GeoInformatica

, Volume 7, Issue 4, pp 283–313 | Cite as

Efficient Flow Computation on Massive Grid Terrain Datasets

  • Lars Arge
  • Jeffrey S. Chase
  • Patrick Halpin
  • Laura Toma
  • Jeffrey S. Vitter
  • Dean Urban
  • Rajiv Wickremesinghe
Article

Abstract

As detailed terrain data becomes available, GIS terrain applications target larger geographic areas at finer resolutions. Processing the massive datasets involved in such applications presents significant challenges to GIS systems and demands algorithms that are optimized for both data movement and computation. In this paper we present efficient algorithms for flow routing on massive grid terrain datasets, extending our previous work on flow accumulation. Our algorithms are developed in the framework of external memory algorithms and use I/O-techniques to achieve efficiency. We have implemented the algorithms in the Terraflow system, which is the first comprehensive terrain flow software system designed and optimized for massive data. We compare the performance of Terraflow with that of state-of-the-art commercial and open-source GIS systems. On large terrains, Terraflow outperforms existing systems by a factor of 2 to 1,000, and is capable of solving problems no system was previously able to solve.

alogorithms I/O-complexity flow routing large data Terraflow flow direction flow accumulation watershed 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A. Aggarwal and J.S. Vitter. “The Input/Output complexity of sorting and related problems,” Communications of the ACM, Vol. 31(9):1116–1127, 1988.Google Scholar
  2. 2.
    L. Arge. “The buffer tree: A new technique for optimal I/O-algorithms,” in Proc. Workshop on Algorithms and Data Structures, LNCS 955, 334–345, 1995.Google Scholar
  3. 3.
    L. Arge. “External memory data structures,” in J. Abello, P.M. Pardalos, and M.G.C. Resende (Eds.), Handbook of Massive Data Sets, 313–358, Kluwer Academic Publishers, 2002.Google Scholar
  4. 4.
    L. Arge, R. Barve, D. Hutchinson, O. Procopiuc, L. Toma, D.E. Vengroff and R. Wickremesinghe. TPIE User Manual and Reference (edition 082902). Duke University. The manual and software distribution are available on the web at http://www.cs.duke.edu/TPIE/, 2002.Google Scholar
  5. 5.
    L. Arge, L. Toma, and J.S. Vitter. “I/O-efficient algorithms for problems on grid-based terrains,” in Proc. Workshop on Algorithm Engineering and Experimentation (electronic proceedings). To appear in ACM Journal of Experimental Algorithmics. 2000.Google Scholar
  6. 6.
    G.S. Brodal and J. Katajainen. “Worst-case efficient external-memory priority queues,” in Proc. Scandinavian Workshop on Algorithms Theory, LNCS 1432, 107–118, 1998.Google Scholar
  7. 7.
    C. Ehlschlaeger. “Using the AT search algorithm to develop hydrologic models from digital elevation data,” in International Geographic Information Systems (IGIS) Symposium, 275–281. U.S. Army Construction Engineering Research Laboratory. Baltimore, MD, 18–19 March 1989.Google Scholar
  8. 8.
    Environmental Systems Research Inc. ARC/INFO Professional GIS. Version 7.1.2, 1997.Google Scholar
  9. 9.
    J. Fairfield and P. Leymarie. “Drainage network from grid digital elevation model,” Water Resource Research, Vol. 27:709–717, 1991.Google Scholar
  10. 10.
    T. Freeman. “Calculating catchment area with divergent flow based on a regular grid,” Computers and Geosciences, Vol. 17:413–422, 1991.Google Scholar
  11. 11.
    J. Garbrecht and L. Martz. TOPAZ Topographic Parameterization Software. http://grl.ars.usda.gov/topaz/TOPAZ1.HTM.Google Scholar
  12. 12.
    J. Garbrecht and L. Martz. “Numerical definition of drainage network and subcatchment areas from digital elevation models,” Computers and Geosciences, Vol. 18(6):747–761, 1992.Google Scholar
  13. 13.
    J. Garbrecht and L. Martz. “The assignment of drainage directions over flat surfaces in raster digital elevation models,” Journal of Hydrology, Vol. 193:204–213, 1997.Google Scholar
  14. 14.
    Grass Development Team. GRASS GIS homepage. http://www.baylor.edu/grass/Google Scholar
  15. 15.
