1. NOTE, THIS PPT LARGELY SWIPED FROM HTTP://WWW.UWYO.EDU/RS4111/

2. Understanding Multispectral Reflectance  Remote sensing measures reflected “light” (EMR)  Different materials reflect EMR differently  Basis for distinguishing materials

3. Types of Reflectance  Specular  Mirrors or surfaces of lakes, for example  Angle of incidence = angle of reflection  Diffuse (aka Lambertian)  Reflects equally in all directions  Usually we assume Lambertian reflectance for natural surfaces  Idealized—not really found in nature but often close

5. Reflectance of Materials  Varies with wavelength  Varies with geometry  Diagnostic of different materials What kinds of reflectance do you see here? Why do the different ponchos look different (e.g. pink vs. green)?

6. Some Important Terms  Irradiance (E λ ) (Incoming light from sun)  Radiance (L λ ) (Light received at satellite) EλEλ L λ

7. Reflectance spectra

9. How do you get spectra?  Measure in the field with field spectroradiometers  Measure in the lab  Collect from image data  Look at spectral libraries: http://speclab.cr.usgs.gov/spectral- lib.html)

10. Spectral Investigations

11. Spectral Properties of Vegetation  Unlike minerals, vegetation is composed of a limited set of spectrally active compounds  Relative abundance of compounds, including water, indicates veg. condition  Vegetation structure has significant influence on reflectance.  Spatial scale of reflectance measurement is critical.

13. Plant Pigments  Chlorophyll A (green)  Chlorophyll B (green)  Others: e.g., β – carotene (yellow) and Xanthophylls (red)

14. Leaf Structure

15. Leaf Water Content

17. The Red Edge

18. Multiple Leaf Layers  Reflectance increases with the number of leaf layers in a non-linear fashion  Eventually, with enough layers, the reflectance “saturates” (stops increasing)

20. Digital Data Digital Data Format and Storage

21. Remotely Sensed Data  Represent the amount of light reflecting off the ground and reaching the satellite sensor.  Continuous change from place to place, or not?  Often cover large areas (lots of data!)  Multiple images (bands) are collected simultaneously for each place in an image  What data model (raster or Vector) might be best for this image?

22. Raster Data  Imaginary matrix (row & column format) is placed over the feature (e.g., the ground)  Some phenomenon (e.g. amount of reflected light) is measured  A value (called a digital number or DN) representing the strength of the signal (amount of light) is assigned to each grid cell (pixel).

23. Somewhere on earth

24. Overlay raster grid

25. Assign DNs to pixels 3247679311 10579 35 23 11 56 43 89 21 213 245 201 179 136155 55 203 16363 211189 145 109122202

27.  Images are presented as 2-d arrays (matrices). Each pixel has a location (x,y) in the array.  Position of pixel often described in terms of image columns and rows (called image coordinates) or map coordinates (e.g. latitude/longitude). F(3,2) F(1,4) How do we keep track of pixel locations?

28. Image Bands  You can think of image bands (also called channels and sometimes layers) as a collection of pictures taken simultaneously of the same place, each of which measures reflected light from a different part of the spectrum.  Together, image bands allow us to create spectral curves for each pixel.

29. How are images stored?  Many proprietary image file formats (e.g., Erdas Imagine)  Typically include 1) a header and 2) the image data  Image data can be organized in several ways  Band Sequential (BSQ)  Band Interleaved by Line (BIL)  Band Interleaved by Pixel (BIP)  You sometimes have to know (or guess) file structures to import images into image processing software. (Indian [IRS] data story.)

30. Example Band 1Band 2Band 3 1 11 111 111 3 33 333 333 2 22 222 222 3 bands, 9 pixels each

31. Case 1  Band sequential ( BSQ ) format  Band #1 is stored first  Followed by #2, #3  Bands are stored sequentially 1 11 111 111 3 33 333 333 2 22 222 222 111111111222222222333333333111111111222222222333333333

32. Case 2  BIL format  Line #1, band #1 is stored first  Followed by line #1, band #2  Bands are inter- leaved by line 1 11 111 111 3 33 333 333 2 22 222 222 111222333111222333111222333111222333111222333111222333

33. Case 3  BIP format  Pixel #1, Line #1, band #1 is stored first  Followed by Pixel #1, line #1, band #2  Bands are inter- leaved by PIXEL 1 11 111 111 3 33 333 333 2 22 222 222 123123123123123123123123123123123123123123123123123123

34. Histograms  A histogram is a graph showing the number of pixels in a single band corresponding to each possible DN.  Histograms give us information about the data distribution in each band (e.g. normal, skewed, bimodal, etc.)  We use information from histograms for contrast stretching, atmospheric correction, statistical analyses, and many other applications.

35. # of pixels Describe the shape of this histogram.

36. Contrast Stretching  Computer monitors have a range of brightness that they use to display images.  Unprocessed remotely sensed images often don’t use the full range, resulting in a “washed-out” image.  Contrast stretching changes (usually temporarily) the DNs to take advantage of the full tonal range available.  Usually best not to permanently change the DNs. Why?

37. High ContrastLow Contrast

38. Types of Contrast Stretches  Contrast stretches can be linear (DNs stretched evenly across the available range of values)  …or they can be nonlinear (some DNs changed more than others)  Within each of these are many different stretching algorithms.  Which you choose depends on what you are trying to see in an image.  Erdas and other image display software often applies a temporary contrast stretch automatically to make images looks crisp.

39. Linear Contrast Stretch Non-linear Contrast Stretch

40. Laramie Landsat 8 Image Linear stretch (min-max) Standard Deviation stretch Cuts off extremes and linearly stretches remaining pixels