Abstract
Quantifying the spatial pattern of landscapes has become a common task of many studies in landscape ecology. Most of the existing software to compute landscape metrics is not well suited to be used in interactive environments such as Jupyter notebooks nor to be included as part of automated computational workflows. This article presents PyLandStats, an open-source Pythonic library to compute landscape metrics within the scientific Python stack. The PyLandStats package provides a set of methods to quantify landscape patterns, such as the analysis of the spatiotemporal patterns of land use/land cover change or zonal analysis. The implementation is based on the prevailing Python libraries for geospatial data analysis in a way that they can be forthwith integrated into complex computational workflows. Notably, the provided methods offer a large variety of options so that users can employ PyLandStats in the way that best supports their needs. The source code is publicly available, and is organized in a modular object-oriented structure that enhances its maintainability and extensibility.
Highlights
Landscape ecology is based on the notion that the spatial pattern of landscapes strongly influences the ecological processes that occur upon them [1]
In a context of significant advances in geographical information systems (GIS) and increasing availability of land use/land cover (LULC) datasets, landscape metrics have been implemented within a variety of software packages [6]
Landscape metrics might be classified into two main groups
Summary
Citation: Bosch M (2019) PyLandStats: An opensource Pythonic library to compute landscape metrics. PLoS ONE 14(12): e0225734. https://doi. org/10.1371/journal.pone.0225734 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0225734 Data Availability Statement: All the data files are available at a GitHub repository https://github.com/ martibosch/pylandstats-notebooks/tree/biorxiv/ data and have been derived from the Corine Land Cover datasets https://land.copernicus.eu/paneuropean/corine-land-cover.
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