Satellite Sensors – ArchaeologicalApplicationsAnthony (Ant) BeckTwitter: AntArchPotential of satellite images and hyper/mu...
Overview•The Satellite Platform•Archaeological Prospection•Landscape Survey in data poor environments•Exemplar: Homs, Syri...
OverviewThere is no need to take notes:Slides –Text –http://dl.dropbox.com/u/393477/MindMaps/Events/ConferencesAndWorkshop...
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 PhotographyDetailedmappingField backdropSmall area
Characteristics of the satellite platformSpatial Resolution - 1m IkonosDetailedmappingField backdropLarge area
Characteristics of the satellite platformSpatial Resolution - 30m LandsatLandscapemapping• Soils• Geology• Vegetation• Lan...
Characteristics of the satellite platformSpatial Resolution - 30m Landsat (geology bands)Landscapemapping• Soils• Geology•...
Characteristics of the satellite platformTemporal Resolution
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 archaeologistsCost• It’s perceived to be expensiveComplexity...
Characteristics of the satellite platformMy issues with satellite applicationsA solution searching for a problem• Does it ...
Archaeological ProspectionWhat is the basis for detectionDiscovery requires the detection of one or more siteconstituents....
Archaeological Prospection What is the basis for detection                            Micro-Topographic variations        ...
Archaeological ProspectionWhat is the basis for detectionWe detect Contrast:• Between the expression of the remains  and t...
Archaeological ProspectionWhat is the basis for detection
Archaeological ProspectionWhat is the basis for detection
Archaeological ProspectionWhat is the basis for detection
Archaeological ProspectionSummaryThe sensor must have:• The spatial resolution to resolve the feature• The spectral resolu...
Satellite images for archaeological prospectionHigh spatial resolution opticalEssentially large footprint vertical photogr...
Satellite images for archaeological prospectionHigh spatial resolution opticalThat’s it.
Satellite images for archaeological prospectionHigh spatial resolution optical                                    Nothing ...
Satellite images for archaeological prospectionHigh spatial resolution opticalWell there’s a bit more –Image sources• Majo...
Satellite images for archaeological prospectionHigh spatial resolution optical – WorldView - 2                    New: goo...
However, prospection is not everythingWhy use satellites when it’s already known!
However, prospection is not everythingLandscape surveyIts not just about finding stuff• Its about placing it in a context ...
However, prospection is not everythingLandscape surveyThis is an inventory problem• OK we need to do more prospection!  • ...
However, prospection is not everythingLandscape survey – Types of surveyReconnaissance survey: (Detection)• primarily desi...
However, prospection is not everythingLandscape survey – Types of surveyLandscape research: (Identification)• to form theo...
However, prospection is not everythingLandscape survey – Desk Based Assessments
However, prospection is not everythingLandscape survey – Desk Based AssessmentsSources that are normally considered for re...
Landscape Survey in data poor environments                      Ecological Setting                           Hinterland   ...
Landscape Survey in data poor environmentsNature of the evidence – DBA resources• Regional and national site inventories. ...
Landscape Survey in data poor environmentsUnderstand the nature of the study area• The geology and soil types in the study...
Landscape Survey in data poor environmentsUnderstand the nature of the study area• How any of the above conditions may cha...
Landscape Survey in data poor environmentsImage SelectionWhat has an impact on the derivatives you want to create:• Enviro...
Landscape Survey in data poor environmentsImage SelectionRule of thumb: Landscape Themes• Stereoscopic or Radar imagery fo...
Landscape Survey in data poor environmentsImage SelectionRule of thumb:• high spatial resolution (0.5-2 metres) and medium...
Landscape Survey in data poor environmentsImage Selection – What to consultOn-line streaming• Bing Maps• Yahoo Maps• Googl...
Landscape Survey in data poor environmentsImage Selection – What to consultThe libraries of free or low cost imagery• Spot...
Landscape Survey in data poor environmentsImage Selection – What to consultThe libraries of free or low cost imagery• Land...
Landscape Survey in data poor environmentsImage Pre-processingAtmospheric CorrectionGeo-referencingCo-referencingOrthorect...
Landscape Survey in data poor environmentsTheme Extraction - DTM• Two sources  • Radar/LiDAR  • Photogrammetry/Computer vi...
Landscape Survey in data poor environmentsTheme Extraction - DTM• Photogrammetry  • Stereo pairs    • Corona (5m results) ...
