Sensitivity assessment and evaluation of a spatially explicit land-use model for Southern Amazonia

摘要

Land-use and land cover change (LULCC), in particular in Amazonia has exerted and will exert crucial influence on global climate and environmental change. Many models were applied to reproduce observed LULCC and explore possible future conversion trends. Results thus far have shown that LULCC modeling, especially in a regional context in Amazonia, needs further research in order to assess the change trajectories that were observed since the end of the 20th century in a complete and cogent way. The lack of modeling results that reproduce observed LULCC dynamics is mostly based upon uncertainties that arise when employing different sets of initial land use data, model input data (drivers), and methods to estimate parameter weights. Also uncertainties in regard to model structure and, thus different representations of modeled processes, have to be taken into account. We therefore chose the well-established dynamic, spatially explicit, integrated LULCC modeling framework, LandSHIFT, to investigate the effect of (1) different initial land-cover products, (2) input variables derived from the most commonly utilized databases and (3) the variety of model parameter weights for suitability analysis resulting from different methods used for model parameterization, on modeling results. We then analyzed the resulting model output in order to determine the ability of the model to capture observed LULCC with respect to the chosen combination of input and methods. We measured the predictive performance of the land-use modeling framework by calculating model efficiency as well as Fuzzy Kappa coefficient. The two Brazilian federal states Mato Grosso and Para were chosen as focus of this study because they are characterized by highly dynamic LULCC processes as well as large areas of intact natural vegetation that are threatened to be destroyed due to agricultural and pasture expansion. Our findings show that a high degree of uncertainty regarding LULCC can be expected, depending on th