Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

Enguehard, Lea; Falco, Nicola; Schmutz, Myriam; Newcomer, Michelle E.; Ladau, Joshua; Brown, James B.; Bourgeau-Chavez, Laura; Wainwright, Haruko M.

doi:10.15485/1876578

Title: Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

Dataset
Other Related Research

Abstract

The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims 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

Authors:

;

; Brown, James B.;

;

Lawrence Berkeley National Laboratory; Lawrence Berkeley National Laboratory
Lawrence Berkeley National Laboratory
Bordeaux Institut National Polytechnique

Publication Date:: Fri Jul 08 04:00:00 UTC 2022

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem; COMPASS-FME

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES; COASTAL WETLANDS; EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > DIGITAL ELEVATION/TERRAIN MODEL (DEM); ECOSYSTEM CHARACTERIZATION; Enhanced Vegetation Index; FUNCTIONAL ZONATION; LAKE ERIE; Land covers; PLANT PRODUCTIVITY; REMOTE SENSING; Soil texture; TERRESTRIAL-ACQUATIC INTERFACE; Topographical metrics

OSTI Identifier:: 1876578

DOI:: https://doi.org/10.15485/1876578

Citation Formats


                    Enguehard, Lea, Falco, Nicola, Schmutz, Myriam, Newcomer, Michelle E., Ladau, Joshua, Brown, James B., Bourgeau-Chavez, Laura, and Wainwright, Haruko M. Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”.  United States: N. p., 2022. 
        Web.  doi:10.15485/1876578.

Copy to clipboard


                    Enguehard, Lea, Falco, Nicola, Schmutz, Myriam, Newcomer, Michelle E., Ladau, Joshua, Brown, James B., Bourgeau-Chavez, Laura, & Wainwright, Haruko M. Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”.  United States.  doi:https://doi.org/10.15485/1876578

Copy to clipboard


                    Enguehard, Lea, Falco, Nicola, Schmutz, Myriam, Newcomer, Michelle E., Ladau, Joshua, Brown, James B., Bourgeau-Chavez, Laura, and Wainwright, Haruko M. 2022.  
"Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”".  United States.  doi:https://doi.org/10.15485/1876578.  https://www.osti.gov/servlets/purl/1876578. Pub date:Fri Jul 08 04:00:00 UTC 2022

Copy to clipboard


                    
@article{osti_1876578,

  title        = {Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”},

  author       = {Enguehard, Lea and Falco, Nicola and Schmutz, Myriam and Newcomer, Michelle E. and Ladau, Joshua and Brown, James B. and Bourgeau-Chavez, Laura and Wainwright, Haruko M.},

  abstractNote = {The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims 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},

  doi          = {10.15485/1876578},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Jul 08 04:00:00 UTC 2022},

  month        = {Fri Jul 08 04:00:00 UTC 2022}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.15485/1876578

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

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

Journal Article Enguehard, Léa ; Falco, Nicola ; Schmutz, Myriam ; ... - Remote Sensing

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, includingmore »« less
Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie

Journal Article Enguehard, Léa ; Falco, Nicola ; Schmutz, Myriam ; ... - Remote Sensing

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, includingmore »« less
Data for Bandopadhyay et al., "Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces"

Dataset Bandopadhyay, Sreejata ; Danczak, Robert ; Patel, Kaizad ; ...

These data are from Bandopadhyay et al., "Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces". This study aims to understand the soil microbial ecology along terrestrial-aquatic interfaces of a freshwater and estuarine region and how it relates to organic matter. We analyzed soil microbial (16S rRNA gene) and organic matter (Fourier-transform ion cyclotron resonance mass spectrometry, FTICR-MS) composition from upland (forested), transition (stressed forest), and wetland positions at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. This dataset includes 16S rRNA gene amplicon data (only processed file typesmore »« less
Data used in Wainwright, H.M. et al. 2021, “Watershed zonation through hillslope clustering for tractably quantifying above- and belowground watershed heterogeneity and functions”

Dataset Wainwright, Haruko ; Uhlemann, Sebastian ; Franklin, Maya ; ...

This data package contains spatial data layers and processing scripts used in Wainwright, H.M. et al. 2021, “Watershed zonation approach for tractably quantifying above-and- belowground watershed heterogeneity and functions”. The purpose of the data and paper is to develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by applying clustering methods to multiple spatial data layers. The data package contains the geotiff files of spatial data layers, and the processed data values corresponding to the figures in the paper. The Data_description file describe each file in details. The spatial data sets (geotiff) are includedmore » in the zip files.« less
Soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie.

Dataset Ehosioke, Solomon ; Adebayo, Moses B. ; Bailey, Vanessa L. ; ...

Uploaded raw datasets from electrical resistivity tomography (ERT), ground penetrating radar (GPR), electromagnetic induction (EMI) and insitu sensors were acquired across land-lake interfaces along Lake Erie. These datasets were useful to delineate the soil architecture and subsurface stratigraphic heterogeneities. We combined multiple geophysical methods with lithostratigraphic logs from borehole cores and data from soil and borehole sensors to investigate spatial heterogeneities across land-lake interfaces. The apparent electrical conductivity (Eca) maps matched closely with the soil maps from public database and also detected additional soil units that were missing in the traditional soil maps. This implies that electromagnetic induction (EMI) couldmore »« less

Similar Records