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Article

Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment

by 1,2,3, 3, 4, 3, 5,* and 3
1
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
2
School of Computer Sciences, China University of Geosciences, Wuhan 430074, China
3
School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
4
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
5
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editors: Gui Jin, Qian Zhang and Feng Wu
Water 2021, 13(11), 1585; https://doi.org/10.3390/w13111585
Received: 17 April 2021 / Revised: 27 May 2021 / Accepted: 27 May 2021 / Published: 4 June 2021
(This article belongs to the Special Issue Optimal Utilization and Management of Natural Resources)
The hydrological information derived from a digital elevation model is very important in distributed hydrological modeling. As part of alpine hydrological research on stream network modeling using remote sensing data in the northeast of the Tibetan Plateau, three digital elevation model (DEM) datasets were obtained for the purpose of hydrological features, mainly including channel network, watershed extent and terrain character. The data sources include the airborne light detection and ranging (LiDAR) with point spacing of 1 m, the High Mountain Asia (HMA) DEM and the Shuttle Radar Topography Mission (SRTM) DEM. Mapping of the watershed and stream network was conducted using each of the three DEM datasets. The modeled stream networks using the different DEMs were verified against the actual network mapped in the field. The results show that the stream network derived from the LiDAR DEM was the most accurate representation of the network mapped in the field. The SRTM DEM overestimated the basin hypsometry relative to the LiDAR watershed at the lowest elevation, while the HMA DEM underestimated the basin hypsometry relative to the LiDAR watershed at the highest elevation. This may be because, compared with the SRTM DEM and the HMA DEM, the LiDAR DEM has higher initial point density, accuracy and resolution. It can be seen that the LiDAR data have great potential for the application in hydrologic modeling and water resource management in small alpine catchments. View Full-Text
Keywords: hydrological modeling; LiDAR; stream network hydrological modeling; LiDAR; stream network
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MDPI and ACS Style

Cao, H.; Pan, Z.; Chang, Q.; Zhou, A.; Wang, X.; Sun, Z. Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment. Water 2021, 13, 1585. https://doi.org/10.3390/w13111585

AMA Style

Cao H, Pan Z, Chang Q, Zhou A, Wang X, Sun Z. Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment. Water. 2021; 13(11):1585. https://doi.org/10.3390/w13111585

Chicago/Turabian Style

Cao, Hong, Zhao Pan, Qixin Chang, Aiguo Zhou, Xu Wang, and Ziyong Sun. 2021. "Stream Network Modeling Using Remote Sensing Data in an Alpine Cold Catchment" Water 13, no. 11: 1585. https://doi.org/10.3390/w13111585

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