Collection and Interpretation of Remote Sensing Data, Kasper Johansen, University of Queensland

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Collection and Interpretation of Remote Sensing Data, Kasper Johansen, University of Queensland

  1. 1. Collection and Interpretation of Remote Sensing Data 600m|______| 140m|______| 70m|______| Dr. Kasper Johansen, Email: k.johansen@uq.edu.au Biophysical Remote Sensing GroupSchool of Geography, Planning and Environmental Management The University of Queensland GTAQ 2012 1
  2. 2. Objectives of talk To present how to collect and access remote sensing image data and introduce selected image interpretation approaches and study exercises for high school students GTAQ 2012 2
  3. 3. Outline of talk Introduction to remote sensing Collection of remote sensing data Interpretation of remote sensing data:  Short study on linking field and image data  Student exercise 1  Short study on image interpretation cues  Student exercise 2 Summary of this talk Questions and Resources Demonstration of Remote Sensing Toolkit for learning purposes GTAQ 2012 3
  4. 4. What is Remote Sensing and Why Use It The science and art of obtaining information about an object, area or phenomenon through the analysis of data collected by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand et al., 2004:1) Not a Remote Sensing Measurement GTAQ 2012 4
  5. 5. What is Remote Sensing and Why Use It Rockhampton/Gladstone MODIS Image February 11, 2003 Source CSIRO GTAQ 2012 5
  6. 6. What is Remote Sensing and Why Use It The science and art of obtaining information about an object, area or phenomenon through the analysis of data collected by a device that is not in contact with the object, area or phenomenon under investigation (Lillesand et al., 2004:1) Remote Sensing Measurement Not a Remote Sensing Measurement Rockhampton Gladstone Susp. sediment concentration GTAQ 2012 6
  7. 7. What is Remote Sensing and Why Use It GTAQ 2012 7
  8. 8. Applications: Cyclone Yasi GTAQ 2012 8
  9. 9. Applications: Biomass mapping by Satellite Measured Measured in field GTAQ 2012 9
  10. 10. Application: Surface Temperature http://earthobservatory.nasa.gov/IOTD/view.php?id=36699 GTAQ 2012 10
  11. 11. Application: Elevation mapping Digital Elevation Model  LIDAR GTAQ 2012 11
  12. 12. Application: Elevation mapping LiDAR (Light Detection and Ranging):  LiDAR pulses from airborne transmitter  Height difference between surface features = Time difference for returns  High positional accuracy  Very suitable for deriving vegetation structural and geomorphic information ENVM3201 - April 2011 12
  13. 13. Application: Elevation mapping LiDAR data examples with high point density ENVM3201 - April 2011 13
  14. 14. Application: Elevation mapping Predicted for 5.4 m GTAQ 2012 14
  15. 15. Application: Elevation mappingPredicted floodingJan 2011 GTAQ 2012 15
  16. 16. Application: Elevation mappingActual floodingJan 2011 GTAQ 2012 16
  17. 17. Application: Coral reef mapping GTAQ 2012 17
  18. 18. GPS towedEvery dot is a photo on the reef by diver
  19. 19. Application: Coral reef mapping GTAQ 2012 19
  20. 20. Remote Sensing Applications Response to increasing application areas = increasing data dimensionality and availability Need to carefully select data and balance spatial resolution, spectral resolution, temporal resolution, acquisition costs and processing costs Always question where data comes from and how it was derived - metadata Push towards public access to data sets and open source processing tools to increase data sharing GTAQ 2012 20
  21. 21. Remote Sensing Applications GTAQ 2012 21
