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

Abstract

Future land use projections are needed to inform long-term planning and policy. However,most projections require downscaling into spatially explicit projection rasters for ecosystemservice analyses. Empirical demand-allocation algorithms input coarse-level transition quotasand convert cells across the raster, based on a modeled probability surface. Such algorithmstypically employ contagious and/or random allocation approaches. We present ahybrid seeding approach designed to generate a stochastic collection of spatial realizationsfor distributional analysis, by 1) randomly selecting a seed cell from a sample of n cells, then2) converting patches of neighboring cells based on transition probability and distance to theseed. We generated a collection of realizations from 2001–2011 for the conterminous USAat 90m resolution based on varying the value of n, then computed forest area by fragmentationclass and compared the results with observed 2011 forest area by fragmentation class.We found that realizations based on values of n � 256 generally covered observed forestfragmentation at regional scales, for approximately 70% of assessed cases. We also demonstratethe potential of the seeding algorithm for distributional analysis by generating 20trajectories of realizations from 2020–2070 from a single example scenario. Generating alibrary of such trajectories from across multiple scenarios will enable analysis of projectedpatterns and downstream ecosystem services, as well as their variation.

Citation

Brooks, Evan B.; Coulston, John W.; Riitters, Kurt H.; Wear, David N. 2020. Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. PLOS ONE. 15(10): e0240097-. https://doi.org/10.1371/journal.pone.0240097.