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.We will demonstrate applications for a large area such as Central America and for smaller regions like the Zona Atlantica (Costa Rica) and Sibuyan Island (Philippines). For all these applications the model was calibrated with historical data. Validation took place by comparing model output with data from an independent data set. The validation results indicate good agreement of modeled land use dynamics with observed dynamics. Through scenario studies, the impacts of changing conditions in the near future at different levels were studied, such as the effects of changing national demands for food products, national policies concerning market liberalization or nature protection, but also regional and local developments like migration and land degradation. Time horizons of 15 to 20 years were used in the scenario studies. The evaluation of different scenarios, based on different policies and development trajectories can help to assess these spatial patterns and their effects on nutrient balances and erosion, food production, and other ecosystem functions. In this way, spatial modeling can improve land use planning and inform policymaking.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
- Spatial Explicit Land Use Change Scenarios for Policy Purposes: Some Applications of the CLUE Framework
Peter H. Verburg
Free De Koning
- Springer US
- Chapter 14