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Cross correlation of CIELAB color reflectance data from archive photographs and line-scan images of sediment

Published online by Cambridge University Press:  02 October 2019

Della K. Murton*
Affiliation:
Department of Geography, University of Cambridge, Cambridge, CB2 3EN, United Kingdom Present address: Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, United Kingdom
Simon J. Crowhurst
Affiliation:
Department of Earth Sciences, University of Cambridge, Cambridge, CB2 3EQ, United Kingdom
*
*Corresponding author e-mail address: dkf20@cam.ac.uk (D.K. Murton).

Abstract

Archive color photographs—particularly of sites where sediment is no longer available—are an underused resource that potentially contains detailed paleoenvironmental information. To investigate this potential, two sets of digital images were taken, at different times, of loess, glaciolacustrine, and deep-sea sediments. The first image set was taken using standard digital cameras. Lighting conditions and sediment surface preparation varied, in a similar way to characteristics likely to be encountered in archive photographs. The second image set was taken by a high-resolution, line-scan camera with an integrated light source. CIELAB (Commission Internationale de l'Eclairage) color reflectance data were obtained from both image sets and analyzed by cross correlation. Of the three reflectance parameters (L*, a*, and b*), L* reflectance is the most compromised by differences in ambient lighting or moisture content. Textural distinctions appear to be an important factor influencing the cross correlations and produce multiple, relatively weak solutions for the glaciolacustrine sediments, whereas the texturally uniform loess and deep-sea sediments produce a single, best-fit solution. Comparison of a* reflectance records from an undated marine sequence with a chronologically constrained sequence from the same site indicates the potential to apply color reflectance to produce preliminary age models.

Type
Research Article
Copyright
Copyright © University of Washington. Published by Cambridge University Press, 2019 

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