r.futures.gridvalidation

NAME

r.futures.gridvalidation - Module for validating land change simulation on a grid

KEYWORDS

raster, statistics, accuracy, validation

SYNOPSIS

r.futures.gridvalidation

r.futures.gridvalidation --help

r.futures.gridvalidation simulated=name reference=name [original=name] output=name [region=name] [subregions=name] nprocs=integer [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

simulated=name [required]: Simulated land use raster
reference=name [required]: Reference land use raster
original=name: Original land use raster; Required for kappa simulation
output=name [required]: Vector output with values as attributes
region=name: Name of saved region
subregions=name: Name of input vector map; Vector areas for validation
nprocs=integer [required]: Number of parallel processes; Default: 1

DESCRIPTION
EXAMPLES
REFERENCES
SEE ALSO
AUTHORS

DESCRIPTION

Tool r.futures.gridvalidation allows to validate land change simulation results spatially. It is a wrapper around r.futures.validation that computes validation metrics for each cell of a grid or for each polygon of a vector layer. It computes:

Allocation disagreement (total and per class), see Pontius et al, 2011
Quantity disagreement (total and per class), see Pontius et al, 2011
Cohen's Kappa
Kappa simulation, see van Vliet et al, 2011

When original is provided and the input rasters contain only binary categories (0 for undeveloped and 1 for developed), the tool additionally computes change detection metrics (see r.futures.validation for details). When more than two categories are present, these metrics are skipped.

The tool operates in two modes:

Grid mode (region): A saved region is divided into grid cells and metrics are computed for each cell. Cell size of the region should be larger than the cell size of the current computational region. The output is a point vector layer with each point at the center of a grid cell.
Subregion mode (subregions): Metrics are computed for each polygon of the input vector layer. The output is a copy of the input vector with metrics added as attribute columns.

This tool can be used for any number of classes. Input raster original represents the initial conditions and is needed for Kappa simulation and for change detection metrics.

EXAMPLES

Validate FUTURES output by computing validation metrics on a 5km grid. First, reclassify FUTURES output (where -1 is undeveloped, 0 is initially developed, and 1 to N is the step when a cell became developed) to binary (0 = undeveloped, 1 = developed). Create a file reclass_rules.txt with the following content:

-1 = 0 undeveloped
0 thru 1000 = 1 developed

Then save a region used as a grid and reclassify:

g.region res=5000 -a save=grid
r.reclass input=simulated_2016 output=simulated_2016_reclass rules=reclass_rules.txt

Run the grid validation:

r.futures.gridvalidation simulated=simulated_2016_reclass reference=reference_2016 \
    original=orig_2001 output=validation_grid region=grid nprocs=4

REFERENCES

Validation references:

Robert Gilmore Pontius Jr & Marco Millones (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32:15, 4407-4429
Jasper van Vliet, Arnold K. Bregt, Alex Hagen-Zanker (2011) Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecological Modelling, Volume 222, Issue 8.

FUTURES references:

Meentemeyer, R. K., Tang, W., Dorning, M. A., Vogler, J. B., Cunniffe, N. J., & Shoemaker, D. A. (2013). FUTURES: Multilevel Simulations of Emerging Urban-Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm. Annals of the Association of American Geographers, 103(4), 785-807. DOI: 10.1080/00045608.2012.707591
Dorning, M. A., Koch, J., Shoemaker, D. A., & Meentemeyer, R. K. (2015). Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies. Landscape and Urban Planning, 136, 28-39. DOI: 10.1016/j.landurbplan.2014.11.011
Petrasova, A., Petras, V., Van Berkel, D., Harmon, B. A., Mitasova, H., & Meentemeyer, R. K. (2016). Open Source Approach to Urban Growth Simulation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 953-959. DOI: 10.5194/isprsarchives-XLI-B7-953-2016
Sanchez, G.M., A. Petrasova, A., M.M. Skrip, E.L. Collins, M.A. Lawrimore, J.B. Vogler, A. Terando, J. Vukomanovic, H. Mitasova, and R.K. Meentemeyer (2023). Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk. Sci Rep 13, 18869. DOI: 10.1038/s41598-023-46195-9

AUTHORS

Corresponding author:
Anna Petrasova, akratoc ncsu edu, Center for Geospatial Analytics, NCSU

Original standalone version:
Ross K. Meentemeyer,
Wenwu Tang,
Monica A. Dorning,
John B. Vogler,
Nik J. Cunniffe,
Douglas A. Shoemaker
(Department of Geography and Earth Sciences, UNC Charlotte)
Jennifer A. Koch (Center for Geospatial Analytics, NCSU)

Port to GRASS and GRASS-specific additions:
Vaclav Petras, NCSU GeoForAll

Development pressure, demand, calibration, validation, preprocessing tools and maintenance:
Anna Petrasova, NCSU GeoForAll

Climate forcing submodel:
Anna Petrasova, NCSU GeoForAll
Georgina Sanchez, Center for Geospatial Analytics, NCSU

Zoning:
Margaret Lawrimore, Center for Geospatial Analytics, NCSU
Anna Petrasova, NCSU GeoForAll

SOURCE CODE

Available at: r.futures.gridvalidation source code (history)

Accessed: Monday Jul 27 12:29:17 2026