Modelling Regional Land Change Scenarios to Assess Land Abandonment and Reforestation Dynamics in the Pyrenees(France)

Over the last decades and centuries,European mountain landscapes have experienced substantial transformations.Natural and anthropogenic LULC changes(land use and land cover changes), especially agro-pastoral activities,have directly influenced the spatial organization and composition of European mountain landscapes.For the past sixty years, natural reforestation has been occurring due to a decline in both agricultural production activities and rural population.Stakeholders, to better anticipate future changes,need spatially and temporally explicit models to identify areas at risk of land change and possible abandonment.This paper presents an integrated approach combining forecasting scenarios and a LULC changes simulation model to assess where LULC changes may occur in the Pyrenees Mountains,based on historical LULC trends and a range of future socio-economic drivers.The proposed methodologyconsiders local specificities of the Pyrenean valleys,sub-regional climate and topographical properties,and regional economic policies.Results indicate that some regions are projected to face strong abandonment, regardless of the scenario conditions.Overall, high rates of change are associated with administrative regions where land productivity is highly dependent on socio-economic drivers and climatic and environmental conditions limit intensive(agricultural and/or pastoral) production and profitability.The combination of the results for the four scenarios allows assessments of where encroachment(e.g.colonization by shrublands) and reforestation are the most probable.This assessment intends to provide insight into the potential future development of the Pyrenees to help identify areas that are the most sensitive to change and to guide decision makers to help their management decisions.

A cellular automata downscaling based 1 km global land use datasets (2010–2100)

Global climate and environmental change studies require detailed land-use and land-cover （LULC） information about the past, present, and future. In this paper, we discuss a methodology for downscaling coarse-resolution （i.e., half-degree） future land use scenarios to finer （i.e., 1 km） resolutions at the global scale using a grid-based spatially explicit cellular automata （CA） model. We account for spatial heterogeneity from topography, climate, soils, and socioeconomic variables. The model uses a global 30 m land cover map （2010） as the base input, a variety of biogeographic and socioeconomic variables, and an empirical analysis to downscale coarse-resolution land use information （specifically urban, crop and pasture）. The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100 （with four representative concentration pathway （RCP） scenarios--RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5） at a 1 km resolution within a globally consistent framework. The data are freely available for download, and will enable researchers to study the impacts of LULC change at the local scale.