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Remote Sensing of Ancient Paths: A Case Study from the Middle East

Kalayci Tuna


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  <identifier identifierType="DOI">10.5281/zenodo.1286154</identifier>
  <creators>
    <creator>
      <creatorName>Kalayci Tuna</creatorName>
      <affiliation>CNR-IBAM</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Remote Sensing of Ancient Paths:  A Case Study from the Middle East</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-06-09</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Presentation</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1286154</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1286153</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/geomop</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;pre&gt;This study focuses on Early Bronze Age movement network in Upper Mesopotamia and in particular aims to investigate differential movement practices which lasted half a millennium. 
The main hypothesis of the study is that variations in the movement levels must have differentially changed soil physical characteristics (e.g. soil compaction/moisture) 
so that differences in traffic is potentially detectable on satellite data.

As for the methodology, publicly available satellite sensor data (panchromatic and multispectral) are exploited to generate proxy variables of ancient movement, 
mainly in the form of soil and vegetation indices. Index values from ancient paths are cross-compared with areas which must have faced no traffic so that a relative 
traffic metric can be estimated. The model, in return, differentiates between high and low traffic carrying roads. As for the secondary aim, the study investigates 
sensor data in time-series form and intends to understand the spectral response of linear archaeological features under differential climatic conditions.&lt;/pre&gt;</description>
    <description descriptionType="Other">747493 — GeoMOP — H2020-MSCA-IF-2016</description>
  </descriptions>
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