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Hyperspectral Remote Sensing
--an indirect trait measuring method
Jin Wu
05/02/2012
Outline
Part 1: Terminologies & Tools of RS Techniques
Part 2: RS Approaches to Estimating Leaf/Canopy Traits
Part 3: Famous RS Traits Studies and Related Ecological
Application
Part 4: Hand-on Experience of Using RS Traits Approach
Part 1: Terminologies
Three Pathways of Light-Leaf Interaction
Source: http://micro.magnet.fsu.edu/cells/leaftissue/leaftissue.html
Incoming Light!
Leaf Tissue!
Reflection!
Transmission!
Absorption!
Part 1: Terminologies
Three Pathways of Light-Leaf Interaction
Source: http://micro.magnet.fsu.edu/cells/leaftissue/leaftissue.html
Incoming Light!
Leaf Tissue!
Reflection & Reflectance!
Transmission
& Transmittance
Absorption
& Absorptance!
Part 1: Terminologies
Three Pathways of Light-Leaf Interaction
Source: http://micro.magnet.fsu.edu/cells/leaftissue/leaftissue.html
Incoming Light!
Leaf Tissue!
Reflection & Reflectance!
Transmission
& Transmittance
Absorption
& Absorptance!
1=Reflectance+Transmittance+Absorptance!
Part 1: Tools
Source: http://www.asdi.com/
ASD Field Spec 4 – Leaf Level Measurements
(Analytical Spectra Device)!
Main Computer of ASD !
Integrating Sphere!
Leaf Clip!
Reflectance!
Transmittance!
Reflectance!
Part 1: Tools
SOC 710 Camera – Canopy Level Measurements
(Surface Optics Corporation)!
AVIRIS Camera – Canopy Level Measurements
(Airborne Visible Infrared Imaging Spectrometer )!
Reflectance!
Reflectance!
Part 1: Example from ASD measurements
Reflectance!
Transmittance!
absorptance!
Part 1: Example from ASD measurements
Chl=37.4 (ug/cm2)!
Chl=52.4 (ug/cm2)!
Chl=56.9(ug/cm2)!
LSA=0.0021 (g/cm2)!
LSA=0.0043 (g/cm2)!
LSA=0.0041 (g/cm2)!
Cw=0.024 (cm)!
Cw=0.006 (cm)!
Cw=0.011 (cm)!
Part 2: RS Based Trait Estimation
Approach 1: Vegetation Index !
Approach 3: Mutiple-Variable Regression!
Approach 2: Processed Based Models !
Part 2: RS Based Trait Estimation
Approach 1: Vegetation Index !
Figure 3 from Gamon et al. 1995.
Ecological Application!
Three vegetation types in California!
NDVI (Normalized Difference Vegetation Index)!
Red! NIR!
Part 2: RS Based Trait Estimation
Approach 1: Vegetation Index !
Figure 3 from Hilker et al. 2010.
Remote Sensing of Environment!
Two forest types in Canada!
PRI (Photosynthetic Reflectance Index)!
Light use efficiency
generated by eddy
covariance
measurement!
Xanthophyll induced absorption feature at 531
nm, which is intimately linked to the biochemical
mechanism down-regulating photosynthesis
Part 2: RS Based Trait Estimation
Approach 2: Processed Based Models (Prospect Model) !
atmosphere!
leaf!
atmosphere!
(1) Simulate the three pathways of
light-leaf interaction!
(2) Describe the multiple scattering
of light inside the leaf!
(3) Leaf absorption is related to
leaf chemical content and each
chemical has unique absorption
spectra!
Jacquemoud and Baret, 1990, Remote Sensing of Environment
Part 2: RS Based Trait Estimation
Approach 2: Processed Based Models (Prospect Model) !
e.g. unique absorption spectra!e.g. Model Assessment!
Fig 6 in (Jacquemoud and Baret, 1990)
Fig 11 in (Feret et al., 2008)
Chlorophyll (ug/cm2)! Carotenold (ug/cm2)!
Leaf Water Depth (cm)! Leaf Mass Area (g/cm2)!
Part 2: RS Based Trait Estimation
Approach 3: Mutiple-Variable Regression
or Partial Least Square Regression Analysis (Asner et al. 2009) !
Yn!m, hyperspectral reflectance or transmittance, n is the number of
leaf samples, m is the number of spectral bands
Xn!p, leaf traits, n is the number of leaf samples, p is the number of leaf
traits
Bn!n, leaf spectral Weightings
en!m, spectral residual errors
Assumptions: leaf spectral properties quantitatively represent a suite
of biochemicals and SLA in the foliage of tropical forest tree species
Part 2: RS Based Trait Estimation
Approach 3: Mutiple-Variable Regression
or Partial Least Square Regression Analysis (Asner et al. 2009) !
162 species of canopy trees, including 121 genera, 51 families, across
11 tropical forests sites were used to test leaf spectral-traits relationship
Assumptions: leaf spectral properties quantitatively represent a suite
of biochemicals and SLA in the foliage of tropical forest tree species
8 leaf traits: SLA (cm2/g), Water (g/g), N (%), P(%), Chl a (mg/g),
Chl b (mg/g), Car (mg/g), Anth (mmol/g)
Part 2: RS Based Trait Estimation
Approach 3: Mutiple-Variable Regression
or Partial Least Square Regression Analysis (Asner et al. 2009) !"#$%&'()*+!
