A presentation given by Anthony Beck at EARSeL Gent on 20/09/12 describing some of the multi-temporal issues associated with archaeological detection. This presentation is primarily based on the research of David Stott.

1. Using multi-temporal benchmarking to
determine optimal sensor deployment:
advances from the DART project.
David Stott, Ant Beck and Doreen Boyd
Twitter: AntArch (in using the hashtag #EARSeL)
3rd EARSEL Workshop: Advances in remote sensing
for archaeology and cultural heritage management
19th to 22nd September 2012
School of Computing
Faculty of Engineering

2. Presentation overview
•Detection summary
•Why do we need the DART project?
•Ground observation benchmarking at DART
•Data examples
•Multi-temporal spectroradiometry
•Conclusions

3. Archaeological Prospection
What is the basis for detection
We detect Contrast:
• Between the expression of the remains
and the local 'background' value
Direct Contrast:
• where a measurement, which exhibits a
detectable contrast with its
surroundings, is taken directly from an
archaeological residue.
Proxy Contrast:
• where a measurement, which exhibits a
detectable contrast with its
surroundings, is taken indirectly from an
archaeological residue (for example from
a crop mark).

4. Archaeological Prospection
What is the basis for detection
Micro-Topographic variations
Soil Marks
• variation in mineralogy and
moisture properties
Differential Crop Marks
• constraint on root depth and
moisture availability changing
crop stress/vigour
Proxy Thaw Marks
• Exploitation of different thermal
capacities of objects expressed
in the visual component as
thaw marks
Now you see me
dont

5. Archaeological Prospection
Summary
The sensor must have:
• The spatial resolution to resolve the feature
• The spectral resolution to resolve the contrast
• The radiometric resolution to identify the change
• The temporal sensitivity to record the feature when the contrast is
exhibited
The image must be captured at the right time:
• Different features exhibit contrast characteristics at different times

6. What’s the problem? ‘Things’ are not well understood
Environmental processes
Sensor responses (particularly new
sensors)
Constraining factors (soil, crops etc.)
Bias and spatial variability
Techniques are scaling!
• Geophysics!
IMPACTS ON
• Deployment
• Management

7. What do we do about this?
Go back to first principles:
• Understand the phenomena
• Understand the sensor
characteristics
• Understand the relationship
between the sensor and the
phenomena
• Understand the processes better
• Understand when to apply
techniques

8. What do we want to achieve with this?
Increased understanding
which could lead to:
• Improved detection in marginal
conditions
• Increasing the windows of
opportunity for detection
• Being able to detect a broader
range of features

10. DART: Ground Observation Benchmarking
Try to understand the periodicity of change
• Requires
• intensive ground observation
• at known sites (and their surroundings)
• In different environmental settings
• under different environmental conditions

11. DART: Ground Observation Benchmarking
Based upon an understanding of:
• Nature of the archaeological residues
• Nature of archaeological material (physical and chemical structure)
• Nature of the surrounding material with which it contrasts
• How proxy material (crop) interacts with archaeology and surrounding
matrix

20. DART: Ground Observation Benchmarking
Based upon an understanding of:
• Sensor characteristics
• Spatial, spectral, radiometric and temporal
• How these can be applied to detect contrasts
• Environmental characteristics
• Complex natural and cultural variables that can change rapidly over
time

21. DART: it’s all part of a process

22. DART: it’s all part of a process

23. DART: Sites
Location
• Diddington, Cambridgeshire
• Harnhill, Gloucestershire
Both with
• contrasting clay and 'well draining'
soils
• an identifiable archaeological
repertoire
• under arable cultivation
Contrasting Macro environmental
characteristics

24. Show sites here

25. DART: Probe Arrays

27. DART: Probe Arrays
As design
As built

28. DART: Probe Arrays

29. DART
ERT
Ditch
Rob Fry
B’ham TDR
Imco TDR
Spectro-radiometry transect

30. DART
ERT
Ditch
Rob Fry
B’ham TDR
Imco TDR
Spectro-radiometry transect

31. DART: Field Measurements
Spectro-radiometry
• Soil
• Vegetation
• Up to every 2 weeks
Crop phenology
• Height
• Growth (tillering)
Flash res 64
• Including induced events

