Transcript of "Seeing the Unseen- Improving aerial archaeological prospection"
Seeing the unseen:Improving aerialprospection outsidethe visible spectrumDavid Stott, Anthony Beck,Doreen Boyd & Anthony CohnSchool of ComputingFaculty of Engineering
Overview• An introduction to the DART project• The problem • Contrast • Principles of detection• Preliminary results • Lots of graphs• Further work • Problems • Proposed analyses
The DART project• Detecting Archaeological Residues using remote Sensing Techniques• Soil properties • University of Birmingham • University of Winchester• Geophysics • Bradford University• Optical (aerial and satellite detection) • University of Leeds • University of Nottingham
How do we detect archaeological features?•Contrast with the background•Changeable: • Land use • Cultivation regime • Vegetation • Species & variety • Growth stage (phenology) • Soils • Weather
The problem• Observer directed aerial photography • Bias • Soils (Jessica Mills & Rog Palmer) • Honeypots (Dave Cowely & Kenny Brophy) • Visible spectrum• Sensors • Underutilised because we don’t know how best to use them • Hyperspectral • Focus on data reduction • Very few archaeologically commissioned flights • Thermal
Aims• To understand how archaeological features interact with and influence the surrounding environment • If we do this we can work out how to detect them better • Improved exploitation of existing sensors • Improved development of new sensorsThis aims of this project are:• To identify optimal timing for acquiring aerial and satellite imagery for archaeological prospection • Commissioning new imagery • Evaluating existing archives
What I’m doing: Fieldwork• Measurements taken on transects across linear features on at least a monthly basis• Spectro-radiometry (more on this in a minute)• Surface properties • Vegetation coverage (near vertical close-range photography) • Vegetation growth stage • Height • Feekes scale • Vegetation density • Leaf Area Index (LAI)
Spectra-ma-what-now? • Spectroradiometry • ASD FieldSpec Pro • Produces a spectral profile • 350nm-2500nm (Visible-Short Wave Infrared) • c. 1.4-2nm Sampling interval interpolated to 1nm • Usable 2hrs either side of solar noon • Needs clear-ish skies
Flights• Environment agency • CASI • High spatial resolution ortho-photography • 28th June 2011• NERC ARSF • Eagle (visible – near-IR) & Hawk (near-IR – SWIR) • High spatial resolution ortho-photography • Thermal? • 14th June 2011, 23rd March 2012 • 3 further flights during 2012
Problems• 2011 Driest spring in eastern England for 100 years • Extreme conditions • 2012 due to be an even more extreme drought • I want it to be a bad spring and a worse summer (sorry. Kind of) • Not much subtlety in the vegetation marks…
“Why do you need hyperspectralwhen you can see the cropmarks onthe ground like this”
Solution: Extend temporal depth?• Can I use lower spatial resolution satellite data? • Paleochannels as a proxy for archaeological vegetation marks? • Need to test this
Further work: Analysis• Building an ontology • Identifying diagnostic absorption features • Well known from precision agriculture & remote sensing• Using this to evaluate contrast • Python code to compare spectra • Field spectra (high temporal resolution, low spatial coverage) • Aerial spectra (low temporal resolution, high spatial coverage)• Correlating contrast to environmental variables • Weather • Soil moisture
Further work: Building a knowledge-based system• Testing • Using this to predict contrast in 2013 • Using this to predict contrast in archive imagery • NERC flights? • Geoeye satellite data? • Aerial photos?
Finally• DART is Open Science! • PLEASE re-use our data • Servers online spring-summer 2012• www.dartproject.info• @DART_Project• http://www.flickr.com/groups/dartproject/
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