Landscape Survey in data poor environmentsTheme Extraction - LandscapeSatellite imagery has an established pedigree of doi...
Landscape Survey in data poor environmentsTheme Extraction - LandscapeClassification systems• Approaches generally segment...
Problems of the satellite platformTheme Extraction - Landscape
Landscape Survey in data poor environmentsTheme Extraction - Landscape
Landscape Survey in data poor environmentsArchaeological Prospection – Positive Evidence
Landscape Survey in data poor environmentsArchaeological Prospection – Negative Evidence
Landscape Survey in data poor environmentsArchaeological Prospection – Image Enhancement
Landscape Survey in data poor environmentsArchaeological Prospection – Documentation or KTKnowledge Transfer is importantG...
Exemplar: Homs, Syria
Exemplar: Homs, SyriaOverview – SHR ProjectTo establish a framework to understand settlement dynamicsand diversity in the ...
Exemplar: Homs, SyriaPreliminary Enquiries• The main agricultural season was between October (seeding) and May  (harvestin...
Exemplar: Homs, SyriaPreliminary Enquiries• Site soil colour in the marl zones was significantly different to off-site soi...
Exemplar: Homs, SyriaImage Selection – implications from the zone• Apart from the irrigated areas crop cover is only signi...
Exemplar: Homs, SyriaImage Selection                    Corona KH-4B photography (1970)                    1.83 - 2.5 m pa...
Exemplar: Homs, SyriaImage Selection
Exemplar: Homs, SyriaImage Pre-processingAtmospheric correctionGeo-referencing Corona (using Ikonos as a backdrop)
Exemplar: Homs, SyriaLandscape ThemesThemes include• Land use and cover (topography)  • Communication networks (Ikonos, Co...
Exemplar: Homs, SyriaLandscape Themes – Classification SystemsUsed standard classification system (USGS)• Designed with re...
Exemplar: Homs, SyriaLandscape Themes – Classification SystemsSegmented the imagery into contiguous parcels with different...
Exemplar: Homs, SyriaLandscape ThemesThe USGS classification means these views can be refined atdifferent scales• Vary fie...
Exemplar: Homs, SyriaProspection – The Basalt
Exemplar: Homs, SyriaProspection – The BasaltComplex and intensive multi-period palimpsest of upstandingstructural feature...
Exemplar: Homs, SyriaProspection – The BasaltDetected by:• Topographic effect (shadows)• Spectral responseRequirements:• H...
Exemplar: Homs, SyriaProspection – The Basalt, Image EnhancementInternal Geometries of Ikonos imagery highly accurate• The...
Exemplar: Homs, SyriaProspection – The Basalt , Image Enhancement
Exemplar: Homs, SyriaProspection – The Basalt, Image EnhancementLinear enhancements• Edge detection• Crisp• Generally unsu...
Exemplar: Homs, SyriaProspection – The BasaltSimply a process of digitising results• Ikonos fused imagery  • Finer resolut...
Exemplar: Homs, SyriaProspection – The Basalt
Exemplar: Homs, SyriaProspection – The Marl
Exemplar: Homs, SyriaProspection – The MarlDispersed remains punctuated by soil marks and tellsSmallest feature is c. 10s ...
Exemplar: Homs, SyriaProspection – The MarlSimply a process of digitising resultsAdding an attribute for the source (so yo...
Exemplar: Homs, SyriaProspection – The Marl, Lab WorkSoil Colour difference recorded between on and off site soils• Dry: O...
Exemplar: Homs, SyriaProspection – The Marl, Lab WorkFactors influencing soil colour include:• Mineralogy• Chemical consti...
Exemplar: Homs, SyriaProspection – The Marl, Lab WorkSoil samples were taken across a number of site transectsAnalysed for...
Exemplar: Homs, SyriaProspection – The Marl, Lab Work
Exemplar: Homs, SyriaProspection – The Marl, Lab WorkConcluded difference in spectral reflectance principally due tovariat...
Exemplar: Homs, SyriaProspection – The Marl, Lab WorkThis confirmed hypothesis about data collection duringperiods of peak...
Exemplar: Homs, SyriaProspection – The Marl, Lab Work                            IncreaseAn anomaly                  small...
Exemplar: Homs, SyriaProspection – The Marl, Image EnhancementArchaeological residues as localised background soilvariatio...
