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

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 th...

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Bibliographic Details
Published in:Journal of geophysical research. Biogeosciences 2019-06, Vol.124 (6), p.1618-1636
Main Authors: Falco, Nicola, Wainwright, Haruko, Dafflon, Baptiste, Léger, Emmanuel, Peterson, John, Steltzer, Heidi, Wilmer, Chelsea, Rowland, Joel C., Williams, Kenneth H., Hubbard, Susan S.
Format: Article
Language:English
Subjects:
Data integration
Detection
Distribution
Ecosystems
Electrical resistivity
Engineering education
Environmental changes
estimation of plant community distribution
Floodplains
Frameworks
Geophysical data
Geophysics
Heterogeneity
Hydrologic data
interaction aboveground‐belowground
Investigations
Landscape
Learning algorithms
Lidar
Machine learning
Meadows
Microtopography
Monitoring
mountainous floodplain‐hillslope system
Mountains
Multisensor fusion
Niches
Nutrient cycles
Perturbation
Perturbations
Physiological responses
Plant communities
Plant populations
Precipitation
Remote sensing
Resolution
River basin development
River basins
Rivers
Satellite imagery
Satellites
Shrubs
Snowmelt
Soil moisture
Soil properties
Soils
Spatial distribution
Spatial resolution
Spatial variations
Tomography
Topography
Variability
Watersheds
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Summary: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. Plain Language Summary In this study, we aim to understand how soil and topographic properties influence the spatial distribution of plant communities within a floodplain‐hillslope system, located in a mountainous East River watershed in Colorado. Watersheds are vulnerable to environmental change, including earlier snowmelt, changes in precipitation, and temperature trends, all of which can alter plant communities and associated water and nutrient cycles within the watershed. However, tractable yet accurate quantification of plant communities is challenging to do at a scale that also permits inve
ISSN:2169-8953
2169-8961
DOI:10.1029/2018JG004394