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https://github.com/martibosch/pylandstats
Computing landscape metrics in the Python ecosystem
https://github.com/martibosch/pylandstats
land-change-analysis landscape-ecology landscape-metrics python raster
Last synced: 4 days ago
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Computing landscape metrics in the Python ecosystem
- Host: GitHub
- URL: https://github.com/martibosch/pylandstats
- Owner: martibosch
- License: gpl-3.0
- Created: 2018-11-12T15:13:10.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T00:14:41.000Z (2 months ago)
- Last Synced: 2024-10-29T16:17:22.375Z (2 months ago)
- Topics: land-change-analysis, landscape-ecology, landscape-metrics, python, raster
- Language: Python
- Homepage: https://doi.org/10.1371/journal.pone.0225734
- Size: 851 KB
- Stars: 83
- Watchers: 5
- Forks: 16
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.python.org/pypi/pylandstats/)
[](https://anaconda.org/conda-forge/pylandstats)
[](https://pylandstats.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/martibosch/pylandstats/blob/main/.github/workflows/tests.yml)
[](https://results.pre-commit.ci/latest/github/martibosch/pylandstats/main)
[](https://codecov.io/github/martibosch/pylandstats)
[](https://github.com/martibosch/pylandstats/blob/master/LICENSE)
# PyLandStats
Open-source library to compute landscape metrics in the Python ecosystem (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](https://doi.org/10.1371/journal.pone.0225734)
## Features
- Read GeoTiff files of land use/cover:
```python
import pylandstats as pls
ls = pls.Landscape("../data/processed/veveyse-AS18_4.tif")
ls.plot_landscape(legend=True)
```
- Compute pandas data frames of landscape metrics at the patch, class and landscape level:
```python
class_metrics_df = ls.compute_class_metrics_df(
metrics=["proportion_of_landscape", "edge_density", "euclidean_nearest_neighbor_mn"]
)
class_metrics_df
```
| class_val | proportion_of_landscape | edge_density | euclidean_nearest_neighbor_mn |
| --------: | ----------------------: | -----------: | ----------------------------: |
| 1 | 7.749572 | 19.102211 | 309.244705 |
| 2 | 56.271868 | 50.599270 | 229.079970 |
| 3 | 33.946252 | 38.167200 | 253.299859 |
| 4 | 2.032308 | 3.722177 | 552.835154 |
- Analyze the spatio-temporal evolution of landscapes:
```python
import matplotlib.pyplot as plt
input_filepaths = [
"../data/processed/veveyse-AS97R_4.tif",
"../data/processed/veveyse-AS09R_4.tif",
"../data/processed/veveyse-AS18_4.tif",
]
sta = pls.SpatioTemporalAnalysis(input_filepaths, dates=["1992", "2004", "2012"])
sta.plot_metric("contagion")
```
- Zonal analysis of landscapes
See the [documentation](https://pylandstats.readthedocs.io/en/latest/?badge=latest) and the [pylandstats-notebooks](https://github.com/martibosch/pylandstats-notebooks) repository for a more complete overview.
## Installation
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 in order to define zones by vector geometries in `ZonalAnalysis`, or in order to use the the `BufferAnalysis` and `SpatioTemporalBufferAnalysis` classes, PyLandStats requires [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 aforementioned features (without having to use conda).
### Development install
To install a development version of PyLandStats, you can first use conda to create an environment with all the dependencies and activate it as in:
```
$ conda create -n pylandstats -c conda-forge geopandas matplotlib-base rasterio scipy openblas
$ conda activate pylandstats
```
and then clone the repository and use pip to install it in development mode
```
$ git clone https://github.com/martibosch/pylandstats.git
$ cd pylandstats/
$ pip install -e .
```
## Acknowledgments
- The computation of the adjacency matrix in [transonic](https://github.com/fluiddyn/transonic) has been implemented by Pierre Augier ([paugier](https://github.com/paugier))
- Several information theory-based metrics from Nowosad and Stepinski \[1\] were added by [achennu](https://github.com/achennu)
- With the support of the École Polytechnique Fédérale de Lausanne (EPFL)
- The Corine Land Cover datasets used for the test datasets were produced with funding by the European Union
## References
1. Nowosad, J., & Stepinski, T. F. (2019). Information theory as a consistent framework for quantification and classification of landscape patterns. Landscape Ecology, 34(9), 2091-2101.