The document summarizes the DART project which aims to better understand how archaeological features interact with their environment to improve detection techniques. It discusses using spectroradiometry to measure spectral profiles across archaeological linear features over time. Preliminary flights captured imagery using sensors like CASI and thermal. Challenges included drought conditions reducing vegetation marks. Further work involves analyzing spectral data to identify diagnostic features and building a knowledge system to predict contrast in new and archive imagery.

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/