Airborne remote sensing
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This presentation discusses using airborne remote sensing to detect archaeological features through vegetation marks. It summarizes that spectro-radiometry shows good contrast in foliar pigmentation over time, while crop structure remains similar. Full waveform LiDAR correlates well with hyperspectral data and detects archaeological features through vegetation height more than other metrics like intensity. Different sensors and analysis techniques are needed depending on each field's variability, context and small archaeological signals within large remote sensing datasets.
Airborne remote sensing
- 1. School of Computing Faculty of Engineering DART workshop: Airborne remote sensing David Stott
- 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. 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. 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. Some challenges ● High dimesnionality of data: – Hyperspectral can have 100s of bands – Full wave-form LiDAR – Lots of redundancy ● No unique spectral signature
- 8. 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
- 9. Methodology: Ground-based ● Multi-temporal ground measurements (monthly): – Spectro-radiometry ● ASD FieldSpec Pro ● 350-2500nm @ 3nm (ish) sampling interval
- 10. Fiber-optic probe Tired old laptop (needs an LPT port…) Reflectance pane Instrument (20Kg of back pain)
- 11. 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
- 13. Airborne data ● NERC ARSF: – Eagle and Hawk hyperspectral (VIS-SWIR) – Full waveform LiDAR – Survey camera ● Geomatics Group: – CASI hyperspectral – Discrete LiDAR – Orthophotography
- 14. Methodology: Analyses ● Python software: – Spectral analysis (imagery & spectroradiometry): ● Continuum removal ● Vegetation analyses ● Red edge position – LiDAR: ● Multi-temporal vegetation mass ● Full-waveform
- 15. Jun 14th 2011
- 16. Jun 29th 2011
- 17. Jul 15th 2011
- 18. Spectral differences ● Example from Diddington June-July 2011 – Spectradiometry shows good contrast – Continuum removed spectra from 670nm absorption feature – Band normalised by area
- 21. 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)
- 23. Full waveform ● Looked at correlation between hyperspectral and full waveform LiDAR – Reflectance @ 1062nm (Hawk) & intensity @ 1064nm (LiDAR)
- 24. 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
- 25. 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
- 39. 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
- 40. Further work ● LiDAR: – Scan angle – Spatial analyses ● Statistical comparison of sensors – Comparison of contrasts on and off the features ● Writing it all up
- 41. Acknowledgements ● NERC ARSF ● Royal Agricultural College ● Thornhill Estates ● DART community