Field Spectroscopy, Hyperspectral Imaging, Applications in Vegetation and Soils Analysis

1
Field Spectroscopy, Hyperspectral Imaging,
Applications in Vegetation and Soils Analysis

• Alexander F. H. Goetz
• University of Colorado and
• Analytical Spectral Devices Inc.
• goetz_at_cses.colorado.edu
• Beijing and NanJing, China
• June 28-29 and July 1-2, 2004
• Lecture 2

2
Spectroscopy of Vegetation

• Brian Curtiss
• Analytical Spectral Devices, Inc.
• 5335 Sterling Drive, Suite A
• Boulder, CO 80301-2354, USA
• 303-444-6522 FAX [phone]
• curtiss_at_asdi.com
• http//www.asdi.com/

3
Spectral Properties of Vegetation

• Unlike minerals, all vegetation is composed of a
  limited set of spectrally active compounds
• The relative abundances of these compounds,
  including water, are indicators of the condition
  of the vegetation and of the environment in which
  the vegetation is growing
• Vegetation architecture has a very strong
  influence on the overall characteristics of the
  reflectance spectrum
• The spatial scale of the reflectance measurement
  is important in determining the observed
  reflectance

4
Spectral Properties of Vegetation (cont.)

• Reflectance in the visible and near infrared
  region (350 to 800 nm) is dominated by absorption
  from chlorophyll and other accessory pigments
• Reflectance in the SWIR (800 to 2500 nm) is
  dominated by absorption from liquid water in the
  plants tissue
• Reflectance in the SWIR is modified by minor
  absorption features associated with C-H, N-H, and
  CH2 bearing compounds such as starches, proteins,
  oils, sugars, lignin and cellulose.

5
Scale Dependence of Vegetation Spectra

• Important at all scales
• Viewing geometry
• Geometry and spectral characteristics of
  illumination source(s)
• Leaf / Needle scale
• relative abundance of biochemicals
• cellular structure
• Branch scale
• leaf / needle scale reflectance
• leaf / needle angle distribution
• leaf / needle shape and density

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Scale Dependence of Vegetation Spectra (cont.)

• Crown scale
• branch scale reflectance
• branch angle distribution
• crown geometry
• branch geometry and density
• background reflectance
• Canopy scale
• crown scale reflectance
• crown density
• background reflectance (soil, understory, litter)

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Information Content of Vegetation Spectra

• Leaf / Needle scale
• leaf / needle age
• water and nutrient availability
• evidence of other environmental stress (both
  biotic and abiotic)
• Branch scale
• branch scale architecture (relates to species)
• evidence of environmental stress
• Crown scale
• tree age
• crown scale architecture (relates to species)
• evidence of environmental stress
• Canopy scale
• tree size distribution
• canopy scale architecture (relates to species
  assemblages)

8
Information Extraction from Vegetation Spectra

• Limitations of spectral libraries
• the multitude of factors affecting vegetation
  spectra makes it difficult to adequately populate
  a spectral library
• large within-species variability
• strong dependence on growing season climate
• scale dependence of vegetation spectra
• dependence of vegetation spectra on view and
  illumination geometry
• Successful use of spectral libraries
• crop type identification
• species identification in arid lands
• plant community identification

9
Information Extraction from Vegetation Spectra
(cont.)

• Limitations of model-based approaches
• often requires extensive field and laboratory
  measurements
• often are season specific
• often are specific to a particular plant
  community or group of communities (e.g. C4 forage
  grasses)
• Successful use of model-based approaches
• quantification of canopy biochemicals
• tree crown size gt stem diameter gt timber volume
• stress across an environmental gradient
• crop yield

10
Similarity of Vegetation Spectra
single leaf spectra all have same set
of absorption features but with varying depths
11
Major Spectral Features Vegetation
12
Leaf Structure
A Upper leaf cuticle B Palisade mesophyll cells
containing the majority of chloroplasts C Spongy
mesophyll cells with a large area of cell wall
interfaces D Lower cuticle containing stomates
Schematic cross-section of a typical broadleaf
showing the basic photon interactions that occur
when light strikes the leaf surface. A photon
may be (1) specularly reflected, (2) diffusely
reflected, (3) absorbed in leaf photosynthetic
apparatus, (4) scattered from inside the leaf
back in the general direction from which it
originally entered, adding to the leaf
reflectance, (5) transmitted or scattered out of
the leaf in the same general direction as it
originally traveled, adding to leaf transmittance.
13
Model-based Analysis of Vegetation Spectra

