Accounting#

Foreground patch size Accounting labels and measures all individual foreground patches in a binary raster, classifying them into user-defined size classes. The result is a spatially explicit map and tabular summary statistics describing the patch size distribution across the landscape. Further details about Accounting analysis are available in the Accounting product sheet.

Accounting Classes#

Foreground patches are labelled and classified into up to 6 size classes based on user-defined area thresholds. Each class groups patches whose size in pixels falls within a specific range, from the smallest isolated patches to the largest connected foreground areas.

The number of classes depends on the number of thresholds provided: 1 threshold produces 2 classes, 2 thresholds produce 3 classes, and so on up to a maximum of 5 thresholds producing 6 classes. Classes are assigned from smallest to largest and colour-coded in the output map as follows:

Accounting size classes, pixel values and colors.#
Class	Pixel Value	Color	Size Range
1	103	Black	Smallest patches [1 – threshold 1]
2	33	Red	[threshold 1 + 1 – threshold 2]
3	65	Yellow	[threshold 2 + 1 – threshold 3]
4	1	Orange	[threshold 3 + 1 – threshold 4]
5	9	Brown	[threshold 4 + 1 – threshold 5]
6	17	Green	Largest patches [> last threshold]

In addition to the foreground classes, the output map encodes background and special pixel values:

Accounting background and special pixel values.#
Pixel Value	Color	Meaning
0	Grey	Background (value 1 in input)
129	White	NoData (value 0 in input)
105	Blue	Special background (value 3 in input)
176	Light Blue	Special background (value 4 in input)

Note

Thresholds are expressed in pixels. To convert to area units, multiply by the pixel area (e.g. for a 25 m resolution raster, 1 pixel = 0.0625 ha). For example, a threshold of 200 pixels at 25 m resolution corresponds to 12.5 hectares.

Usage#

import pyguidos as pg

result = pg.acc(
    in_tiff="my_map.tif",
    thresholds=[10, 100, 1000, 10000],
    outdir="output/",
    statists=True,
    stat_files=True,
    verb=False
)

Parameters#

Parameter	Type	Default	Description
`in_tiff`	str or Path	–	Path to input GeoTIFF
`thresholds`	list, tuple or array	–	1 to 5 unique positive integers defining size class boundaries
`outdir`	str or Path	None	Output directory
`statists`	bool	True	Compute statistics
`stat_files`	bool	True	Write statistics to files
`verb`	bool	False	Print progress messages

Thresholds#

The thresholds parameter defines the patch size class boundaries in pixels. For example:

thresholds = [10, 100, 1000, 10000]

This creates 5 size classes:

Class	Size range
1	1 – 10 pixels
2	11 – 100 pixels
3	101 – 1000 pixels
4	1001 – 10000 pixels
5	> 10000 pixels

Note

A minimum of 1 and a maximum of 5 thresholds are allowed. Duplicate values are automatically removed and the list is sorted before processing.

Output Files#

File	Description
`<name>_acc.tif`	Accounting result GeoTIFF with colour palette
`<name>_acc.txt`	Statistics report

Results#

The acc() function returns a dict. The structure is nested as follows:

output paths (dict or None)
- path tif (str): Absolute path to the resulting Accounting GeoTIFF.
- path txt (str): Absolute path to the statistics text 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)
- pxl numb (dict): A dictionary where keys are class IDs and values are the total number of pixels belonging to that accounting class.
- patch numb (dict): A dictionary where keys are class IDs and values represent the total number of discrete patches identified for that accounting class.

result = pg.acc("my_map.tif", thresholds=[10, 100, 1000, 10000])

# 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}

# Per-class pixel and patch counts
print(result["output stats"])
# {'pxl numb': Counter({...}), 'patch numb': Counter({...})}

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

Note

result["output paths"] is None when stat_files=False. All other keys are always populated regardless of stat_files.

Computing Statistics Separately#

If you already have an accounting output GeoTIFF, you can compute statistics without rerunning the analysis:

stats = pg.acc_stats(
    acc_tiff="output/my_map_acc.tif",
    stat_files=True,
    outdir="output/",
    source_tiff="my_map.tif"
)

Note

acc_stats() requires the input GeoTIFF to be an pyGuidos (or GTB) output raster file. See Input Format for details.