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| Titel |
On the uncertainty of stream networks derived from elevation data: the error propagation approach |
| VerfasserIn |
T. Hengl, G. B. M. Heuvelink, E. E. Loon |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 14, no. 7 ; Nr. 14, no. 7 (2010-07-02), S.1153-1165 |
| Datensatznummer |
250012360
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| Publikation (Nr.) |
 copernicus.org/hess-14-1153-2010.pdf |
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| Zusammenfassung |
| DEM error propagation methodology is extended to the derivation of
vector-based objects (stream networks) using geostatistical simulations.
First, point sampled elevations are used to fit a variogram model. Next 100
DEM realizations are generated using conditional sequential Gaussian
simulation; the stream network map is extracted for each of these
realizations, and the collection of stream networks is analyzed to quantify
the error propagation. At each grid cell, the probability of the occurrence
of a stream and the propagated error are estimated. The method is illustrated
using two small data sets: Baranja hill (30 m grid cell size; 16 512
pixels; 6367 sampled elevations), and Zlatibor (30 m grid cell size; 15 000
pixels; 2051 sampled elevations). All computations are run in the open source
software for statistical computing R: package geoR is used
to fit variogram; package gstat is used to run sequential Gaussian
simulation; streams are extracted using the open source GIS SAGA via
the RSAGA library. The resulting stream error map (Information
entropy of a Bernoulli trial) clearly depicts areas where the extracted
stream network is least precise – usually areas of low local relief and
slightly convex (0–10 difference from the mean value). In both cases,
significant parts of the study area (17.3% for Baranja Hill; 6.2% for
Zlatibor) show high error (H>0.5) of locating streams. By correlating the
propagated uncertainty of the derived stream network with various land
surface parameters sampling of height measurements can be optimized so
that delineated streams satisfy the required accuracy level. Such error
propagation tool should become a standard functionality in any modern GIS.
Remaining issue to be tackled is the computational burden of geostatistical
simulations: this framework is at the moment limited to small data sets with
several hundreds of points. Scripts and data sets used in this article are
available on-line via the www.geomorphometry.org website and can be
easily adopted/adjusted to any similar case study. |
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