71
Furthermore, coupling of spectral and functional trait databases (e.g., ecosis.org) will
facilitate more rapid development and testing of new functional algorithms or the
expansion of the scope of inference of existing models. In addition, the inclusion of
high spectral resolution sensors on unmanned aerial systems (UASs, Shiklomanov
et al. 2019) provides the opportunity to leverage similar scaling approaches as presented in this chapter with UAS observations to provide unprecedented temporal coverage and targeted spatial sampling that can be used to understand ecosystem in new
detail or aid in the scaling from the plant to grid cell. In all, functional trait maps from
imaging spectroscopy will supplement data and approaches presented by Butler et al.
(2017) or Moreno-Martínez et al. (2018) for broad-scale trait characterization.
Acknowledgments The authors would like to thank Anna Schweiger, Erin Hestir, and Jeannine
Cavender-Bares for their careful reviews, input, and suggestions on earlier versions of this chapter
as part of the and the National Institute of Mathematical Biology and Synthesis Working Group on
Remotely Sensing Biodiversity. Special thanks to Tiffany Bowman and Yelena Belyavina for assistance with graphics. S.P.S was supported by the Next-Generation Ecosystem Experiments
(NGEEs) in the Arctic and tropics that are supported by the Office of Biological and Environmental
Research in the Department of Energy, Office of Science, and through the United States Department
of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory. P.T. acknowledges
support from NSF Emerging Frontiers Macrosystems Biology and NEON-Enabled Science (MSBNES) grant 1638720, USDA McIntire-Stennis WIS01809 and Hatch WIS01874, NASA
Biodiversity Program grant 80NSSC17K0677, and AIST program grant 80NSSC17K0244.
References
Aber JD, Melillo JM (1982) Nitrogen immobilization in decaying hardwood leaf litter as a function of initial nitrogen and lignin content. Can J Bot 60:2261–2269
Ahl DE, Gower ST, Burrows SN, Shabanov NV, Myneni RB, Knyazikhin Y (2006) Monitoring
spring canopy phenology of a deciduous broadleaf forest using modis. Remote Sens Environ
104:88–95
Ainsworth EA, Serbin SP, Skoneczka JA, Townsend PA (2014) Using leaf optical properties to
detect ozone effects on foliar biochemistry. Photosynth Res 119:65–76
Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Remote
Sens Environ 64:234–253
Asner GP, Nepstad D, Cardinot G, Ray D (2004) Drought stress and carbon uptake in an
Amazon forest measured with spaceborne imaging spectroscopy. Proc Natl Acad Sci U S A
101:6039–6044
Asner GP, Knapp DE, Boardman J, Green RO, Kennedy-Bowdoin T, Eastwood M, Martin RE,
Anderson C, Field CB (2012) Carnegie Airborne Observatory-2: increasing science data
dimensionality via high-fidelity multi-sensor fusion. Remote Sens Environ 124:454–465
Asner GP, Martin RE (2008) Spectral and chemical analysis of tropical forests: scaling from leaf
to canopy levels. Remote Sens Environ 112:3958–3970
Asner PG, Martin ER (2015) Spectroscopic remote sensing of non-structural carbohydrates in forest canopies. Remote Sens 7(4)
Asner GP, Martin RE, Anderson CB, Knapp DE (2015) Quantifying forest canopy traits: imaging
spectroscopy versus field survey. Remote Sens Environ 158:15–27
Asner GP, Brodrick PG, Anderson CB, Vaughn N, Knapp DE, Martin RE (2016) Progressive forest canopy water loss during the 2012-2015 California drought. Proc Natl Acad Sci U S A
113:E249–E255
3 Scaling Functional Traits from Leaves to Canopies
Furthermore, coupling of spectral and functional trait databases (e.g., ecosis.org) will
facilitate more rapid development and testing of new functional algorithms or the
expansion of the scope of inference of existing models. In addition, the inclusion of
high spectral resolution sensors on unmanned aerial systems (UASs, Shiklomanov
et al. 2019) provides the opportunity to leverage similar scaling approaches as presented in this chapter with UAS observations to provide unprecedented temporal coverage and targeted spatial sampling that can be used to understand ecosystem in new
detail or aid in the scaling from the plant to grid cell. In all, functional trait maps from
imaging spectroscopy will supplement data and approaches presented by Butler et al.
(2017) or Moreno-Martínez et al. (2018) for broad-scale trait characterization.
Acknowledgments The authors would like to thank Anna Schweiger, Erin Hestir, and Jeannine
Cavender-Bares for their careful reviews, input, and suggestions on earlier versions of this chapter
as part of the and the National Institute of Mathematical Biology and Synthesis Working Group on
Remotely Sensing Biodiversity. Special thanks to Tiffany Bowman and Yelena Belyavina for assistance with graphics. S.P.S was supported by the Next-Generation Ecosystem Experiments
(NGEEs) in the Arctic and tropics that are supported by the Office of Biological and Environmental
Research in the Department of Energy, Office of Science, and through the United States Department
of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory. P.T. acknowledges
support from NSF Emerging Frontiers Macrosystems Biology and NEON-Enabled Science (MSBNES) grant 1638720, USDA McIntire-Stennis WIS01809 and Hatch WIS01874, NASA
Biodiversity Program grant 80NSSC17K0677, and AIST program grant 80NSSC17K0244.
References
Aber JD, Melillo JM (1982) Nitrogen immobilization in decaying hardwood leaf litter as a function of initial nitrogen and lignin content. Can J Bot 60:2261–2269
Ahl DE, Gower ST, Burrows SN, Shabanov NV, Myneni RB, Knyazikhin Y (2006) Monitoring
spring canopy phenology of a deciduous broadleaf forest using modis. Remote Sens Environ
104:88–95
Ainsworth EA, Serbin SP, Skoneczka JA, Townsend PA (2014) Using leaf optical properties to
detect ozone effects on foliar biochemistry. Photosynth Res 119:65–76
Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Remote
Sens Environ 64:234–253
Asner GP, Nepstad D, Cardinot G, Ray D (2004) Drought stress and carbon uptake in an
Amazon forest measured with spaceborne imaging spectroscopy. Proc Natl Acad Sci U S A
101:6039–6044
Asner GP, Knapp DE, Boardman J, Green RO, Kennedy-Bowdoin T, Eastwood M, Martin RE,
Anderson C, Field CB (2012) Carnegie Airborne Observatory-2: increasing science data
dimensionality via high-fidelity multi-sensor fusion. Remote Sens Environ 124:454–465
Asner GP, Martin RE (2008) Spectral and chemical analysis of tropical forests: scaling from leaf
to canopy levels. Remote Sens Environ 112:3958–3970
Asner PG, Martin ER (2015) Spectroscopic remote sensing of non-structural carbohydrates in forest canopies. Remote Sens 7(4)
Asner GP, Martin RE, Anderson CB, Knapp DE (2015) Quantifying forest canopy traits: imaging
spectroscopy versus field survey. Remote Sens Environ 158:15–27
Asner GP, Brodrick PG, Anderson CB, Vaughn N, Knapp DE, Martin RE (2016) Progressive forest canopy water loss during the 2012-2015 California drought. Proc Natl Acad Sci U S A
113:E249–E255
3 Scaling Functional Traits from Leaves to Canopies