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Refinements in Eelgrass Mapping at Tabusintac Bay (New Brunswick, Canada): A Comparison between Random Forest and the Maximum Likelihood Classifier
Canadian Journal of Remote Sensing ( IF 2.6 ) Pub Date : 2020-09-02 , DOI: 10.1080/07038992.2020.1824118
David Forsey 1 , Armand LaRocque 1 , Brigitte Leblon 1 , Marc Skinner 2, 3 , Angela Douglas 4
Affiliation  

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.

中文翻译:

Tabusintac Bay(加拿大新不伦瑞克省)鳗草制图的改进:随机森林和最大似然分类器之间的比较

摘要 鳗草(Zostera marina L.)是一种生长在加拿大大西洋沿岸地区的海洋被子植物。鳗草草甸提供多种生态系统服务,虽然它们被认为是重要的栖息地,但它们在加拿大大西洋地区的位置、范围和健康状况却知之甚少。这项研究检验了 WorldView-2 光学卫星图像在新不伦瑞克省 Tabusintac 绘制鳗草存在的有效性,这是一个河口泻湖,鳗草覆盖范围广泛。图像使用两个监督分类器进行分类:参数最大似然分类器 (MLC) 和非参数随机森林 (RF) 分类器。虽然预计 RF 会产生更高的分类精度,但在这种特定情况下,它并不比 MLC 好多少。总体验证准确率为 96。RF 和 MLC 分类器均为 36%。最后,将我们 2014 年的分类图像与 2008 年的鳗草分布图进行比较,可以看出两年间海湾中鳗草的范围有所增加。
更新日期:2020-09-02
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