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| Titel |
Mapping geomorphology based on the information from existing geomorphological maps with a multiple-point geostatistics technique |
| VerfasserIn |
Lucie Babel, Ekkamol Vannametee, Martin Hendriks, Jasper Schuur, Steven de Jong, Marc Bierkens, Derek Karssenberg |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250091713
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| Publikation (Nr.) |
 EGU/EGU2014-12616.pdf |
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| Zusammenfassung |
| Geomorphological maps are valuable tools for studying land surface processes. Information
obtained from the field geomorphological maps can be, in turn, used in mapping
geomorphology at another area. In this study, we present an application of the multiple-point
geostatistics (MPG) technique in geomorphological mapping. This technique makes use of
the field geomorphological maps, together with the topographical data obtained from the
Digital Elevation Model (DEM), to derive the knowledge on the formation of different
geomorphological units in the landscape (e.g. river terrace, alluvial fan, badlands) as a
basis in mapping areas with unknown geomorphology. This approach starts from
characterizing the occurrence of each geomorphological class as a function of land surface
attributes (i.e. attribute pattern), which consists of DEM derivatives discretized
into classes (e.g. slope class) observed at that cell location, and geomorphology at
multiple neighboring locations. The latter gives information on the spatial relation
between geomorphological units. Number of cell occurrences per geomorphological
class per attribute pattern is counted and stored in the frequency database, which
will be subsequently used in the mapping. In the mapping stage, the algorithm
assigns a realization of a geomorphological class to the target mapping cell, based
on the probability function of geomorphological occurrences conditioned to the
observed attribute pattern at the target mapping cell, as retrieved from the frequency
database.
The approach is tested to map the geomorphology in the 280 km2 Buëch catchment,
French Alps. We use 4 morphometric attributes, extracted from a 37.5-m DEM resolution
(i.e. height above the nearest drainage, slope gradient, profile curvature, and slope
variability); and 2 non-morphometric attributes (i.e. geomorphology observed at 1-cell and
10-cells downstream from a cell of interest). Mapping is done using different frequency
databases created from different training areas with sizes ranging between 7-28 km2
(2.5-10% of the mapping area). The MPG technique yields the geomorphological map with
the highest cell accuracy of 51.2% evaluated against the field geomorphological map, using
the training image size with 10% of the mapping area. The unit mapped with the
highest accuracy is the debris slope, while hogback and glacis were mapped with
the lowest accuracy. The mapping accuracy is highest for training areas with a
size of 7.5-10% of the total area. Reducing the size of the training images resulted
in a decreased mapping quality, as the frequency database only represents local
characteristics of the geomorphology that are not representative for other areas. Increasing
the size of training images beyond this range may not considerably increase the
mapping quality. This will, instead, result in a redundancy of information and more
variations in geomorphological class occurrences per attribute patterns in the frequency
database, reducing the capability to discriminate between geomorphological units. |
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