A presentation given by Anthony Beck at the workshop "Potential of satellite images and hyper/multi-spectral recording in archaeology" Poznan – 31st June 2012

1. Satellite Sensors – Archaeological
Applications
Anthony (Ant) Beck
Twitter: AntArch
Potential of satellite images and hyper/multi-spectral
recording in archaeology
Poznan – 31st June 2012
School of Computing
Faculty of Engineering

2. Overview
•The Satellite Platform
•Archaeological Prospection
•Landscape Survey in data poor environments
•Exemplar: Homs, Syria
•The Future
•Conclusions

3. 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

4. Characteristics of the satellite platform
Sensor Types – Active and Passive

5. Characteristics of the satellite platform
Spatial Resolution
Multi-spectral
4 bands

6. Characteristics of the satellite platform
Spatial Resolution - 20cm Aerial Photography
Detailed
mapping
Field backdrop
Small area

7. Characteristics of the satellite platform
Spatial Resolution - 1m Ikonos
Detailed
mapping
Field backdrop
Large area

8. Characteristics of the satellite platform
Spatial Resolution - 30m Landsat
Landscape
mapping
• Soils
• Geology
• Vegetation
• Land use
• etc
Long history
Multi-spectral
Multi-temporal

9. Characteristics of the satellite platform
Spatial Resolution - 30m Landsat (geology bands)
Landscape
mapping
• Soils
• Geology
• Vegetation
• Land use
• etc
Long history
Multi-spectral
Multi-temporal

10. Characteristics of the satellite platform
Temporal Resolution

11. Characteristics of the satellite platform
Temporal Resolution

12. Characteristics of the satellite platform
Spectral Resolution

13. Characteristics of the satellite platform
A large archive

14. Problems of the satellite platform
Atmospheric Attenuation

15. Problems of the satellite platform
Topographic Distortion

16. Problems of the satellite platform
Pixel Mixing

17. Problems of the satellite platform
Classification

18. Characteristics of the satellite platform
Perceived issues for archaeologists
Cost
• It’s perceived to be expensive
Complexity
• It’s perceived to be complex to
understand and process
Temporal constraints
• Revisits are frequent
• Times of collection are fixed
The ‘Google Earth’ effect

19. Characteristics of the satellite platform
My issues with satellite applications
A solution searching for a problem
• Does it have a place in well understood
landscapes?
Cropmarks
• Unless you’ve got lots of money, why
would you want to prospect for spatio-
temporally ephemeral cropmarks with a
sensor with a large synoptic footprint
Everyone focuses on prospection
at the expense of
• The Landscape
• Integrated Cultural Resource
Management

20. 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 sites are physical and chemical phenomena.
• There are different kinds of site constituents.
• The abundance and spatial distribution of different constituents vary
both between sites and within individual sites.
• These attributes may be masked or accentuated by a variety of other
phenomena.
• Importantly from a remote sensing perspective archaeological site do
not exhibit consistent spectral signatures

21. 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

22. 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).

23. Archaeological Prospection
What is the basis for detection

24. Archaeological Prospection
What is the basis for detection

25. Archaeological Prospection
What is the basis for detection

26. 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

27. Satellite images for archaeological prospection
High spatial resolution optical
Essentially large footprint vertical photographs
Lower spatial resolution than aerial (0.5 – 4m)
Panchromatic (higher spatial resolution)
4 band multi-spectral (lower spatial resolution)
• Blue
• Green
• Red
• Near Infra-Red

28. Satellite images for archaeological prospection
High spatial resolution optical
That’s it.

29. Satellite images for archaeological prospection
High spatial resolution optical
Nothing more to say
really

30. Satellite images for archaeological prospection
High spatial resolution optical
Well there’s a bit more –
Image sources
• Major providers (GeoEye, DigitalGlobe), archive and bespoke
• Declassified Cold War ‘spy’ photography
• Before modern ‘destructive modification’
Free viewers
• Google, Yahoo, Bing
• No control over the data

31. Satellite images for archaeological prospection
High spatial resolution optical – WorldView - 2
New: good water penetration
New: Yellowness (crop)
New: Red-edge (crop)
New: NIR (crop/biomass)

32. However, prospection is not everything
Why use satellites when it’s already known!

33. However, prospection is not everything
Landscape survey
It's not just about finding stuff
• It's about placing it in a context where it can be useful
Most countries do not have mature cultural management
frameworks
• e.g. Homs region of Syria or Vidisha area of India
• Archaeological inventory is significantly biased towards large and
prominent landscape features
• What about the rest of the landscape?

