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A Spatially Explicit Comparison of Quantitative and Categorical Modelling Approaches for Mapping Seabed Sediments Using Random Forest

1
Department of Geography, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
2
Geological Survey of Norway (NGU), Postal Box 6315 Torgarden, 7491 Trondheim, Norway
3
Department of Geography & Planning, 117 Science Place, University of Saskatchewan, Saskatoon, SK S7N 5C8, Canada
4
Ivany Campus, Nova Scotia Community College, 80 Mawiomi Place, Dartmouth, NS B2Y 0A5, Canada
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(6), 254; https://doi.org/10.3390/geosciences9060254
Received: 31 March 2019 / Revised: 24 May 2019 / Accepted: 3 June 2019 / Published: 6 June 2019
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Abstract

Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods. View Full-Text
Keywords: marine habitat mapping; benthic habitat mapping; grain size modelling; spatial autocorrelation; multiscale; marine geology marine habitat mapping; benthic habitat mapping; grain size modelling; spatial autocorrelation; multiscale; marine geology
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Misiuk, B.; Diesing, M.; Aitken, A.; Brown, C.J.; Edinger, E.N.; Bell, T. A Spatially Explicit Comparison of Quantitative and Categorical Modelling Approaches for Mapping Seabed Sediments Using Random Forest. Geosciences 2019, 9, 254.

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