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| Titel |
Perspective – Synthetic DEMs: A vital underpinning for the quantitative future of landform analysis? |
| VerfasserIn |
John K. Hillier, Giulia Sofia, Susan Conway |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250102283
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| Publikation (Nr.) |
 EGU/EGU2015-1593.pdf |
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| Zusammenfassung |
| Physical processes, including anthropogenic feedbacks, sculpt planetary surfaces (e.g.,
Earth’s). A fundamental tenet of Geomorphology is that the shapes created, when combined
with other measurements, can be used to understand those processes. Morphological data,
including metrics and mapping (manual and automated), are a key resource in this endeavour.
However, how good are these data that analyses rely on? Artificial or synthetic
DEMs are widely used to examine the distortions of ‘noise’ (e.g., on topographic
parameters), but only rarely to make strong ’absolute’ statements about landform
detection and quantification; e.g., 84% of the river channels in the real landscape are
found, or 47% of all actual drumlins H > 3 m are mapped. In theory synthetic
DEMs a priori containing known, idealised components can give such absolute
conclusions regarding effectiveness if they can be constructed so as to represent well
the actual landscapes. So, do we need good realistic synthetic DEMs, how can we
best construct them, and what for? From our perspective, they are vital to verify
the statistics that will link physics-driven models of processes to morphological
observations, allowing quantitative hypotheses to be formulated and tested. We
will outline current approaches, and some speculations about the future, but we
are seeking a discussion on how best to construct realistic synthetic DEMs and
proceed with uncertainty-aware landscape analysis to examine physical processes. |
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