DART - improving the science. Bradford 21022012

School of Computing Faculty of Engineering

DART - Improving the science

underpinning archaeological detection

Anthony (Ant) Beck

Twitter: AntArch

Bradford – 21st February 2012

Overview

•How do we detect stuff

•Why DART

•Going back to first principles

•DART overview

•Data so far

•Open Science

Overview

There is no need to take notes:

Slides –

Text –

http://dl.dropbox.com/u/393477/MindMaps/Events/Conference

sAndWorkshops.html

There is every need to ask questions

The slides and text are release under a Creative Commons by

attribution licence.

Archaeological Prospection

What is the basis for detection

Archaeological Prospection

What is the basis for detection

At the small scale:

• The archaeological record can be

considered as a more or less

continuous spatial distribution of

artefacts, structures, organic

remains, chemical residues,

topographic variations and other

less obvious modifications

Archaeological Prospection

What is the basis for detection

At the large scale:

• The distribution is far from even, with

large areas where archaeological

remains are widely and infrequently

dispersed. There are other areas,

however, where materials and other

remains are abundant and clustered.

It is these peaks of abundance that

are commonly referred to as sites,

features, anomalies (whatever!).

Archaeological Prospection

What is the basis for detection

Discovery requires the detection of one or more site

constituents.

The important points for archaeological detection are:

Archaeological Prospection Archaeological sites are physical and chemical phenomena

Archaeological Prospection There are different kinds of site constituents

Archaeological Prospection The abundance and spatial distribution of different

constituents vary both between and within individual sites

Archaeological Prospection These attributes may be masked or accentuated by a variety

of other phenomena

Archaeological Prospection Importantly from a remote sensing perspective archaeological

site do not exhibit consistent spectral signatures

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).

Archaeological Prospection

What is the basis for detection

Archaeological Prospection

What is the basis for detection

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

Archaeological Prospection

What is the basis for detection

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 Now you dont

Why DART? Isn’t everything rosy in the garden?

Why DART? ‘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

Why DART? Precision agriculture

Why DART? Precision agriculture

Why DART? Traditional AP exemplar

Why DART? Traditional AP exemplar

Significant bias in its application

• in the environmental areas where it is

productive (for example clay

environments tend not to be

responsive)

• Surveys don’t tend to be systematic

• Interpretation tends to be more art

than science

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

What do we do about this? Understand the

phenomena

How does the object generate an

observable contrast to it's local

matrix?

• Physical

• Chemical

• Biological

• etc

Are the contrasts permanent or

transitory?

What do we do about this? Understand the

phenomena

If transitory why are they

occurring?

• Is it changes in?

• Soil type

• Land management

• Soil moisture

• Temperature

• Nutrient availability

• Crop type

• Crop growth stage

What do we do about this? Understand the

relationship between the sensor and the phenomena

What do we do about this? Understand the

relationship between the sensor and the phenomena Spatial Resolution

What do we do about this? Understand the

relationship between the sensor and the phenomena

Radiometric resolution

determines how finely a system can

represent or distinguish differences of

intensity

Radiometric Resolution

What do we do about this? Understand the

relationship between the sensor and the phenomena Temporal Resolution

What do we do about this? Understand the

relationship between the sensor and the phenomena Spectral(?) Resolution

What do we do about this? Example from multi or

hyper spectral imaging

DART

DART - Collaborators

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

• 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

DART: Ground Observation Benchmarking

Try to understand the periodicity of change

• Require intensive ground observation (spectro-radiometry, soil and crop

analysis) at known sites (and their surroundings) in a range of different

environments under different environmental conditions

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

DART: Field Measurements

Spectro-radiometry

• Soil

• Vegetation

• Every 2 weeks

Crop phenology

• Height

• Growth (tillering)

Flash res 64

• Including induced events

DART: Field Measurements

Resistivity

Ground penetrating radar

Embedded Soil Moisture and

Temperature probes

• Logging every hour

Weather station

• Logging every half hour

DART: Field Measurements

Aerial data

• Hyperspectral surveys

• CASI

• EAGLE

• HAWK

• LiDAR

• Traditional Aerial Photographs

Low level photography

• 1 photo every 30 minutes

DART: Probe Arrays

DART: Probe Arrays

DART: Weather Station

Davis Vantage Pro Weather station

• Collects a range of technical data e.g.

