1. Remote Sensing of Vegetation
Slides from J. Jensen adapted by K. Debure, 2009

2. Remote Sensing of Vegetation
Spectral Characteristics
Temporal Characteristics

3. Remote Sensing of Vegetation: Spectral Characteristics
Kirchoff’s Radiation Law:
Dividing each of the variables by the original incident radiant flux:
allows us to rewrite the initial equation as:
where r is spectral hemispherical reflectance,  is spectral hemispherical absorptance, and  is spectral hemispherical transmittance.

4. Remote Sensing of Vegetation: Spectral Characteristics
Most remote sensing systems function in the 0.35 – 3.0 mm region and measure primarily reflected energy. Thus, it can be useful to write the equation as:

5. Dominant Factors Controlling Leaf Reflectance
Water
absorption bands:
0.97 mm
1.19 mm
1.45 mm
1.94 mm
2.70 mm

6. Photosynthetic processes determine how a leaf or plant canopy appears radiometrically. The following are needed for photosynthesis to occur:
Carbon dioxide (CO2)
Water (H2O)
Irradiance (El) measured in W m-2

7. CO2 enters through the stoma.
Cross-section through A Hypothetical and Real Leaf Revealing the Major Structural Components that Determine the Spectral Reflectance
of Vegetation
Top layer is cuticular – often thick for plants that grow in bright sun, thin for shade plants. (reflects very little light)
CO2 enters through the stoma.
Photosynthesis occurs in
the palisade parenchyma cells and
the spongy parenchyma mesophyll cells

8. Absorption Spectra of Chlorophyll a and b, b-carotene, Phycoerythrin,
and Phycocyanin Pigments
lack of
absorption
Absorption Efficiency
Chlorophyll a peak absorption is at 0.43 and 0.66 mm.
Chlorophyll b peak absorption is at 0.45 and 0.65 mm.
Optimum chlorophyll absorption windows are:
mm and mm
Absorption Efficiency
Lower absorption of green wavelength light that causes healthy green foliage to appear green to the eye.

9. Litton Emerge Spatial, Inc
Litton Emerge Spatial, Inc., CIR image (RGB = NIR,R,G) of Dunkirk, NY, at 1 x 1 m obtained on December 12, 1998.
Natural color image (RGB = R,G,B) of a N.Y. Power Authority lake at 1 x 1 ft obtained
on October 13, 1997.

10. Spectral Reflectance Characteristics of
Sweetgum Leaves (Liquidambar styraciflua L.)

11. Spectral Reflectance Characteristics of Selected Areas of
Blackjack Oak Leaves

12. Hemispherical Reflectance, Transmittance, and Absorption Characteristics of Big Bluestem Grass

13. Hypothetical Example of Additive Reflectance from A Canopy with Two Leaf Layers

14. Distribution of Pixels in a Scene in
Red and Near-infrared Multispectral Feature Space

15. Reflectance Response of a Single Magnolia Leaf
(Magnolia grandiflora) to Decreased Relative Water Content

16. 224 channels each 10 nm wide with 20 x 20 m pixels
Imaging Spectrometer Data of Healthy Green Vegetation in the San Luis Valley of Colorado Obtained on September 3, 1993 Using AVIRIS
224 channels each 10 nm wide with 20 x 20 m pixels

17. Temporal (Phenological) Characteristics
Remote Sensing of Vegetation
Temporal (Phenological) Characteristics

18. Phenological Cycle of Hard Red Winter Wheat in the Great Plains

19. “Green is green is green.”
-noted remote sensing scientist, Dave Simonett-
If two different crops were planted at the same time and had complete canopy closure on the same date as the remotely sensed data were collected, the spectral reflectance of the two crops would appear to be very similar throughout the spectrum.

20. “Green is green is green.”
-noted remote sensing scientist, Dave Simonett-
Discrimination between two crops using relatively coarse spectral resolution remote sensor data is possible if crops are:
Planted at slightly different times in the growing season
Receiving different amounts of irrigation that affect the resulting biomass
Maturing at different rates through fertilization or weeding
Planted with dramatically different row spacing or field orientation
Characterized by different canopy structures

21. Phenological Cycles of San Joaquin and Imperial Valley, CA Crops and Landsat Multispectral Scanner Images of One Field During a Growing Season

22. Landsat Thematic Mapper Imagery of the Imperial Valley, California Obtained on December 10, 1982

23. Landsat Thematic Mapper Color Composites and Classification Map of a Portion of the Imperial Valley, California

24. Phenological Cycles of Soybeans and Corn in South Carolina

25. Phenological Cycles of Winter Wheat, Cotton, and Tobacco in South Carolina

26. Phenological Cycle of Cattails and Waterlilies in Par Pond, SC.

27. Infrared/Red Ratio Vegetation Index
The near-infrared (NIR) to red simple ratio (SR) is the first true vegetation index:
It takes advantage of the inverse relationship between chlorophyll absorption of red radiant energy and increased reflectance of near-infrared energy for healthy plant canopies (Cohen, 1991) .

28. Normalized Difference Vegetation Index
The generic normalized difference vegetation index (NDVI):
has provided a method of estimating net primary production over varying biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et al., 1995), monitoring phenological patterns of the earth’s vegetative surface, and of assessing the length of the growing season and dry-down periods (Huete and Liu, 1994).

29. Time Series of 1984 and 1988 NDVI Measurements Derived from AVHRR Global Area Coverage (GAC) Data for the Region around El Obeid, Sudan, in Sub-Saharan Africa