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The Role of Lidar Intensity Data in Interpreting Archaeological Landscapes
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The Role of Lidar Intensity Data in Interpreting Archaeological Landscapes

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Laser intensity images, created by visualising the amplitude of each returned laser pulse as a greyscale image, are often ignored when working with the results of lidar surveys. Fortunately a growing ...

Laser intensity images, created by visualising the amplitude of each returned laser pulse as a greyscale image, are often ignored when working with the results of lidar surveys. Fortunately a growing body of research demonstrates the value and uses of intensity imagery. In this paper (focusing on data collected by Optech instruments) we summarise the problems inherent in collecting and using intensity data, review examples of its use in a variety of environmental disciplines and discuss experiments to determine its potential uses for archaeological studies.

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The Role of Lidar Intensity Data in Interpreting Archaeological Landscapes The Role of Lidar Intensity Data in Interpreting Archaeological Landscapes Presentation Transcript

  • Extracting Information from Lidar Intensity Images Keith Challis IBM Vista, Institute of Archaeology and Antiquity, University of Birmingham U B
  • Extracting Information from Lidar Intensity Images
    • The Intensity Record
    • Intensity Potential
    • Working with Intensity Data
    • Intensity: A Way Forward in Archaeology
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • The Intensity Record
  • Extracting Information from Lidar Intensity Images
    • Amplitude of the returned laser pulse
    • Highly collimated beam of laser light
    • Relatively unaffected by illumination issues, shading, etc.
    • NIR reflectance (c.1040nm)
    • A laser illuminated 2D landscape image
    • 8 bit / 10 bit scale
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Scan angle and incidence angle may have a profound effect on recorded intensity
    • Gross et al
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images Lidar Intensity Keith Challis, IBM Vista, University of Birmingham Processing variables effect intensity values: Boyd and Hill 2007
  • Extracting Information from Lidar Intensity Images
    • System variables
    • Distance between the lidar system and the target (the range – controlled largely by the altitude of the survey aircraft, but also influenced by topographic variation and the scan angle of individual lidar pulses),
    • Peak pulse power of the laser system, which may vary as a factor of pulse frequency,
    • Beam divergence,
    • Laser footprint size (a product of range and beam divergence)
    • Angle of incidence
    • Processing procedures
    • Interpolation technique applied to convert the point cloud into a regular grid
    • Target variables
    • Cross sectional area of target within the laser footprint,
    • Target reflectivity
    • Surface roughness
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Intensity data require correction for analytical use
    • Data driven and model driven approaches in literature
    • Morsdorf et al 2008
    • Starek et al 2006
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Intensity Potential
  • Extracting Information from Lidar Intensity Images Lidar Intensity Keith Challis, IBM Vista, University of Birmingham General spectral reflectance of common materials Visible Lidar
  • Extracting Information from Lidar Intensity Images Lidar Intensity Keith Challis, IBM Vista, University of Birmingham Moisture % Organic %
  • Extracting Information from Lidar Intensity Images
    • Strong relationship between sediment moisture and reflectance
    • Attempts to calibrate intensity against targets of know properties
    • Kaasalainen et al 2007
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Normalised intensity values reflect species variation on mixed woodland
    • Donoghue et al 2007
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Normalised intensity values provide improved depiction of glacier surface properties compared to DTM
    • Arnold et al 2008
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Intensity values reflect variation in lava flow surface allowing delineation of separate flow events
    • Mazzarini et al
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Radiometric correction of intensity data allows reduction in variation within data
    • Pseudo-reflectance thus calculated offers potential for classification of land cover
    • Coren and Sterzai 2008
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Normalised intensity data compared to sediment properties
    • Knowledge-based analysis of intensity for geoarchaeological mapping
    • Challis et al 2011
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Analysis of intensity for upland environments assist in delineation of archaeological earthworks
    • Kincey forthcoming
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Analysis and Interpretation A Geoarchaeological case Study
  • Extracting Information from Lidar Intensity Images
    • The intensity record can be highly problematic
    • Uncalibrated data from multiple flights is not suitable for quantitative analysis
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
    • Calibration required information on targets of known NIR reflectance
    • System variables may be ameliorated by normalisation
    • Even simple normalisation to range may improve analytical results
  • Extracting Information from Lidar Intensity Images
    • Six study windows in the Trent Valley, English Midlands
    • Simultaneous collection of Lidar and ground data
    • Soil moisture, organics, electrical resistance
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Moisture
    • Weak negative correlation (R 2 0.25)
    • No significant variation FP/LP
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Organics
    • Moderate negative correlation R 2 0.32
    • Not accounted for, proxy effects of vegetation
    • Some potential but much more work required
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images
    • Some evidence for anthropogenic cropmarks (Roman villa at Cromwell)
    • Even though poor conditions and late in season (end July 2007)
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Intensity: A Way Forward in Archaeology
  • Extracting Information from Lidar Intensity Images
    • The potential of intensity data for archaeological studies requires further systematic investigation
    • There is a case for flying at non-traditional times (eg summer) for some archaeological surveys where crop mark evidence is anticipated
    • Anticipated use of intensity should inform survey strategy (eg flight lines, altitude, single epoch, etc)
    • Work is required on calibration of intensity data to targets of known reflectance
    • Work is required on normalisation procedures for intensity data
    • Procedures need to be developed to fuse intensity data and other RS data (eg for object driven classification of land cover)
    • In UK, examination of Optech Gemini intensity data needed
    • Future work on full waveform intensity
    Lidar Intensity Keith Challis, IBM Vista, University of Birmingham
  • Extracting Information from Lidar Intensity Images Lidar Intensity Keith Challis, IBM Vista, University of Birmingham Toolkit approaches to archaeological remote sensing
    • Keith Challis
    • IBM Vista
    • University of Birmingham
    • Edgbaston
    • Birmingham
    • B15 2TT
    • ++ 44 121 4151041
    • [email_address]
    • www.vista.bham.ac.uk
    Thanks Lidar Intensity Keith Challis, IBM Vista, University of Birmingham