,&%-./)*+!
Part 2: RS Based Trait Estimation
Approach 3: Mutiple-Variable Regression
or Partial Least Square Regression Analysis (Asner et al. 2009) !
012! 3.4%&%+5!5&(.5$!6('%!-.4%&%+5!$7%/5&(8!$%+$.)'.59:!!
0;2! <6.$!$7%/5&(8!=%.>6)+>$!/(+!#%!!(778.%-!5*!*56%&!5&*7./(8!?*&%$5$@!!
Part 2: RS Based Trait Estimation
Approach 3: Mutiple-Variable Regression
Extend to Canopy and Regional Scale (Asener and Martin, 2009) !
Part 3: Ecological Application
e.g.1: Spectra-Biodiversity
(Asner et al. 2009) !
Sp
ectr
al W
avel
eng
th!
(1) Different species have
unique combinations of leaf
chemicals (Figure 4)!
(2) Unique Spectral Signal
(Figure 8). The same color
denotes a similar spectral
response !
Part 3: Ecological Application
e.g.1: Spectra-Biodiversity (Asner et al. 2009) !
(1)! Spectral signal are very similar as chemical signal;
(2) Spectra-species richness response curve is easy to saturate.!
Part 3: Ecological Application
e.g.2: Spectra-Biological Invasion
(Doughty et al. 2011) !
(1) PLS regression analysis!
(2) Data collected at 2 Hawaii
sites and 1 B2 site!
(b) Canopy Level!
(a) Leaf Level!
A: light saturated photosynthesis!
Amax: CO2 saturated
photosynthesis!
R: Respiration rate!
Summary
1. Three Approaches are Currently Used in Estimating Plant Traits!
2. Current Advanced Hyperspectral Remote Sensing Might Contribute!
(1) Biodiversity Research!
(2) Ecosystem Functioning !
(3) Biological Invasion… !
(1) Vegetation Indices!
(2) Processes Based Model (Prospect Model)!
(3) Multiple-Variable Analysis!
Part 4: Hand-On Experience
1. Prospect Model!
Please Refer to: http://teledetection.ipgp.jussieu.fr/prosail/!
Download ,A"B,CD<EFG(58(#H&(&! and you can change the
chemical parameters to see how it will affect spectral signal!
Download ,A"B,CD<EFG(58(#F.+'%&$.*+H&(&, and you can
estimate the leaf chemistry if you have the leaf spectra!
(There are actually some default data when you download it, and
you can just play with it)!
Part 4: Hand-On Experience
2. Regular Camera! Can regular Camera be able to track leaf chemical?!
Three Undergraduate!
Jianfei Chen!
Han Zhao !
Yuyan Zhu !
Part 4: Hand-On Experience
2. Regular Camera! Can regular Camera be able to track leaf chemical?!
LAI (m2/m2)!
G/(R+G+B)!
2G-RBi!
Leaf Area (m2)!
Part 4: Hand-On Experience
2. Regular Camera!
Leaf Density (g/cm3)!Leaf Density (g/cm3)!
Single Leaf:! Multiple-Layer Leaf:!
*!
R2=0.71
P=0.000!
R2=0.80
P=0.000!
Can regular Camera be able to track leaf chemical?!
Part 4: Hand-On Experience
3. Other Materials!
Technique Detail: http://spectranomics.stanford.edu/technical_information
Useful Video: http://spectranomics.stanford.edu/
Appendix: How do we monitor phenology?
Regular Camera (RGB camera)
Three Primary Colors!
http://en.wikipedia.org/wiki/RGB_color_model
Appendix: How do we monitor phenology?
Regular Camera (RGB camera)
Grey Scale!
Relative
Brightness!
Digital
Number!
255!
0!0!
1!
242!
217!
191!
166!
127!
89!
64!
38!
0.95!
0.85!
0.75!
0.65!
0.50!
0.35!
0.25!
0.15!
Winter! Spring!
Images of Bartlett Forest!
R!
G!
B!
R!
G!
B!
Relative Brightness! Relative Brightness!
Appendix: How do we monitor phenology?
Regular Camera (RGB camera)
Grey Scale!
Relative
Brightness!
Digital
Number!
255!
0!0!
1!
242!
217!
191!
166!
127!
89!
64!
38!
0.95!
0.85!
0.75!
0.65!
0.50!
0.35!
0.25!
0.15!
Winter! Spring!
Images of Bartlett Forest!
Relative Brightness! Relative Brightness!
G/(R+G+B)!
2G-RBi! 2G-RBi!
G/(R+G+B)!
Appendix: How do we monitor phenology?
Regular Camera (RGB camera)
Images of Bartlett Forest at different season in 2008!
Bartlett Forest in 2008!
Richardson. 2010. Dublin Land Product Validation Subgroup.
2G
-RB
i!
MO
DIS
EV
I!
0!
0.2!
0.4!
0.6!
0.8!
1.0!
0!
0.2!
0.4!
0.6!
0.8!
1.0!
Jan! Mar! May!Jul! Sep! Nov! Jan! Jan! Mar! May!Jul! Sep! Nov! Jan!
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