34. However, prospection is not everything
Landscape survey
This is an inventory problem
• OK we need to do more prospection!
• Bring on the planes!
• NO
If we were to start from the beginning would we do it all the
same way again
• Learn from our experiences
This is what I hope to show in the rest of the presentation

35. However, prospection is not everything
Landscape survey – Types of survey
Reconnaissance survey: (Detection)
• primarily designed to detect all the positive and negative archaeological
evidence within a study area.
Evaluation survey: (Recognition)
• to assess the archaeological content of a landscape using survey
techniques that facilitate subsequent field-prospection, statistical
hypothesis building or the identification of spatial structure.

36. However, prospection is not everything
Landscape survey – Types of survey
Landscape research: (Identification)
• to form theoretical understanding of the relationships between
settlement dynamics, hinterlands and the landscape itself.
Cultural Resource Management (CRM): (Management and
Protection)
• primarily designed for management of the available resources. CRM
applications are not necessarily distinct from other survey objectives
although they may be conducted as part of a more general information
capture system.
Improve Reconnaissance Survey and impact on all the others.

37. However, prospection is not everything
Landscape survey – Desk Based Assessments

38. However, prospection is not everything
Landscape survey – Desk Based Assessments
Sources that are normally considered for reference during a
DBA are:
• Regional and national site inventories.
• Public and private collections of artefacts and ecofacts.
• Modern and historical mapping.
• Geo-technical information (such as soil maps and borehole data).
• Historic documents.
• Aerial photography and other remote sensing.
How can satellite imagery help in data poor environments.

39. Landscape Survey in data poor environments
Ecological Setting
Hinterland
Ecofacts
Sites
Artefacts

40. Landscape Survey in data poor environments
Nature of the evidence – DBA resources
• Regional and national site inventories.
• Archaeological inventory is significantly biased towards large and
prominent landscape features
• Public and private collections of artefacts and ecofacts
• Not well documented
• Modern and historical mapping.
• Not available, or available at inappropriate scales
• Geo-technical information (such as soil maps and borehole data).
• Not available, or available at inappropriate scales
• Historic documents.
• ?
• Aerial photography and other remote sensing.

41. Landscape Survey in data poor environments
Understand the nature of the study area
• The geology and soil types in the study area
• The surface vegetation regimes
• The nature, range and size of the archaeological residues
• How these residues may contrast against a background value
• Residue or proxy detection
• Localised masking (i.e. crop, terraces)
• What conditions enhance the contrast between a residue and its
background and when this is maximised

42. Landscape Survey in data poor environments
Understand the nature of the study area
• How any of the above conditions may change during a year
• What resolution is required for detection
• Spatial
• Spectral
• Temporal
• Radiometric

43. Landscape Survey in data poor environments
Image Selection
What has an impact on the derivatives you want to create:
• Environment
• Topography
• Agriculture
• Land use
• Image fidelity
• Cloud Cover, Atmospheric Haze

44. Landscape Survey in data poor environments
Image Selection
Rule of thumb: Landscape Themes
• Stereoscopic or Radar imagery for the generation of Digital Terrain
Models (DTMs)
• Low spatial (>15 metres) and medium-high spectral resolution (>7
bands). This imagery will be primarily used for generating thematic data
such as soil maps.
• medium-high spatial (4-15 metres) and medium spectral resolution
(multispectral in the visible-near infrared and beyond). This imagery will
be primarily used for generating thematic data such as topographic and
land-use maps.

45. Landscape Survey in data poor environments
Image Selection
Rule of thumb:
• high spatial resolution (0.5-2 metres) and medium-low spectral
resolution (panchromatic and multispectral in the visible-near infrared
wavelengths). Used for the location and mapping of fine spatial
resolution archaeological features .
• Other
• There will always be a requirement for other data

46. Landscape Survey in data poor environments
Image Selection – What to consult
On-line streaming
• Bing Maps
• Yahoo Maps
• Google Maps
• Open Street Map
• Open Aerial Map
Use Caution – The ‘Google Earth’ effect
Strongly consider adding new data to the Open collection
movements (OSM empowers local communities)

