonan Song1, Mingming Jia4, Xiaojuan Li1,2, Xiaomeng Liu1, Ruofei Zhong1,2, Dameng Yin3, Shanshan Yang1, Xianxian Guo1   

  1. 1 College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
    2 Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
    3 Department of Geography, University at Buffalo, The State University of New York, 105 Wilkeson Quad, Buffalo, NY 14261, USA
    4 Laboratory for Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
  • Received:2018-03-02 Accepted:2018-08-05 Online:2018-08-20 Published:2018-09-27
  • Contact: Wang Le
  • About author:# Co-first authors


Mangrove forests are ecological communities growing in the intertidal zone of tropical and subtropical coastlines. Due to their high productivity, mangrove forests are essential to persistence of biodiversity along coastlines and have high carbon sequestration ability. In this article we review aspects of monitoring mangrove forests using recent multi-source remote sensing data. First, we reviewed studies on monitoring mangrove dynamics. By integrating object-based and pixel-based classification, high spatial resolution images were used to classify different mangrove species. Landsat images were then used to monitor the dynamics of mangrove forests and analyze factors driving them. Second, we reviewed studies measuring structural parameters of mangroves. Specifically, unmanned aerial vehicle multispectral data and ground-based Light Detection and Ranging (LiDAR) data were used to compute leaf area index of mangrove forests. Finally, we reviewed studies examining physiology and biochemistry parameters. These studies explored adaption of chlorophyll content in mangrove forests under different submergence conditions, whether the invasive species Spartina alterniflora affects the light use efficiency and changed the response of photochemical reflectance index (PRI) to LUE. Our review provides a useful reference for selecting appropriate analytical methods when extracting information of mangroves from remotely sensed data. We emphasize the effectiveness of remote sensing in studying mangrove spatiotemporal patterns, extracting structural parameters, monitoring biochemical parameters, thus aiding efforts to conserve mangrove ecosystems.

Key words: mangrove, biodiversity, multi-source remote sensing, species classification, invasive species