Classification of mangrove species using combined WordView-3 and LiDAR data in Mai Po Nature Reserve, Hong Kong

Li, Q. , Wong, F. and Fung, T. (2019) Classification of mangrove species using combined WordView-3 and LiDAR data in Mai Po Nature Reserve, Hong Kong. Remote Sensing, 11(18), 2114. (doi: 10.3390/rs11182114)

Abstract

Mangroves have significant social, economic, environmental, and ecological values but they are under threat due to human activities. An accurate map of mangrove species distribution is required to effectively conserve mangrove ecosystem. This study evaluates the synergy of WorldView-3 (WV-3) spectral bands and high return density LiDAR-derived elevation metrics for classifying seven species in mangrove habitat in Mai Po Nature Reserve in Hong Kong, China. A recursive feature elimination algorithm was carried out to identify important spectral bands and LiDAR (Airborne Light Detection and Ranging) metrics whilst appropriate spatial resolution for pixel-based classification was investigated for discriminating different mangrove species. Two classifiers, support vector machine (SVM) and random forest (RF) were compared. The results indicated that the combination of 2 m resolution WV-3 and LiDAR data yielded the best overall accuracy of 0.88 by SVM classifier comparing with WV-3 (0.72) and LiDAR (0.79). Important features were identified as green (510–581 nm), red edge (705–745 nm), red (630–690 nm), yellow (585–625 nm), NIR (770–895 nm) bands of WV-3, and LiDAR metrics relevant to canopy height (e.g., canopy height model), canopy shape (e.g., canopy relief ratio), and the variation of height (e.g., variation and standard deviation of height). LiDAR features contributed more information than spectral features. The significance of this study is that a mangrove species distribution map with satisfactory accuracy can be acquired by the proposed classification scheme. Meanwhile, with LiDAR data, vertical stratification of mangrove forests in Mai Po was firstly mapped, which is significant to bio-parameter estimation and ecosystem service evaluation in future studies.

• Item Type: Articles
• Additional Information: This research was funded by a Hong Kong Research Grant Council General Research Grant Project No. 14618715.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Dr Qiaosi
• Authors: Li, Q., Wong, F., and Fung, T.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Remote Sensing
• Publisher: MDPI AG
• ISSN: 2072-4292
• ISSN (Online): 2072-4292
• Copyright Holders: Copyright © 2019 by the authors.
• First Published: First published in Remote Sensing 11(18):2114
• Publisher Policy: Reproduced under a Creative Commons license