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Science underpinning archaeological detection: DART

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A presentation by Anthony Beck presented at the workshop "Potential of satellite images and hyper/multi-spectral recording in archaeology"
Poznan – 31st June 2012

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Science underpinning archaeological detection: DART

  1. 1. DART – Archaeological detectionAnthony (Ant) BeckTwitter: AntArchPotential of satellite images and hyper/multi-spectralrecording in archaeologyPoznan – 31st June 2012School of ComputingFaculty of Engineering
  2. 2. Overview•How do we detect stuff•Why DART•Going back to first principles•DART overview•Platforms•Knowledge base – impact on deployment
  3. 3. Archaeological ProspectionWhat is the basis for detectionWe detect Contrast:• Between the expression of the remains and the local background valueDirect Contrast:• where a measurement, which exhibits a detectable contrast with its surroundings, is taken directly from an archaeological residue.Proxy Contrast:• where a measurement, which exhibits a detectable contrast with its surroundings, is taken indirectly from an archaeological residue (for example from a crop mark).
  4. 4. Archaeological ProspectionThese attributes may be masked or accentuated by avariety of other phenomenahttp://www.youtube.com/v/UfOi_7Os7kA
  5. 5. Archaeological Prospection What is the basis for detection Micro-Topographic variations Soil Marks • variation in mineralogy and moisture properties Differential Crop Marks • constraint on root depth and moisture availability changing crop stress/vigour Proxy Thaw Marks • Exploitation of different thermal capacities of objects expressed in the visual component as thaw marksNow you see me dont
  6. 6. Archaeological ProspectionWhat is the basis for detection
  7. 7. Archaeological ProspectionWhat is the basis for detection
  8. 8. Archaeological ProspectionSummaryThe sensor must have:• The spatial resolution to resolve the feature• The spectral resolution to resolve the contrast• The radiometric resolution to identify the change• The temporal sensitivity to record the feature when the contrast is exhibitedThe image must be captured at the right time:• Different features exhibit contrast characteristics at different times
  9. 9. A multi-sensor environment:which includes ground survey and excavation
  10. 10. Why DART? Isn’t everything rosy in the garden?
  11. 11. Why DART? ‘Things’ are not well understoodEnvironmental processesSensor responses (particularly newsensors)Constraining factors (soil, crops etc.)Bias and spatial variabilityTechniques are scaling!• Geophysics!IMPACTS ON• Deployment• Management
  12. 12. Why DART? Precision agricultureUsing science to maximise crop return
  13. 13. Why DART? Precision agricultureOutlier values are being controlled
  14. 14. Why DART? Traditional AP exemplar
  15. 15. Why DART? Traditional AP exemplarSignificant bias in its application• in the environmental areas where it is productive (for example clay environments tend not to be responsive)• Surveys don’t tend to be systematic• Interpretation tends to be more art than science
  16. 16. What do we do about this?Go back to first principles:• Understand the phenomena• Understand the sensor characteristics• Understand the relationship between the sensor and the phenomena• Understand the processes better• Understand when to apply techniques
  17. 17. What do we want to achieve with this?Increased understandingwhich could lead to:• Improved detection in marginal conditions• Increasing the windows of opportunity for detection• Being able to detect a broader range of features
  18. 18. What do we do about this? Understand thephenomenaHow does the object generate anobservable contrast to its localmatrix?• Physical• Chemical• Biological• etcAre the contrasts permanent ortransitory?
