html5-img
1 / 26

NDVI Anomaly, Kenya, January 2009

NDVI Anomaly, Kenya, January 2009. Vegetation Indices. Enhancing green vegetation using mathematical equations and transformations. Learning Objectives. What are vegetation indices? What do we hope to accomplish with them?

kosey
Download Presentation

NDVI Anomaly, Kenya, January 2009

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NDVI Anomaly, Kenya, January 2009

  2. Vegetation Indices Enhancing green vegetation using mathematical equations and transformations

  3. Learning Objectives • What are vegetation indices? • What do we hope to accomplish with them? • Understand the relationship between spectral indices and spectral reflectance curves. • What features of vegetation spectra are most indices based on? • What are advantages and disadvantages of various algabraic indices?

  4. What is a “vegetation index”? • Some mathematical combination or transformation of spectral bands that accentuates the spectral properties of green plants so that they appear distinct from other image features.

  5. What Should Vegetation Indices Do?? • Indicate the AMOUNT of vegetation (e.g., %cover, LAI, biomass, etc.) • Distinguish between soil and vegetation • Reduce atmospheric and topographic effects if possible

  6. How is Vegetation Spectrally Distinct? • Reflectance in individual wavelength regions (bands)? • Shape of spectral curve created by looking at more than one wavelength regions? • Changes in spectral curves with amount of vegetation? • Others?

  7. Soil Reflectance • Can be bright in NIR (like vegetation) • dry soil especially bright • wet soil much darker than dry soil • Soil can have low red reflectance (like vegetation)

  8. Group Exercise • Given typical green vegetation spectral reflectance, and reflectance soils ranging from dark to bright, propose an algebraic combination of two Landsat 8 bands that will distinguish the plants from the soils!

  9. Vegetation vs. Soil and Water

  10. How can we use this with digital imagery? • Vegetation indices based on accentuating the DIFFERENCE between red and NIR reflectance in image pixels

  11. Difference Vegetation Index (DVI) • Probably the simplest vegetation index • Sensitive to the amount of vegetation • Distinguishes between soil and vegetation • Does NOT deal with the difference between reflectance and radiance caused by the atmosphere or shadows • So for example…can’t distinguish vegetation from soil in shady areas very well. • A problem when there is topography.

  12. Ratio-based Vegetation Indices • Simplest ratio-based index is called the Simple Ratio (SR) or Ratio Vegetation Index (RVI) • High for vegetation • Low for soil, ice, water, etc. • Indicates the amount of vegetation • Reduces the effects of atmosphere and topography

  13. Why Simple Ratios Reduce Atmospheric and Topographic Effects LNIR = ENIRtNIRrNIR/π LRed = ERedtRedrRed/π So = Largely eliminates irradiance from equation and therefore topography Largely eliminates transmittance and therefore atmospheric effects

  14. Problem with SR • Division by zero • Wide range of possible values depending on amount of red reflectance • These problems addressed by development of the NDVI

  15. Normalized Difference Vegetation Index • NDVI = (NIR – Red)/(NIR + Red) • Ranges from -1 to 1 • Never (Rarely?) divide by zero • Indicates amount of vegetation, distinguishes veg from soil, minimizes topographic effects, etc. • A good index! • Does not eliminate atmospheric effects!

  16. NDVI Applications

  17. But…Problem with NDVI (and some other ratios) • Sensitive to soil background reflectance • Non-linear changes in index as amount of vegetation changes • Affected by atmospheric effects • Affected by geometry • Saturation problems • So…use with caution. Great for many applications but not all!

  18. Soil Background Effects

  19. B G R IR IR IR IR IR IR (Amount changes depending on soil) IR IR

  20. Indices get “tuned” to try to reduce these problems. • E.g., Soil Adjusted Vegetation Index (SAVI) • Uses a soil background “fudge factor” SAVI = [(NIR – Red)/(NIR + Red + L)] * (1 + L) L is a soil fudge factor that varies from 0 to 1 depending on the soil. Often set to 1.

  21. Vegetation Amount (LAI)

  22. Choosing an Algabraic Index • Most difference indices fall short in terms of dealing with atmospheric and topographic effects • Most ratio-based indices are functionally equivalent (work about the same) • Some ratio-based indices are computationally “cleaner” • NDVI is often the index of choice and generally performs pretty well, but you must be aware of potential issues

  23. Next Lecture… • Indices based on data transformations and “feature space”

More Related