  22. 22. Collection of Remote Sensing Data How do you get access to remote sensing data and what are the costs?  High spatial resolution imagery:  Geoimage, SKM, AAM, Fugro Spatial Solutions ~ $30/km2  Airborne optical and LiDAR data:  AAM, Fugro, AEROmetrex, ARA, Hyvista ~ $1500/km2  Free Imagery:  USGS EarthExplorer GTAQ 2012 22
  23. 23. GTAQ 2012 23
  24. 24. Collection of Remote Sensing Data How do you get access to remote sensing data and what are the costs?  High spatial resolution imagery:  Geoimage, SKM, AAM, Fugro Spatial Solutions ~ $30/km2  Airborne optical and LiDAR data:  AAM, Fugro, AEROmetrex, ARA, Hyvista ~ $1500/km2  Free Imagery:  USGS EarthExplorer  Google Earth – but not geo-referenced GTAQ 2012 24
  25. 25. GTAQ 2012 25
  26. 26. Collection of Remote Sensing Data How do you get access to remote sensing data and what are the costs?  High spatial resolution imagery:  Geoimage, SKM, AAM, Fugro Spatial Solutions ~ $30/km2  Airborne optical and LiDAR data:  AAM, Fugro, AEROmetrex, ARA, Hyvista ~ $1500/km2  Free Imagery:  USGS EarthExplorer  Google Earth – but not geo-referenced  TERN Data Discovery Portal GTAQ 2012 26
  27. 27. GTAQ 2012 27
  28. 28. GTAQ 2012 28
  29. 29. Interpretation of Remote SensingData at Different Spatial Scales GTAQ 2012 29
  30. 30. Outline of talk GTAQ 2012 30
  31. 31. Outline of talk GTAQ 2012 31
  32. 32. Outline of talk GTAQ 2012 32
  33. 33. GTAQ 2012 33
  34. 34. GTAQ 2012 34
  35. 35. GTAQ 2012 35
  36. 36. Case Study 1Mapping Condition of Savanna Riparian Zones in North Australia GTAQ 2012 36
  37. 37. 1. Tropical Savanna Riparian ZonesAustralian tropical savannas Riparian zones Source: Tropical Savannas CRC, 2003 GTAQ 2012 37
  38. 38. 1. Importance of Riparian Zones Provision of stream shade Prevention of erosion Nutrient source from litter fall Natural filtering of pollutants Wildlife habitat GTAQ 2012 38
  39. 39. 2. ObjectiveTo map biophysical parameters suitablefor assessing the environmental conditionof Australian savanna riparian zones atlocal to regional scales based on theintegration of field survey and high spatialresolution image data. GTAQ 2012 39
  40. 40. 3. Study Area – Daly River 2.4m pixels Darwin 0.6m pixels Approximate scale Katherine 2km I____________IMap of part of the Northern Territory QuickBird image of the Daly River study area GTAQ 2012 40
  41. 41. 3. Study Area – Daly River GTAQ 2012 41
  42. 42. 4. Methods - Field Survey Data Field measurements of 5m x 5m quadrats on both sides of transect line – 10m wide transects Parameters: 1. Riparian zone width 2. River channel width 3. Percentage Canopy Cover 4. Leaf Area Index (LAI) 5. Ground cover 6. High impact weeds 7. Tree species 8. Bank stability 9. Flood damage 10. Vegetation overhang GTAQ 2012 42
  43. 43. 4. Methods - Field Survey Data GTAQ 2012 43
  44. 44. 4. Methods - Field Survey Data GTAQ 2012 44
  45. 45. 4. Methods - Field Survey DataApproximate scale300m I_______________I GTAQ 2012 45
  46. 46. 5. Results – Biophysical Models Daly River 2005 1 y = 1.4419Ln(x) + 1.2253 0.9 R2 = 0.7893, n = 548 Percentage Canopy Cover 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0.2 0.4 0.6 0.8 1 SAVI GTAQ 2012 46
  47. 47. 5. Results – Biophysical Maps Pan-sharpened QuickBird Image Percentage Canopy Cover Map Approximate scale 100m I_________I GTAQ 2012 47
  48. 48. 4. Methods - Field Survey Data GTAQ 2012 48
  49. 49. 5. Results – Biophysical Models Daly River 2005 6 y = 9.5382x - 4.4086 R2 = 0.7206, n = 548 5 Leaf Area Index 4 3 2 1 0 0 0.2 0.4 0.6 0.8 1 SAVI GTAQ 2012 49
  50. 50. 5. Results – Biophysical Maps Leaf Area index Map Pan-sharpened QuickBird Image Approximate scale 100m I_________I GTAQ 2012 50
  51. 51. 4. Methods - Object-based classification Processing sequence for object-based image classification Original image Segmented image Develop rule sets Classified image GTAQ 2012 51