• Wind speed

• Wind direction

• Rainfall

• Temperature

• Humidity

• Solar Radiation

• Barometric Pressure

• And derivatives

• Wind Chill

• Heat Index

DART

ERT

B’ham TDR

Imco TDR

Spectro-radiometry transect

Ditch Rob Fry

DART

ERT

B’ham TDR

Imco TDR

Spectro-radiometry transect

Ditch Rob Fry

DART: Laboratory Measurements

Geotechnical analyses

Geochemical analyses

Plant Biology

DART: Data so far - Temperature

DART: Data so far - Temperature

DART: Data so far - Temperature

DART: Data so far - Temperature

DART: Data so far - Temperature

DART: Data so far - Temperature

Useful tool for

• Scheduling diurnal thermal inertia flights

• Calibrating the TDR readings

DART: Data so far - Permittivity

Key aims

• Investigate the propagation of EM radiation in different soil conditions

(e.g. temperature, magnetic permeability, moisture content, density) and

identify the difference between archaeological residue and the

surrounding soil matrix

• Attempt to use geotechnical properties (e.g. particle size distribution,

moisture content) to predict the geophysical responses of the different

EM sensors used in aerial and geophysical work

• Link the soil properties to local weather and other environmental factors

to enable better planning for collection techniques

DART: Data so far - Permittivity

TDR - How does it work

• Sends a pulse of EM energy

• Due to changes in impedance, at the start and at the end of the probe,

the pulse is reflected back and the reflections can be identified on the

waveform trace

• The distance between these two reflection points is used to determine

the Dielectric permittivity

• Different soils have different dielectric permittivity

• This needs calibrating before soil moisture can be derived from the

sensors

DART: Data so far - Permittivity

DART: Data so far - Permittivity

DART: Data so far - Permittivity

DART: Data so far - Permittivity

Further analysis of permittivity and conductivity against rainfall

Linking the changes to the weather patterns

Comparisons can be made between

• Soils at different depths

• Archaeological and non-archaeological features

• Different soil types at the different locations

DART: Data so far - Permittivity

Conversion to moisture content is also a priority

DART: Data so far – Earth Resistance

DART: Data so far – Earth Resistance

The measured earth resistance of a site will change

throughout the year.

This is due to many factors which exist in the natural

landscape.

It is not the broad change of readings we are interested in

• Not Quantitative

but how the readings within the archaeological feature change

compared to the readings of the natural surrounding soil

• But Relative

DART: Data so far – Earth Resistance

methodology similar to that employed by Parkyn et al. (2011)

Overview

• data points

• lie within the ditch feature

• over the non-archaeological feature

• find an average data value for the feature and the surrounding soil

The percentage difference between these two figures can

then be considered the amount of contrast within the test

area.

The higher the percentage, the better the feature is able to be

defined.

DART: Data so far – Earth Resistance

Methodology:

• The raw geophysical data is despiked

• Specific data points are chosen for examination

• Data values are extracted at different probe separations

• The percentage difference is calculated

R

DART: Data so far – Earth Resistance

Probe Separation (m)

0.25 0.5 0.75 1

June 18.04742552 18.88545 18.896896 16.79403

July 19.13517794 17.15205 17.081613 15.01906

August #N/A #N/A #N/A #N/A

September 8.841189868 13.255 14.512463 15.53069

October 7.988128839 10.97714 12.217018 11.6229

June July August Septemb

er October

0.25 18.047426 19.135178 8.8411899 7.9881288

0.5 18.885449 17.152047 13.255001 10.977143

0.75 18.896896 17.081613 14.512463 12.217018

1 16.794035 15.019057 15.530692 11.622898

5

10

15

20

Co

ntr

ast

Facto

r (%

)

Twin Probe Electrode

Seperation (m)

Change of Contrast Factors with Seasons

0.25 0.5

0.75

Difference in magnitude

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

Spectro-radiometry: Methodology

• Lab-based, background methodology

• Soils

• Soil samples taken along transect

• Reflectance measured with varying moisture content

• Vegetation

• Vegetation samples taken during each field visit

• Measured under artificial light under controlled conditions

0

0.02

0.04

0.06

0.08

0.1

0.12

400 500 600 700

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Wavelength (nm)

Diddington transect 1: Spectroradiometry June 2011

27/06/2011 Archaeology 27/6/2011 Outside archaeology 14/06/2011 Archaeology

14/06/2011 Outside archaeology 08/06/2011 Archaeology 08/06/2011 Outside archaeology

0

0.05

0.1

0.15

0.2

0.25

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0.35

0.4

350 450 550 650 750 850 950 1050 1150 1250 1350 1450 1550 1650 1750 1850 1950 2050 2150 2250 2350 2450

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Wavelength (nm)

Diddington transect 1: Spectroradiometry June 2011

27/06/2011 Archaeology 27/6/2011 Outside archaeology 14/06/2011 Archaeology

14/06/2011 Outside archaeology 08/06/2011 Archaeolgy 08/06/2011 Outside archaeology

DART: Plant Biology

Lab experiments conducted in collaboration with Leeds Plant

Biology in 2011 and repeated in 2012

From soils at Quarry Field

Soil structure appears to be the major component influencing

root penetration and plant health

DART: Knowledge Base

DART: Communication

The case for Open Science from Cameron Neylon

Open Data: Server (in the near future)

The full project archive will be available from the server

Raw Data

Processed Data

Web Services

Will also include

TDR data

Weather data

Subsurface temperature data

Soil analyses

spectro-radiometry transects

Crop analyses

Excavation data

In-situ photos

Open Data: Server (in the near future)

Also:

Hyperspectral data

Thermal imaging

Full Waveform LiDAR

UAV data collection

Formats

Standard interoperable formats

Licences

These are not complete

Most data will be made available under an open re-use licence (see server)

Creative Commons

GPL

Why are we doing this – spreading the love

Why are we doing this – it’s the right thing to do

DART is a publically funded project

Publically funded data should provide benefit to the public

Why are we doing this – IMPACT/unlocking potential

More people use the data then there is improved impact

Better financial and intellectual return for the investors

Why are we doing this – innovation

Reducing barriers to data and knowledge can improve

innovation

Why are we doing this – education

To provide baseline exemplar data for teaching and learning

Why are we doing this – building our network

Find new ways to exploit our data

Develop contacts

Write more grant applications

Questions