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Using multi-temporal benchmarking to determine optimal sensor deployment: advances from the DART project.

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A presentation given by Anthony Beck at EARSeL Gent on 20/09/12 describing some of the multi-temporal issues associated with archaeological detection. This presentation is primarily based on the …

A presentation given by Anthony Beck at EARSeL Gent on 20/09/12 describing some of the multi-temporal issues associated with archaeological detection. This presentation is primarily based on the research of David Stott.

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  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/catikaoe/183454010/We identify contrast Between the expression of the remains and the local 'background' value In most scenarios direct contrast measurements are preferable as these measurements will have less attenuation.Proxy contrast measurements are extremely useful when the residue under study does not produce a directly discernable contrast or it exists in a regime where direct observation is impossible
  • Traces can be identified through evidence Clusters of artefacts Chemical and physical residues Proxy biological variations Changes in surface relief
  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/dartproject/6001577156Dependant on localised formation and deformation Land management
  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/san_drino/1454922072/Environmental processesSensor responses (particularly new sensors)Constraining factors (soil, crops etc.)Bias and spatial variabilityIMPACTS ONDeploymentManagement
  • Image re-used under a creative commons licence: http://www.flickr.com/photos/8203774@N06/2310292882/
  • Image re-used under a creative commons licence: http://www.flickr.com/photos/8203774@N06/2310292882/
  • Try to understand the periodicity of changeRequire intensive ground observation (spectro-radiometry, soil and crop analysis) at known sites (and their surroundings) in a range of different environments under different environmental conditions
  • Based upon an understanding of:Nature of the archaeological residuesNature of archaeological material (physical and chemical structure)Nature of the surrounding material with which it contrastsHow proxy material (crop) interacts with archaeology and surrounding matrixSensor characteristicsSpatial, spectral, radiometric and temporalHow these can be applied to detect contrastsEnvironmental characteristicsComplex natural and cultural variables that can change rapidly over time
  • Based upon an understanding of:Nature of the archaeological residuesNature of archaeological material (physical and chemical structure)Nature of the surrounding material with which it contrastsHow proxy material (crop) interacts with archaeology and surrounding matrixSensor characteristicsSpatial, spectral, radiometric and temporalHow these can be applied to detect contrastsEnvironmental characteristicsComplex natural and cultural variables that can change rapidly over time
  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/arpentnourricier/2385863532Dependant on localised formation and deformation Environmental conditions Soil moisture Crop Temperature and emmisivity
  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/dartproject/6001577156Dependant on localised formation and deformation Land management
  • LocationDiddington, CambridgeshireHarnhill, GloucestershireBoth withcontrasting clay and 'well draining' soilsan identifiable archaeological repertoireunder arable cultivationContrasting Macro environmental characteristics
  • Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
  • Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
  • Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
  • Image re-used under a creative commons licence:http://www.flickr.com/photos/soilscience/5104676427Spectro-radiometrySoilVegetationEvery 2 weeksCrop phenologyHeightGrowth (tillering)Flash res 64Including induced events
  • ResistivityGround penetrating radarEmbedded Soil Moisture and Temperature probesLogging every hour Weather stationLogging every half hour
  • Aerial dataHyperspectral surveysCASIEAGLEHAWKLiDARTraditional Aerial Photographs
  • Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
  • Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
  • Conversion to moisture content is also a priorityRequires calibration using different mixing models including:empiricalsemi-empiricalphysical volumetric phenomenological modelsThis will help us to link the changes in geophysical responses to the composition of the soil and predict future responses, as well as supporting investigations into crop stress and vigour.
  • This is not simply scaling
  • Oooh look- contrast! Archaeology has higher absorption in the vis, increased reflectance in the NIR, indicating more LAI / photosynthesis
  • Less contrast, same trend
  • Senescance- increased reflectance in the red, shallower water absorbtion bands, sloping shoulder.Ooh- look- the relationship observable in the previous months is inverted! what’s going on ‘ere then? (see next slide)
  • Well, knock me over with a feather and colour me purple- the crop over the ditch has matured quicker than the the background- we don’t really know what’s going on here yet but it looks like the growth stage of the wheat has been retarded in the areas off the archaeology…MORE SPECTRAL MEASUREMENTS ARE REQUIRED
  • Senescence- archaeology is more reflective- indicative of greater LAI/Bio-mass
  • Here in the visible spectrum- features are ‘brighter’
  • Archaeology – not archaeology14/6/2011Basically biomass is the major determinant- less contrast in the structure insensitive indices
  • Endmembers used- after curran et al.
  • The spectral plot shows greater absorbance in the visible spectrum, and greater reflectance in the near-infrared part of the spectrum for the areas over the archaeology.670nm absorbtion feature, indicative of chlorophyll and other photosynthetic pigments, shows very little contrast. This means that contrast is more strongly expressed as differences in biomass (i.e. increased Leaf Area Index) than as stress and vigour variations.
  • The spectral plot expresses less contrast than 08/06/11. In the continuum removal reults the greatest contrast can be seen in the 1730nm absorbtion feature, which is sensitive to lignin and cellulose content. This seems to indicate that the background is higher in lignin content than the archaeology. Lignin is a major component of plant stems. -This may be a result of the lignin making a greater contribution to reflectance due to a thinner canopy
  • The spectral plot again shows less contrast than previous weeks. The 670nm absorbtion feature exhibits very little contrast. The reduced reflectance in the near and shortwave infrared indicate that the crop has reached maturity, and is starting to senesce. The greatest contrast is seen in the 1200nm absorbtion feature, which is indicative of foliar water content.
  • Transcript

