Prospection, Prediction and Management of Archaeological Sites in Alluvial Environments
1 like688 views
This document discusses techniques for prospecting, assessing preservation of, and predictively managing archaeological sites in alluvial environments. It describes using aerial photography, multispectral imaging, lidar, and hyperspectral analysis for non-invasive prospection of sites. Predictive models are developed using weighted environmental factors to provide risk scores for archaeological potential and impacts on a parcel-by-parcel basis. Critiques note the challenges of modeling past human behaviors and meanings assigned to model results may be imprecise without detailed records. Overall the techniques show potential but also adoption issues around data availability, costs, and strategic management alignment.
Prospection, Prediction and Management of Archaeological Sites in Alluvial Environments
- 1. Prospection, Prediction and Management of Archaeological Sites in Alluvial Environments Keith Challis, Mark Kincey and Andy J Howard, IBM Vista, University of Birmingham U B
- 2. Outline • Study Areas • Prospection • Assessing Preservation • Predictive Management • Critique
- 3. Study Areas Three, c.300km2 study areas • MTV Derbyshire • MTV Newark • LTV Gainsborough 30k ha terrace 35k ha floodplain 144 SAM 11,222 HER records 2254 Aimee (NMR) records
- 4. Prospection
- 5. Prospection • Traditionally focused on known sites • Conventional photography • Landscape scale • Filling gaps • Newer digital techniques
- 6. Prospection Conventional Aerial Photography • Extensive opportunistic flying • Good years reveal much • Not Systematic • Limited by crop response • Visible spectrum(390- 750nm)
- 7. Prospection
- 8. Prospection Multispectral and Hyperspectral • No systematic surveys • Research suggests huge potential • Extended visibility beyond visible spectrum • Rapid area surveys • Satellite imagery
- 9. Prospection
- 10. Prospection Airborne Lidar • 3D record of topography at very high resolution • Systematic survey by Environment Agency • Upstanding and buried sites • Assessment of preservation • Change detection (multi temporal survey)
- 11. Prospection
- 12. Assessing Preservation Hyperspectral Techniques • Adaptation of vegetation monitoring • Ratios of reflectance in different spectral bands reflect vegetation vigour • NDVI (Normalised difference vegetation) • Tasselled Cap
- 14. Assessing Preservation Airborne Lidar Intensity • NIR reflectance enhanced detection of vegetation and soil properties • Not a robust indicator • No systematic collection • Much work to be done
- 17. Problem • Information on heritage assets resides in expert hands • Issues of availability / confidentiality • Discrete, not continuous record • Articulated need for “red flag” mapping • How to achieve this without alienation of some stakeholders
- 18. Goals • To provide interpreted information to non-expert users • Models rooted in knowledge base • Not to usurp the HER as a source of data or to undermine curatorial prerogative
- 19. Approach: User-focused • Understand what users need, how they think and work • Model the knowledge- based approach of expert users “topsight” (Gelernter 1992) • Presentation of results structured to fit the real- world and in a user friendly medium
- 20. Approach: Simplify • Inductive (data driven) rather than deductive (theory driven) • Simplify and summarise (the detail is in the HER) • Validate through user feedback (rather than test and quantify)
- 21. Approach: Model Objectives The completed models will provide per parcel scores for: • The predicted archaeological potential of all land parcels. • The aggregate bearing potential and value of all land parcels. • The susceptibility of individual land parcels to field evaluation techniques. • The likely physical condition of buried cultural remains. • The risk of encountering buried waterlogged organic remains. • The level of impact that different forms of extraction may have on the archaeological record • The importance of archaeology in the light of regional priorities. • The likely mitigation needs in the light of PPG 16 guidance
- 22. Method: Predictive Models • Classic predictive modelling • Big, empty, heterogeneous areas (2500km2 / 21 sites) • Assess and weigh environmental factors • Weights inform model
- 23. • Such models are inappropriate for the TV • c. 40% of land parcels contain a record • Eg. Newark, 1254 parcels out of 5012 Method: Predictive Models
- 24. Method: Our Data Model • OS MasterMap® as a spatial framework • Raster based GIS models • 50m grid (200k cells)
- 25. Method: Model Building • Source data is rasterised • Simplified scores are applied or extracted • Models are based on weighted means of scores • Blank areas filled using landscape classification and spatial modelling Terrace: Score = 3
- 26. Method: Per Parcel Results • Calculations reclassified to 5 level scale from low risk to high • Aggregated model scores devolved to level of an OS MasterMap® TOID • Built up parcels, water and parcels less than 1ha in extent excluded
- 27. Delivery • Data for stakeholder GIS (tables of values for each TOID)
- 28. Delivery • Interactive using embedded Google Earth application
- 29. Critique • “Topsight” is not necessarily the same thing as predictive modelling or risk mapping • Modelling period based activity and intensity of activity is problematic • It would be possible model individual classes of monument with clear geographic preferences (eg burnt mounds) • Perhaps general models are the most helpful • The meaning of results is imprecise and open to misinterpretation
- 30. Concluding Thoughts Prospection • Non-photographic techniques offer huge potential, but uptake issues (availability, cost, education) • In fact, in times of limited finances reliance on traditional techniques may not be cost-effective Predictive Management • Need for strategic management of heritage assets is axiomatic • System adoption requires clear joined-up thinking at high level • Possible conflict with aggregate resource assessments in England • Do we need another level of information?