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Distinguishing N and P addition from
the air using imaging spectroscopy
Alex Young, Anna Schweiger, Melany Fisk, & Ruth Yanai
Twitter: @bearsofthemoss
Airborne imaging spectroscopy & field studies
Oversold promises vs recent improvements
Low resolution
High resolution
Spatial resolution Spectral resolution
NEON AOP
1. RGB photo
● 5 cm resolution
1. Hyperspectral data
● 450 wavelengths m-2
1. Lidar data
● 1-4 points m-2
Laliberte, Schweiger, Legendre 2019 - Partitioning plant
spectral diversity into alpha and beta components
NEON AOP
1. RGB photo
● 5 cm resolution
1. Hyperspectral data
● 450 wavelengths m-2
1. Lidar data
● 1-4 points m-2
NEON AOP
1. RGB photo
● 5 cm resolution
1. Hyperspectral data
● 450 wavelengths m-2
1. Lidar data
● 1-4 points m-2
Results
●N and P addition changed reflectence
where light is used for photosynthesis
●Basal area relationship
●N addition reduced reflectance
○ More chlorophyll, absorbed more light
● P addition increased reflectance
○ If you have thoughts, post in the chat!
When we told the model the treatment class
(control, N, P, N+P) for 75% of the data (27 plots), on
average we had 83% accuracy for predicting the other
25% (9 plots)
Field-measured resin-available N and P in soil align
with clustering of tree-top spectra
High
soil N
Low
soil N
Low
soil P
High
soil P
●Trees are signals of belowground function
that can be easily observed remotely.
●Developing relationship of light reflectance
and nutrient availability may build on our
understanding of biogeochemistry.
●The NEON AOP could be brought to
Hubbard Brook.
Acknowledgements
• NEON online tutorials & data availability
• The MELNHE Project is funded by USDA NIFA (2019-67019-29464) and
NSF (DEB-1637685) . For more information, please visit
www.esf.edu/melnhe
Distinguishing N and P addition from the air using imaging spectroscopy. Alex Young

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Distinguishing N and P addition from the air using imaging spectroscopy. Alex Young

  • 1. Distinguishing N and P addition from the air using imaging spectroscopy Alex Young, Anna Schweiger, Melany Fisk, & Ruth Yanai Twitter: @bearsofthemoss Airborne imaging spectroscopy & field studies
  • 2.
  • 3. Oversold promises vs recent improvements Low resolution High resolution Spatial resolution Spectral resolution
  • 4. NEON AOP 1. RGB photo ● 5 cm resolution 1. Hyperspectral data ● 450 wavelengths m-2 1. Lidar data ● 1-4 points m-2
  • 5. Laliberte, Schweiger, Legendre 2019 - Partitioning plant spectral diversity into alpha and beta components
  • 6. NEON AOP 1. RGB photo ● 5 cm resolution 1. Hyperspectral data ● 450 wavelengths m-2 1. Lidar data ● 1-4 points m-2
  • 7. NEON AOP 1. RGB photo ● 5 cm resolution 1. Hyperspectral data ● 450 wavelengths m-2 1. Lidar data ● 1-4 points m-2
  • 8.
  • 9. Results ●N and P addition changed reflectence where light is used for photosynthesis ●Basal area relationship ●N addition reduced reflectance ○ More chlorophyll, absorbed more light ● P addition increased reflectance ○ If you have thoughts, post in the chat!
  • 10. When we told the model the treatment class (control, N, P, N+P) for 75% of the data (27 plots), on average we had 83% accuracy for predicting the other 25% (9 plots)
  • 11. Field-measured resin-available N and P in soil align with clustering of tree-top spectra High soil N Low soil N Low soil P High soil P
  • 12. ●Trees are signals of belowground function that can be easily observed remotely. ●Developing relationship of light reflectance and nutrient availability may build on our understanding of biogeochemistry. ●The NEON AOP could be brought to Hubbard Brook. Acknowledgements • NEON online tutorials & data availability • The MELNHE Project is funded by USDA NIFA (2019-67019-29464) and NSF (DEB-1637685) . For more information, please visit www.esf.edu/melnhe