  52. 52. 5. Results – Image Classification Image Classification 2004 Multi-spectral QuickBird image Image Classification 2005 Multi-spectral QuickBird image 23 August 2004 – 13 August 2005 Classification Accuracy - 2004 Classification Accuracy - 2005 100 100 90 90 80 80 70 70Percentage Percentage 60 60 50 50 40 40 30 30 20 20 10 10 0 0 Cleared Water Savanna Riparian Transition Exposed Cleared Water Savanna Riparian Transition Exposed areas zone zone banks areas zone zone banks Land Cover Classes Land Cover ClassesTotal number of samples = 350 Producers Accuracy Users Accuracy Total number of samples = 350 Producers Accuracy Users Accuracy Approximate scale Approximate scale 500m I_________I 500m I_________I GTAQ 2012 52
  53. 53. 5. Results – Image Classification River width of the Daly River - August 2005 100 90 80 River width (m) 70 60 50 40 30 20 10 Average river width = 46.51m 0 0 1082 2129 3446 4511 6582 8068 9239 10434 11548 12559 13767 14917 17267 19073 Distance (m) Riparian zone width, west bank of the Daly River, 2005 140 120 Riparian zone width (m) 100 80 60 40 20 Average riparian zone width, west bank = 53.57m 0 0 962 1921 3270 5119 6548 8004 8907 9858 10888 11828 13723 15412 17247 18890 Distance (m) GTAQ 2012 53
  54. 54. 7. Object 2 - Results GTAQ 2012 54
  55. 55. 5. Results – Bank Stability Map Pan-sharpenedDamage Map Stream Bank Stability Image Flood QuickBird Map Approximate scale Approximate scale 100m I_________I 100m I_________I GTAQ 2012 55
  56. 56. 6. Conclusions Indicators of riparian zone condition that can be mapped with an accuracy feasible for multi-temporal assessment:  Percentage canopy cover  Leaf area index  Bank stability  Flood damage  Riparian zone width  River width Large sample size of field data to improve relationship between field and image based measurements GTAQ 2012 56
  57. 57. Study Exercise 1: Considering Spatial Scale Aim To understand how environmental features (e.g. trees, buildings and landforms) are measured and represented in remotely sensed images. Background Any effective form of remote sensing requires in- depth experience and measurement of the environment you are working in. This suggested field exercise with provide this link which will enable a strong and realistic basis for image analysis and interpretation skills. GTAQ 2012 57
  58. 58. Study Exercise 1: Considering Spatial Scale Tasks  Task 1: Position/Location using a GPS  Task 2: What is in a pixel  Task 3: Identifying features along a transect to match up observations with image data  Task 4: Comparing a high spatial resolution image (e.g. Google Earth) with a Landsat image GTAQ 2012 58
  59. 59. Study Exercise 1: Considering Spatial ScaleTask 1: Position/Location using a GPS Aim: To explain, demonstrate and measure horizontal and vertical position using a global positioning system (GPS) receiver Instrument: Hand held GPS receiver Basic Principles to Explain and Demonstrate:  Measurements of horizontal and vertical position, including map projections, coordinate systems, datum  GPS principles  Measurement accuracy GTAQ 2012 59
  60. 60. Study Exercise 1: Considering Spatial ScaleTask 1: Position/Location using a GPS Record the GPS position of a single location or feature at 1 minute intervals for 5 minutes Use the GPS receiver to accurately map the boundary of two features at the field site GTAQ 2012 60
  61. 61. Study Exercise 1: Considering Spatial ScaleTask 1: Position/Location using a GPS Position Measurement Note taker: EPE Feature- Waypoint name Name (estimated Photo file Easting Northing Height student: positional name: error)Mean positionStandard deviation ofposition GTAQ 2012 61
