Restoration Status Summary#

Restoration Status Summary (RSS) computes patch-based connectivity indices for a binary raster map. RSS characterises the spatial structure of the foreground by analysing the size distribution of individual patches, providing a set of indices that quantify landscape connectivity and restoration potential. Further details about Restoration Status Summary analysis are available in the RSS product sheet.

Note

RSS produces a statistics report only; it does not write a classified output map. All results are returned in the dictionary and, optionally, the .txt report.

Connectivity Indices#

RSS computes the following patch-based connectivity indices:

RSS connectivity indices.#

Full Name

Code

Unit

Description

Critical New Object Area

CNOA

pixels

Minimum area of a new patch that would increase connectivity

Equivalent Connected Area

ECA

pixels

Area of a single patch providing the same connectivity as observed

Reference Area Coverage

RAC

%

Percentage of foreground relative to total foreground and background

Coherence

COH

%

Percentage of foreground pixels effectively connected

Restoration Potential

REST_POT

%

Percentage of foreground pixels that could improve connectivity (100 - COH)

In addition to these indices, RSS reports basic patch size statistics: total number of foreground patches, and the average, median and largest patch size (in pixels).

Parameters#

Parameter

Type

Default

Description

in_tiff

str or Path

–

Path to input GeoTIFF

outdir

str or Path

None

Output directory. If None (default), outputs are written to the input file’s directory.

stat_files

bool

True

Write statistics to files

verb

bool

False

Print progress messages

Example with all parameters:

import pyguidos as pg

result = pg.rss(
    in_tiff="my_map.tif",
    outdir="output/",
    stat_files=True,
    verb=False
)

Output Files#

File

Description

<name>_rss.txt

Statistics report with all connectivity indices

Statistics#

Result Dictionary#

The rss() function returns a dict with three sections:

  • output paths (dict or None)

    • path txt (str): Absolute path to the comprehensive statistics report.

    • Note: This key is None if stat_files=False.

  • input stats (dict)

    • foreground pxl (int): Count of pixels with value 2 (Forest).

    • background pxl (int): Count of pixels with value 1 (Background).

    • missing pxl (int): Count of NoData (0) pixels.

    • backgr3 pxl (int): Count of special background class 3 pixels.

    • backgr4 pxl (int): Count of special background class 4 pixels.

  • output stats (dict)

    • total patches (int): The total number of discrete patches identified in the landscape.

    • average patch size (float): The mean size of patches (in pixel units).

    • median patch size (float): The median size of patches.

    • largest patch size (int): The size of the largest single patch found.

    • CNOA (float): Critical New Object Area.

    • ECA (float): Equivalent Connected Area.

    • RAC (float): Reference Area Coverage.

    • COH (float): Coherence index.

    • REST_POT (float): Restoration Potential index.

Accessing the result:

result = pg.rss("my_map.tif")

# Access statistics
print(result.keys())
# dict_keys(['output paths', 'input stats', 'output stats'])

# Input pixel counts
print(result["input stats"])
# {'foreground pxl': 12500, 'background pxl': 37500,
#  'missing pxl': 0, 'backgr3 pxl': 0, 'backgr4 pxl': 0}

# Connectivity indices
print(result["output stats"])
# {'total patches': 142, 'average patch size': 88.0,
#  'median patch size': 12.0, 'largest patch size': 8542,
#  'ECA': 8764.3, 'COH': 70.1, 'CNOA': 3, 'REST_POT': 29.9, 'RAC': 25.0}

# Output file paths
print(result["output paths"])
# {'path txt': 'output/my_map_rss.txt'}

Note

rss() computes its statistics as part of the main run; there is no separate standalone *_stats() function for this tool.