Transcript Multi-image manipulation

Hyperspectral Remote Sensing
Lecture 12
prepared by R. Lathrop 4/06
How plant leaves reflect light
Graphics from http://landsat7.usgs.gov/resources/remote_sensing/radiation.php
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
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
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)
AVIRIS:Airborne Visible
InfraRed Imaging Spectrometer
Hyperspectral sensing: AVIRIS
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
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
EO-1
• ALI & Hyperion designed to work in tandem
Hyperion over New Jersey
EO1H0140312004120110PY_PF1_01
2004/04/29,
EO1H0140312004120
0 to 9% Cloud Cover
110PY_PF1_01
2004/04/29, 0 to 9%
Cloud Cover
EO1H0140312004120110PY_PF1_01
2004/04/29, 0 to 9% Cloud Cover
EO1H0140312004184110PX_SGS_01
2004/07/02, 10% to 19% Cloud Cover
EO1H0140312
004184110PX
_SGS_01
2004/07/02,
10% to 19%
Cloud Cover
Hyperion Image
EO1H0140312004120110PY 2004/04/29
R 800- G 650- B 550
Fallow field
Active crop
Hyperion Image EO1H0140312004184110PX
R 800- G 650- B 550
Conifer forest
Deciduous forest
2004/07/02
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
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, l>1) and b) noise-dominated eigenimages
(l close to = 1)
MNF Transform: example 1
Plot of eigenvalue number
vs. eigenvalue
Original TM image using
ENVI software
MNF 6 = noise
MNF Transform: example 1
MNF 1
MNF 2
MNF 3
MNF 4
MNF 5
MNF 6
MNF Transform: example 2
Plot of eigenvalue number
vs. eigenvalue
Tm_oceanco_95sep04.img
Original TM image using
ENVI software
MNF 5,6 7 = noise
MNF Transform: example 2
MNF 1
MNF 4
MNF 2
MNF 5
MNF 6
MNF 3
MNF 7
Plant Absorption Spectrum
Image adapted from: http://fig.cox.miami.edu/~cmallery/150/phts/spectra.gif
Hyperspectral Vegetation Indices
• NDVI = (R800 – R680) / (R800 + R680)
(R800 – R705) / (R800 + R705)
at 680
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
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 85:1-7)
Hyperspectral Water Stress Indices
• Water Band Index (WBI) designed to monitor the
vegetation canopy water status (Penuelas et al., 1997,
IJRS 18:2863-2868)
• 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
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) / (R860+R1240)
Detection of Xylella fastidiosa Infection ofAmenity
Trees Using Hyperspectral Reflectance
GH Cook project by Bernie Isaacson Cook 2006
Hyperspectral reflectance curves
1 m HHA00033.SPU 9/6/2005 5:33
0.7
1 b HHA00034.SPU 9/6/2005 5:33
2 m HHA00031.SPU 9/6/2005 5:32
0.6
2 b HHA00032.SPU 9/6/2005 5:32
3 m HHA00035.SPU 9/6/2005 5:33
3 b HHA00036.SPU 9/6/2005 5:34
0.5
4 m HHA00025.SPU 9/6/2005 5:30
4 b HHA00026.SPU 9/6/2005 5:30
0.4
5 m HHA00023.SPU 9/6/2005 5:29
5 b HHA00024.SPU 9/6/2005 5:30
0.3
6 m HHA00029.SPU 9/6/2005 5:31
6 b HHA00030.SPU 9/6/2005 5:32
7 m HHA00027.SPU 9/6/2005 5:31
0.2
7 b HHA00028.SPU 9/6/2005 5:31
8 m HHA00037.SPU 9/6/2005 5:34
0.1
8 b HHA00038.SPU 9/6/2005 5:35
9 m HHA00019.SPU 9/6/2005 5:28
0
400
9 b HHA00020.SPU 9/6/2005 5:28
500
600
Green – not scorched
700
800
900
yellow – scorching
1000
10 m HHA00021.SPU 9/6/2005 5:29
10 b HHA00022.SPU 9/6/2005 5:29
brown - senesced
Hyperspectral Indices Applied
•
Normalized Difference Vegetation
Index at 680nm
•
modified Photochemical Reflectance
Index
•
•
•
Normalized Difference Vegetation Index at
705nm
•
Red wavelengths to Green wavelengths
•
Photosynthetic Active Radiation
•
modified Water Band Index
•
Water Band Index
Photochemical Reflectance Index
Simple Ratio
Negative vs. Symptomatic Positive (Margins and Bases)
Date
X - denotes significant
difference
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
l - denotes significant
difference not detected
Red text - denotes N<10
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
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
Scorch Timeline Datapoints
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
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
Original
spectral
reflectance
profile
2nd
derivative
1st
derivative
The derivative is the slope of the
signal:
Derivative positive (+) 
signal slope increasing
Derivative = 0  slope = 0
Derivative negative (-) 
signal slope decreasing
Graphic from http://www.wam.umd.edu/~toh/spectrum/Differentiation.html
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
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
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
ERDAS Spectral Analysis
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.
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
Spectral matching: Spectral Angle Mapper
Material 1
B
a
n
d
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.
Subpixel Analysis: Unmixing mixed pixels
Spectral endmembers: signature of
“pure”land cover class
endmember1
Unknown pixel represents some
proportion of endmembers based
on a linear weighting of spectral
distance. For example:
60% endmember 2
Band j
20% endmember 1
unknown pixel
20% endmember 1
endmember3
endmember2
Band i
Water Color
a 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
Ocean Color Spectra
Open Ocean
Coastal Ocean
Red Algae bloom
Water Color
a 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
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.
100
Adapted from Robinson, 1985. Satellite Oceanography
10
%R
1
0
400
500
600
700
nm
Water Color
a 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
Suspended Sediment Plume
Water Color
a 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
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
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
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 width:1,500 km; 1.1km GRC
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)
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, Quality=All)
February 3, 2003, 0540hrs GMT
West coast of India
Aqua MODIS Chlorophyll
(SeaWiFS-analog algorithm, Quality=All)
February 3, 2003, 0840hrs GMT
West coast of India
Water-leaving radiance: Atmospherically-corrected
and normalized to a constant sun angle
Level 3
Terra MODIS Normalized Water-leaving Radiance at 443 nm (H. Gordon)
Weekly average March 6 - 13, 2001
NASA/GSFC
http://modis-ocean.gsfc.nasa.gov/dataprod.html
ODIS/Aqua Ocean Weekly Productivity Indices 8-Day L4 Global 4km
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
Hyperion: eo1h0140342004241110ky
Hyperion Image EO1H0140312004184110PX
R 800
G 650
B 550
2004/07/02
Hyperion Image EO1H0140312004184110PX
R 560
G 490
B 450
2004/07/02