Mapping mangrove and other coastal blue carbon vegetation
Field monitoring using high resolution structural volumetric measurement techniques for carbon (i.e Structure from Motion, Lidar, high resolution satellites) with machine learning and deep learning methods are revolutionising many areas of ecological monitoring but as yet have had little application to methods leading to robust carbon accounting.
We aim to address this knowledge gap by integrating sensor technology and machine learning methods into on ground quantification and assessment ecosystems and their blue carbon stores correlating in field measurements to broad scale remote sensing using satellite sensors.
- Map and quantify current and predicted threats and impacts on Blue Carbon communities
- Estimate total Blue Carbon value under different climate and impact scenarios
The successful student will collect field data to validate and model the spatial extent, and variability in marine and coastal habitat. They would model the distribution at various spatial and temporal scales using spatial and remote sensing methods.
- Lovelock, C.E., Atwood, T., Baldock, J., Duarte, C.M., Hickey, S., Lavery, P.S., Masque, P., Macreadie, P.I., Ricart, A.M., Serrano, O. and Steven, A., 2017. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Frontiers in Ecology and the Environment, 15(5), pp.257-265.
- Wernberg, T., Bennett, S., Babcock, R.C., De Bettignies, T., Cure, K., Depczynski, M., Dufois, F., Fromont, J., Fulton, C.J., Hovey, R.K. and Harvey, E.S., 2016. Climate-driven regime shift of a temperate marine ecosystem. Science, 353(6295), pp.169-172.
- Lovelock, C.E., Feller, I.C., Reef, R., Hickey, S. and Ball, M.C., 2017. Mangrove dieback during fluctuating sea levels. Scientific Reports, 7(1), pp.1-8.
- Hickey, S.M., Callow, N.J., Phinn, S., Lovelock, C.E. and Duarte, C.M., 2018. Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach. Estuarine, Coastal and Shelf Science, 200, pp.194-201.
- Martínez, B., Radford, B., Thomsen, M.S., Connell, S.D., Carreño, F., Bradshaw, C.J., Fordham, D.A., Russell, B.D., Gurgel, C.F.D. and Wernberg, T., 2018. Distribution models predict large contractions of habitat‐forming seaweeds in response to ocean warming. Diversity and Distributions, 24(10), pp.1350-1366.
My research interests focus on geographical elements of environmental systems, and how spatial and temporal models can examine environmental change and drivers of change. Particularly, investigating coastal and marine changes in habitat through Geographic Information Systems (GIS) and remote sensing data.
This project will be co-supervised by Dr Ben Radford. Ben is a spatial modeller who works on benthic habitat.
Funding and Collaborations
- Dr Ben Radford Australian Institute of Marine Science
How to Apply
- To be accepted into the Doctor of Philosophy, an applicant must demonstrate they have sufficient background experience in independent supervised research to successfully complete, and provide evidence of English language proficiency
- Requirements specific to this project
- Experience and knowledge in spatial ecology, GIS and field data collection
- English language competence
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