Hyperspectral Remote Sensing

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Hyperspectral Remote Sensing

• Lecture 12
• prepared by R. Lathrop 4/06

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How plant leaves reflect light
Graphics from http//landsat7.usgs.gov/resources/r
emote_sensing/radiation.php
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Reflectance from green plant leaves

• Chlorophyll absorbs in 430-450 and 650-680nm
  region. The blue region overlaps with carotenoid
  absorption, so focus is on red region.
• Peak reflectance in leaves in near infrared
  (.7-1.2um) up to 60 of infrared energy per leaf
  is scattered up or down due to cell wall size,
  shape, leaf condition (age, stress, disease),
  etc.
• Reflectance in Mid IR (2-4um) influenced by water
  content-water absorbs IR energy, so live leaves
  reduce mid IR return

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Hyperspectral Sensing

• Multiple channels (50) at fine spectral
  resolution (e.g., 5 nm in width) across the full
  spectrum from VIS-NIR-MIR to capture full
  reflectance spectrum and distinguish narrow
  absorption features

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Hand-held Spectroradiometer

• Calibrated vs dark vs. bright reference
  standard provided (spectralon white panel - 6
  in image)
• Can use passive sensor to record reflected
  sunlight or active illuminated sensor clip (4)

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AVIRISAirborne Visible InfraRed Imaging
Spectrometer
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Hyperspectral sensing AVIRIS
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Compact Airborne Spectrographic Imager (CASI)

• Hyperspectral 288 channels between 0.4-0.9 mm
  each channel 0.018mm wide
• Spatial resolution depends on flying height of
  aircraft and number of channels acquired

CASI 550
For more info www.itres.com
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EO-1 Hyperion

• The Hyperion collects 220 unique spectral
  channels ranging from 0.357 to 2.576 micrometers
  with a 10-nm bandwidth.
• The instrument operates in a pushbroom fashion,
  with a spatial resolution of 30 meters for all
  bands.
• The standard scene width is 7.7 kilometers.
  Standard scene length is 42 kilometers, with an
  optional increased scene length of 185 kilometers
  
• More info http//eo1.usgs.gov/hyperion.php

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EO-1

• ALI Hyperion designed to work in tandem

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Hyperion over New Jersey
EO1H0140312004120110PY_PF1_01 2004/04/29, 0 to 9
Cloud Cover
EO1H0140312004120110PY_PF1_01 2004/04/29, 0 to 9
Cloud Cover
EO1H0140312004120110PY_PF1_01 2004/04/29, 0 to 9
Cloud Cover
EO1H0140312004184110PX_SGS_012004/07/02, 10 to
19 Cloud Cover
EO1H0140312004184110PX_SGS_012004/07/02, 10 to
19 Cloud Cover
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Hyperion Image EO1H0140312004120110PY
2004/04/29
R 800- G 650- B 550
Fallow field
Active crop
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Hyperion Image EO1H0140312004184110PX
2004/07/02
R 800- G 650- B 550
Conifer forest
Deciduous forest
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Hyperspectral Sensing Analytical Techniques

• Data Dimensionality and Noise Reduction MNF
• Ratio Indices
• Derivative Spectroscopy
• Spectral Angle or Spectroscopic Library Matching
• Subpixel (linear spectral unmixing) analysis

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Minimum Noise Fraction (MNF) Transform

• MNF 2 cascaded PCA transformations to separate
  out the noise from image data for improved
  spectral processing especially useful in
  hyperspectral image analysis
• 1st is based on an estimated noise covariance
  matrix to de-correlate and rescale the noise in
  the data such that the noise has unit variance
  and no band-to-band correlation
• 2nd create separate a) spatially coherent MNF
  eigenimage with large eigenvalues (high
  information content, lgt1) and b) noise-dominated
  eigenimages
• (l close to 1)

