Hyperspectral Remote Sensing --an indirect trait measuring method
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1 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
2 Part 1: Terminologies Three Pathways of Light-Leaf Interaction Incoming Light! Reflection! Leaf Tissue! Absorption! Transmission! Source: Part 1: Terminologies Three Pathways of Light-Leaf Interaction Incoming Light! Reflection & Reflectance! Leaf Tissue! Absorption & Absorptance! Transmission & Transmittance Source:
3 Part 1: Terminologies Three Pathways of Light-Leaf Interaction 1=Reflectance+Transmittance+Absorptance! Incoming Light! Reflection & Reflectance! Leaf Tissue! Absorption & Absorptance! Transmission & Transmittance Source: Part 1: Tools ASD Field Spec 4 Leaf Level Measurements (Analytical Spectra Device)! Reflectance! Transmittance! Integrating Sphere! Main Computer of ASD! Reflectance! Leaf Clip! Source:
4 Part 1: Tools SOC 710 Camera Canopy Level Measurements (Surface Optics Corporation)! Reflectance! AVIRIS Camera Canopy Level Measurements (Airborne Visible Infrared Imaging Spectrometer )! Reflectance! Part 1: Example from ASD measurements Transmittance! absorptance! Reflectance!
5 Part 1: Example from ASD measurements Cw=0.024 (cm)! Cw=0.011 (cm)! Cw=0.006 (cm)! LSA= (g/cm2)! LSA= (g/cm2)! LSA= (g/cm2)! Chl=37.4 (ug/cm2)! Chl=56.9(ug/cm2)! Chl=52.4 (ug/cm2)! Part 2: RS Based Trait Estimation Approach 1: Vegetation Index! Approach 2: Processed Based Models! Approach 3: Mutiple-Variable Regression!
6 Part 2: RS Based Trait Estimation Approach 1: Vegetation Index! NDVI (Normalized Difference Vegetation Index)! Figure 3 from Gamon et al Ecological Application! Red! NIR! Three vegetation types in California! Part 2: RS Based Trait Estimation Approach 1: Vegetation Index! Xanthophyll induced absorption feature at 531 nm, which is intimately linked to the biochemical mechanism down-regulating photosynthesis PRI (Photosynthetic Reflectance Index)! Figure 3 from Hilker et al Remote Sensing of Environment! Light use efficiency generated by eddy covariance measurement! Two forest types in Canada!
7 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! Chlorophyll (ug/cm2)! Carotenold (ug/cm2)! Fig 6 in (Jacquemoud and Baret, 1990) Leaf Water Depth (cm)! Leaf Mass Area (g/cm2)! Fig 11 in (Feret et al., 2008)
8 Part 2: RS Based Trait Estimation Approach 3: Mutiple-Variable Regression or Partial Least Square Regression Analysis (Asner et al. 2009)! Assumptions: leaf spectral properties quantitatively represent a suite of biochemicals and SLA in the foliage of tropical forest tree species Y n!m, hyperspectral reflectance or transmittance, n is the number of leaf samples, m is the number of spectral bands X n!p, leaf traits, n is the number of leaf samples, p is the number of leaf traits B n!n, leaf spectral Weightings e n!m, spectral residual errors Part 2: RS Based Trait Estimation Approach 3: Mutiple-Variable Regression or Partial Least Square Regression Analysis (Asner et al. 2009)! 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 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)
9 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!$7%/5&(8!$%+$.)'.59:!! 0;2!
10 Part 2: RS Based Trait Estimation Approach 3: Mutiple-Variable Regression Extend to Canopy and Regional Scale (Asener and Martin, 2009)! Part 3: Ecological Application e.g.1: Spectra-Biodiversity (Asner et al. 2009)! (1) Different species have unique combinations of leaf chemicals (Figure 4)! (2) Unique Spectral Signal (Figure 8). The same color denotes a similar spectral response! Spectral Wavelength!
11 Part 3: Ecological Application e.g.1: Spectra-Biodiversity (Asner et al. 2009)! (1)! Spectral signal are very similar as chemical signal; (2) Spectra-species richness response curve is easy to saturate.! Part 3: Ecological Application e.g.2: Spectra-Biological Invasion (Doughty et al. 2011)! (1) PLS regression analysis! (2) Data collected at 2 Hawaii sites and 1 B2 site! A: light saturated photosynthesis! R: Respiration rate! Amax: CO2 saturated photosynthesis! (b) Canopy Level! (a) Leaf Level!
12 Summary 1. Three Approaches are Currently Used in Estimating Plant Traits! (1) Vegetation Indices! (2) Processes Based Model (Prospect Model)! (3) Multiple-Variable Analysis! 2. Current Advanced Hyperspectral Remote Sensing Might Contribute! (1) Biodiversity Research! (2) Ecosystem Functioning! (3) Biological Invasion! Part 4: Hand-On Experience 1. Prospect Model! Please Refer to: Download,A"B,CD<EFG(58(#H&(&! and you can change the chemical parameters to see how it will affect spectral signal! Download,A"B,CD<EFG(58(#F.+'%&$.*+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)!
13 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?! Leaf Area (m2)! LAI (m2/m2)! G/(R+G+B)! 2G-RBi!
14 Part 4: Hand-On Experience 2. Regular Camera! Can regular Camera be able to track leaf chemical?! *! R 2 =0.71 P=0.000! R 2 =0.80 P=0.000! Single Leaf:! Leaf Density (g/cm3)! Leaf Density (g/cm3)! Multiple-Layer Leaf:! Part 4: Hand-On Experience 3. Other Materials! Technique Detail: Useful Video:
15 Appendix: How do we monitor phenology? Regular Camera (RGB camera) Three Primary Colors! Relative Brightness! 1! Appendix: How do we monitor phenology? Grey Scale! 0.95! 0.85! 0.75! 0.65! 0.50! 0.35! 0.25! 0.15! 0! Regular Camera (RGB camera) Digital Number! 255! 242! 217! 191! 166! 127! 89! 64! 38! 0! R! G! B! Winter! Images of Bartlett Forest! R! G! B! Spring! Relative Brightness! Relative Brightness!
16 Relative Brightness! 1! Appendix: How do we monitor phenology? Grey Scale! 0.95! 0.85! 0.75! 0.65! 0.50! 0.35! 0.25! 0.15! 0! Regular Camera (RGB camera) Digital Number! 255! 242! 217! 191! 166! 127! 89! 64! 38! 0! Winter! Images of Bartlett Forest! G/(R+G+B)! 2G-RBi! Relative Brightness! G/(R+G+B)! 2G-RBi! Spring! Relative Brightness! Appendix: How do we monitor phenology? Regular Camera (RGB camera) Images of Bartlett Forest at different season in 2008! 2G-RBi! Bartlett Forest in 2008! 1.0! 1.0! 0.8! 0.8! 0.6! 0.6! 0.4! 0.4! 0.2! 0.2! 0! 0! Jan! Mar! May! Jul! Sep! Nov! Jan! Jan! Mar! May! Jul! Sep! Nov! Jan! MODIS EVI! Richardson Dublin Land Product Validation Subgroup.
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