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

Nicola Falco, Haruko Wainwright, Baptiste Dafflon, Emmanuel Léger, John Peterson, Heidi Steltzer, Chelsea Wilmer, Joel C. Rowland, Kenneth H. Williams, Susan S. Hubbard

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

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.

Original languageEnglish
Pages (from-to)1618-1636
Number of pages19
JournalJournal of Geophysical Research: Biogeosciences
Volume124
Issue number6
DOIs
StatePublished - Jun 2019
Externally publishedYes

Funding

This material is based upon work supported as part of the Watershed Function Scientific Focus Area funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award DE‐AC02‐05CH11231. The data sets used in this work, including the LiDAR, ERT, and derived products, can be found in Falco et al. (2019). This material is based upon work supported as part of the Watershed Function Scientific Focus Area funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award DE-AC02-05CH11231. The data sets used in this work, including the LiDAR, ERT, and derived products, can be found in Falco et al. ().

Keywords

  • estimation of plant community distribution
  • geophysics
  • interaction aboveground-belowground
  • machine learning
  • mountainous floodplain-hillslope system
  • remote sensing

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