Towards a generic theoretical framework for pattern-based LUCC modeling

Pattern-based land use and land cover change (LUCC) modeling is implemented in various software, such as the CLUE family, LCM or Dinamica EGO. These tools are now relatively mature, but their conceptual foundations are not fully discussed in the literature. In particular, these modeling environments exhibit substantially different behaviors for the same problem and the same calibration data and the origin of this difference of behavior cannot be identified on the basis of the published information. These inter-model inconsistencies are revisited ab initio, focusing on allocation. The concept of allocation bias is defined and allows us to pinpoint a number of conceptual errors, biases and algorithmic inaccuracies in the existing LUCC modeling environments we have tested. We describe error- and bias-free allocation methods, implemented in our own software CLUMPY, which is shown to outperform these software in terms of formal correctness and accuracy. For the purpose of error and bias identification, it is crucial that validation be performed in explanatory variable space, not on maps.