47. Landscape Survey in data poor environments
Image Selection – What to consult
The libraries of free or low cost imagery
• Spot maps
• Cheap ortho-rectified 2.5m imagery
• 2 euro per kilometer
• A good backdrop for rectification in lie of mapping or other ground
control
• 10m RMSE
• They also do Elevation models
• Corona/Hexagon/Gambit
• Historic Imagery
• variable parameters
• 60's onwards

48. Landscape Survey in data poor environments
Image Selection – What to consult
The libraries of free or low cost imagery
• Landsat
• Family of sensors operating from 1973 onwards
• Multispectral
• ASTER
• DEM
• Multispectral
• SRTM
Bespoke

49. Landscape Survey in data poor environments
Image Pre-processing
Atmospheric Correction
Geo-referencing
Co-referencing
Orthorectification
To what degree of accuracy
• Fit for purpose
• To enable it to be confidently identified on
the ground

50. Landscape Survey in data poor environments
Theme Extraction - DTM
• Two sources
• Radar/LiDAR
• Photogrammetry/Computer vision/SFM
• Many free sources of data
• Shuttle Radar Topographic Mapping: SRTM
• 3 arc seconds
• c.90m
• ASTER
• GDEM2 released October 17th 2011
• 1 arc seconds
• c. 30m

51. Landscape Survey in data poor environments
Theme Extraction - DTM
• Photogrammetry
• Stereo pairs
• Corona (5m results)
• beware of clouds 
• beware of trees 

52. Landscape Survey in data poor environments
Theme Extraction - Landscape
Satellite imagery has an established pedigree of doing this
• Corine Land Cover
• NASA Global Maps
• Soil Maps
• Vegetation maps
Processing is dependent on
• Type of theme
• Desired scale

53. Landscape Survey in data poor environments
Theme Extraction - Landscape
Classification systems
• Approaches generally segment the imagery into contiguous parcels with
different characteristics
• colour (spectral response)
• texture
• tone
• pattern
• other association information
• These parcels are then 'identified'
• Mapped to a classification system
• Recommendations
• Established methodologies
• Established classification system (See previous)

54. Problems of the satellite platform
Theme Extraction - Landscape

55. Landscape Survey in data poor environments
Theme Extraction - Landscape

56. Landscape Survey in data poor environments
Archaeological Prospection – Positive Evidence

57. Landscape Survey in data poor environments
Archaeological Prospection – Negative Evidence

58. Landscape Survey in data poor environments
Archaeological Prospection – Image Enhancement

59. Landscape Survey in data poor environments
Archaeological Prospection – Documentation or KT
Knowledge Transfer is important
Good access is important
Consider Open approaches (OSM, Open Archaeology Map)
• Ethics?

60. Exemplar: Homs, Syria

61. Exemplar: Homs, Syria
Overview – SHR Project
To establish a framework to understand settlement dynamics
and diversity in the Homs region, Syria.
C. 650 sq km
2 principal contrasting environmental zones
• Basalt
• Marl
Initial program of surface/site survey
No sites and monuments record!
• No aerial photography available (‘closed skies’)
• Satellite imagery evaluated as a prospection tool

62. Exemplar: Homs, Syria
Preliminary Enquiries
• The main agricultural season was between October (seeding) and May
(harvesting).
• Establishing sites from crop marks would be difficult due to the
perceived lack of negative features (i.e. ‘positive’ mud-brick construction
as opposed to ‘negative’ postholes and ditches).
• Except for fluvial margins, the landscape could be considered as either
completely bare soil or a combination of bare soil and crop throughout
the year.

63. Exemplar: Homs, Syria
Preliminary Enquiries
• Site soil colour in the marl zones was significantly different to off-site soil
colour when dry and similar when wet.
• Areas of high artefact density had a positive relationship with areas of
light soil colour in the marl.
• The majority of walls in the basalt zone have a width of between 0.5 and
2m.
• Heavy mechanisation was introduced in the 70s
• Bulldozers
• Deep plough

64. Exemplar: Homs, Syria
Image Selection – implications from the zone
• Apart from the irrigated areas crop cover is only significant in the few
months preceding harvest (May).
• Atmospheric dust, if applicable, will be at its lowest during the significant
rains (December to May).
• Cloud cover could significantly impact imagery between December and
May.
• Sites in the marl exhibit greater contrast during periods of (hyper) aridity
from September to December.
• The smallest sites in the basalt zone will require very fine (high)
resolution imagery with good image fidelity (i.e. low dust levels)

