Spatial ecological complexity measures in GRASS GIS

Duccio Rocchini*, Vaclav Petras, Anna Petrasova, Yann Chemin, Carlo Ricotta, Alessandro Frigeri, Martin Landa, Matteo Marcantonio, Lucy Bastin, Markus Metz, Luca Delucchi, Markus Neteler

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.
Original languageEnglish
JournalComputers and Geosciences
VolumeIn press
Early online date18 May 2016
DOIs
Publication statusE-pub ahead of print - 18 May 2016

Bibliographical note

© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Supplementary data availalbe on the journal website.

Keywords

  • free and open source software
  • remote sensing
  • spatial complexity
  • spatial ecology

Fingerprint Dive into the research topics of 'Spatial ecological complexity measures in GRASS GIS'. Together they form a unique fingerprint.

  • Cite this

    Rocchini, D., Petras, V., Petrasova, A., Chemin, Y., Ricotta, C., Frigeri, A., Landa, M., Marcantonio, M., Bastin, L., Metz, M., Delucchi, L., & Neteler, M. (2016). Spatial ecological complexity measures in GRASS GIS. Computers and Geosciences, In press. https://doi.org/10.1016/j.cageo.2016.05.006