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MNF Transform example 1
Plot of eigenvalue number vs. eigenvalue MNF 6
noise
Original TM image using ENVI software
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MNF Transform example 1
MNF 1
MNF 2
MNF 3
MNF 5
MNF 6
MNF 4
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MNF Transform example 2
Plot of eigenvalue number vs. eigenvalue MNF 5,6
7 noise
Tm_oceanco_95sep04.img Original TM image using
ENVI software
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MNF Transform example 2
MNF 1
MNF 2
MNF 3
MNF 7
MNF 5
MNF 6
MNF 4
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Plant Absorption Spectrum
Image adapted from http//fig.cox.miami.edu/cmal
lery/150/phts/spectra.gif
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Hyperspectral Vegetation Indices

• NDVI (R800 R680) / (R800 R680) at 680
• (R800 R705) / (R800 R705)
  at 705
• Where 680nm and 705nm are chlorophyll absorption
  maxima and 800 is NIR reference wavelength. 705nm
  may be more sensitive to red edge shifts

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Hyperspectral Vegetation Indices

• Photochemical Reflectance Index (PRI) designed to
  monitor the diurnal activity of xanthophyll cycle
  pigments and the diurnal photosynthetic
  efficiency of leaves
• PRI (R531 R570) / (R531 R570)
  where 531nm is the xanthophyll
  cycle wavelength and 570nm is a reference
  wavelength
• (Gamon et al., 1990, Oecologia 851-7)

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Hyperspectral Water Stress Indices

• Water Band Index (WBI) designed to monitor the
  vegetation canopy water status (Penuelas et al.,
  1997, IJRS 182863-2868)
  
• WBI R970 / R900
  where 970nm is the trough in the
  reflectance spectrum of green vegetation due to
  water absorption (trough tends to disappear as
  canopy water content declines) and
  900nm is a reference wavelength

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Hyperspectral Water Stress Indices

• Moisture Stress Index (MSI) contrast water
  absorption in the MIR with vegetation reflectance
  (leaf internal structure) in the NIR
• MSI MIR / NIR or R1600/R820
• Normalized Difference Water Index
  NDWI (R860-R1240) / (R860R1240)

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Detection of Xylella fastidiosa Infection
ofAmenity Trees Using Hyperspectral
ReflectanceGH Cook project by Bernie Isaacson
Cook 2006
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Hyperspectral reflectance curves
Green not scorched yellow scorching
brown - senesced
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(No Transcript)
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Hyperspectral Indices Applied

• Normalized Difference Vegetation Index at 705nm
• Red wavelengths to Green wavelengths
• Photosynthetic Active Radiation
• modified Water Band Index
• Water Band Index

• Normalized Difference Vegetation Index at 680nm
• modified Photochemical Reflectance Index
• Photochemical Reflectance Index
• Simple Ratio

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• Negative vs. Symptomatic Positive (Margins and
  Bases)

Date PRI mPRI NDVI680 NDVI705 WBI mWBI SR PAR redgrn
14-Jul x x l x x l l l x
18-Jul x x l x l x l x x
22-Jul x l l x l x l l l
28-Jul x x l x l l l x x
11-Aug x l l x l l l l x
22-Aug x x l l x x l x l
26-Aug x x l x l l l x x
X - denotes significant difference
l - denotes significant difference not detected
Red text - denotes Nlt10
Negative vs. Symptomatic Positive (Margins Only)
Date PRI mPRI NDVI680 NDVI705 WBI mWBI SR PAR redgrn
14-Jul x x l x l l l l x
18-Jul l x l x l x l l x
22-Jul x l l x l x l l l
28-Jul x x l x l l l x x
11-Aug x l l x l l l l l

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Pre-Visual Stress Detection?

• Hypothesis Change in reflectance detectable
  before visual symptoms
• Where can symptoms be detected?
• Infected vs. Uninfected
• Symptomatic (showing scorch) vs. Asymptomatic
  (tree infected but no symptoms)

Slide adapted from B. Isaacson
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Scorch Timeline Datapoints
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Derivative Spectroscopy

• First order quantify slope, the rate of change
  in spectra curve
• Second order identify slope inflection points
• Third order identify maximum or minimum
• Pros can be insensitive to illumination
  intensity variations
• Con sensitive to noise

