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Remote sensing

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Remote sensing

  1. 1. ND GIS Users Workshop Bismarck, ND October 24-26, 2005ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Introduction toIntroduction to Remote SensingRemote Sensing Gregory VandebergGregory Vandeberg Assistant Professor of GeographyAssistant Professor of Geography Image: NASA 2005
  2. 2. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 OutlineOutline  Remote Sensing DefinedRemote Sensing Defined  ResolutionResolution  Electromagnetic Energy (EMR)Electromagnetic Energy (EMR)  TypesTypes  InterpretationInterpretation  ApplicationsApplications
  3. 3. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Remote Sensing DefinedRemote Sensing Defined  Remote Sensing is:Remote Sensing is:  ““The art and science of obtaining informationThe art and science of obtaining information about an object without being in direct contact withabout an object without being in direct contact with the object” (Jensen 2000).the object” (Jensen 2000).  There is a medium of transmission involved.There is a medium of transmission involved.
  4. 4. ND GIS Users Workshop Bismarck, ND October 24-26, 2005
  5. 5. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Remote Sensing DefinedRemote Sensing Defined  EnvironmentalEnvironmental Remote Sensing:Remote Sensing:  …… the collection of information about Earth surfacesthe collection of information about Earth surfaces and phenomena using sensors not in physical contactand phenomena using sensors not in physical contact with the surfaces and phenomena of interest.with the surfaces and phenomena of interest.  We will focus on data collected from an overheadWe will focus on data collected from an overhead perspective via transmission of electromagneticperspective via transmission of electromagnetic radiation.radiation.
  6. 6. ND GIS Users Workshop Bismarck, ND October 24-26, 2005ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Source: Jensen (2000)
  7. 7. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Remote Sensing DefinedRemote Sensing Defined  Remote Sensing Includes:Remote Sensing Includes:  A) The mission plan and choice of sensors;A) The mission plan and choice of sensors;  B) The reception, recording, and processing of theB) The reception, recording, and processing of the signal data; andsignal data; and  C) The analysis of the resultant data.C) The analysis of the resultant data.
  8. 8. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Energy Source or Illumination (A) Radiation and the Atmosphere (B) Interaction with the Target (C) Recording of Energy by the Sensor (D) Transmission, Reception, and Processing (E) Interpretation and Analysis (F) Application (G) Source: Canadian Centre for Remote Sensing Remote Sensing Process Components
  9. 9. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 ResolutionResolution  AllAll remote sensing systems haveremote sensing systems have four typesfour types ofof resolution:resolution:  SpatialSpatial  SpectralSpectral  TemporalTemporal  RadiometricRadiometric
  10. 10. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 High vs. Low? Spatial Resolution Source: Jensen (2000)
  11. 11. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Source: Jensen (2000) Spectral Resolution
  12. 12. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Temporal Resolution Time July 1 July 12 July 23 August 3 11 days 16 days July 2 July 18 August 3
  13. 13. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Radiometric Resolution 6-bit range 0 63 8-bit range 0 255 0 10-bit range 1023
  14. 14. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Electromagnetic RadiationElectromagnetic Radiation
  15. 15. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Electromagnetic SpectrumElectromagnetic Spectrum
  16. 16. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Signature SpectraSignature Spectra
  17. 17. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Types of Remote SensingTypes of Remote Sensing  Aerial PhotographyAerial Photography  MultispectralMultispectral  Active and Passive Microwave and LIDARActive and Passive Microwave and LIDAR
  18. 18. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Aerial PhotosAerial Photos  Balloon photographyBalloon photography (1858)(1858)  Pigeon camerasPigeon cameras (1903)(1903)  Kite photographyKite photography (1890)(1890)  Aircraft (WWI andAircraft (WWI and WWII)WWII)  Space (1947)Space (1947) Images: Jensen (2000)
  19. 19. ND GIS Users Workshop Bismarck, ND October 24-26, 2005
  20. 20. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 MultispectralMultispectral  NOAA-AVHRR (1100 m)NOAA-AVHRR (1100 m)  GOES (700 m)GOES (700 m)  MODIS (250, 500, 1000 m)MODIS (250, 500, 1000 m)  Landsat TM and ETM (30 – 60 m)Landsat TM and ETM (30 – 60 m)  SPOT (10 – 20 m)SPOT (10 – 20 m)  IKONOS (4, 1 m)IKONOS (4, 1 m)  Quickbird (0.6 m)Quickbird (0.6 m)
