1. Using vegetation indices (NDVI) to study vegetation

2. Chlorophyll - Amount of chlorophyll in leaves affects the spectral signature in the visible.
Cells known as ‘spongy mesophyll’ are responsible for reflection of NIR.
Reflection occurs where the walls of these cells meet with air spaces inside the leaf.

3. Amount of near infrared energy reflected is a function of
Chlorophyll in healthy vegetation absorbs most of visible red and blue for photosynthesis.
Amount of near infrared energy reflected is a function of
internal structure
amount of moisture

4. Vegetation in imagery Distinct appearance in certain spectral bands
Multispectral imagery valuable for study of vegetation.
Distinct appearance in certain spectral bands
Distinguishes it from other objects in landscape.
Spectral signature varies with species and envir. factors
ID plants in various stages of life cycle or states of health.
Large areas can be studied quickly.
Esp. useful in remote areas (tropical rainforest)
Possible to obtain accurate quantitative information from imagery, together with field data.

5. Vegetation in imagery
Examples;
Est. # of acres of forest harvested for timber.
Predict regional or global yields of crops (wheat, soybeans)
Est. quantity of phytoplankton in oceans.

6. Healthy vegetation - high reflectance in NIR & low reflectance in red.

7. Landsat Thematic Mapper Imagery
Band Wavelength
to Blue
to Green
to Red
to Near IR
to Short Wave IR
to Thermal IR.
to Short Wave IR

10. Sometimes air spaces can be filled with water, thus a plant's state of hydration can significantly affect the reflectance in NIR.
Different species have different leaf cell structures, which affects reflectance of NIR.
Related factors – leaf size and orientation also affect reflectance of NIR.
For example, broad, thin leaves of deciduous plants are more reflective than needles of coniferous trees.

11. Most NIR that is not reflected by leaves is transmitted.
provides info to analyst
In a dense forest canopy, leaves underneath often reflect the energy transmitted by the top layer of leaves.
So, sections of a forest with a dense canopy will exhibit higher DN values in the near infrared band than sections with sparse canopy.

12. Differences among plant species;
amounts of chlorophyll
different leaf structures, shapes or orientation
causes species to absorb, reflect, or transmit differently.
Veg. may have different spectral signature when it is;
Emergent
Mature
Undergoing normal seasonal changes
Dormant
Healthy veg. contains more chl. than stressed or diseased.
Variations in spectral sigs. can be used to study vegetation through image interpretation.

13. When leaves lose their chlorophyll in autumn their spectral characteristics change.
Deciduous more reflective in NIR than conifers.

14. false-color composite - brightest red near river, indicating most vigorous vegetation, may be deciduous trees, shrubs, and grass.
darker red regions surrounding are coniferous forest.

15. Vegetative index - calculated (or derived) from remotely-sensed data to quantify vegetative cover on Earth's surface.
Normalized Difference Vegetative Index (NDVI) most widely used.

16. Ratio between measured reflectivity in red and near infrared.
Gives info on absorption of chlorophyll in leafy green vegetation and density of green vegetation on the surface.
Also, contrast between vegetation and soil is at a maximum.

17. Normalized Difference Vegetation Index (NDVI) has been in use for many years to measure and monitor plant growth (vigor), vegetation cover, and biomass production from multispectral satellite data.

18. NDVI is calculated from the visible and near-infrared light reflected by vegetation.
Healthy vegetation (left) absorbs most of the visible light that hits it, and reflects a large portion of the near-infrared light.
Unhealthy or sparse vegetation (right) reflects more visible light and less near-infrared light.
The numbers on the figure above are representative of actual values, but real vegetation is much more varied.

19. NDVI - ratio of red and near infrared (NIR) spectral bands :
NDVI = (NIR - red) / (NIR + red)
Resulting index value is sensitive to presence of vegetation on land surfaces and used to address vegetation type, amount, and condition.
Advanced Very High Resolution Radiometer (AVHRR).
used to generate NDVI images of large portions of Earth on regular basis to provide global images that portray seasonal and annual changes to vegetative cover.
Thematic Mapper (TM and Enhanced Thematic Mapper Plus (ETM+) bands 3 and 4 also provides Red and NIR measurements:
NDVI = (Band 4 - Band 3) / (Band 4 + Band 3)

20. Primary differences between AVHRR and Landsat NDVI is resolution.
AVHRR resolution is 1km and NDVI is 8 km
Landsat NDVI resolution is 30 m
AVHRR data - frequent global NDVI products
Landsat 7 ETM+ data - greater detail covering less area.

21. NDVI equation produces values in the range of - 1. 0 to 1
NDVI equation produces values in the range of to 1.0, where vegetated areas will typically have values greater than zero and negative values indicate non-vegetated surface features such as water, barren, ice, snow, or clouds.

22. Erdas: Create NDVI Index
NDVI -1.0 to 1.0
Black values = -0.30
Whites values = 0.44