1. Karnieli: Introduction to Remote Sensing
April 17
Karnieli: Introduction to Remote Sensing
Vegetation Indices
Prof. Arnon Karnieli
The Remote Sensing Laboratory
Jacob Blaustein Institute for Desert Research
Ben-Gurion University of the Negev
Sede-Boker Campus 84990, ISRAEL
Topic #9: Vegetation Indices

2. Karnieli: Introduction to Remote Sensing
April 17
Definition
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

3. Karnieli: Introduction to Remote Sensing
April 17
Simple Ratio (SR)
Karnieli: Introduction to Remote Sensing
SR = NIR/Red
Topic #9: Vegetation Indices

4. Karnieli: Introduction to Remote Sensing
April 17
Vegetation health
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

5. Karnieli: Introduction to Remote Sensing
April 17
Characteristics of SR
Karnieli: Introduction to Remote Sensing
The SR is based on the difference between the maximum absorption of radiation in the red (due to the chlorophyll pigments) and the maximum reflection of radiation in the NIR (due to the leaf cellular structure), and the fact that soil spectra, lacking these mechanisms, typically do not show such a dramatic spectral difference.
The values of the SR range from 0 to infinity
SR uses radiance, surface reflectance (), or apparent reflectance (measured at the top of the atmosphere) rather than digital numbers.
Topic #9: Vegetation Indices

6. Karnieli: Introduction to Remote Sensing
April 17
NIR-Red scatterplot
Karnieli: Introduction to Remote Sensing
SR = NIR/Red
NIR
Topic #9: Vegetation Indices

7. Karnieli: Introduction to Remote Sensing
April 17
Example
Karnieli: Introduction to Remote Sensing
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8. Karnieli: Introduction to Remote Sensing
April 17
Example (Cont.)
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

9. Karnieli: Introduction to Remote Sensing
April 17
The Normilized Difference Vegetation Index
Karnieli: Introduction to Remote Sensing
NDVI = (NIR- Red)/(NIR+ Red)
Topic #9: Vegetation Indices

10. Karnieli: Introduction to Remote Sensing
April 17
SR vs. NDVI
Karnieli: Introduction to Remote Sensing
NDVI has the advantage of varying between -1 and +1, while the SR ranges from 0 to infinity.
In NDVI it is easier to separate snow, clouds, and water (negative values) from soil and vegetation (positive values).
Topic #9: Vegetation Indices

11. Karnieli: Introduction to Remote Sensing
April 17
SR vs. NDVI
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

12. Karnieli: Introduction to Remote Sensing
April 17
Interpretation of NDVI
Karnieli: Introduction to Remote Sensing
High index values  dense/health vegetation
Low index values  sparse/stress vegetation
Typical NDVI values:
Bare soils: 0.08 – 0.1
Desert vegetation: 0.1 – 0.3
Tropical forest: 0.4 – 0.6
Water, snow, clouds: <0
Topic #9: Vegetation Indices

13. Karnieli: Introduction to Remote Sensing
April 17
Applications of NDVI
Karnieli: Introduction to Remote Sensing
High Correlation with:
Photosynthetic activity
Vegetation cover
Leaf area index
Green leaf biomass
Carbon fluxes
Foliar loss and damage
Chlorophyll content
Also used for:
Crop classification
Plant Phenology
Change detection
Topic #9: Vegetation Indices

14. Karnieli: Introduction to Remote Sensing
April 17
Leaf Area Index (LAI)
Karnieli: Introduction to Remote Sensing
Leaf Area Index is defined as the total one- side green leaf area per unit ground surface area (m2/ m2).
LAI example
LAI = 6 means 6m2 leaf area per 1m2 ground area.
Topic #9: Vegetation Indices

15. Karnieli: Introduction to Remote Sensing
April 17
Leaf Area Index (LAI)
Karnieli: Introduction to Remote Sensing
It is an important biological parameter because:
It defines the area that interacts with solar radiation and provides the remote sensing signal
It is the surface that is responsible for carbon absorption and exchange with the atmosphere..
Methods
Direct:
Indirect:
Destructive sampling
Radiation measurements
Remote sensing
Topic #9: Vegetation Indices

