hyperspectral remote sensing lecture 12 prepared by r. lathrop 4/06

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 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 & 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

• 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

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

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

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.

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, SeaWiFS & MODIS

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

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

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