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.
Using multi-temporal benchmarking to determine optimal sensor deployment: advances from the DART project.
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
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
9.
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
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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
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
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
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
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
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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
Image re-used under a Creative Commons licence: http://www.flickr.com/photos/catikaoe/183454010/We identify contrast Between the expression of the remains and the local 'background' value In most scenarios direct contrast measurements are preferable as these measurements will have less attenuation.Proxy contrast measurements are extremely useful when the residue under study does not produce a directly discernable contrast or it exists in a regime where direct observation is impossible
Traces can be identified through evidence Clusters of artefacts Chemical and physical residues Proxy biological variations Changes in surface relief
Image re-used under a Creative Commons licence: http://www.flickr.com/photos/dartproject/6001577156Dependant on localised formation and deformation Land management
Image re-used under a Creative Commons licence: http://www.flickr.com/photos/san_drino/1454922072/Environmental processesSensor responses (particularly new sensors)Constraining factors (soil, crops etc.)Bias and spatial variabilityIMPACTS ONDeploymentManagement
Image re-used under a creative commons licence: http://www.flickr.com/photos/8203774@N06/2310292882/
Image re-used under a creative commons licence: http://www.flickr.com/photos/8203774@N06/2310292882/
Try to understand the periodicity of changeRequire 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
Based upon an understanding of:Nature of the archaeological residuesNature of archaeological material (physical and chemical structure)Nature of the surrounding material with which it contrastsHow proxy material (crop) interacts with archaeology and surrounding matrixSensor characteristicsSpatial, spectral, radiometric and temporalHow these can be applied to detect contrastsEnvironmental characteristicsComplex natural and cultural variables that can change rapidly over time
Based upon an understanding of:Nature of the archaeological residuesNature of archaeological material (physical and chemical structure)Nature of the surrounding material with which it contrastsHow proxy material (crop) interacts with archaeology and surrounding matrixSensor characteristicsSpatial, spectral, radiometric and temporalHow these can be applied to detect contrastsEnvironmental characteristicsComplex natural and cultural variables that can change rapidly over time
Image re-used under a Creative Commons licence: http://www.flickr.com/photos/arpentnourricier/2385863532Dependant on localised formation and deformation Environmental conditions Soil moisture Crop Temperature and emmisivity
Image re-used under a Creative Commons licence: http://www.flickr.com/photos/dartproject/6001577156Dependant on localised formation and deformation Land management
Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
Image re-used under a creative commons licence:http://www.flickr.com/photos/soilscience/5104676427Spectro-radiometrySoilVegetationEvery 2 weeksCrop phenologyHeightGrowth (tillering)Flash res 64Including induced events
ResistivityGround penetrating radarEmbedded Soil Moisture and Temperature probesLogging every hour Weather stationLogging every half hour
Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
Image reused under a Creative Commons Licence:http://www.flickr.com/photos/kubina/279523019Geotechnical analysesGeochemical analysesPlant Biology
Conversion to moisture content is also a priorityRequires calibration using different mixing models including:empiricalsemi-empiricalphysical volumetric phenomenological modelsThis will help us to link the changes in geophysical responses to the composition of the soil and predict future responses, as well as supporting investigations into crop stress and vigour.
This is not simply scaling
Oooh look- contrast! Archaeology has higher absorption in the vis, increased reflectance in the NIR, indicating more LAI / photosynthesis
Less contrast, same trend
Senescance- increased reflectance in the red, shallower water absorbtion bands, sloping shoulder.Ooh- look- the relationship observable in the previous months is inverted! what’s going on ‘ere then? (see next slide)
Well, knock me over with a feather and colour me purple- the crop over the ditch has matured quicker than the the background- we don’t really know what’s going on here yet but it looks like the growth stage of the wheat has been retarded in the areas off the archaeology…MORE SPECTRAL MEASUREMENTS ARE REQUIRED
Senescence- archaeology is more reflective- indicative of greater LAI/Bio-mass
Here in the visible spectrum- features are ‘brighter’
Archaeology – not archaeology14/6/2011Basically biomass is the major determinant- less contrast in the structure insensitive indices
Endmembers used- after curran et al.
The spectral plot shows greater absorbance in the visible spectrum, and greater reflectance in the near-infrared part of the spectrum for the areas over the archaeology.670nm absorbtion feature, indicative of chlorophyll and other photosynthetic pigments, shows very little contrast. This means that contrast is more strongly expressed as differences in biomass (i.e. increased Leaf Area Index) than as stress and vigour variations.
The spectral plot expresses less contrast than 08/06/11. In the continuum removal reults the greatest contrast can be seen in the 1730nm absorbtion feature, which is sensitive to lignin and cellulose content. This seems to indicate that the background is higher in lignin content than the archaeology. Lignin is a major component of plant stems. -This may be a result of the lignin making a greater contribution to reflectance due to a thinner canopy
The spectral plot again shows less contrast than previous weeks. The 670nm absorbtion feature exhibits very little contrast. The reduced reflectance in the near and shortwave infrared indicate that the crop has reached maturity, and is starting to senesce. The greatest contrast is seen in the 1200nm absorbtion feature, which is indicative of foliar water content.