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@PHDTHESIS{Pipaud:817365,
author = {Pipaud, Isabel},
othercontributors = {Lehmkuhl, Frank and Wellmann, Jan Florian and Stauch,
Georg},
title = {{A}utomating delineation and classification of alluvial
fans by the use of object-based geomorphometry and machine
learning},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2021-03874},
pages = {1 Online-Ressource : Illustrationen, Diagramme},
year = {2020},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2021; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2020},
abstract = {Automating the mapping of landforms via digital elevation
data is a key research topic in the field of
geomorphometry. The acquisition of extensive thematic
datasets has the potential to foster scientific progress in
geomorphology and related disciplines by enhancing our
understanding of how formative processes are reflected in
geomorphometric variables and by facilitating the transfer
of in-field results to larger study areas. In the wake of
continuously improving spatial resolutions of DEMs,
traditional pixel-based classification approaches fail in
accounting for the irregular boundaries of landforms. To
address this fundamental issue, object-based frameworks
developed in the field of remote sensing have been adopted
for geomorphometric research. With respect to alluvial fans,
all workflows presented hitherto are, however, deficient
in delineating laterally adjoining fans as individual
landforms. Since coalescing fans represent a common scenario
for (semi-) arid mountain ranges, further research is
warranted. Although object-based image analysis (OBIA)
gained considerable emphasis during the last two decades,
scientific progress is complicated by substantial
challenges. (1.) The sensitivity of segmentation algorithms
to local discontinuities requires a proper choice of
parameters. The appropriate selection of scaling factors or
bandwidths is, however, an issue which is not
comprehensively resolved to this date, because segmentation
is an ill-structured problem which is not conducive to an
a-prori assessment. (2.) Existing adaptions of object-based
image analysis (OBIA) in the field of geomorphometry
neglect that by formalizing the local altitude field,
morphometric variables act (i) as an interface to the land
surface characteristics of interest and (ii) are strongly
influenced by the terrain rendition characteristics of the
DEM in use. This doctoral dissertation first sets the stage
by evaluating freely available near-global DEM data— ASTER
GDEM, AW3D30, SRTM1 (30 m posting each), and a
self-processed TanDEM-X DEM (15 m posting). Both
geomorphological mapping and geomorphometric processing
attest TanDEM-X a superior terrain rendition up to moderate
relief. In settings of strong topographic contrasts,
however, challenges toward interferometric processing result
in a deterioration of terrain representation. SRTM1 shows an
overall consistent depiction of terrain, but is
characterized by an autocorrelated grainy texture of
moderate magnitude. The most recent 30 m-digital elevation
model (DEM) product, ALOS World-3D DEM with 1'' (~30 m)
posting (AW3D30), shows improvements over Shuttle Radar
Topography Mission DEM with 1'' posting (SRTM1) by
displaying only uncorrelated systematic noise of relatively
low magnitude, which suitable filtering can remove. In
contrast to the aforementioned DEM products, ASTER GDEM
displays a striking bumpy texture, and it is strongly
discouraged to use the DEM in geomorphometric research.
Since DEM characteristics exert a strong influence on
significant ranges of morphometric variables, this doctoral
dissertation challenges the de facto standard of utilizing
rule-based classification schemes for object-based analysis
in the field of geomorphometry. As an alternative, a
framework is proposed where both segmentation and
classification are controlled by supervised machine
learning in the following way: (1.) The approach recognizes
that segmentation and classification need to be addressed
with individual compilations of morphometric variables. In
the case of alluvial fans, a robust segmentation of
laterally adjoining fans can be accomplished by
incorporating the sine and cosine of slope aspect in
addition to slope and curvature values. For classification,
tailored algorithms have been developed to formalize the
morphometric signature of alluvial fans. (2.) A
high-performing object-based workflow is thus compulsorily
feature-specific. To formalize the morphometric signature
of a landform, one-class estimators are for the first time
integrated into an object-based workflow. (3.) To account
for the ill-structured nature of segmentation, the issue of
selecting appropriate bandwidths for segmentation is tackled
from an a-posteriori perspective. Specifically,
segmentation is first performed for a range of
parametrizations, and all raw datasets are classified.
Subsequently, the final thematic dataset is compiled by
applying an overlap resolve algorithm on the raw
segmentation data, selecting final objects according to
their estimator scores and topological relationships. The
framework was first tested for the delineation and
discrimination of alluvial fans situated in a single study
area within the Mongolian Altai, using a spatial variant of
the mean-shift algorithm for segmentation and a one-class
support vector machine (OCSVM) for classification. The
framework is able to accurately delineate laterally
coalescing fans with the exception of fans grading into a
bajada, i.e., fans displaying diffuse and ambiguous lateral
delimitations. By successively adapting the feature space
representation yielded by the one-class support vector
machine (OCSVM) estimator, a promising diagnostic ability
could be achieved by using just eleven training samples. The
sensitivity of the OCSVM algorithm to the choice of its
parameters hampers, however, the transferability of the
workflow to different landforms and DEM datasets. To
address this issue, this thesis introduces a new one-class
random forest (OCRF) and trials the algorithm with a
training dataset of 306 fans compiled from seven different
study areas. Owing to a high diagnostic ability and a
robustness towards its parametrization and morphometric
variables with little discriminatory potential, the OCRF
improves the delineation and classification of alluvial
fans at the expense of requiring a larger training dataset.
Furthermore, the non-parametric nature of OCRF can be
capitalized on by performing a fully data-driven
optimization of parameters and DEM preprocessing.
Eventually, the geomorphometric variables developed in this
thesis are utilized to assess whether controlling factors
can be inferred from the geomorphometry of alluvial fan
surfaces. While the findings demonstrate that scaling
relationships exert a dominant influence on the expected
range of morphometric parameter values, it could be shown
that the differentiation between tectonic activity and the
capability of the fan–catchment system in evacuating
sediment can be improved by quantifying the degree of fan
progradation. In essence, this doctoral dissertation
showcases that object-based analysis in the field of
geomorphometry poses different demands on methodological
frameworks as is the case in the field of remote sensing,
and the term object-based morphometric analysis (OBMA) is
suggested to emphasize this difference. The methodological
framework developed in this thesis offers excellent
prospects for an extensive use of object-based techniques in
the fields of geomorphology and geomorphometry. Libraries
of feature perimeters and feature signatures can be set up
in a staggered, semi-automated manner, by first performing
an OBMA analysis using an OCSVM or a comparable algorithm,
and switching to a one-class random forest (OCRF) once
suffcient training data has been acquired. For the thematic
mapping of larger portions of the geomorphological
inventory, it is envisaged to pursue a stratified approach
where landform types are delineated and identified in a
serial manner, utilizing object-based morphometric analysis
(OBMA) workflows tailored towards each geomorphic feature
of interest.},
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-2021-03874},
url = {https://publications.rwth-aachen.de/record/817365},
}
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