Spatial Explicit Land Use Change Scenarios for Policy Purposes: Some Applications of the CLUE Framework

Abstract

Worldwide Land Use and Land Cover Change (LULCC) plays an important role in global climate change. To be able to describe and analyze possible LULCC in the near future, spatially explicit modeling approaches are necessary. In this paper, applications of a LULCC-model, CLUE (Conversion of Land Use and its Effects), are demonstrated. The model uses quantitative information on the main drivers of land use change at different spatial scales, derived from historical and actual land use patterns. For a base-resolution (usually in the order of several square kilometers), georeferenced data for the study areas were collected and stored, and aggregated to different resolutions to create a series of artificial spatial scales. The collected data contains information on biophysical and socioeconomic variables that were considered theoretically important land use drivers; the relative contribution at various spatial scales remained to be determined. By means of stratified, multi-scale, spatial statistical analyses, the relative importance of the independent drivers in explaining land use patterns were quantitatively determined for different spatial scales. The multi-scale information on land use drivers was implemented in the dynamic CLUE model that simulates LULCC, using time steps of one year. It consists of a demand and allocation module. The demand module estimates the demanded physical output from agricultural land use systems at the highest aggregation level considered. In a multi-scale iteration-algorithm, the allocation module calculates spatially explicit LULCC at the level of individual cells (for different resolutions), considering (changes in) national demands as well as site-specific values of biophysical and socio-economic drivers.