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Lecture Notes – Vegetation indices Fred Watson, ENVS 436/536, CSUMB, Fall 2010 Many of these slides are from Jianglong Zhang and Cindy Schmidt.

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Presentation on theme: "Lecture Notes – Vegetation indices Fred Watson, ENVS 436/536, CSUMB, Fall 2010 Many of these slides are from Jianglong Zhang and Cindy Schmidt."— Presentation transcript:

1 Lecture Notes – Vegetation indices Fred Watson, ENVS 436/536, CSUMB, Fall 2010 Many of these slides are from Jianglong Zhang and Cindy Schmidt

2 Vegetation Indices Highlights subtle variations in the spectral responses of vegetation: –Reflects strongly in near IR; absorbs strongly in red –Can discriminate between healthy and stressed vegetation pre-visually Image via Zhang

3 (Some) Vegetation Indices Ratio Vegetation Index: NIR/Red –Range: 0 (bare soil) to >20 (dense vegetation) –Sensitive to variations in dense canopies –Rarely used Normalized Difference Vegetation Index: NDVI = ( NIR – Red ) / ( NIR + Red ) Range: -1 (bare soil) to 1 (dense vegetation) Sensitive to low levels of vegetation cover Compensates for unwanted variation due to changes in illumination etc. Very common Slide via Zhang

4 Vegetation Indices Image via Zhang

5 Here’s a video of NDVI changing across the lower 48 states: http://www.nass.usda.gov/research/vc2003.htm Link via Zhang

6 Image via Zhang

7 Image Ratioing Benefit: –Looking at relative brightness values, not absolute so radiometric errors are reduced. Ex: absolute reflectance values for forested areas may vary depending on orientation relative to sun illumination; however the ratio of the reflectance values should be similar. Slide via Zhang

8 Landsat TM image NDVI image Slide via Zhang

9 Study of Yellowstone meadow phenology lead by Watson, Thein, et al. (start by showing video)

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15 EcoViz: Yellowstone Ecosystem Science and Visualization Meadow phenology survey

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21 MODIS phenology

22 Forest cover survey

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25 Leaf Area & Water versus Forest Age

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