  19. 19. What do we do about this? Understand thephenomenaIf transitory why are theyoccurring?• Is it changes in? • Soil type • Land management • Soil moisture • Temperature • Nutrient availability • Crop type • Crop growth stage
  20. 20. What do we do about this? Understand therelationship between the sensor and the phenomena
  21. 21. What do we do about this? Understand therelationship between the sensor and the phenomena Spatial Resolution
  22. 22. What do we do about this? Understand therelationship between the sensor and the phenomena Radiometric ResolutionRadiometric resolutiondetermines how finely a system canrepresent or distinguish differences ofintensity
  23. 23. What do we do about this? Understand therelationship between the sensor and the phenomena Temporal Resolution
  24. 24. What do we do about this? Understand therelationship between the sensor and the phenomena Spectral(?) Resolutionhttp://www.youtube.com/v/Nh-ZB5bxPhc
  25. 25. What do we do about this? Understand theprocesses betterSo what causes theselocalised variations?• Local conditions structure how any contrast difference is exhibited: • Soil type • Crop type • Moisture • Nutrients • Diurnal temperature variations
  26. 26. What do we do about this? Understand theprocesses betterExpressed contrast differenceschange over time• Seasonal variations• crop phenology (growth)• moisture• temperature• nutrients• Diurnal variations• sun angle (topographic features)• temperature variations
  27. 27. What do we do about this? Understand theprocesses betterExacerbated by anthropogenicactions• Cropping• Irrigation• Harrowing
  28. 28. What do we do about this? Example from multi orhyper spectral imaging
  29. 29. DART
  30. 30. DART - Collaborators
  31. 31. DART: Ground Observation BenchmarkingTry to understand the periodicity of change• Requires • intensive ground observation • at known sites (and their surroundings) • In different environmental settings • under different environmental conditions
  32. 32. DART: Ground Observation BenchmarkingBased upon an understanding of:• Nature of the archaeological residues • Nature of archaeological material (physical and chemical structure) • Nature of the surrounding material with which it contrasts • How proxy material (crop) interacts with archaeology and surrounding matrix• Sensor characteristics • Spatial, spectral, radiometric and temporal • How these can be applied to detect contrasts• Environmental characteristics • Complex natural and cultural variables that can change rapidly over time
  33. 33. DART: SitesLocation• Diddington, Cambridgeshire• Harnhill, GloucestershireBoth with• contrasting clay and well draining soils• an identifiable archaeological repertoire• under arable cultivationContrasting Macro environmentalcharacteristics
  34. 34. http://prezi.com/_tntxlrctptg/dart-sites/
  35. 35. DART: Probe Arrays
  36. 36. DART: Probe Arrays
  37. 37. DART: Field MeasurementsSpectro-radiometry• Soil• Vegetation • Every 2 weeksCrop phenology• Height• Growth (tillering)Flash res 64• Including induced events
  38. 38. DART: Field MeasurementsResistivityWeather station• Logging every half hour
  39. 39. DART: Probe Arrays
  40. 40. DART: Field MeasurementsAerial data• Hyperspectral surveys • CASI • EAGLE • HAWK• LiDAR• Traditional Aerial Photographs
  41. 41. DART: Laboratory MeasurementsGeotechnical analysesParticle sizeSheer strengthetc.Geochemical analysesPlant Biology
  42. 42. DART: Laboratory MeasurementsPlant Biology • Soil and leaf water content • Rate of germination • Root studies (emergence) • Root length and density. • Growth analysis • Root – Shoot biomass ratio. • Number of Leaves • Total plant biomass • Number of Tillers • Biochemical analysis: Protein and • Stem length chlorophyll analysis. • Total plant height • Broad spectrum analysis of soil • Drought experiment (Nutrient content) and C-N ratios of leaf. • A - Ci Curve • Chlorophyll a fluorescence
  43. 43. DART ERT Ditch Rob Fry B’ham TDR Imco TDR Spectro-radiometry transect
  44. 44. DART ERT Ditch Rob Fry B’ham TDR Imco TDR Spectro-radiometry transect
  45. 45. DART – exemplarsHyperspectral (400-2500nm) ERT DitchHigh resolution Vertical Rob Fry B’ham TDR Imco TDR Spectro-radiometry transect
  46. 46. DART – exemplarsAirborne Laser ScanningDiscrete Echo and Full Waveform ERT Ditch Rob Fry
  47. 47. DART – exemplarsObliques ERT DitchUAV Rob Fry B’ham TDR
  48. 48. DART: Data so far - Temperature
  49. 49. DART: Data so far - Temperature
