Hyperspectral Remote Sensing --an indirect trait measuring...

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!




Incoming Light!

Leaf Tissue!

Reflection & Reflectance!

Transmission

& Transmittance

Absorption

& Absorptance!




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 !



Figure 3 from Gamon et al. 1995.

Ecological Application!

Three vegetation types in California!

NDVI (Normalized Difference Vegetation Index)!

Red! NIR!



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


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


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)!


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




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)



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!?*&%$5$@!!



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 !


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.!


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)!


2. Regular Camera! Can regular Camera be able to track leaf chemical?!

Three Undergraduate!

Jianfei Chen!

Han Zhao !

Yuyan Zhu !


2. Regular Camera! Can regular Camera be able to track leaf chemical?!

LAI (m2/m2)!

G/(R+G+B)!

2G-RBi!

Leaf Area (m2)!


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?!


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



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!



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)!



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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