• M. Neteler
  • D.E. Beaudette
  • P. Cavallini
  • L. Lami
  • J. Cepicky
Part of the Advances in Geographic Information Science book series (AGIS, volume 2)


GRASS is a full featured, general purpose Open Source geographic information system (GIS) with raster, vector and image processing capabilities. There has been constant development of the software since 1982, with recent major improvements reflecting renewed efforts by the international development team to make it one of the core components of the Open Source geospatial software stack. It can handle 2D and 3D raster data, includes a topological 2D/3D vector engine, network analysis functions, and SQL-based attribute management. This chapter presents an overview and practical examples of the GRASS 6 capabilities relevant to environmental and planning applications including new functionality. Enhancements to 3D visualization and approaches to environmental models are also discussed, as well as image processing routines pertaining to LIDAR and multi-band imagery. Integration of GRASS with other Open Source software packages for geostatistical analysis, cartographic output and Web GIS applications are described. Trends for future development are also discussed.


Geographic Information System Application Programming Interface Digital Terrain Model Structure Query Language Digital Surface Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Beaudette DE (2007) Producing press-ready maps with GRASS and GMT. J Open Source Geospatial Foundation 1:29–35Google Scholar
  2. Bivand R (2007) Using the R-GRASS interface. J Open Source Geospatial Foundation 1:36–38Google Scholar
  3. Blazek R (2005) Introducing the linear reference system in GRASS. Int J Geoinformatics 1(3):95–100Google Scholar
  4. Bouktif S, Antoniol G, Merlo E, Neteler M (2006) A novel approach to optimize clone refactoring activity. In GECCO ’06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, ACM Press, New York, USA, pp 1885–1892Google Scholar
  5. Brovelli M, Cannata M, Longoni U (2004) LIDAR data filtering and DTM interpolation within GRASS. Trans GIS 8(2):155–174CrossRefGoogle Scholar
  6. Hofierka J, Mitasova H, Neteler M (2008) Terrain parameterization in GRASS. In: Hengl T, Reuter H (eds) Geomorphometry: concepts, software, applications, Developments in Soil Science, Vol. 33, Elsevier, Amsterdam pp. 387–410.Google Scholar
  7. Issaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, EnglandGoogle Scholar
  8. Jenson JR (2000) Remote sensing of the environment: and earth science perspective. Prentice Hall, New JerseyGoogle Scholar
  9. Jolma A, Ames D, Horning N, Neteler M, Racicot A, Sutton T (2006) Free and open source geospatial tools for environmental modeling and management. In: Voinov A (ed) Proc. iEMSs 2006, Session W13, July 9–13, 2006, Burlington, Vermont, USAGoogle Scholar
  10. Lime S (1996) UMN MapServer, University of Minnesota, USA, [Computer Program]. Available: http://mapserver.gis.umn.eduGoogle Scholar
  11. Lo CP, Yeung AKW (2006) Concepts and techniques of geographic information systems. Prentice Hall, New JerseyGoogle Scholar
  12. Mitasova H, Hofierka J (1993) Interpolation by regularized spline with tension: II. Application to terrain modeling and surface geometry analysis. Math Geol 25(6):657–669CrossRefGoogle Scholar
  13. Mitasova H, Mitas L (1993) Interpolation by regularized spline with tension: I. Theory and implementation. Math Geol 25(6):641–655CrossRefGoogle Scholar
  14. Neteler M, Grasso D, Michelazzi I, Miori L, Merler S, Furlanello C (2005) An integrated toolbox for image registration, fusion and classification. Int J Geoinformatics 1:51–61Google Scholar
  15. Neteler M, Mitasova H (2008) Open source GIS: A GRASS GIS approach. 3 edn. Springer, New YorkGoogle Scholar
  16. R Development Core Team (2006) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. ISBN 3-900051-07-0Google Scholar
  17. Rizzoli A, Neteler M, Rosà R, Versini W, Cristofolini A, Bregoli M, Buckley A, Gould E (2007) Early detection of TBEv spatial distribution and activity in the Province of Trento assessed using serological and remotely-sensed climatic data. Geospatial Health 1(2):169–176Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • M. Neteler
    • 1
  • D.E. Beaudette
    • 2
  • P. Cavallini
    • 3
  • L. Lami
    • 3
  • J. Cepicky
    • 4
  1. 1.Fondazione Mach - Centre for Alpine Ecology38100 TrentoItaly
  2. 2.Department of Land, Air and Water ResourcesUniversity of CaliforniaDavisUSA
  3. 3.FaunaliaPiazza Garibaldi 556025 PontederaItaly
  4. 4.Help Service - Remote Sensing s.r.o.Cernoleska 160025601 - BenesovCzech Republic

Personalised recommendations