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Derivative Spectroscopy Blue Shift of
Red Edge
As chlorophyll degrades, less absorption in the
red. Leads to a shift in the Red Edge (i.e.,
between 690 and 740nm) towards the blue
Stressed plant
R
Blue Shift
Red Edge inflection point
Normal plant
Spectral wavelength
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Original spectral reflectance profile
1st derivative
The derivative is the slope of the signal
Derivative positive () ? signal slope
increasing Derivative 0 ? slope 0 Derivative
negative (-) ? signal slope decreasing
2nd derivative
Graphic from http//www.wam.umd.edu/toh/spectrum/
Differentiation.html
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Derivative spectroscopy

• Red Edge inflection point (point where the slope
  is maximum) at the center of the 690-740nm range
• Corresponds to the maximum in the 1st derivative
• Corresponds to the zero-crossing (point where the
  signal crosses the y 0 line going either from
  positive to negative or vice versa) in the second
  derivative

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Spectra Matching

• Spectra Matching takes an atmospherically
  corrected unknown pixel and compares it to
  reference spectra
• Reference spectra determined from
• In situ or lab spectro-radiometer measurements
• Spectral image end-member analysis
• Theoretical calculations
• Number of different matching algorithms

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Spectra Matching spectral libraries
USGS Digital Spectral Library covers the UV to
the NIR and includes samples of mineral, rocks,
soils, vegetations, microorganism and man-made
materials http//speclab.cr.usgs.gov/spectral-lib
.html

Nicolet spectrometer
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ERDAS Spectral Analysis
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From http//speclab.cr.usgs.gov
Reference spectra used in the mapping of
vegetation species. The field calibration
spectrum is from a sample measured on a
laboratory spectrometer, all others are averages
of several spectra extracted from the AVIRIS
data. Each curve has been offset from the one
below it by 0.05.
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The continuum-removed chlorophyll absorption
spectra from Figure 1 are compared. Note the
subtle changes in the shapes of the absorption
between species.
From http//speclab.cr.usgs.gov
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Spectral matching Spectral Angle Mapper
Material 1
Band Y
Reference material
Material 2
Band X
Spectral Angle Mapper computes similarity
between unknown and reference spectra as an angle
between 0 and 90 (or as cosine of the angle).
The lower the angle the better the match.
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Subpixel Analysis Unmixing mixed pixels
Spectral endmembers signature of pureland
cover class
endmember1
Unknown pixel represents some proportion of
endmembers based on a linear weighting of
spectral distance. For example 60 endmember
2 20 endmember 1 20 endmember 1
Band j
unknown pixel
endmember3
endmember2
Band i
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Water Colora function of organic and inorganic
constituents

• Suspended sediment/mineral brought into water
  body by erosion and transport or wind-driven
  resuspension of bottom sediments
• Phytoplankton single-celled plants also
  cyanobacteria
• Dissolved organic matter (DOM) due to
  decomposition of phytoplankton/bacteria and
  terrestrially-derived tannins and humic substances

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Ocean Color Spectra
Open Ocean Coastal Ocean
Red Algae bloom
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Water Colora function of organic and inorganic
constituents

• Phytoplankton contain photosynthetically active
  pigments including chlorophyll a which absorbs in
  the blue (400-500nm) and red (approx. 675nm)
  spectral regions increase in green and NIR
  reflectance
• Suspended sediment and DOM will confound the
  chlorophyll signal. Typical occurrence in
  coastal or Case II waters as compared to CASE I
  mid-ocean waters

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Ocean Color function of chlorophyll and other
phytoplankton pigments
Typical reflectance curve for CASE 1 waters where
phytoplankton dominant ocean color signal. Arrow
shows increasing chlorophyll concentration,
dashed line clear water spectrum. Adapted from
Robinson, 1985. Satellite Oceanography
100
10
R
1
0
400 500 600
700 nm
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Water Colora function of organic and inorganic
constituents

• Suspended sediment/minerals increases
  volumetric scattering and peak reflectance shifts
  toward longer wavelengths as more suspended
  sediments are added
• Near IR reflectance also increases
• Size and color of sediments may also affect the
  relative scattering in the visible

