% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Boemke:977017,
author = {Boemke, Bruno Johannes},
othercontributors = {Lehmkuhl, Frank and Maier, Andreas},
title = {{U}nlocking pattern extraction from geospatial big data -
methodological innovations in remote sensing and geospatial
analysis for environmental and geoarchaeological research},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-00528},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2024},
abstract = {In recent decades, the amount of openly available geodata
has increased exponentially. To a large part, this can be
attributed to the technological progress in satellite remote
sensing, producing world-wide coverage from a large variety
of sensors on a sub-daily basis. In addition, improved
accessibility and a growing user base have favoured
methodological advances in geospatial analysis as well as an
increase in the number of derived products such as, e.g.,
land cover classifications. While this opens up new
opportunities for geospatial applications, the vast amounts
of geodata also challenge studies in terms of selection,
filtering, harmonizing, processing and interpretation. To
fully utilize the opportunities that the increasing amount
of geodata offers while tackling its challenges, new and
innovative methodologies for different geospatial
applications are needed. This cumulative dissertation is a
contribution to this goal in the fields of environmental
science and geoarchaeology. It presents three novel and
experimental approaches on how to effectively utilize a
large variety and/or long time series of geodatasets and
analyse them sensibly within the framework of a specific
research question. The first approach explores the
possibilities and limitations of assessing complex aeolian
dune field morphology and evolution using synthetic aperture
radar (SAR) satellite data. This study relies on the
Sentinel-1 mission, which acquires data in volumes of
approximately 600 gigabytes per day. To analyse this
continuous stream of geospatial big data, the study examines
the key interaction mechanism between C-Band radar and sand
dunes and introduces a visual pattern extraction method
based on continuous wavelet transfer. This novel method is
applied to the Western Mongolian dune field Bor Khyar. The
results give new insights into the temporal and spatial
dynamics of dune scales and their response to aeolian
activity, revealing local differences as well as inter- and
intra-annual variations in the dune morphology. The second
approach is a methodological contribution to the field of
archaeological predictive modelling. The main challenge of
this study is the extraction of a thematic pattern from a
small sample of 23 available Upper Palaeolithic sites in
Lower Austria. This is achieved using a novel approach
combining a classical deductive method with the capabilities
of machine learning. This way, ten spatial predictors
representing morphological, hydrological, and
sedimentological factors of the paleo-environment are
analysed for optimal, viable, and non-viable value ranges
and combined mathematically. The resulting predictive model
reveals several spatial dynamics of site probability and
shows high compliance with known sites in the study area. In
stark contrast to this study, which is challenged by the
small number of available sites, the third study conducts
geoarchaeological pattern extraction based on a
substantially bigger dataset of close to 4200 European Upper
and Final Palaeolithic sites. The main aim of this study is
to explore whether the site distribution is representative
of human distribution in the paleo-landscape or if sampling
biases obscure this information. To this goal, eight
Pan-European geodatasets representing both
settlement-relevant factors of the paleo-environment and
discovery-relevant biases of the modern to contemporary
landscape are analysed using a combination of geospatial and
geostatistical methods. The results show that the actual
distribution of sites seems to be most strongly influenced
by sampling biases. The influence of the settlement factor,
however, is still significant when comparing site subsets
from different regions, different Upper Palaeolithic
periods, and, especially, between open-air and cave sites.
The implications of this study are substantial for
geoarchaeological approaches, as the sampling bias is often
overlooked or underestimated as a factor actively
influencing the distribution of known sites. All three
approaches present a novel methodological approach in their
respective field of study and outline a workflow that can be
adapted and built on. For the availability to a broader
audience, all studies are published as open access. In
addition, the results of both geoarchaeological approaches
are distributed as open data in universally usable geodata
formats. As such, they can serve as foundation and
inspiration for many future studies that utilize geospatial
big data for environmental and geoarchaeological research.},
cin = {551610 / 530000},
ddc = {550},
cid = {$I:(DE-82)551610_20140620$ / $I:(DE-82)530000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2024-00528},
url = {https://publications.rwth-aachen.de/record/977017},
}
guest ::