Skip to main content
SpringerLink
Log in

GRASS

  • Reference work entry
Encyclopedia of GIS

  • 115 Accesses

Synonyms

Geographic Resources Analysis Support Software; GRASS-GIS; Visualization tool

Definition

GRASS-GIS (Geographic Resources Analysis Support Software) is a powerful open source software for geospatial analysis and modeling that can manage both raster and vector data. In addition it supports three dimensional modeling with 3D raster voxel or 3D vector data and contains several image processing modules to manipulate remote sensing data. It includes visualization tools and interacts with other related software such as the statistical software package R, gstat and Quantum GIS. GRASS supports a wide variety of GIS formats through the use of the GDAL/OGR library. It also supports Open Geospatial Consortium (OGC)—conformal Simple Features and can connect to spatial databases such as PostGIS via ODBC (Open DataBase Connectivity). GRASS datasets can be published on the internet with the UMN Mapserver software.

The software is published under the terms of the GNU General Public Licence...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Recommended Reading

  1. Ameskamp, M.: Three-dimensional Rule-based Continuous Soil Modelling. PhD thesis, Christian-Albrechts-Universitaet Kiel (1997)

    Google Scholar 

  2. Baker, W.L., Weisberg, P.J.: Using GIS to model tree population parameters in the rocky mountain national park forest-tundra ecotone. J. Biogeogr. 24, 513–526 (1997)

    Article  Google Scholar 

  3. Bivand, R.S.: Integrating grass 5.0 and r: GIS and modern statistics for data analysis. 7th Scandinavian Research Conference on Geographical Information Science. Aalborg, Denmark (1999)

    Google Scholar 

  4. Bivand, R.S.: Using the r statistical data analysis language on grass 5.0 GIS database files. Comp. & Geosci. 26, 1043–1052 (2000)

    Article  Google Scholar 

  5. Bivand, R.S., Neteler, M.: Open source geocomputation: using the r data analysis language integrated with grass GIS and postgresql data base systems (2000)

    Google Scholar 

  6. Brandt, R., Groenewoudt, B.J., Kvamme, K.L.: An experiment in archaeological site location: Modeling in the netherlands using GIS techniques. World Arch. 24(2), 268–282 (1992)

    Article  Google Scholar 

  7. Ciolli,M. Zatelli, P.: http://www.ing.unitn.it/~grass/conferences/grass2002/proceedings/proceedings/abstracts.html. online, 12 (2006)

  8. Clerici, A., Perego, S., Tellini, C., Vescovi, P.: A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphol. 48(4), 349–364 (2002)

    Article  Google Scholar 

  9. Cullmann, J., Mishra, V., Peters, R.: Flow analysis with wasim-eth model parameter sensitivity at different scales. Adv. Geosci. 9, 73–77 (2006)

    Article  Google Scholar 

  10. Deuchler, C., Wählisch, M., Gehrke, S., Hauber, E., Oberst, J., Jaumann, R.: Combining mars data in grass GIS for geological mapping. ISPRS Congress, 12–23 July 2004 Istanbul, Turkey, vol.#160;35, ISPRS, Istanbul (2004)

    Google Scholar 

  11. Ducke, B.: Archaeological predictive modelling in intelligent network structures. Doerr, M., Sarris, A. (eds.): CAA 2002 The Digital Heritage of Archaeology. Proceedings of the 30th CAA conference held at Heraklion, Crete, Greece, 2–6 April 2002, pages 267–273, Athens (2003)

    Google Scholar 

  12. Frankenberger, J.R., Brooks, E.S., Walter, M.T., Walter, M.F., Steenhuis, T.S.: A GIS-based variable source area hydrology model. Hydrol. Process 13, 805–822 (1999)

    Article  Google Scholar 

  13. Frigeri, A., Federico, C., Pauselli, C., Minelli, G.: Identifying wrinkle ridges from mars mgs and viking mission data: Using grass GIS in planetary geology. Trans. GIS 8(2), 255–262 (2004)

    Article  Google Scholar 

  14. Furlanello, C., Neteler, M., Merler, S., Menegon, S., Fontanari, S., Donini, A., Rizzoli, A., Chemini, C.: GIS and the Random Forest Predictor: Integration in R for Tick-borne Disease Risk Assessment. In: Hornik, K., Leisch, F. (eds.) Proceedings of the 3rd International Workshop on Distributed Statistical Computing, March 20–22, Vienna, Austria (2003)

    Google Scholar 

  15. Garzon, M.B., Blazek, R., Neteler, M., Dios, R.S., Ollero, H.S., Furlanello, C.: Predicting habitat suitability with machine learning models: The potential area of pinus sylvestris l. in the iberian peninsula. Ecol. Modell. 197(3–4), 383–393 (2006)

