Files and links (1)
CC BY V4.0, Open Access
Journal article
Mapping fine-scale seagrass disturbance using bi-temporal UAV-acquired images and multivariate alteration detection
Scientific reports, Vol.14(1), 19083
2024
Abstract
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.
Details
- Title
- Mapping fine-scale seagrass disturbance using bi-temporal UAV-acquired images and multivariate alteration detection
- Authors/Creators
- Jamie Simpson - The University of SydneyKevin P. Davies - The University of SydneyPaul Barber - Murdoch University, Centre for Terrestrial Ecosystem Science and SustainabilityEleanor Bruce - The University of Sydney
- Publication Details
- Scientific reports, Vol.14(1), 19083
- Publisher
- Nature Publishing Group UK
- Identifiers
- 991005695569807891
- Copyright
- © The Author(s) 2024
- Murdoch Affiliation
- Centre for Terrestrial Ecosystem Science and Sustainability
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
3 File views/ downloads
82 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.2 Marine Biology
- 3.2.1182 Coastal Vegetation
- Web Of Science research areas
- Environmental Sciences
- ESI research areas
- Environment/Ecology