Exemplar: Homs, SyriaProspection – The Marl, Image EnhancementRequirements• Moving average kernel  • What size?  • Trial a...
Exemplar: Homs, SyriaProspection – The Marl, Image Enhancement
Exemplar: Homs, SyriaProspection – The Marl: evaluation
Exemplar: Homs, SyriaProspection – The Marl: evaluation
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
ConclusionsSatellite approaches offer a number of benefits• Landscape approaches• Can help develop more interactive or dis...
ConclusionsBe selective• Choose stuff because it  • Adds value  • Solves a problem• Just because you can doesnt mean you  ...
Costs                                                      Cost per Hectare £1,000,000   £100,000     £10,000       £1,000...
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Archaeological detection using satellite sensors

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

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  • Images re-used under a reative Commons licencehttp://www.flickr.com/photos/cbcthermal/1475766746http://www.flickr.com/photos/dartproject/6001559320Active and Passive Thermal Optical Radar
  • High sampling density of relatively large areas
  • All have the same pixel resolution
  • Of the same areaAll have the same pixel resolution
  • Of the same areaAll have the same pixel resolution
  • Of the same areaAll have the same pixel resolution
  • Image re-used under a Creative Commons Licence: http://upload.wikimedia.org/wikipedia/commons/thumb/c/c8/RapidEye_Satellites_Artist_Impression.jpg/1280px-RapidEye_Satellites_Artist_Impression.jpgSun synchronous orbits. Revisits are frequent. Times of collection are fixed Constellations of satellites (Rapid-eye a 1 day re-visit off-nadir)
  • Image re-used under a creative commons licence: http://www.flickr.com/photos/dartproject/6005193142
  • Image re-used under a creative commons licence: http://www.flickr.com/photos/dolescum/3567689465/
  • This image is in the public domain: http://en.wikipedia.org/w/index.php?title=File%3AAerialDigitalPhoto.JPG
  • Image re-used under a creative commons licence: http://www.flickr.com/photos/dartproject/6004647401
  • Image re-used under a creative commons licence: http://www.flickr.com/photos/dartproject/6004646971Image re-used under a creative commons licence: http://www.flickr.com/photos/dartproject/6005192120
  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/san_drino/1454922072/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 Google Earth is NOT a panacea It is an excellent viewer It has access to data That data is outside the control of the user That data may not be appropriate for the archaeological problem in hand
  • Image re-used under an ambiguous licence: http://worrydream.com/ABriefRantOnTheFutureOfInteractionDesign/It’s sexy – and has been misrepresented The recent programme on the BBC
  • 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/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
  • 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 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/dartproject/6001577156Dependant on localised formation and deformation Land management
  • High spatial resolution optical Archive imagery Cheaper Declassified imagery Before destructive modifications Corona Hexagon Gambit KVR Free viewers Google Yahoo Bing Issues Although the images are not as degraded as they used to be There is no control over the collection parameters One can only do qualitative analysis
  • The 'new' bands Coastal Band (400 - 450 nm): This band supports vegetation identification and analysis, and supports bathymetric studies based upon its chlorophyll and water penetration characteristics. Also, this band is subject to atmospheric scattering and will be used to investigate atmospheric correction techniques. Yellow Band (585 - 625 nm): Used to identify "yellow-ness" characteristics of targets, important for vegetation applications. Also, this band assists in the development of "true-color" hue correction for human vision representation. Red Edge Band (705 - 745 nm): Aids in the analysis of vegetative condition. Directly related to plant health revealed through chlorophyll production. Near Infrared 2 Band (860 - 1040 nm): This band overlaps the NIR 1 band but is less affected by atmospheric influence. It supports vegetation analysis and biomass studies.
  • You can detect stuff with satellites If you already know about it -then WHY! What value is being added
  • 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 Exemplar Homs region of Syria. or Vidisha area of indiaData poor environment Archaeological inventory is significantly biased towards large and prominent landscape features What about the rest of the landscape?
  • 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
  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/jeffwerner/797327111/The Institute of Field Archaeologists (IFA) defines a DBA as [11]: “... a programme of assessment of the known or potential archaeological resource within a specified area or site on land, inter-tidal zone or underwater. It consists of a collation of existing written, graphic, photographic and electronic information in order to identify the likely character, extent, quality and worth of the known or potential archaeological resource in a local, regional, national or international context as appropriate.”
