Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico

Understanding and analysis of drivers of land-use and -cover change (LUCC) is a requisite to reduce and manage impacts and consequences of LUCC. The aim of the present study is to analyze drivers of LUCC in Southern Mexico and to see how these are used by different conceptual and methodological approaches for generating transition potential maps and how this influences the effectiveness to produce reliable LUCC models. Spatial factors were tested for their relation to main LUCC processes, and their importance as drivers for the periods 1993–2002 and 2002–2007 was evaluated by hierarchical partitioning analysis and logistic regression models. Tested variables included environmental and biophysical variables, location measures of infrastructure and of existing land use, fragmentation, and demographic and social variables. The most important factors show a marked persistence over time: deforestation is mainly driven by the distance of existing land uses; degradation and regeneration by the distance of existing disturbed forests. Nevertheless, the overall number of important factors decreases slightly for the second period. These drivers were used to produce transition potential maps calibrated with the 1993–2002 data by two different approaches: (1) weights of evidence (WoE) to represent the probabilities of dominant change processes, namely deforestation, forest degradation, and forest regeneration for temperate and tropical forests and (2) logistic RM that show the suitability regarding the different land-use and -cover (LUC) classes. Validation of the transition potential maps with the 2002–2007 data indicates a low precision with large differences between LUCC processes and methods. Areas of change evaluated by difference in potential showed that WoE produce transition potential maps that are more accurate for predicting LUCC than those produced with RM. Relative operating characteristic (ROC) statistics show that transition potential models based on RM do usually better predict areas of no change, but the difference is rather small. The poor performance of maps based on RM could be attributed to their too general representation of suitability for certain LUC classes when the goal is modeling complex LUCC and the LUC classes participate in several transitions. The application of a multimodel approach enables to better understand the relations of drivers to LUCC and the evaluation of model calibration based on spatial explanatory factors. This improved understanding of the capacity of LUCC models to produce accurate predictions is important for making better informed policy assessments and management recommendations to reduce deforestation.