  21. 21. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 AVHRR (Advanced Very HighAVHRR (Advanced Very High Resolution Radiometer) NASAResolution Radiometer) NASA
  22. 22. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 GOES (Geostationary OperationalGOES (Geostationary Operational Environmental Satellites) IR 4Environmental Satellites) IR 4
  23. 23. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 MODIS (250 m)MODIS (250 m)
  24. 24. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Landsat TMLandsat TM (False Color Composite)(False Color Composite)
  25. 25. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 SPOT (2.5 m)SPOT (2.5 m)
  26. 26. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 QUICKBIRD (0.6 m)QUICKBIRD (0.6 m)
  27. 27. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 IKONOS (4 m Multispectral)IKONOS (4 m Multispectral)
  28. 28. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 IKONOS (1 m Panchromatic)IKONOS (1 m Panchromatic)
  29. 29. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 RADARRADAR (Radio Detection and Ranging)(Radio Detection and Ranging) Image: NASA 2005
  30. 30. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 LIDARLIDAR (Light Detection and Ranging)(Light Detection and Ranging) Image: Bainbridge Island, WA courtesy Pudget Sound LIDAR Consortium, 2005
  31. 31. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image InterpretationElements of Image Interpretation  Shape:Shape:  Many natural and human-made features haveMany natural and human-made features have unique shapes.unique shapes.  Often used are adjectives like linear,Often used are adjectives like linear, curvilinear, circular, elliptical, radial, square,curvilinear, circular, elliptical, radial, square, rectangular, triangular, hexagonal, star,rectangular, triangular, hexagonal, star, elongated, and amorphous.elongated, and amorphous.
  32. 32. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) ShapeShape
  33. 33. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image InterpretationElements of Image Interpretation  Shadow:Shadow:  Shadow reduction is of concern in remote sensingShadow reduction is of concern in remote sensing because shadows tend to obscure objects thatbecause shadows tend to obscure objects that might otherwise be detected.might otherwise be detected.  However, the shadow cast by an object may beHowever, the shadow cast by an object may be the only real clue to its identity.the only real clue to its identity.  Shadows can also provide information on theShadows can also provide information on the height of an object either qualitatively orheight of an object either qualitatively or quantitatively.quantitatively.
  34. 34. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) ShadowShadow
  35. 35. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image InterpretationElements of Image Interpretation  Tone and Color:Tone and Color:  AA bandband of EMR recorded by a remote sensingof EMR recorded by a remote sensing instrument can be displayed on an image ininstrument can be displayed on an image in shades of gray ranging from black to white.shades of gray ranging from black to white.  These shades are called “tones”, and can beThese shades are called “tones”, and can be qualitatively referred to as dark, light, orqualitatively referred to as dark, light, or intermediate (humans can see 40-50 tones).intermediate (humans can see 40-50 tones).  Tone is related to the amount of light reflectedTone is related to the amount of light reflected from the scene in a specific wavelength intervalfrom the scene in a specific wavelength interval (band).(band).
  36. 36. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) Tone and ColorTone and Color
  37. 37. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image InterpretationElements of Image Interpretation  Texture:Texture:  Texture refers to the arrangement of tone or colorTexture refers to the arrangement of tone or color in an image.in an image.  Useful because Earth features that exhibit similarUseful because Earth features that exhibit similar tones often exhibit different textures.tones often exhibit different textures.  Adjectives include smooth (uniform,Adjectives include smooth (uniform, homogeneous), intermediate, and rough (coarse,homogeneous), intermediate, and rough (coarse, heterogeneous).heterogeneous).
  38. 38. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) TextureTexture
  39. 39. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image InterpretationElements of Image Interpretation  Pattern:Pattern:  Pattern is the spatial arrangement of objects onPattern is the spatial arrangement of objects on the landscape.the landscape.  General descriptions include random andGeneral descriptions include random and systematic; natural and human-made.systematic; natural and human-made.  More specific descriptions include circular, oval,More specific descriptions include circular, oval, curvilinear, linear, radiating, rectangular, etc.curvilinear, linear, radiating, rectangular, etc.