  • 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.
  • Nature of the evidence Regional and national site inventories. Archaeological inventory is significantly biased towards large and prominent landscape features Public and private collections of artefacts and ecofacts. Crude Difficult to access 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.
  • Nature of the evidence Regional and national site inventories. Archaeological inventory is significantly biased towards large and prominent landscape features Public and private collections of artefacts and ecofacts. Crude Difficult to access 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.
  • 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
  • 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
  • Image Selection What has an impact on the derivatives you want to create Environment Topography Agriculture Land use Image fidelity Cloud Cover Atmospheric haze
  • Image Selection Broadly 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.
  • Image Selection 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
  • On-line streamingBing Maps Yahoo Maps Google Maps Open Street Map Open Aerial MapUse Caution – The ‘Google Earth’ effectStrongly consider adding new data to the Open collection movements (OSM empowers local communities)
  • The libraries of free or low cost imagerySpot maps Cheap ortho-rectified 2.5m imagery2 euro per kilometerA good backdrop for rectification in lie of mapping or other ground control10m RMSEThey also do Elevation modelsCorona/Hexagon/Gambit Historic Imageryvariable parameters60's onwards
  • The libraries of free or low cost imageryLandsatFamily of sensors operating from 1973 onwardsMultispectralASTER DEMMultispectralSRTM Bespoke
  • Geo-referencing Co-referencing OrthorectificationTo what degree of accuracy Fit for purpose To enable it to be confidently identified on the ground Example of Basalt versus Marl in homs
  • I assume most of this will be covered by GeertTwo sourcesRadar/LiDARPhotogrammetry/Computer visionMany free sources of dataShuttle Radar Topographic Mapping: SRTM3 arc secondsc.90mASTERGDEM2 released October 17th 20111 arc secondsc. 30m
  • I assume most of this will be covered by GeertPhotogrammetryStereo pairsCorona (5m results) – beware of clouds 
  • Landscape theme generation 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 ~ Corine Land Cover ~ USGS ~ NASA Global Maps ~ Soil Maps ~ Vegetation maps
  • Image re-used under a creative commons licence: http://www.flickr.com/photos/dartproject/6004646971Image re-used under a creative commons licence: http://www.flickr.com/photos/dartproject/6005192120
  • Understanding what form of derivatives are required Traditional Mapping Elevation dataLand use Soils Archaeological mapping
  • Archaeological Prospection Positive evidence the identification of an actual archaeological residue, or the interpretation, by proxy, of objects that would lead one to assume that archaeological residues exist
  • Archaeological Prospection Negative evidence \r\n Negative evidence is the identification of features that appear to be archaeological but are in fact natural features or residues of other processes.\r\n
  • Archaeological Prospection Image enhancement Techniques that can be used to enhance visual or quantitative identification
  • Archaeological Prospection Documentation Image interpretation keys Strongly consider adding new data to the Open collection movements (OSM empowers local communities)
  • To establish a framework to understand settlement dynamics and diversity in the Homs region, Syria.C. 650 sq km2 principal contrasting environmental zonesBasaltMarlInitial program of surface/site surveyNo sites and monuments record!No aerial photography available (‘closed skies’)Satellite imagery evaluated as a prospection tool
  • 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.
