archaeological detection using satellite sensors

School of ComputingFaculty of Engineering

Satellite Sensors – Archaeological Applications

Anthony (Ant) Beck

Twitter: AntArch

Potential of satellite images and hyper/multi-spectral recording in archaeology

Poznan – 31st June 2012

Overview

• The Satellite Platform

• Archaeological Prospection

• Landscape Survey in data poor environments

• Exemplar: Homs, Syria

• The Future

• Conclusions

Overview

There is no need to take notes:

Slides –

Text – http://dl.dropbox.com/u/393477/MindMaps/Events/ConferencesAndWorkshops.html

There is every need to ask questions

http://dl.dropbox.com/u/393477/MindMaps/Events/ConferencesAndWorkshops.html

http://dl.dropbox.com/u/393477/MindMaps/Events/ConferencesAndWorkshops.html

Characteristics of the satellite platformSensor Types – Active and Passive

Characteristics of the satellite platformSpatial Resolution

Multi-spectral

4 bands

Characteristics of the satellite platformSpatial Resolution - 20cm Aerial Photography

Detailed mapping

Field backdrop

Small area

Characteristics of the satellite platformSpatial Resolution - 1m Ikonos

Detailed mapping

Field backdrop

Large area

Characteristics of the satellite platformSpatial Resolution - 30m Landsat

Landscape mapping• Soils

• Geology

• Vegetation

• Land use

• etc

Long history

Multi-spectral

Multi-temporal

Characteristics of the satellite platformSpatial Resolution - 30m Landsat (geology bands)

Landscape mapping• Soils

• Geology

• Vegetation

• Land use

• etc

Long history

Multi-spectral

Multi-temporal

Characteristics of the satellite platformTemporal Resolution

Characteristics of the satellite platformSpectral Resolution

Characteristics of the satellite platformA large archive

Problems of the satellite platformAtmospheric Attenuation

Problems of the satellite platformTopographic Distortion

Problems of the satellite platformPixel Mixing

Problems of the satellite platformClassification

Characteristics of the satellite platformPerceived 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

Characteristics of the satellite platformMy 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

Archaeological ProspectionWhat 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


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


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 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

Satellite images for archaeological prospectionHigh 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

Satellite images for archaeological prospection High spatial resolution optical

That’s it.


Nothing more to say really


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

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)

However, prospection is not everythingWhy use satellites when it’s already known!

However, prospection is not everythingLandscape 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?

However, prospection is not everythingLandscape 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

However, prospection is not everythingLandscape 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.

However, prospection is not everythingLandscape 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.

However, prospection is not everythingLandscape survey – Desk Based Assessments

However, prospection is not everythingLandscape 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.

Landscape Survey in data poor environments

Artefacts

EcofactsSites

Hinterland

Ecological Setting

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.

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

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

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


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.


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

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)


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


The libraries of free or low cost imagery• Landsat

• Family of sensors operating from 1973 onwards

• Multispectral

• ASTER

• DEM

• Multispectral

• SRTM

Bespoke

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

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

Landscape Survey in data poor environments Theme Extraction - DTM

• Photogrammetry

• Stereo pairs

• Corona (5m results)

• beware of clouds

• beware of trees

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


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)

Problems of the satellite platform Theme Extraction - Landscape

Landscape Survey in data poor environments Archaeological Prospection – Positive Evidence

Landscape Survey in data poor environments Archaeological Prospection – Negative Evidence

Landscape Survey in data poor environments Archaeological Prospection – Image Enhancement

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?

Exemplar: Homs, Syria

Exemplar: Homs, SyriaOverview – 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

Exemplar: Homs, SyriaPreliminary 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.

Exemplar: Homs, SyriaPreliminary 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

Exemplar: Homs, SyriaImage 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)

Exemplar: Homs, SyriaImage Selection

Ikonos 11 bit digital imagery (1999 - present)

1 m panchromatic/colour 0.45-0.9m4 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

Corona KH-4B photography (1970)

1.83 - 2.5 m panchromatic

Photogrammetrically scanned to 8 bit raster imagery

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

Exemplar: Homs, SyriaImage Selection

Exemplar: Homs, SyriaImage Pre-processing

Atmospheric correction

Geo-referencing Corona (using Ikonos as a backdrop)

Exemplar: Homs, SyriaLandscape 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

Exemplar: Homs, SyriaLandscape 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

Exemplar: Homs, SyriaLandscape 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

Exemplar: Homs, SyriaLandscape Themes

The USGS classification means these views can be refined at different scales• Vary field based on Classification ID

Exemplar: Homs, SyriaProspection – 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


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

Exemplar: Homs, SyriaProspection – 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

Exemplar: Homs, SyriaProspection – The Basalt , Image Enhancement

Exemplar: Homs, SyriaProspection – 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


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

Exemplar: Homs, SyriaProspection – 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)


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

Exemplar: Homs, SyriaProspection – 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


Factors influencing soil colour include:• Mineralogy

• Chemical constituents

• Soil moisture

• Soil structure

• Particle Size

• Organic matter content

Soil Moisture %


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


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


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


An anomaly Increasesmall stones (6-20mm)coarse sand (0.6 - 2mm)Decreasesilt (0.002-0.0063mm) Theoretically reflectance should increase in the visible/NIR as:Increased silicate to clay/silt ratio.Decreased moisture retention.

Exemplar: Homs, SyriaProspection – 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


Requirements• Moving average kernel

• What size?

• Trial and Error gave 200m

• processor intensive

Exemplar: Homs, SyriaProspection – The Marl: evaluation

Prospection – finding stuff!

Exemplar: Homs, SyriaGeneral– multispectral helps

Exemplar: Homs, SyriaGeneral– Time change analysis

Exemplar: Homs, SyriaGeneral– Image Interpretation Keys

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

Conclusions

Be selective• Choose stuff because it

• Adds value

• Solves a problem

• Just because you can doesn't mean you should

CostsCost per Hectare

£1

£10

£100

£1,000

£10,000

£100,000

£1,000,000

Not comparing like with like for archaeological value

archaeological detection using satellite sensors

Education

satellite platformpixel

archaeological detection

archaeological prospectionwhat

radiometric resolution

satellite platformmy

archaeological sites

adetectable contrast

proxy contrast