The Theoretical Architecture of the MOLUSCE Plugin and an Assessment of Application Results in Land Cover Change Prediction
DOI:
https://doi.org/10.53463/ecopers.20260468Keywords:
MOLUSCE, land use/cover change, cellular autoata, artificial neural network, geographic information systems, predictive modellingAbstract
Projecting land use and land cover change is central to landscape planning and natural resource management. MOLUSCE (Modules for Land Use Change Simulations) combines Markov chain, cellular automata and machine learning based transition potential models in one open source QGIS plugin. This study dissects the theoretical architecture of the plugin, then comparatively discusses 28 applications from nine countries. Its four transition potential methods (neural network, logistic regression, weights of evidence, multi-criteria evaluation) are compared through the reported accuracy figures. The neural network-cellular automata combination is the most frequently adopted configuration, while controlled comparisons show hybrid configurations achieving higher accuracy. Although most applications report Kappa values of 0.70-0.95, a model performing well in training may decline markedly at validation. Predictions consistently indicate expanding built-up areas and retreating natural cover. MOLUSCE is a reliable decision support tool, but its success depends less on the tool than on input data quality and the choice of driver variables and parameters.
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