Foliar functional traits from imaging spectroscopy across biomes in eastern North America.
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| Title: | Foliar functional traits from imaging spectroscopy across biomes in eastern North America. |
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| Authors: | Wang, Zhihui1 (AUTHOR) zwang896@wisc.edu, Chlus, Adam1 (AUTHOR), Geygan, Ryan1 (AUTHOR), Ye, Zhiwei1 (AUTHOR), Zheng, Ting1 (AUTHOR), Singh, Aditya2 (AUTHOR), Couture, John J.3 (AUTHOR), Cavender‐Bares, Jeannine4 (AUTHOR), Kruger, Eric L.1 (AUTHOR), Townsend, Philip A.1 (AUTHOR) |
| Source: | New Phytologist. Oct2020, Vol. 228 Issue 2, p494-511. 18p. |
| Subjects: | Spectral imaging, Partial least squares regression, Standard deviations, Biomes, Temperate forests |
| Geographic Terms: | North America |
| Abstract: | Summary: 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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