Zonal analysis

class pylandstats.ZonalAnalysis(landscape, masks_arr, attribute_name=None, attribute_values=None, **kwargs)

__init__(landscape, masks_arr, attribute_name=None, attribute_values=None, **kwargs)

Parameters:

• landscapes (list-like) – A list-like of Landscape objects or of strings/file objects/ pathlib.Path objects so that each is passed as the landscape argument of Landscape.__init__
• masks_arr (list-like or np.ndarray) – A list-like of numpy arrays of shape (width, height), i.e., of the same shape as the landscape raster image. Each array will serve to mask the base landscape and define a region of study for which the metrics will be computed separately. The same information can also be provided as a single array of shape (num_masks, width, height).
• attribute_name (str, optional) – Name of the attribute that will distinguish each landscape
• attribute_values (str, optional) – Values of the attribute that correspond to each of the landscapes

compute_class_metrics_df(metrics=None, classes=None, metrics_kws=None)

Computes the data frame of class-level metrics, which is multi-indexed by the class and attribute value.

Parameters:

• metrics (list-like, optional) – A list-like of strings with the names of the metrics that should be computed in the context of this analysis case
• classes (list-like, optional) – A list-like of ints or strings with the class values that should be considered in the context of this analysis case
• metrics_kws (dict, optional) – Dictionary mapping the keyword arguments (values) that should be passed to each metric method (key), e.g., to exclude the boundary from the computation of total_edge, metric_kws should map the string ‘total_edge’ (method name) to {‘count_boundary’: False}. The default empty dictionary will compute each metric according to FRAGSTATS defaults.

Returns:

df – Dataframe with the values computed for each class, attribute value (multi-index) and metric (columns)

Return type:

pd.DataFrame

compute_landscape_metrics_df(metrics=None, metrics_kws=None)

Computes the data frame of landscape-level metrics, which is indexed by the attribute value.

Parameters:

• metrics (list-like, optional) – A list-like of strings with the names of the metrics that should be computed. If None, all the implemented landscape-level metrics will be computed.
• metrics_kws (dict, optional) – Dictionary mapping the keyword arguments (values) that should be passed to each metric method (key), e.g., to exclude the boundary from the computation of total_edge, metric_kws should map the string ‘total_edge’ (method name) to {‘count_boundary’: False}. The default empty dictionary will compute each metric according to FRAGSTATS defaults.

Returns:

df – Dataframe with the values computed at the landscape level for each attribute value (index) and metric (columns)

Return type:

pd.DataFrame

plot_landscapes(cmap=None, legend=True, subplots_kws=None, show_kws=None, subplots_adjust_kws=None)

Plots each landscape snapshot in a dedicated matplotlib axis by means of the Landscape.plot_landscape method of each instance

Parameters:

• cmap (str or ~matplotlib.colors.Colormap, optional) – A Colormap instance
• legend (bool, optional) – If True, display the legend of the land use/cover color codes
• subplots_kws (dict, default None) – Keyword arguments to be passed to plt.subplots
• show_kws (dict, default Nonte) – Keyword arguments to be passed to rasterio.plot.show
• subplots_adjust_kws (dict, default None) – Keyword arguments to be passed to plt.subplots_adjust

Returns:

fig – The figure with its corresponding plots drawn into its axes

Return type:

matplotlib.figure.Figure

plot_metric(metric, class_val=None, ax=None, metric_legend=True, metric_label=None, fmt='--o', plot_kws=None, subplots_kws=None, metric_kws=None)

Parameters:

• metric (str) – A string indicating the name of the metric to plot
• class_val (int, optional) – If provided, the metric will be plotted at the level of the corresponding class, otherwise it will be plotted at the landscape level
• ax (axis object, optional) – Plot in given axis; if None creates a new figure
• metric_legend (bool, default True) – Whether the metric label should be displayed within the plot (as label of the y-axis)
• metric_label (str, optional) – Label of the y-axis to be displayed if metric_legend is True. If the provided value is None, the label will be taken from the settings module
• fmt (str, default '--o') – A format string for plt.plot
• plot_kws (dict, default None) – Keyword arguments to be passed to plt.plot
• subplots_kws (dict, default None) – Keyword arguments to be passed to plt.subplots, only if no axis is given (through the ax argument)
• metric_kws (dict, default None) – Keyword arguments to be passed to the method that computes the metric (specified in the metric argument) for each landscape

Returns:

ax – Returns the Axes object with the plot drawn onto it

Return type:

axis object

class pylandstats.BufferAnalysis(landscape, base_mask, buffer_dists, buffer_rings=False, base_mask_crs=None, landscape_crs=None, landscape_transform=None)

__init__(landscape, base_mask, buffer_dists, buffer_rings=False, base_mask_crs=None, landscape_crs=None, landscape_transform=None)

