Title :
Area Partitioning for Channel Network Extraction Using Digital Elevation Models and Remote Sensing
Author :
Shen, Dayong ; Sheng, Yongwei
Author_Institution :
Dept. of Geogr., Univ. of California, Los Angeles (UCLA), Los Angeles, CA, USA
fDate :
3/1/2012 12:00:00 AM
Abstract :
Digital elevation models (DEMs) have been widely used in drainage network extraction; however, DEM-based methods often experience challenges in flat areas, where remote sensing imagery is usually informative. This letter proposes an idea of area partition in drainage network extraction. The entire study area is partitioned based on the number of maximum elevation gradients (MEGs). Areas with multiple MEGs (MMEGs) normally include flat areas and, therefore, can be problematic when using DEM-based methods. Remote sensing information can be used together with DEMs to extract drainage networks in these areas. On the contrary, drainage networks can be well defined solely from DEMs in single MEG areas. In a case study, the area partition strategy has been applied and tested in the Yarlung Tsangpo River basin in the southern region of the Tibetan Plateau. Validated using a manually interpreted channel network from high-resolution data sets, this approach generated a nonbroken channel network covering 98.0% of the reference network. These results show that applying remote sensing information only in MMEG areas performs better than throughout the entire study area.
Keywords :
digital elevation models; feature extraction; geophysical image processing; geophysical techniques; remote sensing; rivers; China; Southern Tibetan Plateau; Yarlung Tsangpo River basin; area partitioning; channel network extraction; digital elevation models; drainage network extraction; multiple maximum elevation gradients; remote sensing; Data mining; Digital elevation models; Earth; Feature extraction; Remote sensing; Satellites; Streaming media; Channel network; digital elevation model (DEM); geographic information system (GIS); remote sensing; satellite imagery;
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2011.2163812