32. DART: Field Measurements
Resistivity
Weather station
• Logging every half hour

33. DART: Field Measurements
Aerial data
• Hyperspectral surveys
• CASI
• EAGLE
• HAWK
• LiDAR
• Traditional Aerial Photographs
• UAV

34. DART: Laboratory Measurements
Geotechnical analyses
Particle size
Sheer strength
etc.
Geochemical analyses

35. DART: Laboratory Measurements
Plant Biology • Soil and leaf water content
• Rate of germination • Root studies
(emergence)
• Root length and density.
• Growth analysis
• Root – Shoot biomass ratio.
• Number of Leaves
• Total plant biomass
• Number of Tillers
• Biochemical analysis: Protein and
• Stem length chlorophyll analysis.
• Total plant height • Broad spectrum analysis of soil
• Drought experiment (Nutrient content) and C-N ratios of
leaf.
• Chlorophyll a fluorescence

36. DART: Data so far - Temperature

37. DART: Data so far –
Temperature

38. DART: Data so far – Earth Resistance

39. DART: Data so far – Earth Resistance

40. Remote sensing

41. Spectro-radiometry: Methodology
• Recorded monthly
• Twice monthly at Diddington during the growing season
• Transects across linear features
• Taken in the field where weather conditions permit
• Surface coverage evaluated using near-vertical photography
• Vegetation properties recorded along transect
• Chlorophyll (SPAD)
• Height

44. To the visible

45. To the visible....... And beyond (08/06/2011)

46. But what about time? (14/06/2011)

48. Senescing (29/06/2011)

50. Senescant (15/07/2011)

53. Some rights
reserved by
ZakVTA

54. Analysis
• We are looking at relative contrast
• Identifying quantitative differences in the density of
vegetation
• Identifying qualitative differences in vegetation stress &
vigour:
• How to make this independent of density?
• Accounting for minor variations
• Making sure things are comparable
• Illumination geometry
• Methodological blunders

55. Vegetation indices
• Mostly simple ratios
• Chlorophyll & biomass (R750 - R705)
ND705 =
(R750 + R705)
• Carotenoid / chlorophyll
• Photo-chemical Reflectance Index (PRI) PRI
( R531 R570)
( R531 R570)
(R800 - R445)
• Structure Insensitive Pigment Index (SIPI) SIPI =
(R800 + R680)
(R680 - R500)
PRSI =
• Plant Senescance Reflectance Index (PRSI) (R750)

57. Continuum removal
• Methodology explored by Kokaly & Clark (1999) and Curran
et al (2001)
• Used to quantify leaf biochemical properties
• Uses diagnostic absorption features
• Chlorophyll a+b
• Lignin
• Cellulose
• Proteins
• Water

58. Continuum removal
Designation Start Centre End Indicates
(nm) (nm) (nm) (nm)
470 408 484 518 Chlorophyll a, b.
670 588 672 750 Red edge, stress
1200 1116 1190 1284 Water
1730 1634 1708 1786 Lignin
2100 2006 2188 2196 Nitrogen, starch
2300 2222 2306 2378 Nitrogen, protein, lignin

59. Continuum removal
• Band Normalised to depth of Centre of absorption feature
(BNC)
BNC (1 ( R / Ri )) /(1 Rc / Ric ))
• Band Normalised to Area of absorption feature (BNA)
BNA = (1- (R / Ri )) / A

63. Conclusions
• Successful vegetation-mark detection depends on identifying the
influence of the archaeological feature on its surroundings.
• Hyper-spectral remote sensing enables us to look for specific indications
of this influence.
• Attempting to use brute force computation to do this potentially leads to
many false positives
• the spectral responses of archaeological features are not unique
• the available data is very large.
• To use this data successfully requires a knowledge-led approach.
• This means a better understanding of how plants, land
management, soil, weather, and the archaeology interact over time.
• Data mining of our benchmark data
• Help us ----- It’s open-data 

64. Questions