Sensitivity of channel mapping techniques to uncertainty in digital elevation data

International Journal of Geographical Information Science

Volumn 20, Issue 6, 2006, Pages 669-692

  Lindsay, J B ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Channels; Digital elevation model; Drainage network; Error; LiDAR; Stochastic simulation

Indexed keywords

EID: 33745098029     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658810600661433     Document Type: Article

References (54)

1. Band, L. E., 1999. “ Spatial hydrography and landforms. ”. In Geographical Information Systems, Volume 1, Principles and Technical Issues, 2, Edited by:Longley, P. A, Goodchild, M. F, Maguire, D. J, and Rhind, D. W. 527–542. New York:Wiley.
2. Band, L. E., 1986. Topographic partition of watershed with digital elevation models. Water Resources Research, 22:15–24.
3. Band, L., Peterson, D., Running, S., Coughlan, J., Lammers, R., Dungan, J., and Nemani, R., 1991. Forest ecosystem processes at the watershed scale:basis for distributed simulation. Ecological Modeling, 56:171–196.
4. Burrough, P. A., and McDonnell, R. A., 1998. Principles of Geographical Information Systems, New York:Oxford University Press.
5. Carlisle, B. H., The highs and lows of digital elevation model (DEM) error—developing a spatially distributed DEM error model. Proceedings of the 5th International Conference on GeoComputation
6. Carr, J. R., 2002. Data Visualization in the Geosciences, London:Prentice-Hall.
7. Charleux-Demargne, J., and Puech, C., 2000. “ Quality assessment for drainage networks and watershed boundaries extraction from a digital elevation model (DEM). ”. In Proceedings of the 8th ACM International Symposium on Advances in Geographic Information Systems 89–94.
8. Douglas, D. H., 1986. Experiments to locate ridges and channels to create a new type of digital elevation model. Cartographica, 23:29–61.
9. Fisher, P., 1998. Improved modelling of elevation error with geostatistics. Geoinformatica, 2:215–233.
10. Fisher, P. F., 1991. First experiments in viewshed uncertainty:the accuracy of the viewable area. Photogrammetric Engineering and Remote Sensing, 57:1321–1327.
11. Gallant, J. C., and Wilson, J. P., 2000. “ Primary topographic attributes. ”. In Terrain Analysis:Principles and Applications Edited by:Wilson, J. P, and Gallant, J. C. 51–86. John Wiley, Hoboken, N.J.
12. Garbrecht, J., and Martz, L., 1994. Grid size dependency of parameters extracted from digital elevation models. Computers & Geosciences, 20:85–87.
13. Gatziolis, D., and Fried, J. S., 2004. Adding Gaussian noise to inaccurate digital elevation models improves fidelity of derived drainage networks. Water Resources Research, 40:W02508
14. Goodchild, M. F., 1980. Algorithm 9:Simulation of autocorrelation for aggregate data. Environment and Planning A, 12:1073–1081.
15. Haycock, N., 2003. Processing of LIDAR Data for the Identification of Critical Peat Mass Areas and Delineation of Peat Gully Network—Outline Notes and Methodology, Pershore, UK:Haycock Associates.
16. Heine, R. A., Lant, C. L., and Sengupta, R. R., 2004. Development and comparison of approaches for automated mapping of stream channel networks. Annals of the Association of American Geographers, 94:477–490.
17. Heuvelink, G. B. M., 1998. Error Propagation in Environmental Modelling, London:Taylor & Francis.
18. Holmes, K. W., Chadwich, O. A., and Kyriakidis, P. C., 2000. Error in a USGS 30-meter digital elevation model and its impact on terrain modelling. Journal of Hydrology, 233:154–173.
19. Hunter, G. J., and Goodchild, M. F., 1997. Modeling the uncertainty of slope and aspect estimates derived from spatial databases. Geographical Analysis, 29:35–49.
20. Istanbulluoglu, E., Tarboton, D. G., Pack, R. T., and Luce, C., 2002. A probabilistic approach for channel initiation. Water Resources Research, 38:1325
21. Jensen, J. R., 2000. Remote Sensing of the Environment:An Earth Resource Perspective, London:Prentice-Hall.
22. Jenson, S. K., 1991. Applications of hydrologic information automatically extracted from digital elevation models. Hydrological Processes, 5:31–41.
23. Jenson, S. K., and Domingue, J. O., 1988. Extracting topographic structure from digital elevation data for geographic information systems analysis. Photogrammetric Engineering and Remote Sensing, 54:1593–1600.
24. Johnston, E. G., and Rosenfeld, A., 1975. Digital detection of pits, peaks, ridges and ravines. IEEE Transactions on Systems, Man and Cybernetics, SMC-5:472–480.
25. Kaufmann, A., 1975. Introduction to the Theory of Fuzzy Subsets, Vol. 1, Fundamental Theoretical Elements, New York:Academic Press.
26. Lane, S. N., 2005. Roughness—time for a re-evaluation?. Earth Surface Processes and Landforms, 30:251–253.
