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I Spy…Seeing Our Environment in a New Light: The Technology of Remote Sensing

I Spy…Seeing Our Environment in a New Light: The Technology of Remote Sensing. by Dr. Kelley A. Crews-Meyer Outreach Lecture Series Volume 22.

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I Spy…Seeing Our Environment in a New Light: The Technology of Remote Sensing

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  1. I Spy…Seeing Our Environment in a New Light:The Technology of Remote Sensing by Dr. Kelley A. Crews-Meyer Outreach Lecture Series Volume 22 Produced by and for the Outreach Lecture Series of the Environmental Science Institute. We request that the use of any of these materials include an acknowledgement of Dr. Kelley A. Crews-Meyer and the Outreach Lecture Series of the Environmental Science Institute of the University of Texas at Austin. We hope you find these materials educational and enjoyable.

  2. I Spy…Seeing Our Environment in a New Light: The Technology of Remote Sensing Dr. Kelley A. Crews-Meyer Department of Geography University of Texas

  3. Pictures are worth a thousand words…

  4. The Earth from SpaceApollo 17 Crew, NASA Credit:Apollo 17 Crew, NASA

  5. Darrell K. Royal Texas Memorial StadiumUniversity of Texas

  6. Daytona 500 RacetrackFebruary 1995, NAPP (USGS EDC) racetrack grandstands Lake Lloyd

  7. Great Lakes, September 1987AVHRR (USGS EDC) Superior Huron Ontario Michigan Erie

  8. Mt. St. Helens, May 1980Aircraft photo (USGS EDC)

  9. The North Pole, June 1992AVHRR (USGS EDC)

  10. The Earth at Night, DMSP mosaic(NASA, Astronomy Picture of the Day) Credit: C. Mayhew & R. Simmon (NASA/GSFC), NOAA/ NGDC, DMSP Digital Archive

  11. Smoke Plumes from Oil Fires Kuwait Oil Fires, April 1991Shuttle (USGS EDC)

  12. Middle East Oil Fires, November 4, 2002MODIS, NASA EOS Credit: Jeff Schmaltz, MODIS Rapid Response Team, NASA GSFC

  13. Far UV Earth from Moon (Apollo 16 crew),(NASA, Astronomy Picture of the Day) Credit: G. Carruthers (NRL) et al., Far UV Camera, Apollo 16, NASA

  14. Thermal (Far Infrared) People(NASA Remote Sensing Tutorial)

  15. Defining Remote Sensing • Remote: not in contact with, away from • Sensing: the act of observation • “Remote Sensing is a technology for sampling electromagnetic radiation to acquire and interpret non-immediate geospatial data from which to extract information about features, objects, and classes on the Earth's land surface, oceans, and atmosphere….” (NASA) • WHAT???

  16. A more workable definition • From a satellite or airplane, we figure out what an object is by the way it reflects “light” • Remember “I SPY” • I is for Image (not a “picture”) • S is Signatures – a unique identifier • P is for Perspective – the bird’s eye view • Y is for You – computers need people

  17. Passive (Optical) Sensor Technology Incoming EMR Reflected EMR Emitted EMR Source: Remote Sensing Core Curriculum, Volume 3 (Jensen)

  18. Visible Portion 0.4 0.5 0.6 0.7 Cosmic Gamma X-rays UV Infrared Microwave Radio/TV Rays Rays Near/Medium/Thermal 10-6 10-4 10-2 1 102 104 106 108 Wavelength in Micrometers (= 103 nanometers or 10-6 meters) Increasing Wavelength Increasing Frequency and Energy Electromagnetic or EM Spectrum

  19. 1 band = 1 B&W photo

  20. Differences in Bands QuickBird, 0.7m Visible (Blue) QuickBird, 0.7m Near Infrared

  21. I is for Image (not a “picture”) 26th Street Littlefield House Geography Bldg. USGS Benchmark 24th Street Fountain Tower You are here! Welch Hall Speedway Guadalupe

  22. The RGB compositing process PAINT PAINT PAINT Infrared band (4) Red band (3) Green band (2) Red color gun Green color gun Blue color gun

  23. Color Theory and Image Processing Traditional Color Practice Additive Color Theory TV / Computer Screens Subtractive Color Theory

  24. 3 for the price of 1… 432 Composite Color Infrared (no camouflage!) 321 Composite True Color

  25. Different Spatial Resolutions* 1-2m 30m 79m 1.1km QuickBird, Landsat TM, Landsat MSS AVHRR IKONOS ETM *Simulated

  26. S is Signatures – a unique identifier… …and very similar to your own signatures Source: NASA Remote Sensing Tutorial

  27. Increasing Wavelength Increasing Frequency and Energy Electromagnetic or EM Spectrum Visible Portion At a given Temperature, T 0.4 0.5 0.6 0.7 Cosmic Gamma X-rays UV Infrared Microwave Radio/TV Rays Rays Near/Medium/Thermal 10-6 10-4 10-2 1 102 104 106 108 Wavelength in Micrometers (= 103 nanometers)