  • 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
  • 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)
  • Themes includeLand use and cover (topography)Communication networks (Ikonos, Corona, Landsat)Hydrology networks (Ikonos, Corona, Landsat)Settlements (Ikonos, Corona, Landsat)Field Systems (Ikonos, Corona)VegetationIdentification - IkonosPresence - LandsatSoil/geology mapsLandsatDEM/DTM - Not discussed further
  • Used standard classification system (USGS)Designed with remote sensing in mindSimilar to CORINE3 Level Nested HierarchyLevel 1 – USGS Coarse Classification (for Landsat)Level 2 – USGS Detailed Classification (for finer spatial/spectral data)Level 3 – Bespoke classification
  • Segmented the imagery into contiguous parcels with different characteristicsCombination of qualitative and quantitative techniquesPrincipal Component AnalysisUnsupervised classificationBand ratiosTransparent overlaysVisual interpretationInsert classification ID
  • The USGS classification means these views can be refined at different scalesVary field based on Classification ID
  • Dispersed remains punctuated by soil marks and tellsSmallest feature is c. 10s of metres in areaDetected bySpectral responseRequirements:Hyper aridNo need to improve Ikonos spatial accuracyMulti-spectral (see comparison later)
  • Dispersed remains punctuated by soil marks and tellsSmallest feature is c. 10s of metres in areaDetected bySpectral responseRequirements:Hyper aridNo need to improve Ikonos spatial accuracyMulti-spectral (see comparison later)
  • Dispersed remains punctuated by soil marks and tellsSmallest feature is c. 10s of metres in areaDetected bySpectral responseRequirements:Hyper aridNo need to improve Ikonos spatial accuracyMulti-spectral (see comparison later)
  • Simply a process of digitising resultsAdding an attribute for the source (so you know where the evidence came from)Conducting field verification (including mapping and grab sample of diagnostic pottery)Undertaking analysisImproved understanding of population dynamics over time
  • Image re-used under a Creative Commons licence: http://www.flickr.com/photos/dartproject/6004648237Factors influencing soil colour include:MineralogyChemical constituentsSoil moistureSoil structureParticle SizeOrganic matter content
  • Soil samples were taken across a number of site transectsAnalysed for:Moist and dry spectro-radiometer readingsParticle size measurementMagnetic susceptibilityGeochemical analysis
  • Concluded difference in spectral reflectance principally due to variations in:moisture contentgrain sizesoil structureSite soils share similar spectral curve to off site soilsMeasurable relative reflectance difference (in this zone)NO unique archaeological spectral curve
  • This confirmed hypothesis about data collection during periods of peak aridityIkonos subsequently collected in January/February 2002Although analysis in SWIR could detect these physical manifestions more effectivelyArchaeological sites in this zone represent localised areas with increased reflectanceThis information can be used to enhance visualisation of residues
  • Archaeological residues as localised background soil variationssubtracting an averaged background soil pixel for an area will theoretically produce a positive value at an archaeological siteOff-site values should produce a value approaching zeroFeatures enhancedArchaeological residuesRoadsBuildingsCropsSmall water bodies
  • RequirementsMoving average kernelWhat size?Trial and Error gave 200mprocessor intensive
  • Enhancement algorithmSignificant improvement in visual detectionReduces variance due to variations in soil typesOriginal dataAppears saturated and washed outIn practice has proven a robust detection techniqueHas identified the majority of surficial sites (only 1 site found exclusively through fieldwalking)
  • Image fusion (to give co-collected imagery the best spectral and spatial characteristics of the component sensors) is goodA transparent overlay of the multispectral over the pan is just as effective
  • Time change analysis Just how representative are your modern interpretations of a landscape that's been messed around. How do you know it's been messed with?
  • Time change analysis Just how representative are your modern interpretations of a landscape that's been messed around. How do you know it's been messed with?
  • Archaeological detection using satellite sensors

    1. 1. Satellite Sensors – ArchaeologicalApplicationsAnthony (Ant) BeckTwitter: AntArchPotential of satellite images and hyper/multi-spectralrecording in archaeologyPoznan – 31st June 2012School of ComputingFaculty of Engineering
    2. 2. Overview•The Satellite Platform•Archaeological Prospection•Landscape Survey in data poor environments•Exemplar: Homs, Syria•The Future•Conclusions
    3. 3. OverviewThere is no need to take notes:Slides –Text –http://dl.dropbox.com/u/393477/MindMaps/Events/ConferencesAndWorkshops.htmlThere is every need to ask questions
    4. 4. Characteristics of the satellite platformSensor Types – Active and Passive
