 |
| Titel |
Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search |
| VerfasserIn |
M. Metz, H. Mitasova, R. S. Harmon |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 2 ; Nr. 15, no. 2 (2011-02-25), S.667-678 |
| Datensatznummer |
250012652
|
| Publikation (Nr.) |
 copernicus.org/hess-15-667-2011.pdf |
|
|
|
|
|
| Zusammenfassung |
| The availability of both global and regional elevation datasets acquired by
modern remote sensing technologies provides an opportunity to significantly
improve the accuracy of stream mapping, especially in remote, hard to reach
regions. Stream extraction from digital elevation models (DEMs) is based on
computation of flow accumulation, a summary parameter that poses performance
and accuracy challenges when applied to large, noisy DEMs generated by
remote sensing technologies. Robust handling of DEM depressions is essential
for reliable extraction of connected drainage networks from this type of
data. The least-cost flow routing method implemented in GRASS GIS as the
module r.watershed was redesigned to significantly improve its speed,
functionality, and memory requirements and make it an efficient tool for
stream mapping and watershed analysis from large DEMs. To evaluate its
handling of large depressions, typical for remote sensing derived DEMs,
three different methods were compared: traditional sink filling, impact
reduction approach, and least-cost path search. The comparison was performed
using the Shuttle Radar Topographic Mission (SRTM) and Interferometric
Synthetic Aperture Radar for Elevation (IFSARE) datasets covering central
Panama at 90 m and 10 m resolutions, respectively. The accuracy assessment
was based on ground control points acquired by GPS and reference points
digitized from Landsat imagery along segments of selected Panamanian rivers.
The results demonstrate that the new implementation of the least-cost path
method is significantly faster than the original version, can cope with
massive datasets, and provides the most accurate results in terms of stream
locations validated against reference points. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|