Fig 1.
Key components of the demand-allocation algorithm presented in this study.
Fig 2.
The conterminous United States (CONUS), as divided into the eight regions used by the Resource Planning Act (RPA) assessments. State boundaries are based on data freely available through the US Census Bureau (https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html).
Table 1.
Transitions between land uses in this study.
Only those marked with an X were considered when generating the transition probability model or the spatial realizations.
Table 2.
Ancillary datasets used to model transition probabilities.
Fig 3.
The conterminous United States (CONUS) was partitioned into 10 groupings of similar land use patterns for purposes of transition probability modeling. The groupings were derived from a k-means cluster analysis based on per-county land use distribution, elevation and slope, population and income, and geographic location. County feature data is freely available upon request from the USDA Forest Service Forest Inventory and Analysis program (https://data.fs.usda.gov/geodata/edw/datasets.php).
Fig 4.
Distance modifier to pixel transition probabilities, as a function of distance to center and local standard deviation of transition probabilities. Note how as the standard deviation increases, the impact of the distance modifier is lessened.
Fig 5.
(a) Land use around Charlotte, North Carolina, United States, and corresponding forest fragmentation classes for (b) 3x3, (c) 9x9, and (d) 27x27 pixel windows. See Table 3 for definitions for the specific classes.
Table 3.
Forest fragmentation classes used, based on percent forest within various window sizes.
Fig 6.
Transition probability rasters.
Example probability rasters of transitions to the Developed land use class from Developed (red), Forest (green), and Agriculture (blue) land use classes. The probability of transitioning from Developed to Developed is set at 1, so the red pixels are intended here as a convenient reference to previously existing Developed area.
Fig 7.
Coverage of observed forest patterns.
Differences between the total observed forest fragmentation and that derived from realizations, with respect to choice of the seeding size, n, and the scale of aggregation for results. Three example forest fragmentation classes (Table 3; Fig 5) are shown here. The green line represents a difference of 0 acres between the observed forest area and realized forest area, for forested pixels of the given fragmentation class at the given scale. Each boxplot represents six realizations per choice of n.
Fig 8.
Coverage of forest patterns by RPA region.
Coverage of observed forest areas by fragmentation class, by window size (when determining percent forest) and RPA region. Values shown represent the minimum deviation of the forest area from the 48 realizations, as compared to the observed time 2 forest area. Values of 0 indicate that the observed time 2 forest area was contained within the distribution of the realized forest areas.
Fig 9.
Projected change in forested area from 2020–2070, by forest fragmentation class (Table 3; Fig 5) and RPA subregion (Fig 2). Change is given as a percentage of the 2020 area. The lines represent the median value across 20 realizations; the polygons represent the corresponding inter-quartile ranges.