Introduction to landscape ecology with R

[This article was first published on rstats on Jakub Nowosad's website, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Maximilian H.K. Hesselbarth and I gave the Introduction to landscape ecology with R workshop during IALE-North America 2020 Annual Meeting. You can find the workshop abstract, slides, and recordings below.


R is a free, open-source programming language created as an environment for statistical computing and visualization. The advantages of using R include its flexibility, ease of collaboration, and focus on reproducibility. Additionally, the concept of packages – collections of R functions, data, and compiled code created by users – allowed for the growth of its capabilities and expansion into many scientific fields. In recent years, R also has become one of the most often used tools in ecology.

R also has a long history of supporting spatial data analysis, including spatial data downloading, preprocessing, visualizing, and modeling. Recently, however, some new packages have appeared which have significantly changed the work with spatial data in R; in particular, the sf package.

The workshop is divided into two parts. The first one introduces participants to the spatial data analysis system in R. The focus is on getting started, with demonstrations of key packages, spatial analysis, and making maps. The second part of the workshop focuses on how to use the landscapemetrics package. This package is based on the main concepts from FRAGSTATS, but it is characterized by a number of advantages. These include, among others, removing existing metric implementation errors, adding new landscape metrics, enabling landscape visualization, and allowing for calculations on large input data. A particular advantage is also an ability to integrate this package with other packages for spatial analysis, so it is possible to download spatial data, process it, calculate landscape metrics and visualize them within one tool.

The workshop is a mixture of theoretical and practical. Pointers to further materials ensure that participants know where to get help and how to take confident next steps after the workshop.


You can find the slides for the talk at


Part I:

Part II:

To leave a comment for the author, please follow the link and comment on their blog: rstats on Jakub Nowosad's website. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)