Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a...

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Bibliographic Details
Main Authors: Léa Enguehard, Nicola Falco, Myriam Schmutz, Michelle E. Newcomer, Joshua Ladau, James B. Brown, Laura Bourgeau-Chavez, Haruko M. Wainwright
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Remote Sensing
Subjects:
coastal wetlands
plant productivity
Great Lakes Region
machine learning
functional zonation
remote sensing
Online Access:https://www.mdpi.com/2072-4292/14/14/3285

Internet

https://www.mdpi.com/2072-4292/14/14/3285

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