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

doi:10.1016/j.cageo.2016.05.006

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 journal › Article › peer-review

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 language	English
Pages (from-to)	166-176
Journal	Computers and Geosciences
Volume	104
Early online date	18 May 2016
DOIs	https://doi.org/10.1016/j.cageo.2016.05.006
Publication status	Published - Jul 2017

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

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10.1016/j.cageo.2016.05.006

Spatial ecological complexity measures in GRASS GIS
© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 2.02 MBLicence: CC BY-NC-ND 3.0

Cite this

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title = "Spatial ecological complexity measures in GRASS GIS",

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.",

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doi = "10.1016/j.cageo.2016.05.006",

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AU - Rocchini, Duccio

AU - Petras, Vaclav

AU - Petrasova, Anna

AU - Chemin, Yann

AU - Ricotta, Carlo

AU - Frigeri, Alessandro

AU - Landa, Martin

AU - Marcantonio, Matteo

AU - Bastin, Lucy

AU - Metz, Markus

AU - Delucchi, Luca

AU - Neteler, Markus

N1 - © 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.

PY - 2017/7

Y1 - 2017/7

N2 - 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.

AB - 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.

KW - free and open source software

KW - remote sensing

KW - spatial complexity

KW - spatial ecology

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SP - 166

EP - 176

JO - Computers and Geosciences

JF - Computers and Geosciences

ER -

Spatial ecological complexity measures in GRASS GIS

Abstract

Bibliographical note

Keywords

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