Premium
Reconstructing a prehistoric topography using legacy point data in a depositional environment
Author(s) -
Vermeer Julian A. M.,
Finke Peter A.,
Zwertvaegher Ann,
Gelorini Vanessa,
Bats Machteld,
Antrop Marc,
Verniers Jacques,
Crombé Philippe
Publication year - 2014
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.3472
Subject(s) - holocene , geology , prehistory , digital elevation model , elevation (ballistics) , kriging , physical geography , sediment , sedimentary depositional environment , remote sensing , paleontology , computer science , geography , geometry , mathematics , structural basin , machine learning
ABSTRACT Reconstruction of past topography is an essential step towards the understanding of past landscapes in terms of biophysical patterns and processes and man–landscape interactions by archaeologists, geomorphologists, geologists and soil scientists. Landscape reconstructions can be based on process knowledge, on data, or on a combination of both. In this case study we focus on a data‐based approach, where knowledge on the geological history is used to interpret and exploit legacy data. As part of a landscape reconstruction of a large area of 584 km 2 a map of the elevation near 10 000 BC was produced. Starting from a present‐day grid digital elevation model (GDEM) that was filtered for human influences, we identified the thickness of accumulated sediments over the Holocene, mapped these and corrected the GDEM. To map the thickness of Holocene sediments we used 72 (OSL and 14 C) dated sediment samples, 731 recent profile descriptions and 3288 legacy profile descriptions. Protocols were formulated based on literature and local correlative studies to convert the legacy profile descriptions into estimates of the thickness of Holocene sediments, with an estimate of the precision. The method of Kriging with uncertain data was applied to obtain a map. Validation at 200 independent test locations with certain data showed a mean error of –6 cm and a standard deviation or error of 16 cm, which was in accordance with the estimated precision of 16 cm. The resulting map indicated zones with marked change that could be studied in more detail. Future reconstructions could employ both process knowledge and data by combining landscape genesis models with legacy data to map model errors and thus increase the quality of the reconstruction. Copyright © 2013 John Wiley & Sons, Ltd.