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Scott, Andea

Publications and source records attributed to Scott, Andea.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Leaf mass per area and leaf water content measurements from field survey in association with NEON AOP survey, East River, CO 2018

Leaf area, wet, and dry weights for leaf samples from sites where field sampling was conducted during the 2018 National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. The sampling sites were located across East River, Washington Gulch, Slate River, and Coal Creek watersheds and contained a mixture of meadow, shrub, and tree sampling sites. This data package contains the leaf area and wet and dry weights for leaves from tree and shrub sampling sites, as well as samples for meadow species present within a given sampling area. Associated data packages contain species composition information at the plot level (DOI: 10.15485/1618130), as well as additional biogeochemical, microbial, and geophysical data as they become available. For full documentation, please see associated reference.This package contains zipped folders of all scans that were used for area calculations for both sampling sites (tree/shrub, site_scans.zip) as well as representative samples from a daily sampling area of meadow species that were found in the 1 m plots (meadow_area_scans.zip). It also contains the macro code that was used in ImageJ for processing of the area calculations. Finally, it includes excel files with the area (cm2), wet weight (g), dry weight (g), leaf mass per area (LMA, g/m2), and leaf water content (%) for each set of scans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Site-level Foliar C, N, delta13C data from samples collected during field survey associated with NEON AOP survey, East River, CO 2018

Carbon (C) and nitrogen (N) weight percent concentrations were obtained from the bulk foliar samples collected across East River, Washington Gulch, Slate River, and Coal Creek watersheds in Gunnison, Colorado during the summer of 2018. These samples were collected from sun-lit leaves of meadow, shrub, and tree vegetation types within 72 hours of the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) hyperspectral survey. These samples were dried and then run on an elemental analyzer coupled to a mass spectrometer for C and N concentrations and delta13C. The intention of these data were to provide ground truth data to develop foliar nutrient models across the imaging spectroscopy domain. Additional associated site metadata and vegetation fractional cover information can be found in the associated data releases (DOI: 10.15485/1618130). Here we provide site level C, N, and delta 13C data in the CN_Results_Foliar.csv. All of these samples were also registered with IGSN codes, and the details from that registration with metadata for the samples is included in foliar_sample_igsn_metadata.csv.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Locations, metadata, and species cover from field sampling survey associated with NEON AOP survey, East River, CO 2018

Locations and descriptions of the sites where field sampling was conducted during the 2018 National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. The sampling sites were located across East River, Washington Gulch, Slate River, and Coal Creek watersheds and contained a mixture of meadow, shrub, and tree sampling sites. This data package contains the location, metadata, species composition, and vector files for the sampling sites in this project. Species vouchers that have been preserved in the Rocky Mountain Biological Laboratory herbarium can be found on the Consortia of Southern Rocky Mountain Herbaria website: http://soroherbaria.org/portal/collections/index.php. Vector files provided here are for areas delineated based on the AOP reflectance data and mosaic developed by Brodrick et al. (doi: 10.15485/1618131), and use with other datasets should be closely evaluated. Subsequent data packages will contain additional biogeochemical, microbial, and geophysical data as they become available for these sampling sites. For full documentation, please see associated reference and additional references will be added here as they become available.

2018 NEON and 2025 CHESS Campaigns↗