27. Lane, S. N., and Chandler, J. H., 2003. Editorial:The generation of high quality topographic data for hydrology and geomorphology:New data sources, new applications and new problems. Earth Surface Processes and Landforms, 28:229–230.
28. Lane, S. N., Westaway, R. M., and Hicks, D. M., 2003. Estimation of erosion nad deposition volumes in a large, gravel-bed, braided river using synoptic remote sensing. Earth Surface Processes and Landforms, 28:249–271.
29. Lane, S. N., Westaway, R. M., and Hicks, D. M., 2004. “ Remotely sensed topographic data for river channel research:the identification, explanation and management of error. ”. In Spatial Modelling of the Terrestrial Environment, Edited by:Drake, R. K, and Barr, D. S. 113–136. Hoboken, NJ:Wiley.
30. Lee, J., Snyder, P. K., and Fisher, P. F., 1992. Modeling the effect of data errors on feature extraction from digital elevation models. Photogrammetric Engineering and Remote Sensing, 58:1461–1467.
31. Lindsay, J. B., 2005. The Terrain Analysis System:A tool for hydro-geomorphic applications. Hydrological Processes, 19:1123–1130.
32. Lindsay, J. B., 2003. A physically based model for calculating contributing area on hillslopes and along valley bottoms. Water Resources Research, 39:1332
33. Lindsay, J. B., and Creed, I. F., in press. Distinguishing between artefact and real depressions in digital elevation data. Computers and Geosciences
34. Lindsay, J. B., and Creed, I. F., 2005a. Removal of artefact depressions from digital elevation models:towards a minimum impact approach. Hydrological Processes, 19:3113–3126.
35. Lindsay, J. B., and Creed, I. F., 2005b. Sensitivity of digital landscapes to artifact depressions in remotely-sensed DEMs. Photogrammetric Engineering and Remote Sensing, 71:1029–1036.
36. McMaster, K. J., 2002. Effects of digital elevation model resolution on derived stream network position. Water Resources Research, 38:1042
37. Measures, R. M., 1984. Laser Remote Sensing:Fundamentals and Applications, Hoboken, NJ:Wiley.
38. Montgomery, D. R., and Dietrich, W. E., 1992. Channel initiation and the problem of landscape scale. Science, 255:826–830.
39. Montgomery, D. R., and Foufoula-Georgiou, E., 1993. Channel network source representation using digital elevation models. Water Resources Research, 29:3925–3934.
40. O'Callaghan, J. F., and Mark, D. M., 1984. The extraction of drainage networks from digital elevation data. Computer Vision, Graphics, & Image Processing, 28:323–344.
41. Oksanen, J., and Sarjakoski, T., 2005. Error propagation of DEM-based surface derivatives. Computers & Geosciences, 31:1015–1027.
42. Peucker, T. K., and Douglas, D. H., 1975. Detection of surface-specific points by local parallel processing of discrete terrain elevation data. Computer Graphics and Image Processing, 4:375–387.
43. Planchon, O., and Darboux, F., 2001. A fast, simple and versatile algorithm to fill the depressions of digital elevation models. Catena, 46:159–176.
44. Qian, J., Ehrich, R. W., and Campbell, J. B., 1990. DNESYS—An expert system for automatic extraction of drainage networks from digital elevation data. IEEE Transactions on Geoscience and Remote Sensing, 28:29–44.
45. Rodriguez-Iturbe, I., Gonzalez-Sanabria, M., and Bras, R. L., 1982. A geomorphoclimatic theory of the instantaneous unit hydrograph. Water Resources Research, 18:877–886.
46. Tarboton, D. G., 1997. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research, 33:309–319.
47. Tarboton, D. G., and Ames, D. P., 2001. “ Advances in the mapping of flow networks from digital elevation data. ”. In World Water and Environmental Resources Congress 20–24 May, Orlando, FL
48. Tarboton, D. G., Bras, R. L., and Rodriguez-Iturbe, I., 1991. On the extraction of channel networks from digital elevation models. Hydrological Processes, 5:81–100.
49. Tribe, A., 1992. Automated recognition of valley lines and drainage networks from digital elevation models:a review and a new method. Journal of Hydrology, 139:263–293.
50. US Army Corps of Engineers. 2001. HEC-HMS Hydrologic Modeling System, User's Manual for Version 2.11. Report CPD-74A, Davis, CA:Hydrologic Engineering Center.
51. Van Niel, K. P., Laffan, S. W., and Lees, B. G., 2004. Effect of error in the DEM on environmental variables for predictive vegetation modelling. Journal of Vegetation Science, 15:747–756.
52. Veregin, H., 1997. The effects of vertical error in digital elevation models on the determination of flow-path direction. Cartography and Geographic Information Systems, 24:67–79.
53. Wechsler, S. P., 2003. Perceptions of digital elevation model uncertainty by DEM users. URISA Journal, 15:57–64.
54. Wechsler, S., 2000. “ Effects of DEM uncertainty on topographic parameters, DEM scale and terrain evaluation. ”. PhD dissertation, College of Environmental Science and Forestry, State University of New York