  62. 62. Study Exercise 1: Considering Spatial Scale Task 1: Position/Location using a GPS Feature Mapping Note taker:Feature 1 Feature- Photo file Easting Northing Height EPE Distance between Waypoint name: Points1)2)3)4)Feature 2 Feature- Photo file Easting Northing Height Distance betweenWaypoint name: points1)2)3)i)n) GTAQ 2012 62
  63. 63. Study Exercise 1:Considering Spatial Scale GTAQ 2012 63
  64. 64. Study Exercise 1:Considering Spatial Scalehttp://www.earthpoint.us/ExcelToKml.aspx GTAQ 2012 64
  65. 65. Study Exercise 1: Considering Spatial ScaleTask 2: What is in a pixel Aim: To measure and assess the effects of increasing the pixel size of an imaging sensor Instrument: 2 x 50 m survey tapes, digital camara, hand held GPS receiver, ranging poles Basic Principles to Explain and Demonstrate:  Principles of multi-spectral optical imaging systems – where do pixels come from  What controls the size of features detectable in an image  Level of spatial detail required for mapping  Common imaging sensor pixel and scene dimensions GTAQ 2012 65
  66. 66. Study Exercise 1: Considering Spatial ScaleTask 2: What is in a pixel Use the two survey tapes to successively mark out the boundaries of image pixels to be measured At each pixel size, take photos from the centre of the pixel and record GPS corner coordinates For each pixel size record the number and percentage coverage of different land cover types (soil, concrete, grass, trees, etc.) GTAQ 2012 66
  67. 67. Study Exercise 1: Considering Spatial ScaleTask 2: What is in a pixel GTAQ 2012 67
  68. 68. Study Exercise 1: Considering Spatial Scale Note taker:Pixel size - no: photo name Waypoint name Easting Northing Height0.5 x 0.5 m 1 2 3 42.4 x 2.4m 1 5 6 710 x 10m 1 8 9 1020 x 20 m 1 11 12 1330 x 30 m 1 14 15 1650 x 50 m 1 17 18 19 GTAQ 2012 68
  69. 69. Study Exercise 1: Considering Spatial ScalePixel composition: Note taker:Pixel size - Surface Cover Type % of pixel Sketch (soil, concrete, grass, trees, asphalt,etc etc covered0.5 x 0.5 m2.4 x 2.4m10 x 10m20 x 20 m30 x 30 m50 x 50 m GTAQ 2012 69
  70. 70. Study Exercise 1: Considering Spatial ScaleTask 3: Identifying features along a transect tomatch up observations with image data Aim: To measure and assess how land cover types are represented in image data Instrument: 1 x 50 m survey tapes, digital camera, hand held GPS receiver Basic Principles to Explain and Demonstrate:  What does the satellite see  What does a pixel look like when multiple land cover types occur within it  Why is there a need for integrating field and image data (calibration and validation of image maps) GTAQ 2012 70
  71. 71. Study Exercise 1: Considering Spatial ScaleTask 3:Identifying features along a transect tomatch up observations with image data Locate a start point of the transect and lay out the 50 m tape Record the positions of the start and end points of the transect using the GPS receiver Take photos along the transect Identify land cover types along the transect line and make notes where along the transect these occur GTAQ 2012 71
  72. 72. Study Exercise 1:Considering Spatial Scale GTAQ 2012 72
  73. 73. Study Exercise 1: Considering Spatial Scale Note taker:Distance no: photo name Land cover Easting Northing Heightalong transect type0m - ? m 1?m- ?m 2 3 4 5 6 7 8 9 10 11 12 Display the location of the transect start and end points in Google Earth Compare land cover observations with those identified in Google Earth and explain any observed differences GTAQ 2012 73
