SimiVal, a multi-criteria map comparison tool for land-change model projections. (August 2016)

• Record Type: Journal Article
• Authors: Bradley, Andrew V.; Rosa, Isabel M.D.; Pontius, Robert G.; Ahmed, Sadia E.; Araújo, Miguel B.; Brown, Daniel G.; Brandão, Amintas; Câmara, Gilberto; Carnerio, Tiago G.S.; Hartley, Andrew J.; Smith, Matthew J.; Ewers, Robert M.
• Is Part Of: Environmental modelling & software. Volume 82(2016:Aug.)
• Page Start: 229
• Page End: 240
• Publication Date: 2016-08
• Subjects: Land-cover modelling -- Validation -- Landscape metrics -- Land-cover change -- Model similarity -- Quantity allocation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.04.016
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: 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'.