Authors / CoAuthors
Li, J. | Siwabessy, P.J.W. | Tran, M. | Huang, Z. | Heap, A.H.
Abstract
Geoscience Australia provides spatial information of seabed environment to support Australian marine zone management. Central to this approach is the prediction of Australia's seabed biodiversity from spatially continuous data of seabed biophysical properties. Seabed hardness is an important environmental property for predicting marine biodiversity and is often inferred from multibeam backscatter data. Although seabed hardness can be measured based on video images, they are only available at a limited number of sampled locations. In this study, we attempt to predict the spatial distribution of seabed hardness using random forest based on video classification and available marine environmental properties. We illustrate the effects of cross-validation methods including a new cross-validation function on the selection of optimal predictive models. We also test the effects of various predictor sets on the predictive accuracy. This study provides an example for predicting the spatial distribution of environmental properties using random forest in R.
Product Type
nonGeographicDataset
eCat Id
74422
Contact for the resource
Custodian
Point of contact
Cnr Jerrabomberra Ave and Hindmarsh Dr GPO Box 378
Canberra
ACT
2601
Australia
Keywords
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- External Publication
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- model
- ( Theme )
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- data
- ( Theme )
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- multibeam
- ( Theme )
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- backscatter
- ( Theme )
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- marine
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- AU
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
Publication Date
2012-07-18T00:00:00
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unknown
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geoscientificInformation
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Unknown
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Extents
[-12.4183, -10.2875, 129.4526, 130.0773]
Reference System
Spatial Resolution
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