Intersect FIA data with spatial polygons
intersectFIA.Rd
Performs spatial intersection between FIA data and user-supplied spatial polygons (sp or sf). Polygon attributes appended to PLOT table, and hence can be used as grouping variables in subsequent calls to rFIA estimator functions. Alternative to the polys
argument in rFIA estimator functions.
Arguments
- db
FIA.Database
orRemote.FIA.Database
object produced fromreadFIA()
orgetFIA()
. If aRemote.FIA.Database
, data will be read in and processed state-by-state to conserve RAM (see details for an example).- polys
sp
orsf
Polygon/MultiPolgyon object; Areal units to bin data for estimation. Separate estimates will be produced for region encompassed by each areal unit. FIA plot locations will be reprojected to match projection ofpolys
object.- nCores
numeric; number of cores to use for parallel implementation. Check available cores using
detectCores
. Default = 1, serial processing.
Details
All polygon attributes will be joined onto the PLOT table.
Primarily useful if you intend to make multiple calls to rFIA estimator functions, e.g., you need to call both tpa
and biomass
and group by spatial polygons in both cases. If using the polys
argument in each function call, spatial intersection will occur mulitple times, and hence be slower than performing the intersection a single time upfront.
References
rFIA website: https://rfia.netlify.app/
FIA Database User Guide: https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi
Bechtold, W.A.; Patterson, P.L., eds. 2005. The Enhanced Forest Inventory and Analysis Program - National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS - 80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 85 p. https://www.srs.fs.usda.gov/pubs/gtr/gtr_srs080/gtr_srs080.pdf
Stanke, H., Finley, A. O., Weed, A. S., Walters, B. F., & Domke, G. M. (2020). rFIA: An R package for estimation of forest attributes with the US Forest Inventory and Analysis database. Environmental Modelling & Software, 127, 104664.
Examples
data(fiaRI)
data(countiesRI)
# Perform spatial intersection
db <- intersectFIA(fiaRI,
countiesRI)
# Group estimates by variable defined
# in `countiesRI`
tpa(db,
grpBy = COUNTY)
#> # A tibble: 30 × 9
#> YEAR COUNTY TPA BAA TPA_SE BAA_SE nPlots_TREE nPlots_AREA N
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <int> <int> <int>
#> 1 2013 bristol 307. 118. 32.5 7.93 3 3 197
#> 2 2013 kent 535. 132. 16.8 6.40 14 14 197
#> 3 2013 newport 325. 122. 7.76 6.01 5 5 197
#> 4 2013 providence 518. 126. 10.6 3.98 53 54 197
#> 5 2013 washington 410. 110. 10.9 6.88 38 40 197
#> 6 2014 bristol 308. 119. 31.5 7.81 3 3 196
#> 7 2014 kent 534. 135. 16.4 6.77 14 14 196
#> 8 2014 newport 311. 118. 9.13 7.08 6 6 196
#> 9 2014 providence 528. 128. 11.0 3.92 52 52 196
#> 10 2014 washington 406. 111. 10.6 6.66 39 41 196
#> # ℹ 20 more rows