GRASS GIS: a peer-reviewed scientific platform and future research repository
Abstract
Geographical Information System (GIS) is known for its capacity to spatially enhance the management of natural resources. While being often used as an analytical tool, it also represents a collaborative scientific platform to develop new algorithms. Thus, it is critical that GIS software as well as the algorithms are open and accessible to anybody [18]. We present how GRASS GIS, a free and open source GIS, is used by many scientists to implement and perform geoprocessing tasks. We will show how integrating scientific algorithms into GRASS GIS helps to preserve reproducibility of scientific results over time [15]. Moreover, subsequent improvements are tracked in the source code version control system and are immediately available to the public. GRASS GIS therefore acts as a repository of scientific peer-reviewed code, algorithm library, and knowledge hub for future generation of scientists. In the field of hydrology, with the various types of actual evapotranspiration (ET) models being developed in the last 20 years, it becomes necessary to inter-compare methods. Most of already published ETa models comparisons address few number of models, and small to medium areas [3, 6, 7, 22, 23]. With the large amount of remote sensing data covering the Earth, and the daily information available for the past ten years (i.e. Aqua/Terra-MODIS) for each pixel location, it becomes paramount to have a more complete comparison, in space and time. To address this new experimental requirement, a distributed computing framework was designed, and created [3, 4]. The design architecture was built from original satellite datasets to various levels of processing until reaching the requirement of various ETa models input dataset. Each input product is computed once and reused in all ETa models requiring such input. This permits standardization of inputs as much as possible to zero-in variations of models to the models internals/specificities. All of the ET models are available in the new GRASS GIS version 7 as imagery modules and replicability is complete for future research. A set of modules for multiscale analysis of landscape structure was added in 1992 by [1], who developed the r.le model similar to FRAGSTATS ([10]). The modules were gradually improved to become r.li in 2006. Further development continued, with a significant speed up [9] and new interactive user interface. The development of spatial interpolation module v.surf.rst started in 1988 [11] and continued by introduction of new interpolation methods and finally full integration into GRASS GIS version 4 [13]. Since then it was improved several times [8]. The module is an important part of GRASS GIS and is taught at geospatial modeling courses, for example at North Carolina State University [14]. GRASS GIS entails several modules that constitute the result of active research on natural hazard. The r.sim.water simulation model [12] for overland flow under rainfall excess conditions was integrated into the Emergency Routing Decision Planning system as a WPS [17]. It was also utilized by [16] and is now part of Tangible Landscape, a tangible GIS system, which also incorporated the r.damflood, a dam break inundation simulation [2]. The wildfire simulation toolset, originally developed by [24], implementing Rothermel's model [21], available through the GRASS GIS modules r.ros and r.spread, is object of active research. It has been extensively tested and recently adapted to European fuel types ([5, 19, 20]). References [1] Baker, W.L., Cai, Y., 1992. The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology, 7(4):291-302. [2] Cannata M. and Marzocchi R., 2012. Two-dimensional dam break flooding simulation: a GIS embedded approach. - Natural Hazards 61(3):1143-1159. [3] Chemin, Y.H., 2012. A Distributed Benchmarking Framework for Actual ET Models. In Evapotranspiration - Remote Sensing and Modeling, Intech (Eds). [4] Chemin, Y. H. , 2014. Remote Sensing Raster Programming, 3rd Ed., Lulu (Eds). [5] Di Leo, M., de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Petroliagkis, T., Camia, A., San-Miguel-Ayanz, J., 2013. Dynamic data driven ensemble for wildfire behaviour assessment: A case study. IFIP Advances in Information and Communication Technology, vol. 413, pp. 11-22, 2013, ISSN:1868-4238. Special issue: "Environmental Software Systems. Fostering sharing information". [6] García, M., Villagarcía, L., Contreras, S., Domingo, F. & Puigdefábregas, J. (2007). Comparison of three operative models for estimating the surface water deficit using aster reflective and thermal data, Sensors 7(6): 860-883. [7] Gao, Y. & Long, D. ,2008. Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on swat, Hydrological Processes 22: 4850-4869. [8] GRASS GIS Trac, changelog for v.surf.rst, 2015. http://trac.osgeo.org/grass/ [9] GRASS GIS Trac, changelog for r.li, 2015. http://trac.osgeo.org/grass/ [10] McGarigal, K., and B. J. Marks. 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA For. Serv. Gen. Tech. Rep. PNW-351 [11] Mitas, L., and Mitasova H., 1988, General variational approach to the approximation problem, Computers and Mathematics with Applications, v.16, p. 983-992. [12] Mitas, L., and Mitasova, H., 1998, Distributed soil erosion simulation for effective erosion prevention. Water Resources Research, 34(3), 505-516. [13] Mitasova, H. and Mitas, L., 1993: Interpolation by Regularized Spline with Tension: I. Theory and Implementation, Mathematical Geology, 25, 641-655. [14] North Carolina State University, Geospatial Modeling Course, GIS/MEA582, 2015. http://courses.ncsu.edu/ [15] Petras, V., Gebbert, S., 2014. Testing framework for GRASS GIS: ensuring reproducibility of scientific geospatial computing. Poster presented at: AGU Fall Meeting, December 15-19, 2014, San Francisco, USA. [16] Petrasova, A., Harmon, B., Petras, V., Mitasova, H., 2014. GIS-based environmental modeling with tangible interaction and dynamic visualization. In: Ames, D.P., Quinn, N.W.T., Rizzoli, A.E. (Eds.), Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15-19, San Diego, California, USA. ISBN: 978-88-9035-744-2 [17] Raghavan, v., Choosumrong, S., Yoshida, D., Vinayaraj, P., 2014. Deploying Dynamic Routing Service for Emergency Scenarios using pgRouting, GRASS and ZOO. In Proc. of FOSS4G Europe, Jacobs University, Bremen, Germany, July 15-17, 2014. [18] Rocchini, D., Neteler, M. ,2012. Let the four freedoms paradigm apply to ecology. Trends in Ecology & Evolution, 27: 310-311. [19] Rodriguez-Aseretto, D., de Rigo, D., Di Leo, M., Cortés, A., and San-Miguel-Ayanz, J., 2013. A data-driven model for large wildfire behaviour prediction in europe. Procedia Computer Science, vol. 18, pp. 1861-1870. [20] de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Di Leo, M., and San-Miguel-Ayanz, J., 2013. An architecture for adaptive robust modelling of wildfire behaviour under deep uncertainty. in Environmental Software Systems. Fostering Information Sharing, ser. IFIP Advances in Information and Communication Technology, J. Ȟrebíˇ cek, G. Schimak, M. Kubásek, and A. Rizzoli, Eds. Springer Berlin Heidelberg, 2013, vol. 413, pp. 367-380. [21] Rothermel, R. C., 1983. How to predict the spread and intensity of forest and range fires. US Forest Service, Gen. Tech. Rep. INT-143. Ogden, Utah. [22] Suleiman, A., Al-Bakri, J., Duqqah, M. & Crago, R. ,2008. Intercomparison ofevapotranspiration estimates at the different ecological zones in jordan, Journal of Hydrometeorology 9(5): 903-919. [23] Timmermans, W. J., Kustas, W. P., Anderson, M. C. & French, A. N. ,2007. An intercomparison of the surface energy balance algorithm for land (sebal) and the two-source energy balance (tseb) modeling schemes, Remote Sensing of Environment 108(4): 369 - 384. [24] Xu, Jianping, 1994. Simulating the spread of wildfires using a geographic information system and remote sensing. Ph. D. Dissertation, Rutgers University, New Brunswick, New Jersey.
- Publication:
-
EGU General Assembly Conference Abstracts
- Pub Date:
- April 2015
- Bibcode:
- 2015EGUGA..17.8314C