    5. 5. Characteristics of the satellite platformSpatial Resolution Multi-spectral 4 bands
    6. 6. Characteristics of the satellite platformSpatial Resolution - 20cm Aerial PhotographyDetailedmappingField backdropSmall area
    7. 7. Characteristics of the satellite platformSpatial Resolution - 1m IkonosDetailedmappingField backdropLarge area
    8. 8. Characteristics of the satellite platformSpatial Resolution - 30m LandsatLandscapemapping• Soils• Geology• Vegetation• Land use• etcLong historyMulti-spectralMulti-temporal
    9. 9. Characteristics of the satellite platformSpatial Resolution - 30m Landsat (geology bands)Landscapemapping• Soils• Geology• Vegetation• Land use• etcLong historyMulti-spectralMulti-temporal
    10. 10. Characteristics of the satellite platformTemporal Resolution
    11. 11. Characteristics of the satellite platformTemporal Resolution
    12. 12. Characteristics of the satellite platformSpectral Resolution
    13. 13. Characteristics of the satellite platformA large archive
    14. 14. Problems of the satellite platformAtmospheric Attenuation
    15. 15. Problems of the satellite platformTopographic Distortion
    16. 16. Problems of the satellite platformPixel Mixing
    17. 17. Problems of the satellite platformClassification
    18. 18. Characteristics of the satellite platformPerceived issues for archaeologistsCost• It’s perceived to be expensiveComplexity• It’s perceived to be complex to understand and processTemporal constraints• Revisits are frequent• Times of collection are fixedThe ‘Google Earth’ effect
    19. 19. Characteristics of the satellite platformMy issues with satellite applicationsA 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 footprintEveryone focuses on prospectionat the expense of• The Landscape• Integrated Cultural Resource Management
    20. 20. Archaeological ProspectionWhat is the basis for detectionDiscovery requires the detection of one or more siteconstituents.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. 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 marksNow you see me dont
    22. 22. Archaeological ProspectionWhat is the basis for detectionWe detect Contrast:• Between the expression of the remains and the local background valueDirect 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. 23. Archaeological ProspectionWhat is the basis for detection
    24. 24. Archaeological ProspectionWhat is the basis for detection
    25. 25. Archaeological ProspectionWhat is the basis for detection
    26. 26. Archaeological ProspectionSummaryThe 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 exhibitedThe image must be captured at the right time:• Different features exhibit contrast characteristics at different times
    27. 27. Satellite images for archaeological prospectionHigh spatial resolution opticalEssentially large footprint vertical photographsLower 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. 28. Satellite images for archaeological prospectionHigh spatial resolution opticalThat’s it.
    29. 29. Satellite images for archaeological prospectionHigh spatial resolution optical Nothing more to say really
    30. 30. Satellite images for archaeological prospectionHigh spatial resolution opticalWell 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. 31. Satellite images for archaeological prospectionHigh spatial resolution optical – WorldView - 2 New: good water penetration New: Yellowness (crop) New: Red-edge (crop) New: NIR (crop/biomass)
    32. 32. However, prospection is not everythingWhy use satellites when it’s already known!
    33. 33. However, prospection is not everythingLandscape surveyIts not just about finding stuff• Its about placing it in a context where it can be usefulMost countries do not have mature cultural managementframeworks• 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. 34. However, prospection is not everythingLandscape surveyThis is an inventory problem• OK we need to do more prospection! • Bring on the planes! • NOIf we were to start from the beginning would we do it all thesame way again• Learn from our experiencesThis is what I hope to show in the rest of the presentation
    35. 35. However, prospection is not everythingLandscape survey – Types of surveyReconnaissance 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. 36. However, prospection is not everythingLandscape survey – Types of surveyLandscape research: (Identification)• to form theoretical understanding of the relationships between settlement dynamics, hinterlands and the landscape itself.Cultural Resource Management (CRM): (Management andProtection)• 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. 37. However, prospection is not everythingLandscape survey – Desk Based Assessments
    38. 38. However, prospection is not everythingLandscape survey – Desk Based AssessmentsSources that are normally considered for reference during aDBA 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. 39. Landscape Survey in data poor environments Ecological Setting Hinterland Ecofacts Sites Artefacts