• Curve-fitting of absorption features
• more commonly applied to mineral spectra
• some success for chlorophyll and water
• features from minor constituents (e.g.
  cellulose) found in the residual spectrum
• Regression-based chemometrics
• commonly used for determination of canopy
  biochemicals (e.g. lignin, cellulose, nitrogen)
• requires many well characterized field samples
  collected near the time of over-flight

14
Model-based Analysis of Vegetation Spectra (cont.)

• Geometric optics
• models canopy as an aggregate of tree crowns
• image spectra are modeled as mixtures of sunlit
  and shaded crown, and, sunlit and shaded
  background
• the model derives the average crown size which is
  then related to stem diameter and timber volume
• Statistical classification
• supervised or unsupervised
• may be used in conjunction with a spectral
  library to aid in the definition of classes

15
Quantitative Reflectance Spectroscopy

• The Bouguer-Beer law is the fundamental
  relationship upon which many spectrometric
  techniques are based
• -log R(?) A(?) ?(?) b(?) c
• R(?) Transmission as a function of wavelength
• A(?) Absorbance as a function of wavelength
  b(?) Absorption coefficient as a function of
  wavelength ?(?) Absorption path length as a
  function of wavelength
• c Concentration of the absorbing compound
• Solving for the concentration gives
• c -log R(?) / ?(?) b(?)
• Reflectance is the quantity that can be measured
  by remote sensing

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Reflectance of Multiple Leaf Layers
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Reflectance of Multiple Leaf Layers (cont.)
Same water concentration, increasing path length
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Wavelength Dependence of Path Length
The amount of liquid water seen varies with
wavelength
19
Single Leaf Spectra
The liquid water absorption feature depths are a
product of water concentration and path length
Pinyon pine has the lowest liquid water
concentration, but the deepest absorption
features at both 0.95µm and 1.2µm
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Liquid Water Band Fitting Single Leaf
Laboratory Spectra
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Liquid Water Band Fitting AVIRIS Spectra
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Liquid Water Band Fitting AVIRIS Spectra (cont.)
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Liquid Water Band Fitting AVIRIS Spectra (cont.)
cellulose
water
golf course
Ponderosa pine
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Chemometric Modeling

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Chemometric Calibration Techniques

• Follows Beers Law
• Linear regression-based models
• calculations using log 1/Reflectance
• Calibration Calibration
  Image
• Samples ??????????????? Model ???
  Spectra
• Develop
  Apply

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Chemometrics Model Development
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Near Infrared Spectroscopy Data Pre-treatments

• Log 1/Reflectance
• Spectral Filtering
• Smoothing
• Multiplicative Scattering Correction
• Spectral Transforms
• Derivatives

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Near Infrared Spectroscopy Chemometric Models

• Simple regression
• Single Wavelength
• Multiple Wavelengths
• Multivariate Regression
• Principal Component Regression
• Partial Least Squares Regression

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Near Infrared Spectroscopy Sample Selection

• Samples Must
• span the range of variability found in the image
• variance for other scene variables must be
  independent
• be collected to be representative of the spatial
  pixel
• For Natural Samples
• many (gt100) samples are required for a good
  calibration
• 10-20 samples required for proof of concept

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Calibration for Nitrogen, Lignin Cellulose in
Fresh Leaves
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Fresh Leaf Calibration Band Positions
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Fresh Vs. Dry Predicted Nitrogen
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AVIRIS Nitrogen PLS Calibration
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AVIRIS Predicted Vs. Actual Nitrogen
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Dry Leaf Band DepthNitrogen Calibration
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Band Depth Predicted Nitrogen Vs. Actual Nitrogen
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Spectra of Vegetation Components

• Canopy biochemicals
• water
• woody components lignin, cellulose
• nitrogen rich components pigments, proteins
• other components sugars, starch, oils, waxes
• Non-vegetative canopy elements
• wood
• bark
• Plant litter
• dry leaves
• decomposing leaves and woody material