65. Exemplar: Homs, Syria
Image Selection
Corona KH-4B photography (1970)
1.83 - 2.5 m panchromatic
Photogrammetrically scanned to 8 bit raster imagery
Ikonos 11 bit digital imagery (1999 - present)
1 m panchromatic/colour 0.45-0.9 m
4 m Multispectral: 0.45-0.52 m Blue
0.52-0.60 m Green
0.63-0.69 m Red
0.76-0.90 m NIR
Landsat 8 bit 7 band (and ETM+) digital imagery (1974 - present)
0.45-0.52 m, 30 m
0.52-0.60 m, 30 m
0.63-0.69 m, 30 m
0.76-0.90 m, 30 m
1.55-1.75 m, 30 m
10.40-12.50 m, 120 m
2.08-2.35 m, 30 m

66. Exemplar: Homs, Syria
Image Selection

67. Exemplar: Homs, Syria
Image Pre-processing
Atmospheric correction
Geo-referencing Corona (using Ikonos as a backdrop)

68. Exemplar: Homs, Syria
Landscape Themes
Themes include
• Land use and cover (topography)
• Communication networks (Ikonos, Corona, Landsat)
• Hydrology networks (Ikonos, Corona, Landsat)
• Settlements (Ikonos, Corona, Landsat)
• Field Systems (Ikonos, Corona)
• Vegetation
• Identification - Ikonos
• Presence - Landsat
• Soil/geology maps
• Landsat
• DEM/DTM - Not discussed further

69. Exemplar: Homs, Syria
Landscape Themes – Classification Systems
Used standard classification system (USGS)
• Designed with remote sensing in mind
• Similar to CORINE
• 3 Level Nested Hierarchy
• Level 1 – USGS Coarse Classification (for Landsat)
• Level 2 – USGS Detailed Classification (for finer spatial/spectral data)
• Level 3 – Bespoke classification

70. Exemplar: Homs, Syria
Landscape Themes – Classification Systems
Segmented the imagery into contiguous parcels with different
characteristics
• Combination of qualitative and quantitative techniques
• Principal Component Analysis
• Unsupervised classification
• Band ratios
• Transparent overlays
• Visual interpretation
Insert classification ID

71. Exemplar: Homs, Syria
Landscape Themes
The USGS classification means these views can be refined at
different scales
• Vary field based on Classification ID

72. Exemplar: Homs, Syria
Prospection – The Basalt

73. Exemplar: Homs, Syria
Prospection – The Basalt
Complex and intensive multi-period palimpsest of upstanding
structural features that covers a large extent
• Cairns
• Walls
• Structures
Smallest feature is c. 1m in size
Structures constructed from basalt

74. Exemplar: Homs, Syria
Prospection – The Basalt
Detected by:
• Topographic effect (shadows)
• Spectral response
Requirements:
• High spatial resolution
• High image fidelity
• High degree of georeferencing accuracy required to locate features on
the ground (<10m RMSE)
• Try mapping all the basalt with aerial photography or GPS! One needs
a metrically accurate system

75. Exemplar: Homs, Syria
Prospection – The Basalt, Image Enhancement
Internal Geometries of Ikonos imagery highly accurate
• Therefore, few GPS points required for re-geo-correction
• Re-geocorrected using Handheld GPS readings
• Prolonged readings over an identifiable tie point
• Ikonos accuracy c. 5-8m
Corona geo-referenced to the Ikonos Basemap
• Difficulty in selecting tie-points due to 30 year time difference
• Corona accuracy > c. 5-8m
Simple technique vastly increased utility of the imagery
• Allowed cheaper desk-based analysis

76. Exemplar: Homs, Syria
Prospection – The Basalt , Image Enhancement

77. Exemplar: Homs, Syria
Prospection – The Basalt, Image Enhancement
Linear enhancements
• Edge detection
• Crisp
• Generally unsuccessful
Image fusion/overlay
• Fuse 1m pan with 4m MS for Ikonos
• Transparent overlay
• Very successful

78. Exemplar: Homs, Syria
Prospection – The Basalt
Simply a process of digitising results
• Ikonos fused imagery
• Finer resolution (spatial and spectral) gave better interpretation
• More modern clutter
• Corona
• Coarser resolution
• Less clutter
• More intact landscape
• Synergies from using both data sets
Adding an attribute for the source (so you know where the
evidence came from)
Undertaking analysis

