Ecosystem Monitoring

Combining Decision Trees with Angle Indices to Identify Mangrove Forest at Shenzhen Bay, China

  • 1. Key Laboratory of Poyang Lake Wetland and Watershed Research, School of Geography and Environment of Jiangxi Normal University, Nanchang 330022, China;
    2. School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received date: 2016-09-18

  Online published: 2017-09-27

Supported by

National Natural Science Foundation of China (41201461)


Mangroves are woody plant communities in the intertidal zone of tropical and subtropical coasts that play an important role in these zones. The infrared wave band is one of the key bands in the remote sensing identification of mangrove forest, and ALI (advanced land imagery) has a large number of infrared bands. Two angle indices were proposed based on liquid water absorption at band 5p and band 5 of EO-1 ALI, denoted as β1.25 and β1.65 respectively. A decision tree method was adopted to identify mangrove forest using remote sensing techniques for β1.25-β1.65 and NDVI (normalized difference vegetation index) for EO-1 ALI imagery acquired at Shenzhen Bay. The results showed that the reflectance of mangrove forests at band 5p and band 5 was significantly lower than that of terrestrial vegetation due to the characteristics of coastal wetlands of mangrove forests. This resulted in a greater β1.25-β1.65 value for mangrove forest than terrestrial vegetation. The decision tree method using β1.25-β1.65 and NDVI effectively identifies mangrove forest from other land cover categories. The misclassification and leakage rates were 4.29% and 5.11% respectively. ALI sensors with many infrared bands could play an important role in discriminating mangrove forest.

Cite this article

LIU Chunyan, GUO Hongqin, ZHANG Xuehong, CHEN Jian . Combining Decision Trees with Angle Indices to Identify Mangrove Forest at Shenzhen Bay, China[J]. Journal of Resources and Ecology, 2017 , 8(5) : 545 -549 . DOI: 10.5814/j.issn.1674-764x.2017.05.012


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