Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie
Name
remotesensing-14-03285-v3.pdf
Description
Published version
Size
5.72 MB
Format
Adobe PDF
Checksum (MD5)
e972e45014385b89fd2dbe9281b08492
Author(s)
Enguehard, Léa
Falco, Nicola
Schmutz, Myriam
Newcomer, Michelle E
Ladau, Joshua
Brown, James B
Bourgeau-Chavez, Laura
Wainwright, Haruko M
Date Issued
July 2022
Journal
Remote Sensing
Publisher
MDPI AG
Citation
Enguehard, Léa, Falco, Nicola, Schmutz, Myriam, Newcomer, Michelle E, Ladau, Joshua et al. 2022. "Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie." Remote Sensing, 14 (14).
Version
Final published version
Abstract
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 functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/rs14143285
