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Selection: Petras:V [5 articles] 

Publications by author Petras:V.
 

Generalized 3D fragmentation index derived from lidar point clouds

  
Open Geospatial Data, Software and Standards, Vol. 2, No. 1. (20 April 2017), https://doi.org/10.1186/s40965-017-0021-8

Abstract

[Background] Point clouds with increased point densities create new opportunities for analyzing landscape structure in 3D space. Taking advantage of these dense point clouds we have extended a 2D forest fragmentation index developed for regional scale analyses into a 3D index for analyzing vegetation structure at a much finer scale. [Methods] Based on the presence or absence of points in a 3D raster (voxel model) the 3D fragmentation index is used to evaluate the configuration of a cell’s 3D neighborhood resulting in fragmentation classes ...

 

How innovations thrive in GRASS GIS

  
In North Carolina GIS Conference, NCGIS2017 (2017)

Abstract

[Poster topic highlights] [::] Algorithms and models included in GRASS GIS remain available long term (Chemin et al., 2015). [::] Analytical tools are not limited to one domain but spread across many fields. [::] New tools can be built based on functionality or code of the existing ones regardless of the particular domain of problems they belong to. [::] Both the functionality and the code are evaluated by the community of users and developers in different fields and scales. [General GRASS GIS highlights] [::] The GRASS GIS development team ...

 

Wildfire modeling in GRASS GIS

  
(April 2014)

Abstract

[Description] This paper introduces implementation of wildfire modeling tool for GRASS GIS named r.fire.spread based on existing r.ros and r.spread modules which were reviewed as part of this project. The new tools was tested in the Lewis Mountain study area in Shenandoah National Park in Virginia and compared to an actual fire which happened in the area in April 2006. [Excerpt: Introduction] The core of wildre spread modeling in GRASS GIS [Neteler et al. 2012] consists of two modules r.ros and r.spread [Xu 1994]. ...

References

  1. Anderson, H.E., 1982. Aids to determining fuel models for estimating fire behavior. In: The Bark Beetles, Fuels, and Fire Bibliography, p.143. http://www.fs.fed.us/rm/pubs_int/int_gtr122.pdf , INRMM-MiD:12114185 .
  2. Clements, C.B., Perna, R., Jang, M., Lee, D., Patel, M., Street, S., Zhong, S., Goodrick, S., Li, J., Potter, B.E., Bian, X., 2007. Observing the dynamics of wildland grass fires: FireFlux-A field validation experiment. Bulletin of the American Meteorological Society 88(9), 1369-1382. https://doi.org/10.1175/BAMS-88-9-1369 .
 

The integration of land change modeling framework FUTURES into GRASS GIS 7

  
In Free and Open Source Software for Geospatial - Open innovation for Europe, Vol. 12 (2015), pp. 21-24

Abstract

Many valuable models and tools developed by scientists are often inaccessible to their potential users because of non-existent sharing infrastructure or lack of documentation. Case in point is the FUTure Urban-Regional Environment Simulation (FUTURES), a patch-based land change model for generating scenario-based regional forecasts of urban growth pattern. Despite a high- impact publication, few scientists, planners, or policy makers have adopted FUTURES due to complexity in use and lack of direct access. We seek to address these issues by integrating FUTURES into GRASS GIS, a free and open source ...

References

  1. Bivand, R. (2007). Using the R–Grass interface. OSGeo Journal, 1, 36-38.
  2. Chemin, Y Petras, V., Petrasova, A., Landa, M., Gebbert, S., Zambelli, P., Neteler, M., Löwe, P., Di Leo, M. (2015). GRASS GIS: a peer-reviewed scientific platform and future research repository. Geophysical Research Abstracts 17, 8314+. INRMM-MiD:13544126
  3. 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
 

GRASS GIS: a peer-reviewed scientific platform and future research repository

  
Geophysical Research Abstracts In European Geosciences Union (EGU) General Assembly 2013, Vol. 17 (2015), 8314

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 ...

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., 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.
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Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/author/Petras:V

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