    Article  Google Scholar 

  16. GRASS Development Team. http://grass.itc.it/. online, 12 (2006)

  17. Grohmann, C.H.: Morphometric analysis in geographic infromation systems: applications of free software grass and r. Comput. & Geosci. 30, 1055–1067 (2004)

    Article  Google Scholar 

  18. Höfle, B., Rutzinger, M., Geist, T., Stötter, J.: Using airborne laser scanning data in urban data management – set up of a flexible information system with open source components. In: Proceedings UDMS 2006: Urban Data Management Symposium, Aalborg, Denmark (2006)

    Google Scholar 

  19. Kaitala, S., Shavykin, A., Volkov, V.A.: Environmental GIS database for the white sea. In: Proceedings of the Open source GIS – GRASS users conference, 11–13 September 2002, Trento, Italy (2002)

    Google Scholar 

  20. Kajiyama, A., Ikawa, N., Masumoto, S., Shiono, K., Raghavan, V.: Three-dimensional geological modeling by foss grass GIS – using some field survey data –. In: Proceedings of the FOSS/GRASS Users Conference – Bangkok, 12–14 September 2004, Bangkog, Thailand (2004)

    Google Scholar 

  21. Lake, M.W.: The use of pedestrian modelling in archaeology, with an example from the study of cultural learning. Environ. Plann. B: Plann. Des. 28, 385–403 (2001)

    Article  Google Scholar 

  22. Lake, M.W., Woodman, P.E., Mithen, S.J.: Tailoring GIS software for archaeological applications: An example concerning viewshed analysis. J. Arch. Sci. 25(1), 27–38 (1998)

    Article  Google Scholar 

  23. Mannelli, A., Ferre, N., Marangon, S.: Analysis of the 1999 2000 highly pathogenic avian influenza (h7n1) epidemic in the main poultry-production area in northern Italy. Prev. Vet. Med., 73, 273–285 (2006)

    Article  Google Scholar 

  24. Merlo, S., Colin, A.: Developing a multidimensional GIS framework for archaeological excavations. In: CIPA 2005, XX International Symposium Torino, Torino (2005)

    Google Scholar 

  25. Mitasova, H., Mitas, L., Brown, W.M., Gerdes, D.P., Kosinovsky, I., Baker, T.: Modelling spatially and temporally distributed phenomena: new methods and tools for grass GIS. Int. J. Geogr. Inf. Syst. 9(4), 433–446 (1995)

    Article  Google Scholar 

  26. Neteler, M., Grasso, D., Michelazzi, I., Miori, L., Merler, S., Furlanello, C.: An integrated toolbox for image registration, fusion and classification. Int. J. Geoinf. Special Issue on FOSS/GRASS 2004 & GIS-IDEAS 2004, 1(1), 51–60 (2005)

    Google Scholar 

  27. Neteler, M., Mitasova, H.: Open Source GIS: A GRASS GIS Approach. Number 773 in SECS. Kluwer Academic Publishers/Springer, Boston, first edition, June 2001. ISBN: 1–4020-8064-6; also published as eBook: ISBN 1-4020-8065-4

    Google Scholar 

  28. Rizzoli, A., Merler, S., Furlanello, C., Genchi, C.: Geographical information system and bootstrap aggregation (bagging) of tree-based classifiers for lyme disease risk prediction in trentino, italian alps. J. Med. Entomol. 39(3), 485–492 (2002)

    Article  Google Scholar 

  29. Romano, N., Palladino, M.: Prediction of soil water retention using soil physical data and terrain attributes. J. Hydro. 265, 56–75 (2002)

    Article  Google Scholar 

  30. Savabi, M.R., Flanagan, D.C., Hebel, B., Engel, B.A.: Application of WEPP and GIS-grass to a small watershed in indiana. J. Soil Water Conserv. 50(5), 477–483 (1995)

    Google Scholar 

  31. Tucker, K., Rushton, S.P., Sanderson, R.A., Martin, E.B., Blaiklock, J.: Modelling bird distributions – a combined GIS and bayesian rule-based approach. Landsc. Ecol. 12(2), 77–93 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Geographic Sciences, Freie Universität Berlin, Berlin, Germany

    Malte Halbey-Martin

Authors
  1. Malte Halbey-Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Shashi Shekhar (McKnight Distinguished University Professor) (McKnight Distinguished University Professor)

  2. Management Science and Information Systems Department, Rutgers Business School Newark and New Brunswick Rutgers, the State University of New Jersey, Newark, NJ, 07102, USA

    Hui Xiong (Assistant Professor) (Assistant Professor)

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Halbey-Martin, M. (2008). GRASS. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_548

Download citation

Publish with us

Policies and ethics

Search

Navigation