Prospective modelling of environmental dynamics. A methodological comparison applied to mountain land cover changes

During the last 10 years, scientists performed significant advances in modelling environmental dynamics. A wide range of new methodological approaches in geomatics - such as neural networks, multi-agent systems or fuzzy logics - was developed. Despite these progresses, the modelling softwares available have to be considered as experimental tools rather than as improved procedures able to work for environmental management or decision support. Particularly, the authors consider that a large number of publications suffer from lakes in the validation of the model results. This contribution describes three different modelling approaches applied to prospective land cover prediction. The first one, a combined geomatic method, uses Markov chains for temporal transition prediction while their spatial assignment is supervised manually by the construction of suitability maps. Compared to this directed method, the two others may be considered as semi automatic because both the polychotomous regression and the multilayer perceptron only need to be optimized during a training step - the algorithms detect themselves the spatial-temporal changes in land cover. The authors describe the three methodological approaches and their practical applications to two mountain studied areas: one in French Pyrenees, the second including a large part of Sierra Nevada, Spain. The article focuses on the comparison of results. The main result is that prediction scores are on the more high that land cover is persistent. They also underline that the geomatic model is complementary to the statistical ones which perform higher overall prediction rate but produce worse simulations when land cover changes are numerous

Modeling the future evolution of Chilean forests to guide current practices. Native forest and industrial timber plantations in Southern Chile

International audienceScientific research builds projects and seeks to achieve specific goals that refer to the principles of scientific inference: deduction, induction and abduction. These inferences correspond to the time path of the prediction-which belongs to the world of rationality and accuracy-and scenarios-which transcribe the uncertain nature of the studied process and can describe, in some cases, a probable future, desirable or not. Because the conclusion of deductive inference stems from premises, predictive simulation must be the result of past observations. Optimization of these results requires a rigorous calibration of the model, in order to reproduce a known situation (past or present). Scenarios are not predictions. For exploratory scenarios (forecasting), plausible hypotheses are built from observed processes and can only be verified a posteriori. The scenario begins with a given situation in the present and moves forward into the future, responding to the question " What may happen if …? " The normative scenario (inductive inference) describes a probable or desirable (or undesirable) future and then moves backwards to the present, i.e. retrospectively. The attitude is proactive towards the future and responds to the question " How can a specific target be reached? " These inferences give rise to specific approaches in terms of modeling and simulation. By focusing on forest dynamics in the south of Chile, this paper presents an expert approach (multi-criteria evaluation with Markovian chains) to map predic-tive and exploratory scenarios. The results open up various interesting lines of discussion in terms of resource management and clearly show the importance of model calibration (choice of data and configuration) upstream of the simulation process