Benchmarking of LUCC modelling tools by various validation techniques and error analysis

This study focuses on various validation and error analysis techniques that are based on map comparisons. After a literature review, authors apply these techniques to analyze the accuracy of LUCC models in terms of quantity, pixel by pixel correctness and LUCC components such as persistence and change. In addition, the fidelity of the spatial patterns and the congruency of the simulation maps from different modelling tools are tested. Finally, an error analysis is conducted that focuses on the magnitude of allocation errors and the magnitude of errors in predicted land use / cover classes. In addition, the impact of training dates on Markov chain predicted LUCC is analyzed.Mentioned techniques of validation and error analysis are illustrated by modelling LUCC of a small study area in the Eastern Pyrenees (France), where current LUCC are driven by spontaneous reforestation, decreasing pastureland and minimal anthropogenic disturbance. This very simple data set is used with three different tools (CA-Markov, LCM and Dinamica Ego) that represent commonly used modelling approaches and there methodological characteristics are highlighted. Applied to this specific dataset, contrasting results occur for different software programs that can help users choose an appropriate modelling approach according to specific model objectives.