    S. Jenson and J. Domingue. “Extracting topographic structure from digital elevation data for geographic information system analysis,” Photogrammetric Engineering and Remote Sensing, Vol. 54(11):1593–1600, 1988.Google Scholar
  16. 16.
    M.V. Kreveld. “Digital elevation models: Overview and selected TIN algorithms,” in M. van Kreveld, J. Nievergelt, T. Roos, and P. Widmayer (Eds.), Algorithmic Foundations of GIS. Springer-Verlag, LNCS 1340, 1997.Google Scholar
  17. 17.
    I. Moore. TAPES: Terrain analysis programs for the environmental sciences. http://cres.anu.edu.au/software/tapes.html.Google Scholar
  18. 18.
    I. Moore, R. Grayson, and A. Ladson. “Digital terrain modelling: A review of hydrological, geomorphological, and biological applications,” Hydrological Processes, Vol. 5:3–30, 1991a.Google Scholar
  19. 19.
    I.D. Moore, R.B. Grayson, and A.R. Ladson. “Digital terrain modelling: A review of hydrological, geomorphological and biological applications,” Hydrological Processes, Vol. 5:3–30, 1991b.Google Scholar
  20. 20.
    D. Morris and R. Heerdegen. “Automatically derived catchment boundary and channel networks and their hydrological applications,” Geomorphology, Vol. 1:131–141, 1988.Google Scholar
  21. 21.
    Nasa Jet Propulsion Laboratory. NASA Shuttle Rader Topography Mission (SRTM). http://www.jpl.nasa.gov/srtm/Google Scholar
  22. 22.
    J.F. O'Callaghan and D.M. Mark. “The extraction of drainage networks from digital elevation data,” Computer Vision, Graphics and Image Processing, Vol. 28, 1984.Google Scholar
  23. 23.
    S. Peckham. The RiverTools home page. http://cires.colorado.edu/people/peckham.scott/RT.html.Google Scholar
  24. 24.
    S. Peckham. Self-similarily in the geometry and dynamics of large river basins. Ph.D. thesis, University of Colorado, Boulder 1995.Google Scholar
  25. 25.
    C.Z. Peng Gao and S. Menon. “An overview of cell based modeling with GIS,” in Second International Conference on Integrating Geographic Information Systems and Environmental Modeling, Breckenridge, CO, USA, 1993.Google Scholar
  26. 26.
    C.Z. Peng Gao and S. Menon. GIS and Environmental Modeling: Progress and Research Issues, Chapter An Overview of Cell-Based Modeling with GIS, pp. 325–332. Boulder: GIS World Books, 1996.Google Scholar
  27. 27.
    D. Tarboton. TARDEM, a suite of programs for the analysis of digital elevation data. http://www.engineering.usu.edu/-cee/faculty/dtarb/tardem.htmlGoogle Scholar
  28. 28.
    D. Tarboton. “A new method for the determination of flow directions and contributing areas in grid digital elevation models,” Water Resources Research, Vol. 33:309–319, 1997.Google Scholar
  29. 29.
    D. Tarboton, R. Bras, and I. Rodriguez-Iturbe. “On the extraction of channel networks from digital elevation data,” Hydrological Processes, Vol. 5:81–100, 1991.Google Scholar
  30. 30.
    A. Tribe. “Automated recognition of valley lines and drainage networks from grid digital elevation models: A review and a new method,” Journal of Hydrology, Vol. 139:263–293, 1992.Google Scholar
  31. 31.
    J.S. Vitter. “External memory algorithms and data structures: Dealing with MASSIVE data,” ACM Computing Surveys, Vol. 33(2):209–271, 2001.Google Scholar
  32. 32.
    D. Wolock. Simulating the variable-source-area of streamflow generation with the watershed model topmodel. Technical report, U.S. Department of the Interior, 1993.Google Scholar
  33. 33.
    D. Wolock and G. McCabe. “Comparison of single and multiple flow direction algorithms for computing topographic parameters in topmodel,” Water Resources Research, Vol. 31:1315–1324, 1995.Google Scholar
  34. 34.
    J. Wood. Automatic surface feature detection from digital elevation data. Technical Report Research Report No. 20, Midlands Regional Research Laboratory. University of Leicester and Loughborouh University of Technology, UK, 1990.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Lars Arge
    • 1
  • Jeffrey S. Chase
    • 1
  • Patrick Halpin
    • 2
  • Laura Toma
    • 1
  • Jeffrey S. Vitter
    • 1
  • Dean Urban
    • 2
  • Rajiv Wickremesinghe
    • 1
  1. 1.Department of Computer ScienceDuke UniversityDurham
  2. 2.Nicholas School of the EnvironmentDuke UniversityDurham

Personalised recommendations