79. Exemplar: Homs, Syria
Prospection – The Basalt

80. Exemplar: Homs, Syria
Prospection – The Marl

81. Exemplar: Homs, Syria
Prospection – The Marl
Dispersed remains punctuated by soil marks and tells
Smallest feature is c. 10s of metres in area
Detected by
• Spectral response
Requirements:
• Hyper arid
• No need to improve Ikonos spatial accuracy
• Multi-spectral (see comparison later)

82. Exemplar: Homs, Syria
Prospection – The Marl
Simply a process of digitising results
Adding an attribute for the source (so you know where the
evidence came from)
Conducting field verification (including mapping and grab
sample of diagnostic pottery)
Conducted validity determination – extensive fieldwalking
Undertaking analysis
• Improved understanding of population dynamics over time

83. Exemplar: Homs, Syria
Prospection – The Marl, Lab Work
Soil Colour difference recorded between on and off site soils
• Dry: On site soils lighter (an increase in chroma)
• Wet: Colour indistinguishable (indicating similar parent regolith)
Indicated that increased contrast would occur at periods of
peak aridity (at least for optical region)
Wanted to understand the cause of the colour change so that
we could model detection with other sensors

84. Exemplar: Homs, Syria
Prospection – The Marl, Lab Work
Factors influencing soil colour include:
• Mineralogy
• Chemical constituents
• Soil moisture
• Soil structure
• Particle Size
• Organic matter content
Soil Moisture %

85. Exemplar: Homs, Syria
Prospection – The Marl, Lab Work
Soil samples were taken across a number of site transects
Analysed for:
• Moist and dry spectro-radiometer readings
• Particle size measurement
• Magnetic susceptibility
• Geochemical analysis

86. Exemplar: Homs, Syria
Prospection – The Marl, Lab Work

87. Exemplar: Homs, Syria
Prospection – The Marl, Lab Work
Concluded difference in spectral reflectance principally due to
variations in:
• moisture content
• grain size
• soil structure
Site soils share similar spectral curve to off site soils
• Measurable relative reflectance difference (in this zone)
• NO unique archaeological spectral curve

88. Exemplar: Homs, Syria
Prospection – The Marl, Lab Work
This confirmed hypothesis about data collection during
periods of peak aridity
• Ikonos subsequently collected in January/February 2002
Although analysis in SWIR could detect these physical
manifestions more effectively
Archaeological sites in this zone represent localised areas
with increased reflectance
• This information can be used to enhance visualisation of residues

89. Exemplar: Homs, Syria
Prospection – The Marl, Lab Work
Increase
An anomaly small stones (6-20mm)
coarse sand (0.6 - 2mm)
Decrease
silt (0.002-0.0063mm)
Theoretically reflectance should
increase in the visible/NIR as:
Increased silicate to clay/silt
ratio.
Decreased moisture retention.

90. Exemplar: Homs, Syria
Prospection – The Marl, Image Enhancement
Archaeological residues as localised background soil
variations
• subtracting an averaged background soil pixel for an area will
theoretically produce a positive value at an archaeological site
• Off-site values should produce a value approaching zero
Features enhanced
• Archaeological residues
• Roads
• Buildings
• Crops
• Small water bodies

91. Exemplar: Homs, Syria
Prospection – The Marl, Image Enhancement
Requirements
• Moving average kernel
• What size?
• Trial and Error gave 200m
• processor intensive

92. Exemplar: Homs, Syria
Prospection – The Marl, Image Enhancement

93. Exemplar: Homs, Syria
Prospection – The Marl: evaluation

94. Exemplar: Homs, Syria
Prospection – The Marl: evaluation

95. Exemplar: Homs, Syria
Prospection – The Marl: evaluation

96. Prospection – finding stuff!

97. Exemplar: Homs, Syria
General– multispectral helps

98. Exemplar: Homs, Syria
General– Time change analysis

99. Exemplar: Homs, Syria
General– Image Interpretation Keys

100. Conclusions
Satellite approaches offer a number of benefits
• Landscape approaches
• Can help develop more interactive or discriminatory strategies
• Use this here (marl)
• Use that there (basalt)
• Providing context
Aerial approaches in the medium term will always provide
better spatial resolution and temporal flexibility

101. Conclusions
Be selective
• Choose stuff because it
• Adds value
• Solves a problem
• Just because you can doesn't mean you
should

102. Costs
Cost per Hectare
£1,000,000
£100,000
£10,000
£1,000
£100
£10
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Not comparing like with like for archaeological value