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

<jats:p>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....

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Bibliographic Details
Main Authors: Enguehard, Léa, Falco, Nicola, Schmutz, Myriam, Newcomer, Michelle E, Ladau, Joshua, Brown, James B, Bourgeau-Chavez, Laura, Wainwright, Haruko M
Other Authors: Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Format: Article
Language:English
Published: MDPI AG 2023
Online Access:https://hdl.handle.net/1721.1/147623

Internet

https://hdl.handle.net/1721.1/147623

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