1. Hyperspectral Remote Sensing
Lecture 12
prepared by R. Lathrop 4/06

2. How plant leaves reflect light
Graphics from

3. Reflectance from green plant leaves
Chlorophyll absorbs in and nm 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

4. 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

5. 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)

6. AVIRIS:Airborne Visible InfraRed Imaging Spectrometer

7. Hyperspectral sensing: AVIRIS

8. Compact Airborne Spectrographic Imager (CASI)
Hyperspectral: 288 channels between mm; each channel 0.018mm wide
Spatial resolution depends on flying height of aircraft and number of channels acquired
CASI 550
For more info:

9. EO-1: Hyperion
The Hyperion collects 220 unique spectral channels ranging from to 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:

10. EO-1
ALI & Hyperion designed to work in tandem

11. Hyperion over New Jersey
EO1H PY_PF1_01
2004/04/29, 0 to 9% Cloud Cover
EO1H PY_PF1_01
2004/04/29, 0 to 9% Cloud Cover
EO1H PY_PF1_01
2004/04/29, 0 to 9% Cloud Cover
EO1H PX_SGS_ /07/02, 10% to 19% Cloud Cover
EO1H PX_SGS_ /07/02, 10% to 19% Cloud Cover

12. Hyperion Image EO1H0140312004120110PY 2004/04/29
R 800- G 650- B 550
Fallow field
Active crop

13. Hyperion Image EO1H0140312004184110PX 2004/07/02
R 800- G 650- B 550
Conifer forest
Deciduous forest

14. 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

15. 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)

16. MNF Transform: example 1
Plot of eigenvalue number vs. eigenvalue
MNF 6 = noise
Original TM image using ENVI software

17. MNF Transform: example 1

18. 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

19. MNF Transform: example 2

20. Plant Absorption Spectrum
Image adapted from:

21. 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

22. 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)

23. Hyperspectral Water Stress Indices
Water Band Index (WBI) designed to monitor the vegetation canopy water status (Penuelas et al., 1997, IJRS 18: )
WBI = R970 / R 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 nm is a reference wavelength

24. 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)

25. Detection of Xylella fastidiosa Infection ofAmenity Trees Using Hyperspectral Reflectance GH Cook project by Bernie Isaacson Cook 2006

26. Hyperspectral reflectance curves
Green – not scorched yellow – scorching brown - senesced

28. 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

29. Negative vs. Symptomatic Positive (Margins and Bases)
Date
PRI
mPRI
NDVI680
NDVI705
WBI
mWBI SR
PAR
redgrn
14-Jul x l
18-Jul
22-Jul
28-Jul
11-Aug
22-Aug
26-Aug
X - denotes significant
difference
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 l
18-Jul
22-Jul
28-Jul
11-Aug

30. 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

31. Scorch Timeline Datapoints

32. 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

33. 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

34. 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

35. Derivative spectroscopy
Red Edge inflection point (point where the slope is maximum) at the center of the nm 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

36. 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

37. 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
Nicolet spectrometer

38. ERDAS Spectral Analysis

39. 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.

40. The continuum-removed chlorophyll absorption spectra from Figure 1 are compared. Note the subtle changes in the shapes of the absorption between species.
From

41. 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.

42. 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
20% endmember 1
Band j
unknown pixel
endmember3
endmember2
Band i

43. 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

44. Ocean Color Spectra
Open Ocean Coastal Ocean Red Algae bloom

45. Water Color a function of organic and inorganic constituents
Phytoplankton: contain photosynthetically active pigments including chlorophyll a which absorbs in the blue ( nm) 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

46. 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, Satellite Oceanography
100 10 %R 1 nm

47. 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

48. Suspended Sediment Plume

49. 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

50. 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) 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)
Bands 1-6 have 20 nm bandwidth; bands 7 and 8 have 40 nm bandwidth.

51. Ocean Color Indices CZCS phytoplankton pigment concentration
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

52. Sea WiFS Launched Aug 1, 1997. Operated by ORBIMAGE
BandWavelength: ; ; ; ; ; ; nm
Sun Synchronous, Equatorial crossing: Noon + 20min
1 day revisit time
10 bit data
Swath width:1,500 km; 1.1km GRC

53. 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)

54. 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

55. 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 , 2001 NASA/GSFC

56. MODIS/Aqua Ocean Weekly Productivity Indices 8-Day L4 Global 4km

57. EO-1: Hyperion
The Hyperion collects 220 unique spectral channels ranging from to 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:

58. Hyperion: eo1h ky

59. Hyperion Image EO1H0140312004184110PX 2004/07/02
B 550

60. Hyperion Image EO1H0140312004184110PX 2004/07/02
B 450