ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 251-256, 2016
https://doi.org/10.5194/isprs-annals-III-7-251-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
A PLUGIN TO INTERFACE OPENMODELLER FROM QGIS FOR SPECIES' POTENTIAL DISTRIBUTION MODELLING
Daniel Becker1, Christian Willmes1, Georg Bareth1, and Gerd-Christian Weniger2 1Institute of Geography, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany
2Neanderthal Museum, Talstraße 300, 40822 Mettmann, Germany
Keywords: openModeller, QGIS, Python, Plugin, Ecological Niche Modelling, ENM, Species Potential Distribution Modelling, SPDM Abstract. This contribution describes the development of a plugin for the geographic information system QGIS to interface the openModeller software package. The aim is to use openModeller to generate species’ potential distribution models for various archaeological applications (site catchment analysis, for example). Since the usage of openModeller’s command-line interface and configuration files can be a bit inconvenient, an extension of the QGIS user interface to handle these tasks, in combination with the management of the geographic data, was required. The implementation was realized in Python using PyQGIS and PyQT. The plugin, in combination with QGIS, handles the tasks of managing geographical data, data conversion, generation of configuration files required by openModeller and compilation of a project folder. The plugin proved to be very helpful with the task of compiling project datasets and configuration files for multiple instances of species occurrence datasets and the overall handling of openModeller. In addition, the plugin is easily extensible to take potential new requirements into account in the future.
Conference paper (PDF, 5069 KB)


Citation: Becker, D., Willmes, C., Bareth, G., and Weniger, G.-C.: A PLUGIN TO INTERFACE OPENMODELLER FROM QGIS FOR SPECIES' POTENTIAL DISTRIBUTION MODELLING, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 251-256, https://doi.org/10.5194/isprs-annals-III-7-251-2016, 2016.

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