Quantitative Methods for Comparing Different Polyline Stream Network Models

Download Download as PDF (Size:388KB)  HTML    PP. 88-98  
DOI: 10.4236/jgis.2014.62010    2,216 Downloads   3,364 Views   Citations


Two techniques for exploring relative horizontal accuracy of complex linear spatial features are described and sample source code (pseudo code) is presented for this purpose. The first technique, relative sinuosity, is presented as a measure of the complexity or detail of a polyline network in comparison to a reference network. We term the second technique longitudinal root mean squared error (LRMSE) and present it as a means for quantitatively assessing the horizontal variance between two polyline data sets representing digitized (reference) and derived stream and river networks. Both relative sinuosity and LRMSE are shown to be suitable measures of horizontal stream network accuracy for assessing quality and variation in linear features. Both techniques have been used in two recent investigations involving extraction of hydrographic features from LiDAR elevation data. One confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes yielded better stream network delineations, based on sinuosity and LRMSE, when using LiDAR-derived DEMs. The other demonstrated a new method of delineating stream channels directly from LiDAR point clouds, without the intermediate step of deriving a DEM, showing that the direct delineation from LiDAR point clouds yielded an excellent and much better match, as indicated by the LRMSE.

Cite this paper

Anderson, D. , Ames, D. and Yang, P. (2014) Quantitative Methods for Comparing Different Polyline Stream Network Models. Journal of Geographic Information System, 6, 88-98. doi: 10.4236/jgis.2014.62010.


[1] Goodchild, M.F. and Gopal, S. (1989) The Accuracy of Spatial Databases. CRC Press, Boca Raton.
[2] Zhang, J. and Goodchild, M.F. (2002) Uncertainty in Geographical Information. CRC Press, Boca Raton.
[3] Congalton, R.G. (2001) Accuracy Assessment and Validation of Remotely Sensed and Other Spatial Information. International Journal of Wildland Fire, 10, 321-328.
[4] Jenson, S. and Domingue, J. (1988) Extracting Topographic Structure from Digital Elevation Data for Geographic Information System Analysis. Photogrammetric Engineering and Remote Sensing, 54, 1593-1600.
[5] Tarboton, D.G., Bras, R.L. and Rodriguez-Iturbe, I. (1991) On the Extraction of Channel Networks from Digital Elevation Data. Hydrological Processes, 5, 81-100.
[6] Olivera, F. (2001) Extracting Hydrologic Information from Spatial Data for HMS Modeling. Journal of Hydrologic Engineering, 6, 524-530. http://dx.doi.org/10.1061/(ASCE)1084-0699(2001)6:6(524)
[7] Yang, P., Ames, D., Glenn, N. and Anderson, D. (2010) Effects of LiDAR Derived DEM Resolution on Hydrographic Feature Extraction. AGU Fall Meeting Abstracts, 7.
[8] Leopold, L.B., Wolman, M.G., Wolman, M.G. and Wolman, M.G. (1957) River Channel Patterns: Braided, Meandering, and Straight. US Government Printing Office, Washington DC.
[9] Friedkin, J.F. (1945) Laboratory Study of the Meandering of Alluvial Rivers.
[10] Schumm, S. (1963) Sinuosity of Alluvial Rivers on the Great Plains. Geological Society of America Bulletin, 74, 10891100. http://dx.doi.org/10.1130/0016-7606(1963)74[1089:SOAROT]2.0.CO;2
[11] Leopold, L.B., Wolman, M.G. and Miller, J.P. (2012) Fluvial Processes in Geomorphology. Courier Dover Publications, Mineola.
[12] Mueller, J.E. (1968) An Introduction to the Hydraulic and Topographic Sinuosity Indexes 1. Annals of the Association of American Geographers, 58, 371-385.
[13] Chorley, R., Schumm, S. and Sugden, D. (1984) Geomorphology, 1984. Methuen, London.
[14] Begin, Z.B. (1985) A Note on the Relationship between Flow Energy and Stream Sinuosity. GSI Current Research, 5, 77-78.
[15] Ebisemiju, F. (1994) The Sinuosity of Alluvial River Channels in the Seasonally Wet Tropical Environment: Case Study of River Elemi, Southwestern Nigeria. Catena, 21, 13-25.
[16] Aswathy, M., Vijith, H. and Satheesh, R. (2008) Factors Influencing the Sinuosity of Pannagon River, Kottayam, Kerala, India: An Assessment Using Remote Sensing and GIS. Environmental Monitoring and Assessment, 138, 173-180.
[17] Downward, S., Gurnell, A. and Brookes, A. (1994) A Methodology for Quantifying River Channel Planform Change Using GIS. IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 224, 449-456.
[18] Heo, J., Duc, T.A., Cho, H.-S. and Choi, S.-U. (2009) Characterization and Prediction of Meandering Channel Migration in the GIS Environment: A Case Study of the Sabine River in the USA. Environmental Monitoring and Assessment, 152, 155-165. http://dx.doi.org/10.1007/s10661-008-0304-8
[19] Committee, F.G.D. (1998) Geospatial Positioning Accuracy Standards, Part 3: National Standard for Spatial Data Accuracy. Subcommittee for Base Cartographic Data, 25 p.
[20] Anderson, D.L. and Ames, D.P. (2011) A Method for Extracting Stream Channel Flow Paths from LiDAR Point Cloud Data. Journal of Spatial Hydrology, 11, 17 p.
[21] Manual, E. (1993) River Hydraulics.
[22] McCuen, R. (1998) Hydrologic Design and Analysis. Prince Hall, New Jersey, 814.

comments powered by Disqus

Sponsors, Associates, and Links >>

Copyright © 2016 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.