  40. 40. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) PatternPattern
  41. 41. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image InterpretationElements of Image Interpretation  Height and Depth:Height and Depth:  As discussed, shadows can often offer clues to theAs discussed, shadows can often offer clues to the height of objects.height of objects.  In turn, relative heights can be used to interpretIn turn, relative heights can be used to interpret objects.objects.  In a similar fashion, relative depths can often beIn a similar fashion, relative depths can often be interpreted.interpreted.  Descriptions include tall, intermediate, and short;Descriptions include tall, intermediate, and short; deep, intermediate, and shallow.deep, intermediate, and shallow.
  42. 42. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Height and DepthHeight and Depth
  43. 43. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image InterpretationElements of Image Interpretation  Association:Association:  This isThis is veryvery important when trying toimportant when trying to interpret an object or activity.interpret an object or activity. AssociationAssociation refers to the fact that certainrefers to the fact that certain features and activities are almost alwaysfeatures and activities are almost always related to the presence of certain otherrelated to the presence of certain other features and activities.features and activities.
  44. 44. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) AssociationAssociation
  45. 45. ND GIS Users Workshop Bismarck, ND October 24-26, 2005
  46. 46. ND GIS Users Workshop Bismarck, ND October 24-26, 2005
  47. 47. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Imaging Tools and DataImaging Tools and Data  Google EarthGoogle Earth  ERDAS ImagineERDAS Imagine  Digital Northern GreatDigital Northern Great PlainsPlains
  48. 48. ND GIS Users Workshop Bismarck, ND October 24-26, 2005ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Case Study 1:Case Study 1: Identification andIdentification and Characterization of MiningCharacterization of Mining Waste Using Landsat TMWaste Using Landsat TM Imagery, Cherokee County,Imagery, Cherokee County, KSKS Gregory S. VandebergGregory S. Vandeberg
  49. 49. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 ProblemProblem  Mining, milling andMining, milling and smelting have disturbedsmelting have disturbed more than 240,000 kmmore than 240,000 km22 earth’s surface (Mooreearth’s surface (Moore and Luoma 1990)and Luoma 1990)  100,000 – 500,000100,000 – 500,000 abandoned mine lands inabandoned mine lands in U.S. (Hauff 2000)U.S. (Hauff 2000)  Mapping andMapping and characterization of thesecharacterization of these areas problematicareas problematic Source: http://www.cma.junta- andalucia.es/guadiamar/accidente_aznalcollar/ aznalcollar_1.html
  50. 50. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 HypothesesHypotheses  Metal miningMetal mining wastes/tailings inwastes/tailings in Cherokee County, KSCherokee County, KS can be identified andcan be identified and mapped using Landsatmapped using Landsat TM imageryTM imagery  Landsat TM data can alsoLandsat TM data can also be used to characterizebe used to characterize the mineralogy of thesethe mineralogy of these wasteswastes
  51. 51. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Previous StudiesPrevious Studies  Use of aerial photographs to identify abandonedUse of aerial photographs to identify abandoned coal mine lands in KS (Kenny and McCauley,coal mine lands in KS (Kenny and McCauley, 1982), and WV (Peplies et al. 1982)1982), and WV (Peplies et al. 1982)  Use of Landsat TM imagery and other remoteUse of Landsat TM imagery and other remote sensing techniques (e.g. AVIRIS) to recognizesensing techniques (e.g. AVIRIS) to recognize mining wastes in Cripple Creek Mining District,mining wastes in Cripple Creek Mining District, CO (Peters et al. 1996, Peters and Hauff 2000)CO (Peters et al. 1996, Peters and Hauff 2000)  Use of Landsat TM imagery to monitorUse of Landsat TM imagery to monitor vegetation and mining in Sudbury, Canadavegetation and mining in Sudbury, Canada (Singhroy 2000)(Singhroy 2000)
  52. 52. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Tri-State Mining DistrictTri-State Mining District  Lead and zinc ores minedLead and zinc ores mined from 1848-1968from 1848-1968  Legacy of mine tailings,Legacy of mine tailings, metal-contaminated soils,metal-contaminated soils, surface water andsurface water and groundwatergroundwater  Over 3 billion metric tonsOver 3 billion metric tons of mine tailings producedof mine tailings produced in district (often referredin district (often referred to as chat)to as chat)  More than 17 historicalMore than 17 historical smelter sitessmelter sites  3 Superfund Sites3 Superfund Sites
  53. 53. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Spruill 1987)
