landscapemetrics: an open‐source R tool to calculate landscape metrics

Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constra...

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Bibliographic Details
Published in:	Ecography (Copenhagen) 2019-10, Vol.42 (10), p.1648-1657
Main Authors:	Hesselbarth, Maximilian H. K., Sciaini, Marco, With, Kimberly A., Wiegand, Kerstin, Nowosad, Jakub
Format:	Article
Language:	eng
Subjects:	Computer programs Ecological monitoring Ecological research Ecology Integration Land economics Landscape landscape analysis landscape indices landscape mosaic model Mathematical models open-source software R software sampling design Software Spatial data Statistical analysis Workflow
Online Access:	Request full text
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Summary:	Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data.
ISSN:	0906-7590 1600-0587