pylandstats 0.1.0

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