GRASS GIS: A multi-purpose open source GIS
By Markus Neteler, M. Hamish Bowman, Martin Landa, Markus Metz. Environmental Modelling & Software, 2012
819 citations recorded by the source corpus.
The GIS software sector has developed rapidly over the last ten years. Open Source GIS applications are gaining relevant market shares in academia, business, and public administration. In this paper, we illustrate the history and features of a key Open Source GIS, the Geographical Resources Analysis Support System (GRASS). GRASS has been under development for more than 28 years, has strong ties into academia, and its review mechanisms led to the integration of well tested and documented algorithms into a joint GIS suite which has been used regularly for environmental modelling. The development is community-based with developers distributed globally. Through the use of an online source code repository, mailing lists and a Wiki, users and developers communicate in order to review existing code and develop new methods. In this paper, we provide a functionality overview of the more than 400 modules available in the latest stable GRASS software release. This new release runs natively on common operating systems (MS-Windows, GNU/Linux, Mac OSX), giving basic and advanced functionality to casual and expert users. In the second part, we review selected publications with a focus on environmental modelling to illustrate the wealth of use cases for this open and free GIS.
Subfields: Geography, Planning and Development, Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/478aba0b04a5704c102edad2ae9ea1e8f1c26653
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Citations in the corpus
- System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 (2015, methodology): “3.4) The code of the remote sensing and image processing tools is partly based on free libraries and open source software including OpenCV (Open Source Computer Vision, http://opencv.org/) ViGrA (Vision with Generic Algorithms, Köthe, 2000), LIBSVM (Chang and Lin, 2011) and GRASS GIS (Geographical R”
- The fate of Madagascar’s rainforest habitat (2019, background): “The latter was linked to GRASS GIS 7.4.1 (Neteler et al. 2012).”
- Comparing sample bias correction methods for species distribution modeling using virtual species (2021, methodology): “A kernel density estimation method for networks, its computational method and a GIS-based tool.”
- Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30 (2017, methodology): “2 (Neteler et al., 2012; GRASS Development Team, 2017) through Python scripts (Python Software Foundation, 2013) using the Pygrass library (Zambelli”
- Potential of remote sensing to predict species invasions (2015, background): “By linking biological processes explicitly to their environmental drivers (not implicitly via proxies), and accessed via data layers of wide spatial and temporal coverage including remote sensing data, PBDMs bridge the gap between bottom-up (primarily physiological and population dynamics) and top-d”
- Climate change, genetic markers and species distribution modelling (2015, neutral): “, 2000) and geostatistical (Diggle & Ribeiro, 2007) modelling tools and the everincreasing speed and storage capacity of personal and mainframe computers, combined with the recent success of R (R Core Team, 2013) and other open-source software platforms (Neteler et al., 2012).”