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Suspended Sediment Plume
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Water Colora function of organic and inorganic
constituents

• Dissolved organic matter DOM strongly absorbs
  shorter wavelengths (e.g., blue)
• High DOM concentrations change the color of water
  to a tea-stained yellow-brown color

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Ocean Color RS Sensors CZCS, SeaWiFS MODIS
Higher spectral resolution bands across the
visible, with concentration in blue and green

Example CZCS wavebands
Band Center Wavelength (nm) Primary Use
1 412 (violet) Dissolved organic matter (incl. Gelbstoffe)
2 443 (blue) Chlorophyll absorption
3 490 (blue-green) Pigment absorption (Case 2), K(490)
4 510 (blue-green) Chlorophyll absorption
5 555 (green) Pigments, optical properties, sediments
6 670 (red) Atmospheric correction (CZCS heritage)
7 765 (near IR) Atmospheric correction, aerosol radiance
8 865 (near IR) Atmospheric correction, aerosol radiance
Bands 1-6 have 20 nm bandwidth bands 7 and 8
have 40 nm bandwidth.
http//daac.gsfc.nasa.gov/CAMPAIGN_DOCS/OCDST/what
_is_ocean_color.html
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Ocean Color Indices
CZCS Ocean color image of the Gulf Stream from
May 8, 1981

• CZCS phytoplankton pigment concentration
• C Lw,443/Lw,550 for low concentrations
• C Lw,520/Lw,550 for higher concentrations
• Where Lw is the water leaving radiance
• 443 and 520 wavebands should decrease due to
  greater absorption as pigment concentrations
  increase, 550 waveband remains generally stable
• Note that these ratios are reversed in form from
  the geological indices with the numerator having
  the absorption peak and the denominator
  representing the stable background

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Sea WiFS

• Launched Aug 1, 1997.
• Operated by ORBIMAGE
• BandWavelength 402-422 433-453 480-500
  500-520 545-565 660-680 745-785 845-885 nm
• Sun Synchronous, Equatorial crossing Noon
  20min
• 1 day revisit time
• 10 bit data
• Swath width1,500 km 1.1km GRC

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NOAA CoastWatch http//coastwatch.noaa.gov/

• NOAA's CoastWatch Program processes and make
  available near real-time oceanographic satellite
  data (both ocean color and SST)

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MODIS Ocean Color

• MODIS on Terra and Aqua offers twice-daily
  coverage and simultaneous measurements of Ocean
  Color and SST.
• 1-km data are available globally, and global
  composites are computed for a variety of spatial
  and temporal resolutions

Terra MODIS Chlorophyll(SeaWiFS-analog
algorithm, QualityAll)February 3, 2003, 0540hrs
GMTWest coast of India
Aqua MODIS Chlorophyll(SeaWiFS-analog algorithm,
QualityAll)February 3, 2003, 0840hrs GMTWest
coast of India
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Water-leaving radiance Atmospherically-corrected
and normalized to a constant sun angle
Level 3Terra MODIS Normalized Water-leaving
Radiance at 443 nm (H. Gordon)Weekly average
March 6 - 13, 2001NASA/GSFC
http//modis-ocean.gsfc.nasa.gov/dataprod.html
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MODIS/Aqua Ocean Weekly Productivity Indices
8-Day L4 Global 4km http//daac.gsfc.nasa.gov/MOD
IS/Aqua/ocean/MYD27W.shtml
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EO-1 Hyperion

• The Hyperion collects 220 unique spectral
  channels ranging from 0.357 to 2.576 micrometers
  with a 10-nm bandwidth.
• The instrument operates in a pushbroom fashion,
  with a spatial resolution of 30 meters for all
  bands.
• The standard scene width is 7.7 kilometers.
  Standard scene length is 42 kilometers, with an
  optional increased scene length of 185 kilometers
  
• More info http//eo1.usgs.gov/hyperion.php

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Hyperion eo1h0140342004241110ky
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Hyperion Image EO1H0140312004184110PX
2004/07/02
R 800 G 650 B 550
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Hyperion Image EO1H0140312004184110PX
2004/07/02
R 560 G 490 B 450