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Related Experiment Video

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Watershed Planning within a Quantitative Scenario Analysis Framework
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Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns.

Evan B Brooks1, John W Coulston2, Kurt H Riitters3

  • 1Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, Virginia, United States America.

Plos One
|October 15, 2020
PubMed
Summary
This summary is machine-generated.

A new hybrid seeding algorithm improves land use projections by creating spatial realizations for ecosystem service analysis. This method accurately models forest fragmentation patterns, aiding long-term planning.

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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.