    40. 40. Landscape Survey in data poor environmentsNature 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. 41. Landscape Survey in data poor environmentsUnderstand 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. 42. Landscape Survey in data poor environmentsUnderstand 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. 43. Landscape Survey in data poor environmentsImage SelectionWhat has an impact on the derivatives you want to create:• Environment• Topography• Agriculture• Land use• Image fidelity • Cloud Cover, Atmospheric Haze
    44. 44. Landscape Survey in data poor environmentsImage SelectionRule 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. 45. Landscape Survey in data poor environmentsImage SelectionRule 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. 46. Landscape Survey in data poor environmentsImage Selection – What to consultOn-line streaming• Bing Maps• Yahoo Maps• Google Maps• Open Street Map• Open Aerial MapUse Caution – The ‘Google Earth’ effectStrongly consider adding new data to the Open collectionmovements (OSM empowers local communities)
    47. 47. Landscape Survey in data poor environmentsImage Selection – What to consultThe 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 • 60s onwards
    48. 48. Landscape Survey in data poor environmentsImage Selection – What to consultThe libraries of free or low cost imagery• Landsat • Family of sensors operating from 1973 onwards • Multispectral• ASTER • DEM • Multispectral• SRTMBespoke
    49. 49. Landscape Survey in data poor environmentsImage Pre-processingAtmospheric CorrectionGeo-referencingCo-referencingOrthorectificationTo what degree of accuracy• Fit for purpose• To enable it to be confidently identified on the ground
    50. 50. Landscape Survey in data poor environmentsTheme 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. 51. Landscape Survey in data poor environmentsTheme Extraction - DTM• Photogrammetry • Stereo pairs • Corona (5m results) • beware of clouds  • beware of trees 
    52. 52. Landscape Survey in data poor environmentsTheme Extraction - LandscapeSatellite imagery has an established pedigree of doing this• Corine Land Cover• NASA Global Maps• Soil Maps• Vegetation mapsProcessing is dependent on• Type of theme• Desired scale
    53. 53. Landscape Survey in data poor environmentsTheme Extraction - LandscapeClassification 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. 54. Problems of the satellite platformTheme Extraction - Landscape
    55. 55. Landscape Survey in data poor environmentsTheme Extraction - Landscape
    56. 56. Landscape Survey in data poor environmentsArchaeological Prospection – Positive Evidence
    57. 57. Landscape Survey in data poor environmentsArchaeological Prospection – Negative Evidence
    58. 58. Landscape Survey in data poor environmentsArchaeological Prospection – Image Enhancement
    59. 59. Landscape Survey in data poor environmentsArchaeological Prospection – Documentation or KTKnowledge Transfer is importantGood access is importantConsider Open approaches (OSM, Open Archaeology Map)• Ethics?
    60. 60. Exemplar: Homs, Syria
    61. 61. Exemplar: Homs, SyriaOverview – SHR ProjectTo establish a framework to understand settlement dynamicsand diversity in the Homs region, Syria.C. 650 sq km2 principal contrasting environmental zones • Basalt • MarlInitial program of surface/site surveyNo sites and monuments record! • No aerial photography available (‘closed skies’) • Satellite imagery evaluated as a prospection tool
    62. 62. 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.
    63. 63. 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
    64. 64. 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)
    65. 65. Exemplar: Homs, SyriaImage 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. 66. Exemplar: Homs, SyriaImage Selection
    67. 67. Exemplar: Homs, SyriaImage Pre-processingAtmospheric correctionGeo-referencing Corona (using Ikonos as a backdrop)
    68. 68. Exemplar: Homs, SyriaLandscape ThemesThemes 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. 69. Exemplar: Homs, SyriaLandscape Themes – Classification SystemsUsed 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. 70. Exemplar: Homs, SyriaLandscape Themes – Classification SystemsSegmented the imagery into contiguous parcels with differentcharacteristics• Combination of qualitative and quantitative techniques • Principal Component Analysis • Unsupervised classification • Band ratios • Transparent overlays• Visual interpretationInsert classification ID
    71. 71. Exemplar: Homs, SyriaLandscape ThemesThe USGS classification means these views can be refined atdifferent scales• Vary field based on Classification ID
    72. 72. Exemplar: Homs, SyriaProspection – The Basalt
    73. 73. Exemplar: Homs, SyriaProspection – The BasaltComplex and intensive multi-period palimpsest of upstandingstructural features that covers a large extent• Cairns• Walls• StructuresSmallest feature is c. 1m in sizeStructures constructed from basalt
