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 acros...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0240097 |
| _version_ | 1819111714435629056 |
|---|---|
| author | Evan B Brooks John W Coulston Kurt H Riitters David N Wear |
| author_facet | Evan B Brooks John W Coulston Kurt H Riitters David N Wear |
| author_sort | Evan B Brooks |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T04:02:00Z |
| format | Article |
| id | doaj.art-48cd0649628546329507b3f08483a16a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T04:02:00Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-48cd0649628546329507b3f08483a16a2022-12-21T18:39:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011510e024009710.1371/journal.pone.0240097Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns.Evan B BrooksJohn W CoulstonKurt H RiittersDavid N WearFuture 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.https://doi.org/10.1371/journal.pone.0240097 |
| spellingShingle | Evan B Brooks John W Coulston Kurt H Riitters David N Wear Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. PLoS ONE |
| title | Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. |
| title_full | Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. |
| title_fullStr | Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. |
| title_full_unstemmed | Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. |
| title_short | Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns. |
| title_sort | using a hybrid demand allocation algorithm to enable distributional analysis of land use change patterns |
| url | https://doi.org/10.1371/journal.pone.0240097 |
| work_keys_str_mv | AT evanbbrooks usingahybriddemandallocationalgorithmtoenabledistributionalanalysisoflandusechangepatterns AT johnwcoulston usingahybriddemandallocationalgorithmtoenabledistributionalanalysisoflandusechangepatterns AT kurthriitters usingahybriddemandallocationalgorithmtoenabledistributionalanalysisoflandusechangepatterns AT davidnwear usingahybriddemandallocationalgorithmtoenabledistributionalanalysisoflandusechangepatterns |