  54. 54. ND GIS Users Workshop Bismarck, ND October 24-26, 2005
  55. 55. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Ragan 1996)
  56. 56. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Gartung, 1931)
  57. 57. ND GIS Users Workshop Bismarck, ND October 24-26, 2005
  58. 58. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (KS Geological Survey)
  59. 59. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (KS Geological Survey)
  60. 60. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Charles Martin, Kansas State)
  61. 61. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Kansas Geological Survey)
  62. 62. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Charles Martin, Kansas State)
  63. 63. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Charles Martin, Kansas State)
  64. 64. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 MethodsMethods  Supervised and Unsupervised Classification ofSupervised and Unsupervised Classification of mining waste and tailings using Landsat 5mining waste and tailings using Landsat 5 Thematic Mapper image (Path 26 and Row 34,Thematic Mapper image (Path 26 and Row 34, acquired June 27, 1992)acquired June 27, 1992)  Geometrically rectified to UTM Zone 15 WGS 84Geometrically rectified to UTM Zone 15 WGS 84 using 11 ground control points and first orderusing 11 ground control points and first order polynomial equation (ERDAS Imagine) afterpolynomial equation (ERDAS Imagine) after subsetting image to county boundariessubsetting image to county boundaries  Radiometric and atmospheric correction usingRadiometric and atmospheric correction using Chavez (1996) COST model (Skirvin 2000)Chavez (1996) COST model (Skirvin 2000)  Use of band ratios to identify broadUse of band ratios to identify broad mineralogical types (Peters and Hauff 2000)mineralogical types (Peters and Hauff 2000)
  65. 65. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Spectral “Signatures”Spectral “Signatures”
  66. 66. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 False Color TM Image ofFalse Color TM Image of Cherokee County, KS (4-3-2)Cherokee County, KS (4-3-2)
  67. 67. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 False Color TM Image (7-4-2)False Color TM Image (7-4-2)
  68. 68. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Unsupervised ClassificationUnsupervised Classification False Color (7-4-2) Unsupervised
  69. 69. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Unsupervised ClassificationUnsupervised Classification AssessmentAssessment MineMine waste/tailingswaste/tailings OtherOther Row TotalsRow Totals Mine waste/Mine waste/ tailingstailings 1010 4040 5050 OtherOther 22 4848 5050 Column totalsColumn totals 1212 8888 100100 58% overall58% overall accuracyaccuracy 83.3% (I)83.3% (I) 20% (II)20% (II) 54% (I)54% (I) 96% (II)96% (II) KAPPA (kKAPPA (khathat) =) = 16%16%
  70. 70. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Supervised ClassificationSupervised Classification False Color (7-4-2) Supervised
  71. 71. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Supervised ClassificationSupervised Classification AssessmentAssessment Mine waste/Mine waste/ tailingstailings OtherOther Row TotalsRow Totals Mine waste/Mine waste/ tailingstailings 88 4242 5050 OtherOther 11 4949 5050 Column totalsColumn totals 99 9191 100100 57% overall57% overall 89% (I)89% (I) 16% (II)16% (II) 54% (I)54% (I) 98% (II)98% (II) KAPPA (kKAPPA (khathat) =) = 14%14%
  72. 72. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Accuracy AssessmentAccuracy Assessment  Conducted using orthophotos from sameConducted using orthophotos from same year with recognition of waste inyear with recognition of waste in piles/barren areaspiles/barren areas  Mining and milling wastes wereMining and milling wastes were incorporated into roads, foundations, etc.incorporated into roads, foundations, etc. so accuracy rates are likely higher thanso accuracy rates are likely higher than presentedpresented
  73. 73. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Mineralogy (3/4-3/1-5/7)Mineralogy (3/4-3/1-5/7) Iron oxidesIron oxides
  74. 74. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Bands 3/1-5/4-5/7Bands 3/1-5/4-5/7 Iron oxides vs. Ferrous/ClayIron oxides vs. Ferrous/Clay
  75. 75. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Bands 5/7-3/1-4/3Bands 5/7-3/1-4/3 Hydrothermal depositsHydrothermal deposits
  76. 76. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 ConclusionsConclusions  Mining wastes/tailings are recognizableMining wastes/tailings are recognizable using Landsat TM imagery, but includeusing Landsat TM imagery, but include many other classes (nonwaste).many other classes (nonwaste).  Only iron oxide minerals readilyOnly iron oxide minerals readily identifiable from Landsat TM imagery foridentifiable from Landsat TM imagery for areaarea

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