Refinements in Eelgrass Mapping at Tabusintac Bay (New Brunswick, Canada): A Comparison between Random Forest and the Maximum Likelihood Classifier: Raffinements de la cartographie de la zostère dans la baie de Tabusintac (Nouveau-Brunswick, Canada): une comparaison entre Random Forest et la méthode de classification par maximum de vraisemblance. Issue 5 (2nd September 2020)

• Record Type: Journal Article
• Authors: Forsey, David; LaRocque, Armand; Leblon, Brigitte; Skinner, Marc; Douglas, Angela
• Is Part Of: Canadian journal of remote sensing. Volume 46:Issue 5(2020)
• Page Start: 640
• Page End: 659
• Publication Date: 2020-09-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2020.1824118
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: Eelgrass ( Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass presence in Tabusintac, New Brunswick, an estuarine lagoon with extensive eelgrass coverage. The imagery was classified using two supervised classifiers: the parametric Maximum Likelihood Classifier (MLC) and the nonparametric Random Forest (RF) classifier. While RF was expected to produce higher classification accuracies, it was shown not to be much better than MLC in this particular context. The overall validation accuracy was 96.36% for both the RF and MLC classifiers. Finally, the comparison of our 2014 classified image with a 2008 eelgrass distribution map shows an increase in eelgrass extent in the bay between both years.