#Vector or raster for natural landscape software
#Vector or raster for natural landscape code

The growing body of R packages related to spatial data processing and analysis results in a high capability of this language for landscape ecology. In addition to handling spatial data, other analytical tasks such as statistical modeling, creation of publication-ready figures, and even complete reports can be done within the R environment (Fig. Thus, the R programming language can be a very powerful tool. Originally introduced as a statistical programming language, a large number of packages designed to analyze spatial data subsequently emerged for the R programming language. First released in 1995, this programming language is among the most popular programming languages today, especially in ecology.

#Vector or raster for natural landscape code

Packages allow to easily share code and make functionality and documentation available to users and by that extend the capability of the R programming language. One successful example of an open-source project is the R programming language, and its extensions called packages.

#Vector or raster for natural landscape software

For more general overviews of open-source software for landscape ecology studies see. Importantly, though not strictly necessary by definition of open-source, most open-source software is also free-of-cost, in contrast to often expensive proprietary software. Furthermore, open-source software allows other scientists to reuse code and not “reinvent the wheel” by customizing existing software to their specific needs. Additionally, open-source software facilitates (computational) reproducibility and allows a better understanding of the methodology of a study. Open-source software development has many advantages, such as fast innovation, transparency, reliability, and longevity, mainly due to many diverse contributors. Open-source software includes all software that is released under licenses that allow for the use, modification and distribution of the software. Software to manage and analyze data becomes increasingly important in modern scientific research and many scientific studies would not have been possible without open-source software. Thus, landscape ecology emphasizes spatial patterns to a high degree and consequently relies on software to preprocess, modify, model, analyze, and visualize these spatial patterns. Because landscapes are defined as mosaics of different land covers, ecosystems, habitat types, or land uses, spatial context is essential and ecological processes can vary spatially. Landscape ecology focuses on how ecological processes are influenced by the heterogeneous landscapes and how the ecological processes themselves influence the landscapes. Suggested future developments include improvement of computation performance additional methods for landscape characterisation such as surface metrics and advanced, accessible visualisation tools. We found that landscape ecologists are generally satisfied with the functionality available within R, and that as a community they are continually further developing the functionality available. Additionally, we outline the results of a survey of R users within the landscape ecology community. In this paper, we examine existing packages for downloading, processing and visualisation of spatial data, as well as those specifically developed for spatial ecological analysis. In particular, the R programming language provides a wealth of community developed tools for landscape ecology.

Landscape ecology, the study of the complex interactions between landscapes and ecological processes, has hugely benefited from the increase in widely available open-source software in recent years.