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Characterizing non-pigment canopy biochemistry from imaging spectrometer
data for studying ecosystem processes
Gregory P. Asner,Gregory P. Asner,
Mary E. Martin,Mary E. Martin,
Scott V. Ollinger, &Scott V. Ollinger, &
Carol A. Carol A. WessmanWessman
Raymond F. KokalyRaymond F. Kokaly
U.S. Geological SurveyU.S. Geological Survey
Ohio State Univ.Ohio State Univ.
General Research Goals
• To quantify non-pigment biochemistry at the leaf and canopy levels using reflectance spectroscopy
• Apply knowledge of leaf spectroscopy for detailed characterization of vegetation
• Quantify canopy biochemistry to increase understanding of fine-scale variations in ecosystem processes over large areas
Types of Remote Sensing
Electromagnetic
Wavelength (m)
Passive
Systems
Active
Systems
0.1 - 0.4 Ultraviolet (UV)
0.4 – 0.7 Visible
0.7 – 1.4
1.4 – 3.0
Near-Infrared
Shortwave Infrared
3.0 – 1,000 Infrared
1,000-
300,000
Microwave
Airborne CamerasAirborne Cameras
RadiometersRadiometers
SpectrometersSpectrometers
LIDARLIDAR
Radar (SAR)Radar (SAR)
•Satellite-basedSatellite-basedImaging Spectroscopy Data: Imaging Spectroscopy Data:
EO-1 HyperionEO-1 Hyperion
• AirborneAirborne Imaging Spectroscopy Data:Imaging Spectroscopy Data:
HyMapHyMapProbeProbeCASICASI AVIRISAVIRIS
Imaging SpectrometersImaging Spectrometers
EO-1EO-1 SpacecraftSpacecraft
NASA ER-2NASA ER-2
Trends in quantifying non-pigment leaf biochemistry
• Greater understanding of lab, field and remote sensing level spectra in relation to biochemical composition
• Increased use of full spectrum or specific biochemical absorption features to characterize vegetation
• Greater application of imaging spectroscopy to understand ecosystem processes
Understanding vegetation spectra in relation to biochemical composition
• Water
• Nitrogen (in chlorophylls and proteins)
• Lignin and Cellulose
• Non-Photosynthetic Vegetation
Kokaly, REMOTE SENS. ENVIRON. 84:437-456 (2003)
Water• Water content/potential is a limiting factor
for plant transpiration (photosynthesis and carbon gain) and thus growth and development
Kokaly and Clark, REMOTE SENS. ENVIRON. 67:267–287 (1999)REMOTE SENS. ENVIRON. 67:267–287 (1999)
Spectroscopic Estimates of Leaf/Canopy Water
• Leaf/Canopy Liquid Water Thickness• Gao & Goetz, 1994
• Canopy Equivalent Water Thickness• Green et al., 1991 & Roberts et al. 1997
• Canopy Relative Water Content• Serrano et al. 2000
• Foliar Water Potential • Stimson et al. 2005
Serrano et al., REMOTE SENS. ENVIRON. 74:570–581 (2000)
NDWI = NDWI = (R860 - R1240)(R860 - R1240)
(R860 + R1240)(R860 + R1240)
NDII = NDII = (R819 - R1649)(R819 - R1649)
(R819 + R1649)(R819 + R1649)
Stimson et al., Remote Sensing of Environment 96 (2005) 108– 118
Foliar Water Potential (MPa) Foliar Water Potential (MPa)
980
nm
1200
nm
Leaf
(Lab)
Canopy
(AVIRIS)
Clark et al., Remote Sensing of Environment 96 (2005) 375 – 398Remote Sensing of Environment 96 (2005) 375 – 398850 1350 1850
Nitrogen
• By dry weight, very low abundance in leaves, only 0.5 to 4%
• Present in important biochemical constituents of plants– Chlorophyll– Protein
• Linked by field studies to rates of ecosystem functioning (carbon fixation) and widely used in ecosystem models
Nitrogen in Proteins
• Proteins composed of amino acids ranging from a 100 to 100,000 in number
RuBisCO
• Ribulose-1,5-bisphosphate carboxylase/oxygenase, catalyzes the first major step of carbon fixation
• RuBisCO is the most abundant protein in leaves (maybe the most abundant on Earth).
