Skip to main content
Log in

Superpixel for seagrass mapping: a novel method using PlanetScope imagery and machine learning in Tauranga harbour, New Zealand

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript


Seagrass ecosystem provides valuable ecosystem services and is significant blue carbon sink. This resource, however, has been degraded across the globe with a loss rate of 7% year−1 to the end of twentieth century. The loss of seagrass meadows might lead to an unexpected emission of CO2 into the atmosphere, aggravating global warming and resulting in potential damages to regional ecology and economies. Accurate mapping of meadows extent in different coverages from remotely sensed data, therefore is in high demand as the first step in the strategy of monitoring, report, verification (MRV) that underpins large scale conservation of global seagrass. Despite the higher accuracy of seagrass mapping in recent years, several challenges still persist, particularly when dealing with degraded, sparse seagrass meadows. In this research, we propose a novel and high accuracy approach for mapping dense and sparse meadows of the small size Zostera muelleri seagrass, using high spatial resolution imagery (PlanetScope) at 3 m spatial resolution, and advanced machine learning (ML) models for a ten-fold cross-validation superpixel-based classification in Tauranga Harbour, New Zealand. We archive high mapping accuracy (overall accuracy = 0.913, Kappa coefficient (κ) = 0.786, Matthews correlation coefficient (MCC) = 0.796 and F1 = 0.908) using the LightGBM model from a set of superpixel image coupled with the Bayesian optimization for hyper-parameter tuning. Our proposed approach is solid and reliable with evidences of improving κ (10%) and MCC (11%) when compared with pixel-based image classification, and is expected to provide novel, effective techniques for quantifying the spatial distribution and area of seagrass ecosystem worldwide.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.


Download references


Our gratefulness to the staffs in the Marine Field Station, Tauranga, New Zealand for supporting the field survey conducted in Tauranga Harbour, New Zealand. A special thanks to the Planet and the Education & Research program ( for providing the PlanetScope images to this study. We also thank for the partly supports of the Core Research Program (No. NCM.DHH.2020.03).


The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations



Conceptualization, NTH; methodology, NTH; software, NTH and TDP; validation, NTH, HQN, TDP; resources, NTH, CTH, IH; writing-original draft preparation, NTH; writing-review and editing, NTH, HQN, TDP, CTH, and IH. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Nam-Thang Ha.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 377 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ha, NT., Nguyen, HQ., Pham, TD. et al. Superpixel for seagrass mapping: a novel method using PlanetScope imagery and machine learning in Tauranga harbour, New Zealand. Environ Earth Sci 82, 154 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: