skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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% validationmore » 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« less

Authors:
ORCiD logo [1];  [2]; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
  1. Stanford University
  2. NASA Jet Propulsion Laboratory (JPL)
Contributors:
Related Person: ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) (United States); A Multiscale Approach to Modeling Carbon and Nitrogen Cycling within a High Elevation Watershed
Sponsoring Org.:
NSF EAR Postdoctoral Fellowship, Chadwick, ID: 1725788; National Science Foundation under NSF-1841547; U.S. Department of Energy BER award, PI: Maher, DE-SC0018155; U.S. DOE > Office of Science > Biological and Environmental Research (BER)
OSTI Identifier:
1618133
Resource Type:
Data
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; hyperspectral; imaging spectroscopy; trait mapping; foliar traits; carbon; Leaf mass per area; leaf water content; EARTH SCIENCE > BIOSPHERE > VEGETATION > NITROGEN; EARTH SCIENCE > BIOSPHERE > VEGETATION > CARBON; EARTH SCIENCE > BIOSPHERE > VEGETATION > EVERGREEN VEGETATION; EARTH SCIENCE > BIOSPHERE > VEGETATION > LEAF CHARACTERISTICS; EARTH SCIENCE > BIOSPHERE > VEGETATION > CANOPY CHARACTERISTICS

Citation Formats

Chadwick, K. Dana, Brodrick, Philip, Grant, Kathleen, Henderson, Amanda, Bill, Markus, Breckheimer, Ian, Williams, C. F. Rick, Goulden, Tristan, Falco, Nicola, McCormick, Maeve, Musinsky, John, Pierce, Samuel, Hastings Porro, Maceo, Scott, Andea, Brodie, Eoin, Hancher, Matt, Steltzer, Heidi, Wainwright, Haruko, Williams, Kenneth, Maher, Katharine, Blonder, Benjamin, Chen, Jiancong, Dafflon, Baptiste, Lawrence, Corey, Sorensen, Patrick, Damerow, Joan, Lamb, Jack, Khurram, Aizah, Polussa, Alexander, Wu Singh, Hans, Varadharajan, Charuleka, and Whitney, Bizuayehu. NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018. United States: N. p., 2020. Web. doi:10.15485/1618133.
Chadwick, K. Dana, Brodrick, Philip, Grant, Kathleen, Henderson, Amanda, Bill, Markus, Breckheimer, Ian, Williams, C. F. Rick, Goulden, Tristan, Falco, Nicola, McCormick, Maeve, Musinsky, John, Pierce, Samuel, Hastings Porro, Maceo, Scott, Andea, Brodie, Eoin, Hancher, Matt, Steltzer, Heidi, Wainwright, Haruko, Williams, Kenneth, Maher, Katharine, Blonder, Benjamin, Chen, Jiancong, Dafflon, Baptiste, Lawrence, Corey, Sorensen, Patrick, Damerow, Joan, Lamb, Jack, Khurram, Aizah, Polussa, Alexander, Wu Singh, Hans, Varadharajan, Charuleka, & Whitney, Bizuayehu. NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018. United States. https://doi.org/10.15485/1618133
Chadwick, K. Dana, Brodrick, Philip, Grant, Kathleen, Henderson, Amanda, Bill, Markus, Breckheimer, Ian, Williams, C. F. Rick, Goulden, Tristan, Falco, Nicola, McCormick, Maeve, Musinsky, John, Pierce, Samuel, Hastings Porro, Maceo, Scott, Andea, Brodie, Eoin, Hancher, Matt, Steltzer, Heidi, Wainwright, Haruko, Williams, Kenneth, Maher, Katharine, Blonder, Benjamin, Chen, Jiancong, Dafflon, Baptiste, Lawrence, Corey, Sorensen, Patrick, Damerow, Joan, Lamb, Jack, Khurram, Aizah, Polussa, Alexander, Wu Singh, Hans, Varadharajan, Charuleka, and Whitney, Bizuayehu. 2020. "NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018". United States. https://doi.org/10.15485/1618133. https://www.osti.gov/servlets/purl/1618133.
@article{osti_1618133,
title = {NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018},
author = {Chadwick, K. Dana and Brodrick, Philip and Grant, Kathleen and Henderson, Amanda and Bill, Markus and Breckheimer, Ian and Williams, C. F. Rick and Goulden, Tristan and Falco, Nicola and McCormick, Maeve and Musinsky, John and Pierce, Samuel and Hastings Porro, Maceo and Scott, Andea and Brodie, Eoin and Hancher, Matt and Steltzer, Heidi and Wainwright, Haruko and Williams, Kenneth and Maher, Katharine and Blonder, Benjamin and Chen, Jiancong and Dafflon, Baptiste and Lawrence, Corey and Sorensen, Patrick and Damerow, Joan and Lamb, Jack and Khurram, Aizah and Polussa, Alexander and Wu Singh, Hans and Varadharajan, Charuleka and Whitney, Bizuayehu},
abstractNote = {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},
doi = {10.15485/1618133},
url = {https://www.osti.gov/biblio/1618133}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2020},
month = {Mon Jun 15 00:00:00 EDT 2020}
}