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"Talking Points" for the RSLarge.ppt presentation The following is not designed as a script, but rather background information for you to develop you own presentation based on these slides. Please feel free to modify both this document, and the associated PowerPoint presentation. If you have any questions or problems, contact me at: Larry Ryan 161 Morse Hall UNH Durham, NH USA 03824 (V) 603,862,0195 (F) 603,862,0243 firstname.lastname@example.org Slide Number Comments 1. This is a drawing of Landsat 5 against the background of the Earth 2. The famous "Blue Marble" photo, taken by Apollo astronauts on the way to the moon. This image makes us aware of the interconnections between the systems of the planet. I like to stress that it shows us as being alone in a very hostile environment, where there are no replacements for things we use up. On "Spaceship Earth," there are no passengers, only crew members. 3. Polar vs. Equatorial Orbiting satellites. Most "Earth Observing" satellites follow polar orbits, at around 700 km, with periods of a little less than 100 min. Equatorial satellites, orbiting at around 32 000 km, take 24 hours to circle the Earth. They thus appear to stand still over the same place all the time, and are called "Geostationary," or "Geosynchronous." Our weather and communications satellites follow such orbits. 4. Landsat "sees" much the way we do. Light from the sun is reflected from the Earth's surface, and reaches the sensors. It is "analog" data. The on-board sensors convert these data to numbers, digital data, which are sent to a ground receiving station. (Landsat also measures the Earth's emitted heat, but we do not use those data in GLOBE.) It is important to remember that satellites do not send us "pictures," only numerical data. "Images" are created when these data are displayed on a computer screen. What we see depends on what the operator wished to highlight in his/her display. These data are called "8-bit" data, which means they are composed of 8 binary digits. This gives the satellite data a range of values from 0 to 255. 5. This chart shows the visible and near-infrared radiation gathered by Landsat's sensors. 6. This shows the parts of Landsat 4 and 5. These had two sets of sensors: The "Multi-Spectral Scanner" (MSS) gathered data at 79 m spatial resolution, and the "Thematic Mapper" ( TM) at 30 m resolution. Our GLOBE images are TM data. Since the early 1990's MSS data have not been collected, but they are available in archives. The new Landsat 7 satellite does not carry a MSS sensor. 7. This slide details the data gathering process in a satellite. The sensors, called "radiometers," gather analog data which are converted to digital data by an "analog-to- digital" converter. These data may be sent immediately to Earth, or stored on board for later broadcast. 8. Details of the orbit of the Landsat series. At an altitude of 705 km, the satellite takes 98.9 minutes to orbit the earth once. Landsat has a special orbit; it is inclined at 98.2 degrees to the Equator. At this inclination, Landsat has what is called a "Sun- synchronous" orbit. Because of this orbit, Landsat always passes over any place on Earth at the same time of day (solar time.) This is done so that the Sun is always in generally the same part of the sky. Therefore, shadows do not change drastically from one image to another. Landsat crosses the Equator going south at 0945. So, our Landsat images are always morning images. 9. As Landsat orbits the Earth, the Earth turns from west to east beneath it. In one orbit, the Earth rotates 2752 km. The effect is that each Landsat pass seems to move 2752 km west of the previous one. At this rate, Landsat images the entire Earth every 16 days. 10. This visual shows the spectral location of the 5 Landsat channels used in GLOBE images. 11. This table list uses of remote sensed data in different parts of the electromagnetic spectrum. 12. This is a small Landsat image of Williamsburg, Virginia. The image is displayed to enhance vegetative features. The green feature in the center is one of the easiest vegetated features on the planet to spot from space. It is a golf course. 13 - 15. As we "zoom in" on this image, we see that it is composed of small, colored squares. These are called "pixels." A pixel in a satellite image is the smallest unit of area on the ground that the satellite can see. All of a pixel is one color, and there are no data for anything smaller that the pixel. For Landsat TM data, one pixel is 30 m on a side. This value, 30 m, is called the "spatial resolution" of the sensor. 16. This slide compares spatial resolution of several popular satellites to a 120 m soccer field. 17. This a view of New England from the AVHRR (Advanced Very High Resolution Radiometer) instrument flown aboard NOAA polar-orbiting satellites. It's pixel size is 1 km. This image is displayed using the near-infrared wavelengths. In this view, reds represent vegetation, and blue - white colors represent mineral materials (urban areas, rock, sand, bare ground.) As a general rule, "the shorter the vegetation, the brighter the color" The darkest reds represent conifers in the mountains of the Adirondacks of New York and the Green, White and Presidential ranges of New England. The medium reds represent mixed and deciduous stands, while the bright pinks general represent grasses. 