PyLandStats documentation!

Open-source Pythonic library to compute landscape metrics within the PyData stack (NumPy, pandas, matplotlib…)

Citation: Bosch M. 2019. “PyLandStats: An open-source Pythonic library to compute landscape metrics”. PLOS ONE, 14(12), 1-19. doi.org/10.1371/journal.pone.0225734

Development:

This documentation is intended as an API reference. See also:

  • pylandstats-notebooks repository (tutorial/thorough overview of PyLandStats)
  • swiss-urbanization repository (example application of PyLandStats to evaluate the spatiotemporal patterns of urbanization in three Swiss urban agglomerations)

Features

  • Compute pandas DataFrames of landscape metrics at the patch, class and landscape level
  • Analyze the spatiotemporal evolution of landscapes
  • Analyze landscape changes accross environmental gradients (zonal analysis)

Using PyLandStats

The easiest way to install PyLandStats is with conda:

$ conda install -c conda-forge pylandstats

which will install PyLandStats and all of its dependencies. Alternatively, you can install PyLandStats using pip:

$ pip install pylandstats

Nevertheless, note that the BufferAnalysis and SpatioTemporalBufferAnalysis classes make use of [geopandas](https://github.com/geopandas/geopandas), which cannot be installed with pip. If you already have [the dependencies for geopandas](https://geopandas.readthedocs.io/en/latest/install.html#dependencies) installed in your system, you might then install PyLandStats with the geo extras as in:

$ pip install pylandstats[geo]

and you will be able to use the BufferAnalysis and SpatioTemporalBufferAnalysis classes (without having to use conda).

Indices and tables