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| Titel |
An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification |
| VerfasserIn |
G. Sofia, P. Tarolli, F. Cazorzi, G. Dalla Fontana |
| 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 ; 15, no. 5 ; Nr. 15, no. 5 (2011-05-06), S.1387-1402 |
| Datensatznummer |
250012776
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| Publikation (Nr.) |
 copernicus.org/hess-15-1387-2011.pdf |
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| Zusammenfassung |
| A statistical approach to LiDAR derived topographic attributes for the
automatic extraction of channel network and for the choice of the scale to
apply for parameter evaluation is presented in this paper. The basis of this
approach is to use distribution analysis and statistical descriptors to
identify channels where terrain geometry denotes significant convergences.
Two case study areas with different morphology and degree of organization
are used with their 1 m LiDAR Digital Terrain Models (DTMs). Topographic
attribute maps (curvature and openness) for various window sizes are derived
from the DTMs in order to detect surface convergences. A statistical
analysis on value distributions considering each window size is carried out
for the choice of the optimum kernel. We propose a three-step method to
extract the network based (a) on the normalization and overlapping of
openness and minimum curvature to highlight the more likely surface
convergences, (b) a weighting of the upslope area according to these
normalized maps to identify drainage flow paths and flow accumulation
consistent with terrain geometry, (c) the standard score normalization of
the weighted upslope area and the use of standard score values as non
subjective threshold for channel network identification. As a final step for
optimal definition and representation of the whole network, a
noise-filtering and connection procedure is applied. The advantage of the
proposed methodology, and the efficiency and accurate localization of
extracted features are demonstrated using LiDAR data of two different areas
and comparing both extractions with field surveyed networks. |
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