  50. 50. DART: Data so far - PermittivityTDR - How does it work• Sends a pulse of EM energy• Due to changes in impedance, at the start and at the end of the probe, the pulse is reflected back and the reflections can be identified on the waveform trace• The distance between these two reflection points is used to determine the Dielectric permittivity• Different soils have different dielectric permittivity • This needs calibrating before soil moisture can be derived from the sensors
  51. 51. DART: Data so far - PermittivityFurther analysis of permittivity and conductivity against rainfallLinking the changes to the weather patternsComparisons can be made between• Soils at different depths• Archaeological and non-archaeological features• Different soil types at the different locationsConversion to moisture content is also a priority
  52. 52. DART: Data so far – Earth Resistance
  53. 53. DART: Data so far – Earth Resistance Probe Separation (m) 0.25 0.5 0.75 1June R 18.04742552 18.88545 18.896896 16.79403July 19.13517794 17.15205 17.081613 15.01906August #N/A #N/A #N/A #N/A Difference in magnitudeSeptember 8.841189868 13.255 14.512463 15.53069 Change of Contrast Factors withOctober 7.988128839 10.97714 12.217018 11.6229 20 Seasons Contrast Factor (%) 15 Twin Probe Electrode Seperation (m) 10 0.2 5 0.5 0.7 5 5 June July August September October 0.25 18.04742 19.13517 8.841189 7.988128 0.5 18.88544 17.15204 13.25500 10.97714 0.75 18.89689 17.08161 14.51246 12.21701 1 16.79403 15.01905 15.53069 11.62289
  54. 54. DART: Data so far – Earth Resistance
  55. 55. Spectro-radiometry: Methodology• Recorded monthly • Twice monthly at Diddington during the growing season• Transects across linear features• Taken in the field where weather conditions permit• Surface coverage evaluated using near-vertical photography• Vegetation properties recorded along transect • Chlorophyll (SPAD) • Height
  56. 56. Diddington transect 1: Spectroradiometry June 2011 0.12Rel 0.1ativ 0.08er 0.06efle 0.04ctan 0.02ce 0 400 500 600 700 Wavelength (nm) 27/06/2011 Archaeology 27/6/2011 Outside archaeology 14/06/2011 Archaeology 14/06/2011 Outside archaeology 08/06/2011 Archaeology 08/06/2011 Outside archaeology
  57. 57. Diddington transect 1: Spectroradiometry June 2011 0.4R 0.35ela 0.3tiv 0.25er 0.2efl 0.15ect 0.1anc 0.05e 0 350 450 550 650 750 850 950 1050 1150 1250 1350 1450 1550 1650 1750 1850 1950 2050 2150 2250 2350 2450 Wavelength (nm) 27/06/2011 Archaeology 27/6/2011 Outside archaeology 14/06/2011 Archaeology 14/06/2011 Outside archaeology 08/06/2011 Archaeolgy 08/06/2011 Outside archaeology
  58. 58. http://prezi.com/-oaoksqr09gx/dart-hyperspectral-the-driest-spring/
  59. 59. DART: Plant BiologyLab experiments conducted in collaboration with Leeds PlantBiology in 2011 and repeated in 2012From soils at Quarry FieldSoil structure appears to be the major component influencingroot penetration and plant health
  60. 60. http://prezi.com/v5kahvg2zmyz/dart-plant-biology/
  61. 61. DART: Knowledge Basehttp://prezi.com/ef_aud--i00t/dart-knowledge-base
  62. 62. DART: Communicationhttp://prezi.com/yo-pijkatt0a/dart-communication-infrastructure/http://dartproject.info/WPBlog/
  63. 63. Open Data: Server (in the near future)The full project archive will be available from the server Raw Data Processed Data Web ServicesWill also include TDR data Weather data Subsurface temperature data Soil analyses spectro-radiometry transects Crop analyses Excavation data In-situ photos ETC.
  64. 64. Why are we doing this – spreading the love
  65. 65. Why are we doing this – it’s the right thing to doDART is a publically funded projectPublically funded data should provide benefit to the public
  66. 66. Why are we doing this – IMPACT/unlocking potentialMore people use the data then there is improved impactBetter financial and intellectual return for the investors
  67. 67. Why are we doing this – innovationReducing barriers to data and knowledge can improveinnovation
  68. 68. Why are we doing this – educationTo provide baseline exemplar data for teaching and learning
  69. 69. Why are we doing this – building our networkFind new ways to exploit our dataDevelop contactsWrite more grant applications
  70. 70. DiscussionSFM Plant Biology Pushbroom Phenology High resolution frame Differential growth parameters Oblique and UAV Data mining (process fromTopographic measurements) From SFM Environmental Full Waveform LiDAR SoilsDetection Temperature Hyperspectral (including thermal) Spectral AnalysisVisualization ERT and tomography Complex data!
  71. 71. Questions
  72. 72. OverviewThere is no need to take notes:Slides – http://goo.gl/ZHYaBText – http://goo.gl/osQZi or http://goo.gl/M5Eu1There is every need to ask questionsThe slides and text are release under a Creative Commons byattribution licence.

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