Hyperspectral Remote Sensing Lecture 12 prepared by R. Lathrop 4/06.
Hyperspectral Remote Sensing Lecture 12 prepared by R. Lathrop 4/06 Updated 1/09.
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Transcript of Hyperspectral Remote Sensing Lecture 12 prepared by R. Lathrop 4/06 Updated 1/09.
Hyperspectral Remote Sensing
Lecture 12
prepared by R. Lathrop 4/06
Updated 1/09
Where in the world?
Learning objectives• Remote sensing science concepts
– hyperspectral vs. broad-band sensors– Derivative spectroscopy– Spectral matching– Applications to the study of vegetation and ocean color
• Math Concepts– Calculating derivatives
• Skills– Applying hyperspectral processing methods
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 (Airbone Visile/IR Imaging System)
(224 detectors, .38-2.5µm full spectrum)
Compact Airborne Spectrographic Imager (CASI)
• Hyperspectral: 288 channels between 0.4-0.9 m; each channel 0.018m wide
• Spatial resolution depends on flying height of aircraft and number of channels acquired
For more info: www.itres.com
CASI 550
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 (Advanced Land Image) & Hyperion
designed to work in tandem
EO1H0140312004184110PX_SGS_012004/07/02, 10% to 19% Cloud Cover
EO1H0140312004120110PY_PF1_012004/04/29, 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
Hyperion over New Jersey
Hyperion Image EO1H0140312004120110PY 2004/04/29
Fallow field
Active crop
R 800- G 650- B 550
Hyperion Image EO1H0140312004184110PX 2004/07/02
Deciduous forest
Conifer forest
R 800- G 650- B 550
Hyperspectral Sensing: Analytical Techniques
• Data Dimensionality and Noise Reduction: MNF (Minimum Noise Function)
• 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, >1) and b) noise-dominated eigenimages
(close1)
MNF Transform: example 1
Original TM image using ENVI software
Plot of eigenvalue number vs. eigenvalue
MNF 6 = noise
MNF Transform: example 1
MNF 5
MNF 1 MNF 2
MNF 6MNF 4
MNF 3
MNF Transform: example 2
Tm_oceanco_95sep04.img Original TM image using ENVI software
Plot of eigenvalue number vs. eigenvalue
MNF 5,6 7 = noise
MNF Transform: example 2
MNF 5
MNF 1 MNF 2
MNF 6MNF 4
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) 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
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
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
400 500 600 700 800 900 1000
1 m HHA00033.SPU 9/6/2005 5:33
1 b HHA00034.SPU 9/6/2005 5:33
2 m HHA00031.SPU 9/6/2005 5:32
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
4 m HHA00025.SPU 9/6/2005 5:30
4 b HHA00026.SPU 9/6/2005 5:30
5 m HHA00023.SPU 9/6/2005 5:29
5 b HHA00024.SPU 9/6/2005 5:30
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
7 b HHA00028.SPU 9/6/2005 5:31
8 m HHA00037.SPU 9/6/2005 5:34
8 b HHA00038.SPU 9/6/2005 5:35
9 m HHA00019.SPU 9/6/2005 5:28
9 b HHA00020.SPU 9/6/2005 5:28
10 m HHA00021.SPU 9/6/2005 5:29
10 b HHA00022.SPU 9/6/2005 5:29
Green – not scorched yellow – scorching brown - senesced
(Water Band Index)
(Photochemical Reflectance Index)
(Photosynthesis active radiation)
(Normalized Difference Vegetation Index)
(Scanning Radiometer)
Hyperspectral Indices Applied• Normalized Difference Vegetation
Index at 680nm
• modified Photochemical Reflectance Index
• Photochemical Reflectance Index
• Simple Ratio
• Normalized Difference Vegetation Index at 705nm
• Red wavelengths to Green wavelengths
• Photosynthetic Active Radiation
• modified Water Band Index
• Water Band Index
Negative vs. Symptomatic Positive (Margins and Bases)
X - denotes significant
difference
Negative vs. Symptomatic Positive (Margins Only)
l - denotes significant difference not detected
Red text - denotes N<10
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
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
Stressed plant
Normal plant
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
‘Red Edge’ inflection point
Spectral wavelength
%R
Blue Shift
Original spectral reflectance profile
1st derivative
2nd derivative
Graphic from http://www.wam.umd.edu/~toh/spectrum/Differentiation.html
The derivative is the slope of the signal: 1st derivative
1st Derivative increasing signal slope increasing in steepness
1st Derivative Max signal slope inflection point
1st Derivative decreasing signal slope decreasing in steepness
Original spectral reflectance profile
1st derivative
2nd derivative
The 2nd derivative is the slope of the 1st derivative:
2nd Derivative positive (+) 1st derivative slope increasing
2nd Derivative = 0 slope = 0
2nd Derivative negative (-) 1st derivative slope decreasing
Graphic from http://www.wam.umd.edu/~toh/spectrum/Differentiation.html
Derivative spectroscopy
• 1st difference, subtract the value for wavelength (i) from wavelength (i+1)
• Example – Band Reflectance– 706 0.111246– 707 0.119605
• 1st Derivative = R (i+1) - R i
0.119605 – 0.11246 = +0.008359
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 (Untraviolet) 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
Band X
Band Y
Material 1
Material 2
Reference material
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.
