A comparative assessment of ensemble-based machine learning and maximum likelihood methods for mapping seagrass using sentinel-2 imagery in Tauranga Harbor, New Zealand

Nam Thang Ha*, Merilyn Manley-Harris, Tien Dat Pham, Ian Hawes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
5 Downloads (Pure)

Abstract

Seagrass has been acknowledged as a productive blue carbon ecosystem that is in significant decline across much of the world. A first step toward conservation is the mapping and monitoring of extant seagrass meadows. Several methods are currently in use, but mapping the resource from satellite images using machine learning is not widely applied, despite its successful use in various comparable applications. This research aimed to develop a novel approach for seagrass monitoring using state-of-the-art machine learning with data from Sentinel-2 imagery. We used Tauranga Harbor, New Zealand as a validation site for which extensive ground truth data are available to compare ensemble machine learning methods involving random forests (RF), rotation forests (RoF), and canonical correlation forests (CCF) with the more traditional maximum likelihood classifier (MLC) technique. Using a group of validation metrics including F1, precision, recall, accuracy, and the McNemar test, our results indicated that machine learning techniques outperformed the MLC with RoF as the best performer (F1 scores ranging from 0.75-0.91 for sparse and dense seagrass meadows, respectively). Our study is the first comparison of various ensemble-based methods for seagrass mapping of which we are aware, and promises to be an effective approach to enhance the accuracy of seagrass monitoring.

Original languageEnglish
Article number355
Pages (from-to)1-16
Number of pages16
JournalRemote Sensing
Volume12
Issue number3
DOIs
Publication statusPublished - 1 Feb 2020
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • seagrass
  • Sentinel-2
  • random forest
  • rotation forest
  • canonical correlation forest
  • maximum likelihood
  • Tauranga
  • machine learning
  • remote sensing

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