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Title: Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data

Abstract

Abstract This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain‐hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain‐hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain‐hillslope transect at 0.50‐m resolution and extracted LiDAR‐derived metrics to model the rapid change in microtopography. We then investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important rolemore » in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long‐term monitoring to capture community‐specific physiological responses to perturbations, offering the possibility of bridging local plot‐scale observations with large landscape monitoring.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1];  [2];  [2]; ORCiD logo [3]; ORCiD logo [1]; ORCiD logo [1]
+ Show Author Affiliations
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Fort Lewis College, Durango, CO (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1572824
Alternate Identifier(s):
OSTI ID: 1560184
Grant/Contract Number:  
AC02-05CH11231; DE‐AC02‐05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 124; Journal Issue: 6; Journal ID: ISSN 2169-8953
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; estimation of plant community distribution; interaction aboveground‐belowground; mountainous floodplain‐hillslope system; remote sensing; geophysics; machine learning

Citation Formats

Falco, Nicola, Wainwright, Haruko, Dafflon, Baptiste, Léger, Emmanuel, Peterson, John, Steltzer, Heidi, Wilmer, Chelsea, Rowland, Joel C., Williams, Kenneth H., and Hubbard, Susan S. Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data. United States: N. p., 2019. Web. doi:10.1029/2018jg004394.
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Falco, Nicola, Wainwright, Haruko, Dafflon, Baptiste, Léger, Emmanuel, Peterson, John, Steltzer, Heidi, Wilmer, Chelsea, Rowland, Joel C., Williams, Kenneth H., & Hubbard, Susan S. Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data. United States. https://doi.org/10.1029/2018jg004394
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Falco, Nicola, Wainwright, Haruko, Dafflon, Baptiste, Léger, Emmanuel, Peterson, John, Steltzer, Heidi, Wilmer, Chelsea, Rowland, Joel C., Williams, Kenneth H., and Hubbard, Susan S. Mon . "Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data". United States. https://doi.org/10.1029/2018jg004394. https://www.osti.gov/servlets/purl/1572824.
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@article{osti_1572824,
title = {Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data},
author = {Falco, Nicola and Wainwright, Haruko and Dafflon, Baptiste and Léger, Emmanuel and Peterson, John and Steltzer, Heidi and Wilmer, Chelsea and Rowland, Joel C. and Williams, Kenneth H. and Hubbard, Susan S.},
abstractNote = {Abstract This study aims to investigate the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain‐hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain‐hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain‐hillslope transect at 0.50‐m resolution and extracted LiDAR‐derived metrics to model the rapid change in microtopography. We then investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long‐term monitoring to capture community‐specific physiological responses to perturbations, offering the possibility of bridging local plot‐scale observations with large landscape monitoring.},
doi = {10.1029/2018jg004394},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 6,
volume = 124,
place = {United States},
year = {Mon May 20 00:00:00 EDT 2019},
month = {Mon May 20 00:00:00 EDT 2019}
}
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https://doi.org/10.1029/2018jg004394
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