  74. 74. Study Exercise 1: Considering Spatial ScaleTask 4: Comparing a high spatial resolution imagewith a Landsat image Aim: To compare two images with different spatial resolutions Questions to Address:  When and why would you use the two different image types?  What are the pros and cons of using the two different image types?  Think of different applications suitable for using the two image types GTAQ 2012 74
  75. 75. Study Exercise 1: Considering Spatial ScaleLandsat image Google Earth image GTAQ 2012 75
  76. 76. Case Study 2Object-Based Mapping of Urban Areas GTAQ 2012 76
  77. 77. Urban Land Cover Mapping QuickBird image from 2005 500 250 0 500 Meters GTAQ 2012 77
  78. 78. Urban Land Cover Mapping500 250 0 500 Meters GTAQ 2012 78
  79. 79. Urban Land Cover Mapping500 250 0 500 Meters GTAQ 2012 79
  80. 80. Urban Land Cover Mapping500 250 0 500 Meters GTAQ 2012 80
  81. 81. Urban Land Cover Mapping500 250 0 500 Meters GTAQ 2012 81
  82. 82. Urban Land Cover Mapping500 250 0 500 Meters GTAQ 2012 82
  83. 83. Urban Land Cover Mapping500 250 0 500 Meters GTAQ 2012 83
  84. 84. Urban Land Cover Mapping500 250 0 500 Meters GTAQ 2012 84
  85. 85. Conclusions Object-based image analysis can be used to map urban land cover classes at high spatial resolution Shape and size of objects and context relationships were found very useful for mapping urban land cover classes GTAQ 2012 85
  86. 86. Study Exercise 2: Interpreting images Aim To build an understanding and experience in the necessary skills for interpreting image data Background Manual interpretation of aerial photos and high spatial resolution image data is a well established science. This science has recently provided the basis for automated mapping approaches using object-based image analysis GTAQ 2012 86
  87. 87. Study Exercise 2: Interpreting images Image Interpretation Cues  Tone/Colour: bright / actual colour  Texture: frequency of change and arrangement of tones  Size: physical size of objects  Shape: shape created by the boundaries of features  Shadows: presence and extent  Pattern: repetition of shape and tonal features  Context (site and association): geographic location constraints of features (e.g. beaches near water), positional association (e.g. aircraft, runway, airport) GTAQ 2012 87
  88. 88. Study Exercise 2: Interpreting images Image Interpretation Cues Terminology  Tone/Colour: bright - dark / actual colour  Texture: smooth - rough  Size: physical size and dimensions of objects  Shape: rectangular, circular, square, oval, etc.  Shadows: presence and extent  Pattern: regular - irregular  Context (site and association): geographic location constraints of features (e.g. beaches near water), positional association (e.g. aircraft, runway, airport) GTAQ 2012 88
  89. 89. Study Exercise 2: Interpreting images Questions:  Identify image interpretation cues for the following land cover types: mangroves, canal estate, sugar cane fields  Identity which interpretation cues are unique for certain land cover classes, which will allow recognition and discrimination and different land cover classes GTAQ 2012 89
  90. 90. Study Exercise 2: Interpreting imagesInterpretation Land- Land- Land- Cue cover/use #1 cover/use #2 cover/use #3Tone –ColourTextureSizeShapePatternShadowContext GTAQ 2012 90
  91. 91. Study Exercise 2:Interpreting images GTAQ 2012 91
  92. 92. Study Exercise 2:Interpreting images GTAQ 2012 92
  93. 93. Study Exercise 2:Interpreting images GTAQ 2012 93
  94. 94. Summary of this talk Brief introduction about Remote Sensing Case study on relating field and image data Study exercise suitable for field trip Case study on automated use of image interpretation cues Study exercise suitable for the classroom Further learning tools and resources GTAQ 2012 94
  95. 95. www.gpem.uq.edu.au/cser-rstoolkit GTAQ 2012 95

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