A presentation by David Stott at the 2012 Computer Applications in Archaeology conference in Southampton

1. Seeing the unseen:
Improving aerial
prospection outside
the visible spectrum
David Stott, Anthony Beck,
Doreen Boyd & Anthony Cohn
School of Computing
Faculty of Engineering

2. Overview
• An introduction to the DART project
• The problem
• Contrast
• Principles of detection
• Preliminary results
• Lots of graphs
• Further work
• Problems
• Proposed analyses

3. The DART project
• Detecting Archaeological Residues using remote Sensing
Techniques
• Soil properties
• University of Birmingham
• University of Winchester
• Geophysics
• Bradford University
• Optical (aerial and satellite detection)
• University of Leeds
• University of Nottingham

7. How do we detect archaeological features?
•Contrast with the background
•Changeable:
• Land use
• Cultivation regime
• Vegetation
• Species & variety
• Growth stage (phenology)
• Soils
• Weather

18. The problem
• Observer directed aerial photography
• Bias
• Soils (Jessica Mills & Rog Palmer)
• Honeypots (Dave Cowely & Kenny Brophy)
• Visible spectrum
• Sensors
• Underutilised because we don’t know how best to use them
• Hyperspectral
• Focus on data reduction
• Very few archaeologically commissioned flights
• Thermal

22. Some rights reserved
by ZakVTA

23. Aims
• To understand how archaeological features interact with
and influence the surrounding environment
• If we do this we can work out how to detect them better
• Improved exploitation of existing sensors
• Improved development of new sensors
This aims of this project are:
• To identify optimal timing for acquiring aerial and satellite
imagery for archaeological prospection
• Commissioning new imagery
• Evaluating existing archives

24. What I’m doing: Fieldwork
• Measurements taken on transects across linear features on
at least a monthly basis
• Spectro-radiometry (more on this in a minute)
• Surface properties
• Vegetation coverage (near vertical close-range photography)
• Vegetation growth stage
• Height
• Feekes scale
• Vegetation density
• Leaf Area Index (LAI)

27. Spectra-ma-what-now?
• Spectroradiometry
• ASD FieldSpec Pro
• Produces a spectral profile
• 350nm-2500nm (Visible-Short Wave Infrared)
• c. 1.4-2nm Sampling interval interpolated to 1nm
• Usable 2hrs either side of solar noon
• Needs clear-ish skies

38. Flights
• Environment agency
• CASI
• High spatial resolution ortho-photography
• 28th June 2011
• NERC ARSF
• Eagle (visible – near-IR) & Hawk (near-IR – SWIR)
• High spatial resolution ortho-photography
• Thermal?
• 14th June 2011, 23rd March 2012
• 3 further flights during 2012

39. Problems…...

40. Problems
• 2011 Driest spring in eastern England for 100 years
• Extreme conditions
• 2012 due to be an even more extreme drought
• I want it to be a bad spring and a worse summer (sorry. Kind of)
• Not much subtlety in the vegetation marks…

41. “Why do you need hyperspectral
when you can see the cropmarks on
the ground like this”

43. Solution: Extend temporal depth?
• Can I use lower spatial resolution satellite data?
• Paleochannels as a proxy for archaeological vegetation marks?
• Need to test this

44. Further work: Analysis
• Building an ontology
• Identifying diagnostic absorption features
• Well known from precision agriculture & remote sensing
• Using this to evaluate contrast
• Python code to compare spectra
• Field spectra (high temporal resolution, low spatial coverage)
• Aerial spectra (low temporal resolution, high spatial coverage)
• Correlating contrast to environmental variables
• Weather
• Soil moisture

45. Further work: Building a knowledge-based system
• Testing
• Using this to predict contrast in 2013
• Using this to predict contrast in archive imagery
• NERC flights?
• Geoeye satellite data?
• Aerial photos?

46. Finally
• DART is Open Science!
• PLEASE re-use our data
• Servers online spring-summer 2012
• www.dartproject.info
• @DART_Project
• http://www.flickr.com/groups/dartproject/