Data Dimensionality reduction: selection of ‘best’ bands
Optimal Hyperion wavebands for predicting rainforest dry weight biomass
From Thenkabail et al., 2004. RSE 90(1):23-43.
Subpixel Analysis: Unmixing mixed pixels
Band i
Band j
Spectral endmembers: signature of “pure”land cover class
unknown pixel
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
endmember1
endmember2endmember3
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
Ocean Color Spectra
Open Ocean Coastal Ocean Red Algae bloom
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
Ocean Color: function of chlorophyll and other phytoplankton pigments
400 500 600 700 nm
1
0
10
100
%R
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
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
Suspended Sediment Plume
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
Ocean Color RS Sensors: CZCS (Coastal Zone Color Scanner), SeaWiFS(Sea-viewing Wide Field of View Sensor) & MODIS (Moderate-resolution Imaging Spectroradiometer)
Higher spectral resolution bands across the visible, with concentration in blue and green
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
Example: CZCS wavebands
Ocean Color Indices
• 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
CZCS Ocean color image of the Gulf Stream from May 8, 1981
Geological Indices=TM Band 5/TM Band 7
Spectral Ratioing: for Absorption Enhancement
• Objective: enhance particular absorption features of materials of interest vs. background reflectance
• Numerator is a baseline of background absorption
• Denominator is an absorption peak for the material of interest (based on absorption spectra)
• As material concentration increases, denominator decreases, index increases
Spectral Ratioing: Geological Indices
• TM5/TM7 to enhance clay minerals– TM5: 1.55->1.75um provides
background reflectance – TM7: 2080->2350um: specific
absorption peak for clay minerals
From ERDAS Field Guide 4th ed.
To more effectively discriminate between the various types of clay minerals can use hyperspectral ratios kaolinite: 2160/2190nm montmorillonite 2220/2250nm illite 2350/2488nm
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 (Satellite Service Technology))
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
Aqua MODIS Chlorophyll(SeaWiFS-analog algorithm, Quality=All)February 3, 2003, 0840hrs GMTWest coast of India
Terra MODIS Chlorophyll(SeaWiFS-analog algorithm, Quality=All)February 3, 2003, 0540hrs GMT
West coast of India
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
Water-leaving radiance: Atmospherically-corrected and normalized to a constant sun angle
MODIS/Aqua Ocean Weekly Productivity Indices 8-Day L4 Global 4km http://daac.gsfc.nasa.gov/MODIS/Aqua/ocean/MYD27W.shtml
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 2004/07/02
R 800G 650B 550
Hyperion Image EO1H0140312004184110PX 2004/07/02
R 560
G 490
B 450
Summary
• Concept of hyperspectral image & major sensors (AVIRIS, Hyperion, etc.);
• Hyperspectral image process methods:
Hyperspectral VI, NDVI, & other ratios;
Normalized Difference Water Index;
Spectral matching;
MNF;
Subpixel analysis;
Homework
1 Homework: Vegetation Indices
2 Reading Field Guide Ch.10-11.
3 Mid-term Exam due to Wednesday, March 7, 2007.