    • 1. Using multi-temporal benchmarking todetermine optimal sensor deployment:advances from the DART project.David Stott, Ant Beck and Doreen BoydTwitter: AntArch (in using the hashtag #EARSeL)3rd EARSEL Workshop: Advances in remote sensingfor archaeology and cultural heritage management19th to 22nd September 2012School of ComputingFaculty of Engineering
    • 2. Presentation overview•Detection summary•Why do we need the DART project?•Ground observation benchmarking at DART•Data examples•Multi-temporal spectroradiometry•Conclusions
    • 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. 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
    • 5. 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
    • 6. What’s the problem? ‘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
    • 7. 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
    • 8. 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
    • 9. 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
    • 10. 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
    • 11. DART: Ground Observation BenchmarkingBased upon an understanding of:• 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
    • 12. DART: it’s all part of a process
    • 13. DART: it’s all part of a process
    • 14. DART: SitesLocation• Diddington, Cambridgeshire• Harnhill, GloucestershireBoth with• contrasting clay and well draining soils• an identifiable archaeological repertoire• under arable cultivationContrasting Macro environmentalcharacteristics
    • 15. Show sites here
    • 16. DART: Probe Arrays
    • 17. DART: Probe Arrays As designAs built
    • 18. DART: Probe Arrays
    • 19. DART ERT Ditch Rob Fry B’ham TDR Imco TDR Spectro-radiometry transect
    • 20. DART ERT Ditch Rob Fry B’ham TDR Imco TDR Spectro-radiometry transect
    • 21. DART: Field MeasurementsSpectro-radiometry• Soil• Vegetation • Up to every 2 weeksCrop phenology• Height• Growth (tillering)Flash res 64• Including induced events
    • 22. DART: Field MeasurementsResistivityWeather station• Logging every half hour
    • 23. DART: Field MeasurementsAerial data• Hyperspectral surveys • CASI • EAGLE • HAWK• LiDAR• Traditional Aerial Photographs• UAV
    • 24. DART: Laboratory MeasurementsGeotechnical analysesParticle sizeSheer strengthetc.Geochemical analyses
    • 25. 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. • Chlorophyll a fluorescence
    • 26. DART: Data so far - Temperature
    • 27. DART: Data so far –Temperature
    • 28. DART: Data so far – Earth Resistance
    • 29. DART: Data so far – Earth Resistance
    • 30. Remote sensing
    • 31. 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
    • 32. To the visible
    • 33. To the visible....... And beyond (08/06/2011)
    • 34. But what about time? (14/06/2011)
    • 35. Senescing (29/06/2011)
    • 36. Senescant (15/07/2011)
    • 37. Some rightsreserved byZakVTA
    • 38. Analysis• We are looking at relative contrast• Identifying quantitative differences in the density of vegetation• Identifying qualitative differences in vegetation stress & vigour: • How to make this independent of density?• Accounting for minor variations • Making sure things are comparable • Illumination geometry • Methodological blunders
    • 39. Vegetation indices• Mostly simple ratios• Chlorophyll & biomass (R750 - R705) ND705 = (R750 + R705)• Carotenoid / chlorophyll • Photo-chemical Reflectance Index (PRI) PRI ( R531 R570) ( R531 R570) (R800 - R445) • Structure Insensitive Pigment Index (SIPI) SIPI = (R800 + R680) (R680 - R500) PRSI = • Plant Senescance Reflectance Index (PRSI) (R750)
    • 40. Continuum removal• Methodology explored by Kokaly & Clark (1999) and Curran et al (2001)• Used to quantify leaf biochemical properties• Uses diagnostic absorption features • Chlorophyll a+b • Lignin • Cellulose • Proteins • Water
    • 41. Continuum removal Designation Start Centre End Indicates (nm) (nm) (nm) (nm) 470 408 484 518 Chlorophyll a, b. 670 588 672 750 Red edge, stress 1200 1116 1190 1284 Water 1730 1634 1708 1786 Lignin 2100 2006 2188 2196 Nitrogen, starch 2300 2222 2306 2378 Nitrogen, protein, lignin
    • 42. Continuum removal• Band Normalised to depth of Centre of absorption feature (BNC) BNC (1 ( R / Ri )) /(1 Rc / Ric ))• Band Normalised to Area of absorption feature (BNA) BNA = (1- (R / Ri )) / A
    • 43. Conclusions• Successful vegetation-mark detection depends on identifying the influence of the archaeological feature on its surroundings.• Hyper-spectral remote sensing enables us to look for specific indications of this influence.• Attempting to use brute force computation to do this potentially leads to many false positives • the spectral responses of archaeological features are not unique • the available data is very large.• To use this data successfully requires a knowledge-led approach.• This means a better understanding of how plants, land management, soil, weather, and the archaeology interact over time. • Data mining of our benchmark data • Help us ----- It’s open-data 
    • 44. Questions