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


Future land use projections are needed to inform long-term planning and policy. However,
most projections require downscaling into spatially explicit projection rasters for ecosystem
service analyses. Empirical demand-allocation algorithms input coarse-level transition quotas
and convert cells across the raster, based on a modeled probability surface. Such algorithms
typically employ contagious and/or random allocation approaches. We present a
hybrid seeding approach designed to generate a stochastic collection of spatial realizations
for distributional analysis, by 1) randomly selecting a seed cell from a sample of n cells, then
2) converting patches of neighboring cells based on transition probability and distance to the
seed. We generated a collection of realizations from 2001–2011 for the conterminous USA
at 90m resolution based on varying the value of n, then computed forest area by fragmentation
class 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 forest
fragmentation at regional scales, for approximately 70% of assessed cases. We also demonstrate
the potential of the seeding algorithm for distributional analysis by generating 20
trajectories of realizations from 2020–2070 from a single example scenario. Generating a
library of such trajectories from across multiple scenarios will enable analysis of projected
patterns 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-.
  • Keywords: projections, stochastic, fragmentation
  • Posted Date: October 16, 2020
  • Modified Date: November 17, 2020
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