Detecting multi-decadal changes in seagrass cover in tauranga harbour, new zealand, using landsat imagery and boosting ensemble classification techniques

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

Seagrass provides a wide range of essential ecosystem services, supports climate change mitigation, and contributes to blue carbon sequestration. This resource, however, is undergoing significant declines across the globe, and there is an urgent need to develop change detection techniques appropriate to the scale of loss and applicable to the complex coastal marine environment. Our work aimed to develop remote-sensing-based techniques for detection of changes between 1990 and 2019 in the area of seagrass meadows in Tauranga Harbour, New Zealand. Four state-of-the-art machine-learning models, Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boost (XGB), and CatBoost (CB), were evaluated for classification of seagrass cover (presence/absence) in a Landsat 8 image from 2019, using near-concurrent Ground-Truth Points (GTPs). We then used the most accurate one of these models, CB, with historic Landsat imagery supported by classified aerial photographs for an estimation of change in cover over time. The CB model produced the highest accuracies (precision, recall, F1 scores of 0.94, 0.96, and 0.95 respectively). We were able to use Landsat imagery to document the trajectory and spatial distribution of an approximately 50% reduction in seagrass area from 2237 ha to 1184 ha between the years 1990–2019. Our illustration of change detection of seagrass in Tauranga Harbour suggests that machine-learning techniques, coupled with historic satellite imagery, offers potential for evaluation of historic as well as ongoing seagrass dynamics.

Original languageEnglish
Article number371
Pages (from-to)1-21
Number of pages21
JournalISPRS International Journal of Geo-Information
Volume10
Issue number6
DOIs
Publication statusPublished - Jun 2021
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2021. 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 mapping
  • Tauranga Harbour
  • change detection
  • landsat
  • random forest
  • support vector machine
  • extreme gradient boost
  • CatBoost
  • machine learning

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