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
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).