Warning

This section is under development.

User Guide

See also

For those who want to contribute, see Development

This guide explains how to use the scripts in this repository to perform APCAC classification on polygons using vector and raster data. The scripts are designed to run within QGIS via the Python Script Tool, supporting both full workflow execution and independent function calls for custom analyses.

Note

This repository is intended to run under the QGIS Python environment.

Installation

1. Install QGIS in advanced mode.

2. During installation, ensure that the following Python libraries are installed in the QGIS environment: - numpy - pandas - geopandas

These are the only external dependencies required to run the scripts.

Workflow

The repository is structured as a function-based workflow, allowing:

• Full APCAC analysis from raster sampling to classification and reporting.

• Independent function calls for hydrology and erosion indices, fuzzification, and risk classification.

• Automatic management of timestamped output folders for reproducible runs and organized outputs.

• Easy integration into QGIS Python tools using importlib.

Scripts operate on raster and vector data, with each catchment receiving a full set of computed attributes. Outputs can be upscaled to coarser maps or used for further GIS analysis.

Output Attributes

The ultimate output of this process is the map of APCAC sampled at polygons, like small catchments or any other scale of interest. The polygon layer is stored in a geopackage with the following core attributes:

Attribute	Type	Description
cd_apcac	text	APCAC code
id_apcac	int	APCAC id
a	float	hydrological importance index
n	float	conservation index
t	float	topography index
s	float	pedological index (soils)
g	float	geological index
c	float	climatological index
v	float	long term conservation change rate (percent)
slope	float	slope in degrees
uslek	float	soil erosivity (USLE equation)
e	float	soil degradation index

Scripts

Scripts are executed via the Python Script Tool in QGIS using the importlib workflow.

See also

More scripts examples are available at the API

The following example shows how to use the analysis_apcac() tool:

# WARNING : run this under a QGIS Python Environment
import importlib.util as iu

# define the paths to this module
the_module = "path/to/classes.py"

# setup module with importlib
spec = iu.spec_from_file_location("module", the_module)
module = iu.module_from_spec(spec)
spec.loader.exec_module(module)

# define the paths to input and output folders
input_dir = "path/to/input_folder"
output_dir = "path/to/output_folder"

# define the path to input database
input_db = "path/to/data.gpkg"

# define the paths to input rasters
raster_files = {
    "t": "path/to/raster_t.tif",
    "s": "path/to/raster_s.tif",
    "g": "path/to/raster_g.tif",
    "c": "path/to/raster_c.tif",
    "n": "path/to/raster_n.tif",
    "v": "path/to/raster_v.tif",
    "slope": "path/to/raster_slope.tif",
    "uslek": "path/to/raster_uslex.tif",
}

raster_multipliers = {
    "t": 1000,
    "s": 100,
    "slope": 100,
}

# call the full APCAC analysis
module.analysis_apcac(
    input_db=input_db,
    input_layer="apcac_bho5k",
    raster_files=raster_files,
    output_folder=output_dir,
    raster_multipliers=raster_multipliers,
    cleanup=True
)

Notes

• Users can call individual functions for custom analyses instead of running the full workflow.

• All outputs are organized in timestamped folders for reproducibility.

• Raster maps form the base for calculations, and zonal statistics are applied to catchments for final aggregation.

• No additional external dependencies are required beyond numpy, pandas, and geopandas.