1. Learning From Paper: Using Printed Satellite Images as a Conservation and Ecosystem Management Tool
A class delivered at the
3rd IUCN World Conservation Congress
17-25 November 2004
Bangkok, Thailand

2. Prepared and presented by:
Karen C. Seto, Ph.D.
Assistant Professor Department of Geological and Environmental Sciences, and Center for Environmental Science and Policy Stanford University, and Ecosystem Management Tools Thematic Leader Commission for Ecosystem Management, IUCN
Gary N. Geller, Ph.D.
ASTER Conservation Liaison Jet Propulsion Laboratory California Institute of Technology
  Ned Horning
Remote Sensing/GIS Program Manager Center for Biodiversity and Conservation American Museum of Natural History

3. Overview Remote sensing overview
Value and limitations of working with paper images
Visual interpretation methods
Hands-on image exploration and interpretation
Discussion of applications where printed images can be used effectively
Wrap-up

4. How can satellite images help conservation practitioners?
Observe: land cover, boundaries, threats, damage, topography…
Monitor: change in forest cover, range condition, land use…
Classify: into vegetation and land use categories, habitats…
Measure: areas, distances, height/elevation…
Detect: fires, resource use violations…

5. Highlights of Earth Remote Sensing
Before primarily aerial and satellite photographs
First Landsat satellite launched
SPOT satellite launched
Indian Remote Sensing Satellite launched
Radarsat launched
IKONOS satellite launched and NASA launched Terra satellite
Today many new satellite and airborne instruments are being developed and launched

6. An image is made up of individual elements called pixels that are arranged in a grid of rows and columns.

7. The sensor acquires several images (bands) at once, each recording a specific color or range of colors. When viewed, each individual band looks like a black and white photograph
                                                             Landsat band 2 - (wavelength range = µm = blue light)

8. For each band the intensity of energy for a specific range of wavelengths (colors) is measured

9. Spectral signatures

10. RGB Band Composite

11. Pixel color and brightness is determined by the pixel value

12. Certain bands or band combinations are better than others for identifying specific land cover features.
Landsat TM Red= band 3, Green =
band 2, Blue = band 1
Landsat TM Red= band 4, Green =
band 5, Blue = band 4

13. Landsat ETM+ band 1 (0.45-0.52 µm, blue-green)
Penetrates water better than the other bands so it is often the band of choice for aquatic ecosystems
Used to monitor sediment in water, mapping coral reefs, and water depth
The “noisiest”  of the Landsat bands since short wavelength blue light is scattered more than the other bands
Rarely used for "pretty picture" type images

14. Landsat ETM+ band 2 (0.52-0.60 µm, green)
Similar qualities to band 1 but not as noisy.
Matches the wavelength for the color green.

15. Landsat ETM+ band 3 (0.63-0.69 µm, red)
Since vegetation absorbs nearly all red light (it is sometimes called the chlorophyll absorption band) this band can be useful for distinguishing between vegetation and soil and in monitoring vegetation health

16. Landsat ETM+ band 4 (0.76-0.90 µm, near infrared)
Since water absorbs nearly all light at this wavelength water bodies appear very dark. This contrasts with bright reflectance for soil and vegetation so it is a good band for defining the water/land interface
Sensitive to vegetation cover
Less affected by atmospheric contamination

17. Landsat ETM+ band 5 (1.55-1.75 µm, mid-infrared)
Very sensitive to moisture and is therefore used to monitor vegetation water stress and soil moisture.
Useful to differentiate between clouds and snow

18. Landsat ETM+ band 6 (10.40-12.50 µm, thermal infrared)
Measures surface temperature.
Geological applications
Differentiate clouds from bright soils since clouds tend to be very cold
The resolution is twice as course as the other bands (60 m instead of 30 m)

19. Landsat ETM+ band 7 (2.08-2.35 µm mid-infrared)
Can detect high surface temperatures
Also used for vegetation moisture although generally band 5 is generally preferred for that application
Commonly used in geology

20. Landsat ETM+ bands 4,3,2 – Peak chlorophyll, land/water boundary, urban areas
Landsat ETM+ bands 3,2,1 – Penetrates shallow water and shows submerged shelf, water turbidity

21. Landsat ETM+ bands 4,5,3 – Land/water boundary, Vegetation type and condition, soil moisture
Landsat ETM+ bands 7,4,2 – Moisture content in vegetation and soils, geological mapping, vegetation mapping

22. MODIS (500m) – Composited using imagery acquired from June – September 2001

23. Landsat ETM+ (30m) - 2 April 2002

24. ASTER (15m) - 8 November 2003

25. CORONA (5m) – 4 March 1967

26. IKONOS (1m) – 29 April 2002

27. IKONOS zoomed

28. Advantages of using paper imagery
No need for a computer or fancy equipment
Inexpensive to create
Very portable and easy to carry in the field
Easy to show other people and often a more effective communication tool
Looks nice on the wall

29. Limitations of using paper imagery
Ancillary data obscures image data
Not possible to zoom into the image
Not possible to change the image enhancement
Can not easily overlay other data layers
Generally more difficult to locate oneself on the image since GPS tracking is not possible

30. Visual Interpretation Skills
How to read a satellite image

31. What do you need to interpret remotely sensed imagery?
Familiarity with the specific area or similar areas
Basic interpretation skills
Image prints that are of sufficient quality
Projection grid marks on the image are helpful to locate oneself on the image using a GPS
Equipment to protect the imagery if working in the field
Tools to transcribe information onto the image

32. Basic Elements of Visual Interpretation
Tone (color)
Size and shape
Texture and pattern
Relative and absolute location
Shadows

33. Tone and Color
Variations in tone and color results in all of the other visual elements
When looking at a image photo we associate specific tones to particular features
Tones change when we enhance an image or when we change the band combination of a color image

34. Size and Shape
Rectangular features often indicate human influence such as agriculture
Size and shape information greatly influenced by image resolution
Knowing the scale of the image helps to convert feature dimensions on the image to actual dimensions

35. Texture and Pattern Varies with image resolution
Often noted by roughness or smoothness
Influenced by shadows

36. Relative and Absolute Location
The location of a feature narrows the list of possible cover types
Relative location particularly useful to determine land use

37. Shadows
Often considered a contaminant but can be very useful to identify features on an image
Helpful to accentuate relief
Shadow effects change throughout the day and throughout the year
Shadows can give an indication to the size of a particular feature

38. Exercises
Purpose: Give you a change to do some of what we just talked about
After the break we will:
Explore different satellite images
Identify features on printed satellite images
Relate features in the image with features in a photograph
Draw lines around features we can see in the satellite images

39. Supplies that will be used in class
Imagery (Landsat ETM+ ASTER, Corona, IKONOS, ground photo)
Topographic map
Transparencies
Masking tape
Black marker
Cotton
Alcohol

40. Exercises

41. Discussion
Questions, additions, and clarifications…

42. Wrap Up What have we learned? After this class
Where satellite images come from and what they are made of
How to interpret images
Methods for using images as an aid to conservation management
After this class
Find some images for your area (use handout)
Play with them using some of the techniques we discussed