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Airborne remote sensing

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A presentation by David Stott at the DART horizon Scanning workshop on the 17th September 2013

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Airborne remote sensing

  1. 1. School of Computing Faculty of Engineering DART workshop: Airborne remote sensing David Stott
  2. 2. Overview ● This presentation covers the airborne side of DART: – LiDAR – Spectroscopy (imaging and field) – Aerial photography ● About cropmarks on arable land: – The nature of contrasts in vegetation marks – How we can use this to improve detection
  3. 3. Aims and Objectives ● Look at contrasts over time: – How they change with weather – How they change with land-use ● How to best detect contrasts with different sensors: – What can the sensors detect? – What is the best context to deploy them in?
  4. 4. Archaeological vegetation marks ● Soil differences influence the development & health of the crop ● Visible as local variation in the crop canopy: – Stress and vigour: ● Variations in foliar chemistry ● Extreme condtions – Canopy structure: ● Leaf Area Index (LAI) ● Tillering / early growth stage development
  5. 5. Some challenges ● High dimesnionality of data: – Hyperspectral can have 100s of bands – Full wave-form LiDAR – Lots of redundancy ● No unique spectral signature
  6. 6. Some challenges ● High dimesnionality of data: – Hyperspectral can have 100s of bands – Lots of redundancy – Full wave-form LiDAR ● No unique spectral signature – Brute force / classifcation approaches are problematic – Changes in soils, land use and crop
  7. 7. Methodology: Ground-based ● Multi-temporal ground measurements (monthly): – Spectro-radiometry ● ASD FieldSpec Pro ● 350-2500nm @ 3nm (ish) sampling interval
  8. 8. Fiber-optic probe Tired old laptop (needs an LPT port…) Reflectance pane Instrument (20Kg of back pain)
  9. 9. Methodology: Ground-based ● Multi-temporal ground measurements (monthly): – Spectro-radiometry ● ASD FieldSpec Pro ● 350-2500nm @ 3nm(ish) sampling interval ● Crop height & density: – Ceptometer (leaf area index) – Surface coverage (near-vertical photos) – Tillering in early growth-stage
  10. 10. Airborne data ● NERC ARSF: – Eagle and Hawk hyperspectral (VIS-SWIR) – Full waveform LiDAR – Survey camera ● Geomatics Group: – CASI hyperspectral – Discrete LiDAR – Orthophotography
  11. 11. Methodology: Analyses ● Python software: – Spectral analysis (imagery & spectroradiometry): ● Continuum removal ● Vegetation analyses ● Red edge position – LiDAR: ● Multi-temporal vegetation mass ● Full-waveform
  12. 12. Jun 14th 2011
  13. 13. Jun 29th 2011
  14. 14. Jul 15th 2011
  15. 15. Spectral differences ● Example from Diddington June-July 2011 – Spectradiometry shows good contrast – Continuum removed spectra from 670nm absorption feature – Band normalised by area
  16. 16. So... ● Spectroradiometry shows good contrast: – Variations in foliar pigmentation change rapidly – Variations in crop structure remain fairly similar ● Can we use the LiDAR to detect the biomass variations? – Higher spatial resolution (~0.4m vs 1m)
  17. 17. Full waveform ● Looked at correlation between hyperspectral and full waveform LiDAR – Reflectance @ 1062nm (Hawk) & intensity @ 1064nm (LiDAR)
  18. 18. Full waveform ● Correlation between archaeological features and full waveform LiDAR Dataset t p Vegetation height 42.9721 2.2E-016 Peak sum 12.968 8.56E-014 Maximum intensity 7.9123 1.327E-015 Peak width 0.4164 0.3385
  19. 19. Full waveform ● Sensor only resolves a single return over low, sparse crop ● Very little variation in the width of the return ● Intensity is usable ● Best results came from using vegetation height model derived from discrete returns
  20. 20. Conclusions so far ● Different sensors and techniques required on a field by field basis ● This is hard: – Variability of the archaeology – Variablility of its context – Small things in big data – (not even mentioning the weather...) ● Spatial resolution is not the be all & end-all
  21. 21. Further work ● LiDAR: – Scan angle – Spatial analyses ● Statistical comparison of sensors – Comparison of contrasts on and off the features ● Writing it all up
  22. 22. Acknowledgements ● NERC ARSF ● Royal Agricultural College ● Thornhill Estates ● DART community

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