Translator Disclaimer
1 September 2017 Combining Decision Trees with Angle Indices to Identify Mangrove Forest at Shenzhen Bay, China
Liu Chunyan, Guo Hongqin, Zhang Xuehong, Chen Jian
Author Affiliations +
Abstract

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 β1.25β1.65 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 β1.25β1.65 value for mangrove forest than terrestrial vegetation. The decision tree method using β1.25β1.65 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.

Liu Chunyan, Guo Hongqin, Zhang Xuehong, and Chen Jian "Combining Decision Trees with Angle Indices to Identify Mangrove Forest at Shenzhen Bay, China," Journal of Resources and Ecology 8(5), 545-549, (1 September 2017). https://doi.org/10.5814/j.issn.1674-764x.2017.05.012
Received: 18 September 2016; Accepted: 1 June 2017; Published: 1 September 2017
JOURNAL ARTICLE
5 PAGES


SHARE
ARTICLE IMPACT
RIGHTS & PERMISSIONS
Get copyright permission
Back to Top