Mapping fine-scale seagrass disturbance using bi-temporal UAV-acquired images and multivariate alteration detection.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 Aug 2024
17 Aug 2024
Historique:
received:
03
04
2024
accepted:
07
08
2024
medline:
18
8
2024
pubmed:
18
8
2024
entrez:
17
8
2024
Statut:
epublish
Résumé
Seagrasses provide critical ecosystem services but cumulative human pressure on coastal environments has seen a global decline in their health and extent. Key processes of anthropogenic disturbance can operate at local spatio-temporal scales that are not captured by conventional satellite imaging. Seagrass management strategies to prevent longer-term loss and ensure successful restoration require effective methods for monitoring these fine-scale changes. Current seagrass monitoring methods involve resource-intensive fieldwork or recurrent image classification. This study presents an alternative method using iteratively reweighted multivariate alteration detection (IR-MAD), an unsupervised change detection technique originally developed for satellite images. We investigate the application of IR-MAD to image data acquired using an unoccupied aerial vehicle (UAV). UAV images were captured at a 14-week interval over two seagrass beds in Brisbane Water, NSW, Australia using a 10-band Micasense RedEdge-MX Dual camera system. To guide sensor selection, a further three band subsets representing simpler sensor configurations (6, 5 and 3 bands) were also analysed using eight categories of seagrass change. The ability of the IR-MAD method, and for the four different sensor configurations, to distinguish the categories of change were compared using the Jeffreys-Matusita (JM) distance measure of spectral separability. IR-MAD based on the full 10-band sensor images produced the highest separability values indicating that human disturbances (propeller scars and other seagrass damage) were distinguishable from all other change categories. IR-MAD results for the 6-band and 5-band sensors also distinguished key seagrass change features. The IR-MAD results for the simplest 3-band sensor (an RGB camera) detected change features, but change categories were not strongly separable from each other. Analysis of IR-MAD weights indicated that additional visible bands, including a coastal blue band and a second red band, improve change detection. IR-MAD is an effective method for seagrass monitoring, and this study demonstrates the potential for multispectral sensors with additional visible bands to improve seagrass change detection.
Identifiants
pubmed: 39154100
doi: 10.1038/s41598-024-69695-8
pii: 10.1038/s41598-024-69695-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
19083Subventions
Organisme : Department of Education and Training | Australian Research Council (ARC)
ID : IC170100023
Informations de copyright
© 2024. The Author(s).
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