  28. P is for Perspective – the bird’s eye view • Perfectly plumb to the point at which you look • Also known as nadir or vertical (as opposed to off vertical or oblique) • The nadir perspective comes more naturally at more macro scales (think road maps) • The nadir perspective also tends to come more naturally to people who walk their landscape rather than drive • Linking the oblique perspective (as you see objects at eye level) with the nadir perspective is key to interp

  29. Test your ‘interp’ skills… (a) A person whose head is in the center, shoulders on each side, and feet sticking out the front or bottom (b) Depending upon where you are, central pivot irrigation, crop circles, or pater noster (glacial) lakes (c) Again, they could be agriculture rows, highway lanes, etc. (d) A house with the roofline evident and a sidewalk or driveway

  30. How important is it to know… • Where your image is located? • That landscape? • The culture? • The scale? • In the absence of fieldwork, other products can help (e.g., written descriptions or ground photos) but there is no substitute for local knowledge

  31. Y is for You – computers need people • Everyday skills • Color, shape, size • Texture, Pattern • Proximity, Relationship • Logical Consistency • There is no completely automated process that is generalizable

  32. You know you’re an analyst if… • …on airplanes you spend most of your time figuring out where you are and what the shapes on the ground are • …you like to solve puzzles, and enjoy problem-solving • …you tend to look for and find patterns, whether in numbers or graphics • …you could draw your neighborhood –nadir perspective-- without thinking • …you pay attention to how things change over time

  33. Deforestation in the Bolivian Rainforest May 2000, Landsat 7 ETM (USGS EDC) • NOTE: • Geometric shapes • Regular pattern • Size versus density • Pasture and agriculture

  34. Salt Cedar in Coke County, Texas November 3, 1999, Landsat 7 (UT CSR Synergy) White areas = development (roads, shelter) • NOTE: • Salt cedar in yellow • Riparian corridor • Water dependence • Worse up-channel Agricultural fields Black lines = GIS buffer analysis

  35. Change in Brasilian Rainforests Landsat MSS (USGS EDC) Road • NOTE: • Linear pattern in removal • Proximity to roads • Enlarging of junction • Inter-annual (seasons?) Dropped line Junction Rivers June 19, 1975 August 1, 1986

  36. Austin, 1951 and 1995 Lamar Blvd Loop 360 Old racetrack Agricultural fields- in Austin! Intense development Near Horseshoe Bend, Barton Creek TOBIN photography (TNRIS) DOQQ (TNRIS)

  37. Texas High Plains Drought Landsat 7 ETM, UT CSR Synerg • NOTE: • Change in ponds • Agricultural areas • Importance of seasons • Near Lubbock, north to Amarillo • 741 composite January 13, 2000 July 5, 1999

  38. Environmental Vulnerability Analysis

  39. Landuse / Landcover Change (LULC) in the Oriente of the northeastern Ecuadorian Amazon

  40. Settlement Patterns in the Oriente

  41. LULC in Nang Rong District, Isaan Region, Northeast Thailand

  42. Northeast Thai Settlement Pattern Thai nuclear village settlement pattern is characterized by a spatial disconnect between people and the land they manage

  43. Global Positioning Systems (GPS) • Fully operational in 1994 • > 20 satellites, 98% operational • Not as available during times of conflict • 6 Orbital Planes • 20,200 km orbit • ~ 12 hour orbital period • Each visible for ~ 5 hours Source: Remote Sensing Core Curriculum

  44. Human-Environment Interactions & Analysis • Population potential created via survey data • Access to surface water • Accessibility to transportation infrastructure (roads, rivers) • Digital elevation model (DEM) • Soil moisture potential • Landcover/Landuse (LCLU) Change, Vegetation Indices WHO, WHAT • Change Detection • Overlay analysis, Lagged correlations • Statistical correlations, Regression analysis • Spatial autocorrelation • Pattern Metrics WHERE, WHEN, WHY, HOW

  45. Change Detection in Thai Forests, 1972 to 1999 Early Deforestation Late Deforestation Stable Forest Early Reclamation Late Reclamation

  46. Visual and Overlay Analysis Lowland areas (180m) Upland areas (400m) Digital elevation model (DEM) with higher elevations in darker tones and rivers shown in blue. Early Deforestation Late Deforestation Stable Forest Early Reclamation Late Reclamation

  47. Pattern Metrics: Graphical Analysis Patches Borders Closeness Mixedness

  48. Pattern Metrics - Mapping LULC Stability PPU Fractal Dimension Highly unstable Unstable Fairly stable Little change Spatial Autocorrelation (30m)

  49. Active Sensor Applications: LIDAR Graphics courtesy of UT Bureau of Economic Geology and UT Center for Space Research Training Materials

  50. UT Memorial Stadium LIDAR-based Digital Elevation Model (DEM) Source: UT Bureau of Economic Geology and UT Center for Space Research

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