Methods For Measuring Effects Of Changes In Tamarisk Evapotranspiration On Groundwater At Southwestern Uranium Mill Tailings Sites

Environmental Monitoring
American Geophysical Union, Fall General Assembly 2016

Abstract: Tamarisk (Tamarix spp.) is a non-native tree that competes with native species for water in riparian corridors of the southwestern U.S. The beetle, Diorhabda carinulata, which was released as a biocontrol agent, may be affecting tamarisk health. After several years of defoliation, tamarisk is now coming back along many southwestern rivers because of dwindling beetle numbers. We studied effects of changes in riparian plant communities dominated by tamarisk on evapotranspiration (ET) at uranium mill tailings sites. We used an unmanned aerial system (UAS) to acquire high resolution spectral data needed to estimate spatial and temporal variability in ET in riparian ecosystems at uranium mill tailings sites adjacent to the San Juan River near Shiprock, New Mexico, and the Colorado River near Moab, Utah. UAS imagery allowed us to monitor changes in phenology, fractional greenness, ET, and effects on water resources at these sites. We timed ground data and UAS image acquisition with an August 2016 Landsat image to assist with spatiotemporal scaling techniques. We measured leaf area index (LAI) and sampled biomass on tamarisk, cottonwood (Populus spp.), and willow (Salix spp.) within the UAS acquisition areas to scale leaf area on individual branches to LAI of whole trees. UAS cameras included a Sony Alpha A5100 for species-level vegetation mapping and a MicaSense Red Edge five-band multispectral camera to map Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The UAS products were correlated with satellite imagery. Our goal was to scale plant water use acquired from UAS imagery to Landsat and/or MODIS to provide a time-series documenting long-term trends and relationships of ET and groundwater elevation. NDVI and EVI were calibrated across UAS, MODIS and Landsat images using regression and ET was calculated using NDVI, EVI, ground meteorological data, and an existing empirical algorithm.

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Authors: W. Waugh, P. L. Nagler, J. Vogel, E. Glenn, U. Nguyen, C. J. Jarchow

Associations: Navarro Research and Engineering; USGS; University of Arizona

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