Evidence map›Paper›PMID 38036628›Full record

ArticleScientific reports2023

Assessment of the utility of underwater hyperspectral imaging for surveying and monitoring coral reef ecosystems.

Matthew S Mills, Mischa Ungermann, Guy Rigot, Joost den Haan, Javier X Leon, Tom Schils

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Matthew S MillsMarine Laboratory, University of Guam, Mangilao, GU, USA. mmills07088@gmail.com.ORCID 0000-0001-5596-6935
Mischa UngermannPlanBlue GmbH, Bremen, Germany.ORCID 0000-0002-2437-4221
Guy RigotPlanBlue GmbH, Bremen, Germany.
Joost den HaanPlanBlue GmbH, Bremen, Germany.ORCID 0000-0002-5397-9027
Javier X LeonSchool of Science, Technology, and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia.ORCID 0000-0002-4201-5804
Tom SchilsMarine Laboratory, University of Guam, Mangilao, GU, USA.ORCID 0000-0002-1516-9082

Funding

NASA 80NSSC17M0052
6 · The paper itself

Abstract

Technological innovations that improve the speed, scale, reproducibility, and accuracy of monitoring surveys will allow for a better understanding of the global decline in tropical reef health. The DiveRay, a diver-operated hyperspectral imager, and a complementary machine learning pipeline to automate the analysis of hyperspectral imagery were developed for this purpose. To evaluate the use of a hyperspectral imager underwater, the automated classification of benthic taxa in reef communities was tested. Eight reefs in Guam were surveyed and two approaches for benthic classification were employed: high taxonomic resolution categories and broad benthic categories. The results from the DiveRay surveys were validated against data from concurrently conducted photoquadrat surveys to determine their accuracy and utility as a proxy for reef surveys. The high taxonomic resolution classifications did not reliably predict benthic communities when compared to those obtained by standard photoquadrat analysis. At the level of broad benthic categories, however, the hyperspectral results were comparable to those of the photoquadrat analysis. This was particularly true when estimating scleractinian coral cover, which was accurately predicted for six out of the eight sites. The annotation libraries generated for this study were insufficient to train the model to fully account for the high biodiversity on Guam's reefs. As such, prediction accuracy is expected to improve with additional surveying and image annotation. This study is the first to directly compare the results from underwater hyperspectral scanning with those from traditional photoquadrat survey techniques across multiple sites with two levels of identification resolution and different degrees of certainty. Our findings show that dependent on a well-annotated library, underwater hyperspectral imaging can be used to quickly, repeatedly, and accurately monitor and map dynamic benthic communities on tropical reefs using broad benthic categories.

Indexed as

AnthozoaCoral ReefsAnimalsEcosystemHyperspectral ImagingReproducibility of Results

Identifiers

PMID38036628
PMCPMC10689744

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.