Abstract
Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental-scale functional diversity.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 494-511 |
| Number of pages | 18 |
| Journal | New Phytologist |
| Volume | 228 |
| Issue number | 2 |
| DOIs | |
| State | Published - Oct 1 2020 |
Bibliographical note
Funding Information:The work was supported by NSF Macrosystems Biology and NEON‐Enabled Science grant 1638720 to PAT, JJC, AS and ELK and NSF‐NASA Dimensions of Biodiversity grants 1342778 and 1342872 to PAT and JC‐B. The trait maps were generated using the computing resources and assistance of the UW‐Madison Center for High Throughput Computing (CHTC) in the Department of Computer Sciences. The CHTC is supported by UW‐Madison, the Advanced Computing Initiative, the Wisconsin Alumni Research Foundation, the Wisconsin Institutes for Discovery, and the National Science Foundation and is an active member of the Open Science Grid supported by the National Science Foundation and the US Department of Energy’s Office of Science. The authors are grateful to Tristan Goulden and the NEON AOP team for providing imaging spectrometer data and insights into AOP data preprocessing. The authors acknowledge the help from NEON site managers with logistics in fieldwork. The authors also thank Hannah Manninen, John Joutras, Andrew Kluck, Tyler Crass, James Fang Gui, Ben Townsend, Ben Spaier, Ittai Herrmann, John Clare, Haley Knight, Dewi Atikah Radin Umar, Ashley Seufzer, Diana Barrera, Alex Horvath, Angad Dhariwal, Josephine Mayhew, Jacob Gold, Abigail Walther, Raina Eddy, Xu, Alex Brito, Anna Schweiger, Cathleen Nguyen, Sarah Hobbie, Richard Lindroth, Kennedy Rubert‐Nason for their help in field data collection, sample processing and chemical analyses. The authors greatly appreciate the efforts of four anonymous reviewers, whose comments substantially improved this article.
Funding Information:
The work was supported by NSF Macrosystems Biology and NEON-Enabled Science grant 1638720 to PAT, JJC, AS and ELK and NSF-NASA Dimensions of Biodiversity grants 1342778 and 1342872 to PAT and JC-B. The trait maps were generated using the computing resources and assistance of the UW-Madison Center for High Throughput Computing (CHTC) in the Department of Computer Sciences. The CHTC is supported by UW-Madison, the Advanced Computing Initiative, the Wisconsin Alumni Research Foundation, the Wisconsin Institutes for Discovery, and the National Science Foundation and is an active member of the Open Science Grid supported by the National Science Foundation and the US Department of Energy?s Office of Science. The authors are grateful to Tristan Goulden and the NEON AOP team for providing imaging spectrometer data and insights into AOP data preprocessing. The authors acknowledge the help from NEON site managers with logistics in fieldwork. The authors also thank Hannah Manninen, John Joutras, Andrew Kluck, Tyler Crass, James Fang Gui, Ben Townsend, Ben Spaier, Ittai Herrmann, John Clare, Haley Knight, Dewi Atikah Radin Umar, Ashley Seufzer, Diana Barrera, Alex Horvath, Angad Dhariwal, Josephine Mayhew, Jacob Gold, Abigail Walther, Raina Eddy, Xu, Alex Brito, Anna Schweiger, Cathleen Nguyen, Sarah Hobbie, Richard Lindroth, Kennedy Rubert-Nason for their help in field data collection, sample processing and chemical analyses. The authors greatly appreciate the efforts of four anonymous reviewers, whose comments substantially improved this article.
Publisher Copyright:
© 2020 The Authors. New Phytologist © 2020 New Phytologist Trust
Keywords
- NEON
- ecosystem processes
- foliar functional traits
- imaging spectroscopy
- trait map database