Prospection, Prediction and Management of Archaeological Sites in Alluvial Environments


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Prospection, Prediction and Management of Archaeological Sites in Alluvial Environments

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Prospection, Prediction and Management of Archaeological Sites in Alluvial Environments

  1. 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. 2. Outline • Study Areas • Prospection • Assessing Preservation • Predictive Management • Critique
  3. 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. 4. Prospection
  5. 5. Prospection • Traditionally focused on known sites • Conventional photography • Landscape scale • Filling gaps • Newer digital techniques
  6. 6. Prospection Conventional Aerial Photography • Extensive opportunistic flying • Good years reveal much • Not Systematic • Limited by crop response • Visible spectrum(390- 750nm)
  7. 7. Prospection
  8. 8. Prospection Multispectral and Hyperspectral • No systematic surveys • Research suggests huge potential • Extended visibility beyond visible spectrum • Rapid area surveys • Satellite imagery
  9. 9. Prospection
  10. 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. 11. Prospection
  12. 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
  13. 13. Assessing Preservation
  14. 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
  15. 15. Assessing Preservation
  16. 16. Predictive Management
  17. 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. 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. 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. 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. 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. 22. Method: Predictive Models • Classic predictive modelling • Big, empty, heterogeneous areas (2500km2 / 21 sites) • Assess and weigh environmental factors • Weights inform model
  23. 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. 24. Method: Our Data Model • OS MasterMap® as a spatial framework • Raster based GIS models • 50m grid (200k cells)
  25. 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. 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. 27. Delivery • Data for stakeholder GIS (tables of values for each TOID)
  28. 28. Delivery • Interactive using embedded Google Earth application
  29. 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. 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?