Extracting low-resolution river networks from high-resolution digital elevation models

被引:27
|
作者
Olivera, F
Lear, MS
Famiglietti, JS
Asante, K
机构
[1] Texas A&M Univ, Dept Civil Engn, College Stn, TX 77843 USA
[2] Hart Crowser Inc, Seattle, WA 98102 USA
[3] Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA 92697 USA
[4] US Geol Survey, Raytheon EROS Data Ctr, Sioux Falls, SD 57198 USA
关键词
river network; surface water; global; continental; geographic information systems (GIS); digital elevation models (DEM);
D O I
10.1029/2001WR000726
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Including a global river network in the land component of global climate models (GCMs) is necessary in order to provide a more complete representation of the hydrologic cycle. The process of creating these networks is called river network upscaling and consists of lowering the resolution of already available fine networks to make them compatible with GCMs. Fine-resolution river networks have a level of detail appropriate for analysis at the watershed scale but are too intensive for global hydrologic studies. A river network upscaling algorithm, which processes fine-resolution digital elevation models to determine the flow directions that best describe the flow patterns in a coarser user-defined scale, is presented. The objectives of this study were to develop an algorithm that advances the previous work in the field by being applicable at a global scale, allowing for the upscaling to be performed in a projected environment, and generating evenly distributed flow directions.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Assessment of methods for extracting low-resolution river networks from high-resolution digital data
    Davies, Helen N.
    Bell, Victoria A.
    [J]. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2009, 54 (01): : 17 - 28
  • [2] Algorithm on extracting high-resolution image from low-resolution image sequence
    Shao, Ling
    Ding, Pei-Lu
    Zhang, Li-Ming
    Hu, Bo
    [J]. 2001, Chinese Institute of Electronics (30):
  • [4] Creating low-cost high-resolution digital elevation models
    Louhaichi, M
    Borman, MM
    Johnson, AL
    Johnson, DE
    [J]. JOURNAL OF RANGE MANAGEMENT, 2003, 56 (01): : 92 - 96
  • [5] High-resolution images from low-resolution compressed video
    Segall, CA
    Molina, R
    Katsaggelos, AK
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2003, 20 (03) : 37 - 48
  • [6] A LOW-RESOLUTION VIEW OF HIGH-RESOLUTION SPECTROMETRY
    TRIMBLE, V
    [J]. PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC, 1995, 107 (716) : 1012 - 1015
  • [7] Estimating Bedding Orientation From High-Resolution Digital Elevation Models
    Cracknell, Matthew J.
    Roach, Michael
    Green, David
    Lucieer, Arko
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (05): : 2949 - 2959
  • [8] High-resolution iris image reconstruction from low-resolution imagery
    Barnard, R.
    Pauca, V. P.
    Torgersen, T. C.
    Plemmons, R. J.
    Prasad, S.
    van der Gracht, J.
    Nagy, J.
    Chung, J.
    Behrmann, G.
    Mathews, S.
    Mirotznik, M.
    [J]. ADVANCED SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, AND IMPLEMENTATIONS XVI, 2006, 6313
  • [9] SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis
    Yin, Yu
    Robinson, Joseph P.
    Jiang, Songyao
    Bai, Yue
    Qin, Can
    Fu, Yun
    [J]. PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2021, 2021, : 1609 - 1617
  • [10] High-resolution image reconstruction from multiple low-resolution images
    Wei, H
    Binnie, TD
    [J]. SEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND ITS APPLICATIONS, 1999, (465): : 596 - 600