Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns.

Area of Science:

• Ecology
• Geospatial Analysis
• Land Use Science

Background:

• Accurate land use projections are crucial for policy and planning.
• Downscaling coarse projections into explicit rasters is necessary for ecosystem service analysis.
• Existing empirical demand-allocation algorithms use contagious or random approaches.

Purpose of the Study:

• To introduce a novel hybrid seeding algorithm for generating stochastic spatial realizations of land use change.
• To assess the algorithm's ability to replicate observed forest fragmentation patterns.
• To demonstrate the algorithm's utility for distributional analysis of future land use scenarios.

Main Methods:

• A hybrid seeding approach involving random seed cell selection and neighbor cell conversion based on probability and distance.
• Generation of multiple spatial realizations for the conterminous USA (2001-2011) at 90m resolution.
• Comparison of computed forest area by fragmentation class with observed data.

Main Results:

• Realizations using n ≤ 256 generally matched observed forest fragmentation at regional scales in about 70% of cases.
• The algorithm successfully generated 20 future land use trajectories (2020-2070) from a single scenario.
• Demonstrated potential for distributional analysis of projected land use patterns and ecosystem services.

Conclusions:

• The hybrid seeding algorithm provides a valuable tool for generating realistic spatial land use projections.
• This approach enhances the analysis of uncertainty in future land use patterns and their impacts.
• A library of trajectories from multiple scenarios will enable comprehensive assessment of ecosystem services variation.