School of ComputingFaculty of Engineering

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

Presentation overview

• Detection summary

• Why do we need the DART project?

• Ground observation benchmarking at DART

• Data examples

• Multi-temporal spectroradiometry

• Conclusions

Archaeological ProspectionWhat 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 ProspectionWhat 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 meNow you dont

Archaeological ProspectionSummary

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

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

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 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

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

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

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

DART: it’s all part of a process

DART: it’s all part of a process

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

Show sites here

DART: Probe Arrays

DART: Probe Arrays

As design

As built

DART: Probe Arrays

DART

ERT

B’ham TDR

Imco TDR

Spectro-radiometry transect

DitchRob Fry

DART

ERT

B’ham TDR

Imco TDR

Spectro-radiometry transect

DitchRob Fry

DART: Field Measurements

Spectro-radiometry• Soil

• Vegetation

• Up to every 2 weeks

Crop phenology• Height

• Growth (tillering)

Flash res 64• Including induced events

DART: Field Measurements

Resistivity

Weather station• Logging every half hour

DART: Field Measurements

Aerial data• Hyperspectral surveys

• CASI

• EAGLE

• HAWK

• LiDAR

• Traditional Aerial Photographs

• UAV

DART: Laboratory Measurements

Geotechnical analyses

Particle size

Sheer strength

etc.

Geochemical analyses

DART: Laboratory Measurements

Plant Biology• Rate of germination

(emergence)

• Growth analysis

• Number of Leaves

• Number of Tillers

• Stem length

• Total plant height

• Drought experiment

• Chlorophyll a fluorescence

• Soil and leaf water content

• Root studies

• Root length and density.

• Root – Shoot biomass ratio.

• Total plant biomass

• Biochemical analysis: Protein and chlorophyll analysis.

• Broad spectrum analysis of soil (Nutrient content) and C-N ratios of leaf.

DART: Data so far - Temperature

DART: Data so far –Temperature

DART: Data so far – Earth Resistance

DART: Data so far – Earth Resistance

Remote sensing

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

Fiber-optic probe

Tired old laptop

(needs an LPT port…)

Reflectance panel

Instrument

(20Kg of back

pain)

To the visible

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

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

Senescing (29/06/2011)

Senescant (15/07/2011)

Some rights reserved by ZakVTA

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

Vegetation indices

• Mostly simple ratios

• Chlorophyll & biomass

• Carotenoid / chlorophyll• Photo-chemical Reflectance Index (PRI)

• Structure Insensitive Pigment Index (SIPI)

• Plant Senescance Reflectance Index (PRSI)

)570531(

)570531(

RR

RRPRI

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

Continuum removal

Designation(nm)

Start(nm)

Centre(nm)

End(nm)

Indicates

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

Continuum removal

• Band Normalised to depth of Centre of absorption feature (BNC)

• Band Normalised to Area of absorption feature (BNA)

))/1/())/(1( icci RRRRBNC

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

Questions