38
Spectra of Cellulose Lignin
The spectra of most woody materials are dominated
by lignins The spectra of leaves/needles show
absorption by both lignin and cellulose Man-made
cotton fabrics have no lignin absorption features
39
Spectra of Dry Plant Materials
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Spectra of Dry Plant Materials (cont.)
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Spectra of Other Vegetative Components
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Wavelengths of Plant Biochemical Absorption
Features
Graphical plot of the major (boxed) and minor
absorption peaks of canopy biochemicals overlaid
with the location of the principal water
absorption regions of the atmosphere as
determined from AVIRIS spectra.
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Wavelengths of Plant Biochemical Absorption
Features (cont.)
Absorption features in the Vis and NIR that have
been related to particular foliar chemical
concentrations.
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Effects of Shadows onVegetation Spectra
The aspect ratio of canopy elements (in this case
crowns) determines the relative amounts of
illuminated and shaded crown and background.
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Effects of Hole Shape onVegetation Spectra
(cont.)
The aspect ratio of holes in the canopy (at all
scales) determines the importance of shadows in
the measured spectrum.
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Viewing Geometry Effects Soil
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Viewing Geometry Effects Grass
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Viewing Geometry Effects Juniper
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Bi-Directional ReflectanceDistribution Function
illumination direction

• Red (680nm) Infrared
  (850nm)
• squares pine circles broadleaf

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Influence of View Geometry on Vegetation Spectra
BRDF of a tallgrass prairie grassland community
at two wavelengths. Solar zenith angle is 48.
The red band is centered at 662nm the IR band is
centered at 826nm.
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Effects of Sensor Spatial Resolution on the
Spectrum of a Vegetation Canopy
The spatial variance of the amount of shaded
canopy is an indicator of the average crown size.
Crown size can then be related to trunk diameter
and other parameters useful for forest management.
52
Effects of View Geometry on Reflectance of
Vegetation Canopies
Mean spectral reflectance of a mixed conifer
stand from view angles of 0 and 15, 30 and 45
in the backscatter direction and in the principal
plane of solar illumination.
53
Time Variation of Vegetation Spectra
Conifers retain needles for several years.
Spectra of each years needles can be quite
different. At the canopy level the number of
years growth retained by a tree strongly
influences the canopy scale spectral reflectance.
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Time Variation of Vegetation Spectra (cont.)
left changes in NDVI for a Indian Grass below
reflectance spectra for three days in the growing
season.
55
The Red Edge
The plot to the left shows a red edge shift to
blue wavelengths for stressed
vegetation. Others have reported a shift to the
red under stressed conditions.
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Mechanisms for the Red Edge Shift
Band broadening can be produced by chloroplast
disruption. Band depth can be reduced by either
chlorophyll loss or an architectural change that
reduces the interaction of illumination with the
chlorophyll in the leaf.
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What Produces the Shift in the Red Edge?

• Anything that reduces the amount of chlorophyll
  seen by the sensor at either the leaf/needle
  scale or at the canopy scale
• actual changes in chlorophyll concentration
• canopy architecture changes
• branch-scale changes in architecture
• changes in vegetation cover
• Anything that disrupts the chloroplast membranes
• These two mechanisms compete under stressful
  conditions
• could get either a red or blue or no shift with
  stress

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Effects of Chronic Stress on Vegetation

• In a natural ecosystem, chronic stress results in
  the establishment of individual trees and/or
  species that have adapted to the stress
• The chlorosis observed in seedlings grown under
  stress is generally not observed in a mature
  forest
• Architectural changes are a common response to
  chronic stress and result in changes in
  reflectance that are observable at leaf to canopy
  scales
• Chances in foliar reflectance may also result
  from chronic stress

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Vegetation Hyperspectral Remote Sensing - Summary

• Step 1 A clearly defined objective examples
• delineation of dominant species assemblages
• mapping of ecosystem productivity
• crop type or yield mapping
• spatial distribution of heavy metal induced
  stress
• Step 2 Identify spectral changes or differences
  associated with process of interest methods
• examination of existing spectral libraries
• examination of study-specific field spectral
• correlation between spectral and non-spectral
  field or laboratory measurements

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Vegetation Hyperspectral Remote Sensing - Summary
(cont.)

• Step 3 Identify best analytical method based on
  available image, field, and laboratory resources
  methods
• spectral library based
• model based
• curve-fitting
• chemometics
• geometric optics
• classification