16. Karnieli: Introduction to Remote Sensing
April 17
Non-linear correlations
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

17. Karnieli: Introduction to Remote Sensing
April 17
Linear correlations
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

18. Karnieli: Introduction to Remote Sensing
April 17
NDVI calculations
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

19. Karnieli: Introduction to Remote Sensing
April 17
NDVI product
Karnieli: Introduction to Remote Sensing
Red
Near Infrared
NDVI
Topic #9: Vegetation Indices

20. Karnieli: Introduction to Remote Sensing
April 17
NDVI - Israel
Karnieli: Introduction to Remote Sensing
NDVI derived from NOAA-AVHRR
Topic #9: Vegetation Indices

21. Karnieli: Introduction to Remote Sensing
April 17
NDVI of snow
Karnieli: Introduction to Remote Sensing
True Color
NDVI
Topic #9: Vegetation Indices

22. Karnieli: Introduction to Remote Sensing
April 17
NDVI Phenology
Karnieli: Introduction to Remote Sensing
Low NDVI VALUE High
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23. Karnieli: Introduction to Remote Sensing
April 17
VI optimization
Karnieli: Introduction to Remote Sensing
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24. Karnieli: Introduction to Remote Sensing
April 17
August Average NDVI, Africa
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

25. Karnieli: Introduction to Remote Sensing
April 17
Maximum Value Composite (MVC)
Karnieli: Introduction to Remote Sensing
2,400 km
Topic #9: Vegetation Indices

26. Karnieli: Introduction to Remote Sensing
April 17
Maximum Value Composite (MVC)
Karnieli: Introduction to Remote Sensing
Pixel 1 Pixel 2 Pixel 3 Pixel 4
0.15
0.10
0.20
0.12
Day 1
0.50
Day 2
0.11
0.09
Day 3
***
0.63
0.25
0.06
Day n
Composite
Time
Topic #9: Vegetation Indices

27. Karnieli: Introduction to Remote Sensing
April 17
MVC - importance
Karnieli: Introduction to Remote Sensing
Forward view angle
Backward view angle
Nadir
2330 km SWATH
Eliminate effects of cloud cover
Eliminate effects of atmospheric aerosols
Eliminate effects of view zenith angle
Eliminate effects of solar zenith angle
Topic #9: Vegetation Indices

28. Karnieli: Introduction to Remote Sensing
April 17
Maximum Value Composite (MVC)
Karnieli: Introduction to Remote Sensing
residual clouds
Topic #9: Vegetation Indices

29. Karnieli: Introduction to Remote Sensing
April 17
NDVI time series
Karnieli: Introduction to Remote Sensing
Movie
Topic #9: Vegetation Indices

30. Karnieli: Introduction to Remote Sensing
April 17
NDVI global coverage
Karnieli: Introduction to Remote Sensing
Movie
Topic #9: Vegetation Indices

31. Soil and Vegetation Reflectance Scatterplot
April 17
Soil and Vegetation Reflectance Scatterplot
Karnieli: Introduction to Remote Sensing
NIR
Red
Topic #9: Vegetation Indices

32. Karnieli: Introduction to Remote Sensing
April 17
Perpendicular Vegetation Index (PVI)
Karnieli: Introduction to Remote Sensing A B C D E
Red Reflectance
NIR Reflectance
Soil Background Line
Greening Line
A, B = Pixels of bare soil
C, D = Pixels of partly green vegetation cover.
E = Pixel of green vegetation
The objective of PVI is to remove the effect of soil brightness and isolate reflectance changes due to vegetation only
Topic #9: Vegetation Indices

33. Karnieli: Introduction to Remote Sensing
April 17
NDVI – soil sensitivity
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

34. Karnieli: Introduction to Remote Sensing
April 17
Soil Adjusted Vegetation Index (SAVI)
Karnieli: Introduction to Remote Sensing
L = 1 For low vegetation density
L = 0.5 For intermediate vegetation density;
L = For high vegetation density.
Topic #9: Vegetation Indices

35. Karnieli: Introduction to Remote Sensing
April 17
SAVI
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

36. Karnieli: Introduction to Remote Sensing
April 17
Soil sensitivity
Karnieli: Introduction to Remote Sensing
NDVI – soil sensitivity
SAVI – soil sensitivity
Topic #9: Vegetation Indices

