Assessment of pixel-oriented k-NN machine learning algorithm performance for the interannual remote sensing monitoring of eelgrass beds at the mouth of the Romaine

Eelgrass cover extent is among the most reliable indicators for measuring changes in coastal ecosystems. Eelgrass has colonized the mouth of the Romaine River and has become a part of environmental monitoring there since 2013. The presence of eelgrass in this area is an essential factor for the earl...

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Bibliographic Details
Published in:	Environmental monitoring and assessment Vol. 195; no. 8; p. 939
Main Authors:	Lemieux, P., Lalumière, C., Fugaru, N., Gilbert, J.-P., Tremblay, A.
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01-08-2023 Springer Nature B.V
Subjects:	Algorithms Aquatic plants Atmospheric Protection/Air Quality Control/Air Pollution Change detection Coastal ecosystems Data acquisition Data collection Detection Earth and Environmental Science Ecology Ecosystem Ecosystems Ecotoxicology Edge detection Environment Environmental Management Environmental Monitoring Environmental science Machine Learning Monitoring Monitoring/Environmental Analysis Pixels Remote monitoring Remote sensing Remote Sensing Technology Review Sea grasses Training Workflow Zosteraceae K-NN Eelgrass Pixel oriented Classification Machine learning
Online Access:	Get full text
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