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Watching the Saltmarsh Grow A High-Resolution Remote Sensing Approach to Quantify the Effects of Wetland Restoration25.68 MB
Published VersionCC BY V4.0, Open Access
Journal article
Watching the Saltmarsh Grow: A High-Resolution Remote Sensing Approach to Quantify the Effects of Wetland Restoration
Remote Sensing, Vol.14(18), pp.1-18
2022
Abstract
Coastal wetlands are restored to regenerate lost ecosystem services. Accurate and frequent representations of the distribution and area of coastal wetland communities are critical for evaluating restoration success. Typically, such data are acquired through laborious, intensive and expensive field surveys or traditional remote sensing methods that can be erroneous. Recent advances in remote sensing techniques such as high resolution sensors (<2 m resolution), object based image analysis and shallow learning classifiers provide promising alternatives but have rarely been applied in a restoration context. We measured the changes to wetland communities at a 200 ha restoring coastal wetland in eastern Australia, using remotely sensed Worldview 2 imagery, object-based image analysis and random forest classification. Our approach used structural rasters (digital elevation and canopy height models) and a multi temporal technique to distinguish between spectrally similar land cover. The accuracy of our land cover maps was high, with overall accuracies ranging between 91 and 95%, and this supported early detection of increases in the area of key ecosystems, including mixed she oak and paperbark (10 ha), mangroves (0.91 ha) and saltmarsh (4.31 ha), over a 5 year monitoring period. Our approach provides coastal managers with an accurate and frequent method for quantifying early responses of coastal wetlands to restoration, which is essential for informing adaptive management in the regeneration of ecosystem services.
Details
- Title
- Watching the Saltmarsh Grow: A High-Resolution Remote Sensing Approach to Quantify the Effects of Wetland Restoration
- Authors
- Ashley Rummell (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringJavier Leon (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringHayden Borland (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringBrittany Elliott (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringBen Gilby (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringChristopher Henderson (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringAndrew Olds (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Remote Sensing, Vol.14(18), pp.1-18
- Publisher
- MDPI AG
- Date published
- 2022
- DOI
- 10.3390/rs14184559
- ISSN
- 2072-4292
- Copyright note
- © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99678891102621
- Output Type
- Journal article
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- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
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