pylandstats 0.3.0

Open-source Python library to compute landscape metrics

PyLandStats

Overview

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

Features

Read GeoTiff files of land use/cover

import pylandstats as pls

ls = pls.read_geotiff('data/vaud_g100_clc00_V18_5.tif')

ls.plot_landscape(legend=True)

Compute pandas DataFrames of landscape metrics at the patch, class and landscape level

patch_metrics_df = ls.compute_patch_metrics_df()
patch_metrics_df.head()

patch_id	class_val	area	perimeter	perimeter_area_ratio	shape_index	fractal_dimension
0	1	115	10600	92.17	2.409	1.130
1	1	13	2600	200.00	1.625	1.100
2	1	2	600	300.00	1.000	1.012
3	1	69	6000	86.96	1.765	1.088
4	1	76	8800	115.79	2.444	1.137

class_metrics_df = ls.compute_class_metrics_df(metrics=['proportion_of_landscape', 'edge_density'])
class_metrics_df

class_val	proportion_of_landscape	edge_density
1	7.702	4.459
2	92.298	4.459

Also analyze the spatio-temporal evolution of the landscape:

input_fnames = [
    'data/vaud_g100_clc00_V18_5.tif',
    'data/vaud_g100_clc06_V18_5a.tif',
    'data/vaud_g100_clc12_V18_5a.tif'
]

sta = pls.SpatioTemporalAnalysis(
    input_fnames, metrics=[
        'proportion_of_landscape',
        'edge_density',
        'fractal_dimension_am',
        'landscape_shape_index',
        'shannon_diversity_index'
    ], classes=[1], dates=[2000, 2006, 2012], 
)

fig, axes = sta.plot_metrics(
    class_val=1,
    metrics=['proportion_of_landscape', 'edge_density', 'fractal_dimension_am'],
    num_cols=3)
fig.suptitle('Class-level metrics (urban)')

See the pylandstats-notebooks repository for a more complete overview

Installation

To install use pip:

$ pip install pylandstats

Or clone the repo:

$ git clone https://github.com/martibosch/pylandstats.git
$ python setup.py install