    74. 74. Exemplar: Homs, SyriaProspection – The BasaltDetected by:• Topographic effect (shadows)• Spectral responseRequirements:• 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. 75. Exemplar: Homs, SyriaProspection – The Basalt, Image EnhancementInternal 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. 5-8mCorona geo-referenced to the Ikonos Basemap• Difficulty in selecting tie-points due to 30 year time difference• Corona accuracy > c. 5-8mSimple technique vastly increased utility of the imagery• Allowed cheaper desk-based analysis
    76. 76. Exemplar: Homs, SyriaProspection – The Basalt , Image Enhancement
    77. 77. Exemplar: Homs, SyriaProspection – The Basalt, Image EnhancementLinear enhancements• Edge detection• Crisp• Generally unsuccessfulImage fusion/overlay• Fuse 1m pan with 4m MS for Ikonos• Transparent overlay• Very successful
    78. 78. Exemplar: Homs, SyriaProspection – The BasaltSimply 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 setsAdding an attribute for the source (so you know where theevidence came from)Undertaking analysis
    79. 79. Exemplar: Homs, SyriaProspection – The Basalt
    80. 80. Exemplar: Homs, SyriaProspection – The Marl
    81. 81. Exemplar: Homs, SyriaProspection – The MarlDispersed remains punctuated by soil marks and tellsSmallest feature is c. 10s of metres in areaDetected by• Spectral responseRequirements:• Hyper arid• No need to improve Ikonos spatial accuracy• Multi-spectral (see comparison later)
    82. 82. Exemplar: Homs, SyriaProspection – The MarlSimply a process of digitising resultsAdding an attribute for the source (so you know where theevidence came from)Conducting field verification (including mapping and grabsample of diagnostic pottery)Conducted validity determination – extensive fieldwalkingUndertaking analysis• Improved understanding of population dynamics over time
    83. 83. Exemplar: Homs, SyriaProspection – The Marl, Lab WorkSoil 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 ofpeak aridity (at least for optical region)Wanted to understand the cause of the colour change so thatwe could model detection with other sensors
    84. 84. Exemplar: Homs, SyriaProspection – The Marl, Lab WorkFactors influencing soil colour include:• Mineralogy• Chemical constituents• Soil moisture• Soil structure• Particle Size• Organic matter content Soil Moisture %
    85. 85. Exemplar: Homs, SyriaProspection – The Marl, Lab WorkSoil samples were taken across a number of site transectsAnalysed for:• Moist and dry spectro-radiometer readings• Particle size measurement• Magnetic susceptibility• Geochemical analysis
    86. 86. Exemplar: Homs, SyriaProspection – The Marl, Lab Work
    87. 87. Exemplar: Homs, SyriaProspection – The Marl, Lab WorkConcluded difference in spectral reflectance principally due tovariations in: • moisture content • grain size • soil structureSite soils share similar spectral curve to off site soils• Measurable relative reflectance difference (in this zone)• NO unique archaeological spectral curve
    88. 88. Exemplar: Homs, SyriaProspection – The Marl, Lab WorkThis confirmed hypothesis about data collection duringperiods of peak aridity• Ikonos subsequently collected in January/February 2002Although analysis in SWIR could detect these physicalmanifestions more effectivelyArchaeological sites in this zone represent localised areaswith increased reflectance• This information can be used to enhance visualisation of residues
    89. 89. Exemplar: Homs, SyriaProspection – The Marl, Lab Work IncreaseAn 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. 90. Exemplar: Homs, SyriaProspection – The Marl, Image EnhancementArchaeological residues as localised background soilvariations• 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 zeroFeatures enhanced• Archaeological residues• Roads• Buildings• Crops• Small water bodies
    91. 91. Exemplar: Homs, SyriaProspection – The Marl, Image EnhancementRequirements• Moving average kernel • What size? • Trial and Error gave 200m • processor intensive
    92. 92. Exemplar: Homs, SyriaProspection – The Marl, Image Enhancement
    93. 93. Exemplar: Homs, SyriaProspection – The Marl: evaluation
    94. 94. Exemplar: Homs, SyriaProspection – The Marl: evaluation
    95. 95. Exemplar: Homs, SyriaProspection – The Marl: evaluation
    96. 96. Prospection – finding stuff!
    97. 97. Exemplar: Homs, SyriaGeneral– multispectral helps
    98. 98. Exemplar: Homs, SyriaGeneral– Time change analysis
    99. 99. Exemplar: Homs, SyriaGeneral– Image Interpretation Keys
    100. 100. ConclusionsSatellite 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 contextAerial approaches in the medium term will always providebetter spatial resolution and temporal flexibility
    101. 101. ConclusionsBe selective• Choose stuff because it • Adds value • Solves a problem• Just because you can doesnt mean you should
    102. 102. Costs Cost per Hectare £1,000,000 £100,000 £10,000 £1,000 £100 £10 £1 I AR ity na s AS ry x no te IR et tiv o D C o or m s is Li al Ik Po C to es m ne R er d ag an Th M n e t io r tu va Na ca h Ex is gl En Not comparing like with like for archaeological value
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