SimiVal, a multi-criteria map comparison tool for land-change model projections

The multiple uses of land-cover models have led to validation with choice metrics or an ad hoc choice of the validation metrics available. To address this, we have identified the major dimensions of land-cover maps that ought to be evaluated and devised a Similarity Validation (SimiVal) tool. SimiVal uses a linear regression to test a modelled projection against benchmark cases of, perfect, observed and systematic-bias, calculated by rescaling the metrics from a random case relative to the observed, perfect case. The most informative regression coefficients, p-value and slope, are plot on a ternary graph of 'similarity space' whose extremes are the three benchmark cases. SimiVal is tested on projections of two deliberately contrasting land-cover models to show the similarity between intra- and inter-model parameterisations. We find metrics of landscape structure are important in distinguishing between different projections of the same model. Predictive and exploratory models can benefit from the tool. SimiVal is a tool that provides a consistent validation of land-cover change maps.The method validates quantity allocation, distance allocation and spatial metrics.Extreme benchmark scores for metrics and boundless metrics are computed.A model projection is validated to the perfect, random and systematic-bias cases.Many model projections can be validated together in 'similarity space'.