18. This slide shows a 15 km x 15 km section of the New England image. Since the pixel size here is 1 km (actually 1.1 km) the section is only 15 pixels x 15 pixels. Little detail is visible at this resolution and scale. 19. This is the same image, though this time processed using other wavelengths to show surface temperature. The darkest blues represent the coolest colors, while the yellows and reds the warmest. The white color over Cape Cod is clouds. 20. This is an image of a major American city from a hypothetical satellite with a pixel size of 100 m. What city is it? 21. Here is the same city at Landsat's 30 m resolution. It is Washington, DC. 22. Here is the same area with a spatial resolution of 5 m. An important lesson is: "Spatial resolution determines feature recognition." I.e.: What you can see depends on the pixel size in your image. 23. This is Atlanta, GA, at 1 m resolution, taken from a Soviet satellite. (See the images in the Ikonos folder.) 24. - 27: These images are all of the Pease Development Center, in NH. They are used frequently in GLOBE remote sensing materials (the Teachers Guide and the Remote Sensing video.) They are acquired with: 24: Landsat MSS with a 79 m pixel 25. Landsat TM with a 30 m pixel 26. The French SPOT satellite with a 20 m pixel 27. The French SPOT's "panchromatic" (black & white) sensor at 10 m Again, spatial resolution determines feature recognition. 28- 30: This sequence shows the effects of pixel composition on its color. 28. This pixel has multiple land cover types within it. What will it look like? 29. Here is the percentage composition of the different land covers in this pixel. The values are "made up." The resulting color (and a pixel has only one color) depends on the weighted average of all the land covers. These are shown in the table. The weighted average is close to the brightness of vegetation. 30. this pixel would appear as vegetation, since its weighted average is in the range of values for vegetation. 31. This visual stresses that your GLOBE Landsat image is actually 5 separate images, one for each of the channels, or wavelength bands, the sensor detects. 32 - 33. These two visuals show the reason Landsat carries sensors for the near-infrared portion of the spectrum. 32. This is part of Cleveland, OH. You can see the airport. The dark band circling part of the city is very large Centennial Park. The dark material looks like a wide river. 32. In this infrared view, we can see that the water course is actually very narrow, surrounded by dark, probably coniferous, vegetation. 34 - 37. This sequence shows how we can use the near infrared channel to draw conclusions about land cover. 34. This is the city of Prague, CZ, seen in "true color," approximately as it would look from space. 35 In the center of this image is the soccer stadium, one of the world's largest 36. Zooming in further on the stadium. What is the playing surface? Grass? Artificial turf? 37. Here, in the infrared view, we see the center of the stadium is red. This means that "real" grass covers the surface. 38 - 41 A similar exercise. 38. This is the University of Texas at El Paso, in true color 39 Here is their football stadium 40 "Zooming in" on the stadium. What is the surface cover here? 41. The IR view shows no red. The stadium playing area is "Astro-turf." 42. This is Las Vegas, NV. The southwest part of the view shows many drainage patterns. Is there vegetation surrounding these watercourses? 43. Here is the same view in IR. There is no vegetation near the watercourses. The only vegetation is in the city, where irrigation provides the necessary water. You can plainly see the golf courses! 44. This is Logan, Utah. The mountains on the right appear to have been carved by water. Is there vegetation there? 45. Seen in IR, these hills have abundant vegetation. This area is far more moist than the area around Las Vegas. 46. This is a "composite" image, made up of several different images from the AVHRR satellite. This composite, assembled over time, shows the landforms of the US. The spatial resolution of this image is 4 km. 47 - 51. This series of images, from the Defense Meteorological Satellite Program, shows that satellites work 24 hours a day. These are nighttime images of: 47. The US 48. Europe 49. Asia 50. Australia 51. South America Advanced Visualizations: The next two slides will be of use if you work with MultiSpec, and examine spectral signatures. 52. This graph, from a ground based sensor, shows the "reflectance" of green vegetation from visible through near-infrared wavelengths. It tells you what factors are responsible for absorption in each area where little energy is being reflected. 53. Water is normally very dark in satellite images. However, suspended surface materials can greatly alter its reflectance. This graph shows the reflectance of surface water with differing amounts of sediment. Note that water is always very dark in the infrared wavelengths.
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