Kokaly, REMOTE SENS. ENVIRON. 75:153–161 (2001)
Kokaly, REMOTE SENS. ENVIRON. 75:153–161 (2001)
Kokaly and Clark, REMOTE SENS. ENVIRON. 67:267–287 (1999)REMOTE SENS. ENVIRON. 67:267–287 (1999)
Nitrogen inChlorophyll
Mutanga et al., Remote Sensing of Environment 96 (2005) 108– 118
Biochemical Components of Plants
• Cellulose– Cellulose monomers (β-glucose) are linked
together through 1→4 glycosidic bonds– Cellulose is a common material in plant cell
walls– Cellulose is the most abundant form of living
terrestrial biomass (R.L. Crawford 1981).
Biochemical Components of Plants
• Lignin– 10% to 40% by dry weight– Lignin is a large macromolecule with molecular
mass in excess of 10,000 amu.– Structural component of plant cell walls.– It is hydrophobic and aromatic in nature.– Lignin concentration in litter has strong
influence on ecosystem processes
CelluloseCellulose
LigninLignin
PineCellulose:Lignin = 1.5
= 3.8
GrassCellulose:Lignin
Kokaly et al., Remote Sensing of Environment, in press
Apply knowledge of leaf spectroscopy for detailed
characterization of vegetation
Cerro Grande FireCerro Grande FireLos Alamos,Los Alamos,New MexicoNew Mexico
PineCellulose:lignin =1.5
=3.8
GrassCellulose:lignin
Kokaly et al., Remote Sensing of Environment, in press
AVIRIS Dry Grass
AVIRISDry Pine
Kokaly et al., Remote Sensing of Environment, in press
Results: AVIRIS Maps
GreenGreenVegetationVegetation
Mineral/AshMineral/Ash
Mineral-1Mineral-1mm
Mineral-2Mineral-2mm
Dry ConiferDry Conifer
Dry & GreenDry & GreenConiferConifer
Straw mattingStraw matting
Ash/CharcoalAsh/Charcoal
Straw mattingStraw matting & Green grass& Green grass
Kokaly et al., Remote Sensing of Environment, in press
GreenGreenVegetationVegetation
Mineral/AshMineral/Ash
Mineral-1Mineral-1mm
Mineral-2Mineral-2mm
Dry ConiferDry Conifer
Dry & GreenDry & GreenConiferConifer
Straw mattingStraw matting
Ash/CharcoalAsh/Charcoal
Straw mattingStraw matting & Green grass& Green grass
Kokaly et al., Remote Sensing of Environment, in press
High Burn SeverityHigh Burn Severity
Kokaly et al., Remote Sensing of Environment, in press
30m
Analysis of spectral shape (full spectrum and/or absorption features) for vegetation
characterization
• Multiple Endmember Spectral Mixture Models:– Roberts, et al. (1998) Remote Sens. Environ. 65:267–279
• Monte Carlo spectral unmixing model:– Asner and Heidebrecht (2002) Int. J. Remote sensing 23:
3939–3958
• Tetracorder, absorption feature analysis:– Clark, et al. (2003) . J. Geophys. Research 108 (E12): 5-1
to 5-44
Understanding ecosystem processes by quantifying canopy biochemistry
• Wessman, et al. (1988) Nature 333: 154–156• Asner and Heidebrecht (2003) IEEE Trans. on
Geoscience & Remote Sensing 41: 1283-1296.• Smith, et al. (2002) Ecological Applications 12:
1286–1302• Ollinger and Smith, (2005) Ecosystems 8: 760–778• Asner et al. (2005), Remote Sensing of Environment
96: 497–508
Asner and Heidebrecht, Int. J. remote sensing, 2002, vol. 23, no. 19, 3939–3958
Asner and Heidebrecht, IEEE Trans. on Geoscience & Remote Sensing (2003), 41:1283-1296.
Non-Photosynthetic Vegetation and Shrubland Ecosystems
Kokaly, REMOTE SENS. ENVIRON. 75:153–161 (2001)
Mutanga et al., Remote Sensing of Environment 96 (2005) 108– 118
• V
Asner and Vitousek,
Proc. Nat. Acad. Sci.,
Vol 102 , 2005.
Smith et al., Ecological
Applications, 12(5), 2002,
1286–1302
Smith et al., Ecological
Applications, 12(5), 2002,
1286–1302
Ollinger and Smith,
Ecosystems (2005) 8: 760–778
Ollinger and Smith, Ecosystems
(2005) 8: 760–778
Ollinger and Smith,
Ecosystems (2005) 8: 760–778
Remaining Challenges/Future Directions
• Overlapping of biochemical absorption features– Leaf water masking effect– Understanding protein/lignin/cellulose changes which
all affect the 2.1 and 2.3 m features
• Quantification of other biochemicals (starch, tannin, hemi-cellulose, phosphorous)
• Independent-site/Multi-site validation• Atmospheric correction needs improvement to
lessen impact of residual features