37. Karnieli: Introduction to Remote Sensing
April 17
Surface color
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

38. Karnieli: Introduction to Remote Sensing
April 17
Atmospheric Resistant Vegetation Index
Karnieli: Introduction to Remote Sensing
The resistance of the ARVI to atmospheric effects (in comparison to the NDVI) is accomplished by a self-correction process for the atmospheric effect on the red channel, using the difference in the radiance between the blue and the red channels to correct the radiance in the red channel.
 = 1.0
Topic #9: Vegetation Indices

39. Karnieli: Introduction to Remote Sensing
April 17
Enhanced Vegetation Index (EVI)
Karnieli: Introduction to Remote Sensing
Normalized Difference
Vegetation Index
Atmospheric Resistant
Vegetation Index
Atmospheric
Resistance
Soil Adjusted
Vegetation Index
Soil Background
Correction
Enhanced Vegetation
Index
The ultimate index!
Topic #9: Vegetation Indices

40. Karnieli: Introduction to Remote Sensing
April 17
Enhanced Vegetation Index (EVI)
Karnieli: Introduction to Remote Sensing
The enhanced vegetation index (EVI) was developed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring while correcting for canopy background signals reducing atmosphere influences.
where  are atmospherically-corrected or partially atmosphere corrected (Rayleigh and ozone absorption) surface reflectances, L is the canopy background adjustment term, and C1, C2 are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band. The coefficients adopted in the EVI algorithm are, L=1, C1 = 6, C2 = 7.5, and G (gain factor) = 2.5.
Topic #9: Vegetation Indices

41. Karnieli: Introduction to Remote Sensing
April 17
Enhanced Vegetation Index (EVI)
Karnieli: Introduction to Remote Sensing
EVI Image of Riparian, Wetland, and Agricultural Areas along the Lower Colorado River and U.S.-Mexico Border
Topic #9: Vegetation Indices

42. Karnieli: Introduction to Remote Sensing
April 17
Ndvi vs. EVI
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

43. Karnieli: Introduction to Remote Sensing
April 17
EVI – global coverage
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

44. Karnieli: Introduction to Remote Sensing
April 17
Blue Shift of the Red edge
Karnieli: Introduction to Remote Sensing
Red Edge
Blue Shift
Topic #9: Vegetation Indices

45. Karnieli: Introduction to Remote Sensing
April 17
Red Edge Position
Karnieli: Introduction to Remote Sensing
B10 B9 B8 B7
Topic #9: Vegetation Indices

46. Karnieli: Introduction to Remote Sensing
April 17
Red edge position
Karnieli: Introduction to Remote Sensing
Topic #9: Vegetation Indices

47. Karnieli: Introduction to Remote Sensing
April 17
Red edge position
Karnieli: Introduction to Remote Sensing
Reflectance
1st Derivative
2nd Derivative
Topic #9: Vegetation Indices

48. Karnieli: Introduction to Remote Sensing
April 17
Red edge position
Karnieli: Introduction to Remote Sensing
False color image
2nd derivative image
Topic #9: Vegetation Indices

49. Karnieli: Introduction to Remote Sensing
April 17
Vegetation Indices – summary (1)
Karnieli: Introduction to Remote Sensing
Simple Ratio:
Normalized Difference Vegetation Index:
Perpendicular Vegetation Index:
Topic #9: Vegetation Indices

50. Karnieli: Introduction to Remote Sensing
April 17
Vegetation Indices (2)
Karnieli: Introduction to Remote Sensing
Soil Adjusted Vegetation Index:
Atmospheric Resistance Vegetation Index:
Enhanced Vegetation Index:
Topic #9: Vegetation Indices

51. Types of vegetation indices
April 17
Types of vegetation indices
Karnieli: Introduction to Remote Sensing
Ratio-based (red and NIR spectral bands):
SR, NDVI, ARVI
Orthogonal-based difference of red and NIR spectral bands:
PVI
Hybrid/combination of the two:
SAVI, EVI
Topic #9: Vegetation Indices