1. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Introduction to Remote Sensing Gregory Vandeberg Assistant Professor of Geography Image: NASA 2005

2. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Outline Remote Sensing Defined Remote Sensing Defined Resolution Resolution Electromagnetic Energy (EMR) Electromagnetic Energy (EMR) Types Types Interpretation Interpretation Applications Applications

3. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Remote Sensing Defined Remote Sensing is: Remote Sensing is: “The art and science of obtaining information about an object without being in direct contact with the object” (Jensen 2000). “The art and science of obtaining information about an object without being in direct contact with the object” (Jensen 2000). There is a medium of transmission involved. There is a medium of transmission involved.

4. ND GIS Users Workshop Bismarck, ND October 24-26, 2005

5. Remote Sensing Defined Environmental Remote Sensing: Environmental Remote Sensing: … the collection of information about Earth surfaces and phenomena using sensors not in physical contact with the surfaces and phenomena of interest. … the collection of information about Earth surfaces and phenomena using sensors not in physical contact with the surfaces and phenomena of interest. We will focus on data collected from an overhead perspective via transmission of electromagnetic radiation. We will focus on data collected from an overhead perspective via transmission of electromagnetic radiation.

6. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Source: Jensen (2000)

7. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Remote 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 the signal data; and B) The reception, recording, and processing of the signal data; and C) The analysis of the resultant data. C) The analysis of the resultant data.

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. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Resolution All remote sensing systems have four types of resolution: All remote sensing systems have four types of resolution: Spatial Spatial Spectral Spectral Temporal Temporal Radiometric Radiometric

10. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 High vs. Low? Spatial Resolution Source: Jensen (2000)

11. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Source: Jensen (2000) Spectral Resolution

12. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Temporal Resolution Time July 1 July 12 July 23August 3 11 days 16 days July 2July 18August 3

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. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Electromagnetic Radiation

15. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Electromagnetic Spectrum

16. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Signature Spectra

17. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Types of Remote Sensing Aerial Photography Aerial Photography Multispectral Multispectral Active and Passive Microwave and LIDAR Active and Passive Microwave and LIDAR

18. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Aerial Photos Balloon photography (1858) Balloon photography (1858) Pigeon cameras (1903) Pigeon cameras (1903) Kite photography (1890) Kite photography (1890) Aircraft (WWI and WWII) Aircraft (WWI and WWII) Space (1947) Space (1947) Images: Jensen (2000)

19. ND GIS Users Workshop Bismarck, ND October 24-26, 2005

20. Multispectral 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. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 AVHRR (Advanced Very High Resolution Radiometer) NASA

22. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 GOES (Geostationary Operational Environmental Satellites) IR 4

23. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 MODIS (250 m)

24. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Landsat TM (False Color Composite)

25. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 SPOT (2.5 m)

26. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 QUICKBIRD (0.6 m)

27. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 IKONOS (4 m Multispectral)

28. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 IKONOS (1 m Panchromatic)

29. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 RADAR (Radio Detection and Ranging) Image: NASA 2005

30. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 LIDAR (Light Detection and Ranging) Image: Bainbridge Island, WA courtesy Pudget Sound LIDAR Consortium, 2005

31. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image Interpretation Shape: Shape: Many natural and human-made features have unique shapes. Many natural and human-made features have unique shapes. Often used are adjectives like linear, curvilinear, circular, elliptical, radial, square, rectangular, triangular, hexagonal, star, elongated, and amorphous. Often used are adjectives like linear, curvilinear, circular, elliptical, radial, square, rectangular, triangular, hexagonal, star, elongated, and amorphous.

32. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000)Shape

33. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image Interpretation Shadow: Shadow: Shadow reduction is of concern in remote sensing because shadows tend to obscure objects that might otherwise be detected. Shadow reduction is of concern in remote sensing because shadows tend to obscure objects that might otherwise be detected. However, the shadow cast by an object may be the only real clue to its identity. However, the shadow cast by an object may be the only real clue to its identity. Shadows can also provide information on the height of an object either qualitatively or quantitatively. Shadows can also provide information on the height of an object either qualitatively or quantitatively.

34. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) Shadow

35. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image Interpretation Tone and Color: Tone and Color: A band of EMR recorded by a remote sensing instrument can be displayed on an image in shades of gray ranging from black to white. A band of EMR recorded by a remote sensing instrument can be displayed on an image in shades of gray ranging from black to white. These shades are called “tones”, and can be qualitatively referred to as dark, light, or intermediate (humans can see 40-50 tones). These shades are called “tones”, and can be qualitatively referred to as dark, light, or intermediate (humans can see 40-50 tones). Tone is related to the amount of light reflected from the scene in a specific wavelength interval (band). Tone is related to the amount of light reflected from the scene in a specific wavelength interval (band).

36. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) Tone and Color

37. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image Interpretation Texture: Texture: Texture refers to the arrangement of tone or color in an image. Texture refers to the arrangement of tone or color in an image. Useful because Earth features that exhibit similar tones often exhibit different textures. Useful because Earth features that exhibit similar tones often exhibit different textures. Adjectives include smooth (uniform, homogeneous), intermediate, and rough (coarse, heterogeneous). Adjectives include smooth (uniform, homogeneous), intermediate, and rough (coarse, heterogeneous).

38. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) Texture

39. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image Interpretation Pattern: Pattern: Pattern is the spatial arrangement of objects on the landscape. Pattern is the spatial arrangement of objects on the landscape. General descriptions include random and systematic; natural and human-made. General descriptions include random and systematic; natural and human-made. More specific descriptions include circular, oval, curvilinear, linear, radiating, rectangular, etc. More specific descriptions include circular, oval, curvilinear, linear, radiating, rectangular, etc.

40. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000)Pattern

41. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image Interpretation Height and Depth: Height and Depth: As discussed, shadows can often offer clues to the height of objects. As discussed, shadows can often offer clues to the height of objects. In turn, relative heights can be used to interpret objects. In turn, relative heights can be used to interpret objects. In a similar fashion, relative depths can often be interpreted. In a similar fashion, relative depths can often be interpreted. Descriptions include tall, intermediate, and short; deep, intermediate, and shallow. Descriptions include tall, intermediate, and short; deep, intermediate, and shallow.

42. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Height and Depth

43. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Elements of Image Interpretation Association: Association: This is very important when trying to interpret an object or activity. This is very important when trying to interpret an object or activity. Association refers to the fact that certain features and activities are almost always related to the presence of certain other features and activities.

44. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Jensen (2000) Association

45. ND GIS Users Workshop Bismarck, ND October 24-26, 2005

47. Imaging Tools and Data Google Earth Google Earth ERDAS Imagine ERDAS Imagine Digital Northern Great Plains Digital Northern Great Plains

48. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Case Study 1: Identification and Characterization of Mining Waste Using Landsat TM Imagery, Cherokee County, KS Gregory S. Vandeberg

49. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Problem Mining, milling and smelting have disturbed more than 240,000 km 2 earth’s surface (Moore and Luoma 1990) Mining, milling and smelting have disturbed more than 240,000 km 2 earth’s surface (Moore and Luoma 1990) 100,000 – 500,000 abandoned mine lands in U.S. (Hauff 2000) 100,000 – 500,000 abandoned mine lands in U.S. (Hauff 2000) Mapping and characterization of these areas problematic Mapping and characterization of these areas problematic Source: http://www.cma.junta- andalucia.es/guadiamar/accidente_aznalcollar/ aznalcollar_1.html

50. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Hypotheses Metal mining wastes/tailings in Cherokee County, KS can be identified and mapped using Landsat TM imagery Metal mining wastes/tailings in Cherokee County, KS can be identified and mapped using Landsat TM imagery Landsat TM data can also be used to characterize the mineralogy of these wastes Landsat TM data can also be used to characterize the mineralogy of these wastes

51. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Previous Studies Use of aerial photographs to identify abandoned coal mine lands in KS (Kenny and McCauley, 1982), and WV (Peplies et al. 1982) Use of aerial photographs to identify abandoned coal mine lands in KS (Kenny and McCauley, 1982), and WV (Peplies et al. 1982) Use of Landsat TM imagery and other remote sensing techniques (e.g. AVIRIS) to recognize mining wastes in Cripple Creek Mining District, CO (Peters et al. 1996, Peters and Hauff 2000) Use of Landsat TM imagery and other remote sensing techniques (e.g. AVIRIS) to recognize mining wastes in Cripple Creek Mining District, CO (Peters et al. 1996, Peters and Hauff 2000) Use of Landsat TM imagery to monitor vegetation and mining in Sudbury, Canada (Singhroy 2000) Use of Landsat TM imagery to monitor vegetation and mining in Sudbury, Canada (Singhroy 2000)

52. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Tri-State Mining District Lead and zinc ores mined from 1848-1968 Lead and zinc ores mined from 1848-1968 Legacy of mine tailings, metal-contaminated soils, surface water and groundwater Legacy of mine tailings, metal-contaminated soils, surface water and groundwater Over 3 billion metric tons of mine tailings produced in district (often referred to as chat) Over 3 billion metric tons of mine tailings produced in district (often referred to as chat) More than 17 historical smelter sites More than 17 historical smelter sites 3 Superfund Sites 3 Superfund Sites

53. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Spruill 1987)

54. ND GIS Users Workshop Bismarck, ND October 24-26, 2005

55. (Ragan 1996)

56. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Gartung, 1931)

57. ND GIS Users Workshop Bismarck, ND October 24-26, 2005

58. (KS Geological Survey)

59. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (KS Geological Survey)

60. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Charles Martin, Kansas State)

61. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Kansas Geological Survey)

62. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Charles Martin, Kansas State)

63. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 (Photo: Charles Martin, Kansas State)

64. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Methods Supervised and Unsupervised Classification of mining waste and tailings using Landsat 5 Thematic Mapper image (Path 26 and Row 34, acquired June 27, 1992) Supervised and Unsupervised Classification of mining waste and tailings using Landsat 5 Thematic Mapper image (Path 26 and Row 34, acquired June 27, 1992) Geometrically rectified to UTM Zone 15 WGS 84 using 11 ground control points and first order polynomial equation (ERDAS Imagine) after subsetting image to county boundaries Geometrically rectified to UTM Zone 15 WGS 84 using 11 ground control points and first order polynomial equation (ERDAS Imagine) after subsetting image to county boundaries Radiometric and atmospheric correction using Chavez (1996) COST model (Skirvin 2000) Radiometric and atmospheric correction using Chavez (1996) COST model (Skirvin 2000) Use of band ratios to identify broad mineralogical types (Peters and Hauff 2000) Use of band ratios to identify broad mineralogical types (Peters and Hauff 2000)

65. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Spectral “Signatures”

66. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 False Color TM Image of Cherokee County, KS (4-3-2)

67. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 False Color TM Image (7-4-2)

68. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Unsupervised Classification False Color (7-4-2)Unsupervised

69. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Unsupervised Classification Assessment Mine waste/tailings Other Row Totals Mine waste/ tailings 104050 Other24850 Column totals 1288100 58% overall accuracy 83.3% (I) 20% (II) 54% (I) 96% (II) KAPPA (k hat ) = 16%

70. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Supervised Classification False Color (7-4-2)Supervised

71. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Supervised Classification Assessment Mine waste/ tailingsOther Row Totals Mine waste/ tailings 84250 Other14950 Column totals 991100 57% overall 89% (I) 16% (II) 54% (I) 98% (II) KAPPA (k hat ) = 14%

72. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Accuracy Assessment Conducted using orthophotos from same year with recognition of waste in piles/barren areas Conducted using orthophotos from same year with recognition of waste in piles/barren areas Mining and milling wastes were incorporated into roads, foundations, etc. so accuracy rates are likely higher than presented Mining and milling wastes were incorporated into roads, foundations, etc. so accuracy rates are likely higher than presented

73. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Mineralogy (3/4-3/1-5/7) Iron oxides

74. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Bands 3/1-5/4-5/7 Iron oxides vs. Ferrous/Clay

75. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Bands 5/7-3/1-4/3 Hydrothermal deposits

76. ND GIS Users Workshop Bismarck, ND October 24-26, 2005 Conclusions Mining wastes/tailings are recognizable using Landsat TM imagery, but include many other classes (nonwaste). Mining wastes/tailings are recognizable using Landsat TM imagery, but include many other classes (nonwaste). Only iron oxide minerals readily identifiable from Landsat TM imagery for area Only iron oxide minerals readily identifiable from Landsat TM imagery for area