An effective accuracy assessment indicator for credible land use change modelling: Insights from hypothetical and real landscape analyses

Land use change (LUC) modelling has been widely used to inform landscape planning and adaptive management practices. The validation of LUC modelling results is critical for justifying the usability of LUC models. Along this line, global-level accuracy assessments with the kappa and error matrix approach are accepted as the common method for the validation of LUC models. However, high global-level accuracy does not always guarantee good model performance and high accuracy in characterizing the local LUC. It is necessary to develop indicators to exactly assess the accuracy of land use modelling in characterizing the detailed land use change. In this study, both hypothetical and real landscapes are used to analyze the differences between global-level and local accuracy assessments, and all possible simulation scenarios are considered in the hypothetical landscape by exhaustive methods. The results derived from the hypothetical landscape show that the local accuracy tends to increase with the increase in the proportion of the regional area showing land use transitions throughout the landscape. A real landscape simulation by the Dyna-CLUE model in the middle reaches of the Heihe River Basin (M-HRB) in China also showed a similar trend, where the land use transition from 2000 to 2015 accounted for approximately 10% of the total area. The simulation results showed high global-level accuracies of 97.17% (2010) and 85.01% (2015). The local accuracies for regions with little land use transition were 98.45% (2010) and 96.56% (2015), but the local accuracies for regions with significant land use transitions were only 0.99% and 6.08% in 2010 and 2015, respectively. According to the global-level accuracy, LUC simulations are reliable, but they do not correctly reflect the local changes in regions with significant land use transitions. Therefore, both global-level accuracy and local accuracy should be used to avoid possible misleading LUC modelling results. An effective simulation and accuracy assessment procedure was proposed in this study to increase the credibility of LUC modelling.