Parameters:

• landscapes (list-like) – A list-like of Landscape objects or of strings/file objects/ pathlib.Path objects so that each is passed as the landscape argument of Landscape.__init__
• base_mask (shapely geometry or geopandas GeoSeries) – Geometry that will serve as a base mask to buffer around
• buffer_dists (list-like) – Buffer distances
• buffer_rings (bool, default False) – If False, each buffer zone will consist of the whole region that lies within the respective buffer distance around the base mask. If True, buffer zones will take the form of rings around the base mask.
• base_mask_crs (dict, optional) – The coordinate reference system of the base mask. Required if the base mask is a shapely geometry or a geopandas GeoSeries without the crs attribute set
• landscape_crs (dict, optional) – The coordinate reference system of the landscapes. Required if the passed-in landscapes are Landscape objects, ignored if they are paths to GeoTiff rasters that already contain such information.
• landscape_transform (affine.Affine) – Transformation from pixel coordinates to coordinate reference system. Required if the passed-in landscapes are Landscape objects, ignored if they are paths to GeoTiff rasters that already contain such information.

compute_class_metrics_df(metrics=None, classes=None, metrics_kws=None)

Computes the data frame of class-level metrics, which is multi-indexed by the class and buffer distance.

Parameters:

• metrics (list-like, optional) – A list-like of strings with the names of the metrics that should be computed in the context of this analysis case
• classes (list-like, optional) – A list-like of ints or strings with the class values that should be considered in the context of this analysis case
• metrics_kws (dict, optional) – Dictionary mapping the keyword arguments (values) that should be passed to each metric method (key), e.g., to exclude the boundary from the computation of total_edge, metric_kws should map the string ‘total_edge’ (method name) to {‘count_boundary’: False}. The default empty dictionary will compute each metric according to FRAGSTATS defaults.

Returns:

df – Dataframe with the values computed for each class, buffer distance (multi-index) and metric (columns)

Return type:

pd.DataFrame

compute_landscape_metrics_df(metrics=None, metrics_kws=None)

Computes the data frame of landscape-level metrics, which is indexed by the buffer distance.

Parameters:

• metrics (list-like, optional) – A list-like of strings with the names of the metrics that should be computed. If None, all the implemented landscape-level metrics will be computed.
• metrics_kws (dict, optional) – Dictionary mapping the keyword arguments (values) that should be passed to each metric method (key), e.g., to exclude the boundary from the computation of total_edge, metric_kws should map the string ‘total_edge’ (method name) to {‘count_boundary’: False}. The default empty dictionary will compute each metric according to FRAGSTATS defaults.

Returns:

df – Dataframe with the values computed at the landscape level for each buffer distance (index) and metric (columns)

Return type:

pd.DataFrame

plot_landscapes(cmap=None, legend=True, subplots_kws=None, show_kws=None, subplots_adjust_kws=None)

Plots each landscape snapshot in a dedicated matplotlib axis by means of the Landscape.plot_landscape method of each instance

Parameters:

• cmap (str or ~matplotlib.colors.Colormap, optional) – A Colormap instance
• legend (bool, optional) – If True, display the legend of the land use/cover color codes
• subplots_kws (dict, default None) – Keyword arguments to be passed to plt.subplots
• show_kws (dict, default Nonte) – Keyword arguments to be passed to rasterio.plot.show
• subplots_adjust_kws (dict, default None) – Keyword arguments to be passed to plt.subplots_adjust

Returns:

fig – The figure with its corresponding plots drawn into its axes

Return type:

matplotlib.figure.Figure

plot_metric(metric, class_val=None, ax=None, metric_legend=True, metric_label=None, fmt='--o', plot_kws=None, subplots_kws=None, metric_kws=None)

Parameters:

• metric (str) – A string indicating the name of the metric to plot
• class_val (int, optional) – If provided, the metric will be plotted at the level of the corresponding class, otherwise it will be plotted at the landscape level
• ax (axis object, optional) – Plot in given axis; if None creates a new figure
• metric_legend (bool, default True) – Whether the metric label should be displayed within the plot (as label of the y-axis)
• metric_label (str, optional) – Label of the y-axis to be displayed if metric_legend is True. If the provided value is None, the label will be taken from the settings module
• fmt (str, default '--o') – A format string for plt.plot
• plot_kws (dict, default None) – Keyword arguments to be passed to plt.plot
• subplots_kws (dict, default None) – Keyword arguments to be passed to plt.subplots, only if no axis is given (through the ax argument)
• metric_kws (dict, default None) – Keyword arguments to be passed to the method that computes the metric (specified in the metric argument) for each landscape

Returns:

ax – Returns the Axes object with the plot drawn onto it

Return type:

axis object