Estimating temporal changes in land area
Hunter Stanke, Jeffrey W. Doser
2021 (last updated February 12, 2025)
Source:vignettes/areaChange.Rmd
areaChange.RmdThe increasing availability of remeasured FIA plots offers the unique
opportunity to move from asking questions about the status of
forest resources, to questions of how resources have changed
over time. For example, we could use the area() function in
rFIA to determine the relative land area occupied by various forest
types in a landscape or region of interest, i.e., it’s current
composition. But how has the composition of that landscape changed in
recent decades? What are the primary drivers of such change? And how
does this shape our thinking of what the landscape will look like in the
future?
rFIA v0.3.1 gave rise to the areaChange() function,
which allows us to address such questions using FIA data alone.
By default, areaChange() will estimate net
annual change in forest land area within a region over time. As always,
you can check out our complete documentation with by clicking on
areaChange() (or opening up the same information in R via
?areaChange):
library(rFIA)
# Download the FIA data for Rhode Island
ri <- getFIA('RI')
# Estimate change in forestland area
areaChange(ri)## # A tibble: 15 × 9
## YEAR PERC_CHNG AREA_CHNG PREV_AREA PERC_CHNG_SE AREA_CHNG_SE PREV_AREA_SE
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2008 -0.612 -2383. 389114. 84.8 84.0 10.4
## 2 2009 -0.424 -1411. 332902. 93.7 92.5 6.17
## 3 2010 -0.590 -2108. 357309. 62.4 61.4 4.82
## 4 2011 -0.131 -466. 356992. 302. 302. 4.52
## 5 2012 0.104 377. 363420. 369. 369. 3.89
## 6 2013 0.224 833. 371634. 176. 175. 3.57
## 7 2014 0.329 1213. 368937. 114. 114. 3.74
## 8 2015 0.383 1404. 366790. 91.3 90.4 3.78
## 9 2016 0.0473 173. 366439. 703. 702. 3.91
## 10 2017 0.0909 331. 363600. 414. 413. 3.86
## 11 2018 -0.0656 -238. 363831. 533. 535. 3.85
## 12 2019 -0.567 -2099. 370239. 57.8 58.6 3.45
## 13 2020 -0.689 -2542. 368700. 51.5 52.1 3.76
## 14 2021 -0.751 -2744. 365531. 49.4 49.9 3.76
## 15 2022 -0.484 -1831. 378440. 68.1 69.1 3.79
## # ℹ 2 more variables: nPlots_AREA <int>, N <int>Here AREA_CHNG is the estimated annual change in forest
land area in acres, and PERC_CHNG is the estimated annual
change as a percentage of previous total forestland. Importantly, the
values estimated above are net changes
(chngType="NET"). In other words, these changes epresent
the combined effects of reversion (non-forest becoming forest) and
diversion (forest becoming non-forest).
Often, however, examining the components of change can yield
valuable insight into the drivers of net changes in forest resources. As
such, areaChange() allows us to explicitly estimate change
components when chngType = "component". In our simple
example, this means estimating the annual rates of reversion and
diversion in our region:
# Estimate change in forestland area by component
areaChange(ri, chngType = 'component')## # A tibble: 45 × 11
## YEAR STATUS1 STATUS2 PERC_CHNG AREA_CHNG PREV_AREA PERC_CHNG_SE AREA_CHNG_SE
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2008 Forest Forest 0 0 375257. NaN NaN
## 2 2008 Forest Non-fo… 19.2 2654. 13857. 5.21 73.9
## 3 2008 Non-fo… Forest 21.4 272. 1268. 1.11 106.
## 4 2009 Forest Forest 0 0 321493. NaN NaN
## 5 2009 Forest Non-fo… 20.0 2281. 11409. 3.66 53.3
## 6 2009 Non-fo… Forest 20.7 869. 4207. 1.23 49.7
## 7 2010 Forest Forest 0 0 340459. NaN NaN
## 8 2010 Forest Non-fo… 19.7 3313. 16850. 3.32 35.8
## 9 2010 Non-fo… Forest 21.1 1205. 5699. 1.89 40.1
## 10 2011 Forest Forest 0 0 343518. NaN NaN
## # ℹ 35 more rows
## # ℹ 3 more variables: PREV_AREA_SE <dbl>, nPlots_AREA <int>, N <int>Here STATUS1 and STATUS2 represent the land
classification at first and second measurements, respectively. For the
2010 inventory, we estimate that 3,313 acres of forestland were diverted
to a non-forest land use annually, and 1,205 acres of non-forest were
reverted back to the forest land base. Here our losses (3,313 acres)
exceed our gains (1,205 acres), and their difference is equal to the net
change (AREA_CHNG) we estimated previously when
chngType = 'NET' (-2,108 acres).
We can extend this example by examining patterns of change across some variable of interest, let’s say ownership classes:
# Grab only the most recent survey
ri_mr <- clipFIA(ri)
# Estimate NET change in forestland area by ownership group
areaChange(ri_mr, grpBy = OWNGRPCD, chngType = 'net')## # A tibble: 2 × 10
## YEAR OWNGRPCD PERC_CHNG AREA_CHNG PREV_AREA PERC_CHNG_SE AREA_CHNG_SE
## <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2022 30 0.487 582. 119620. 177. 174.
## 2 2022 40 -0.932 -2413. 258820. 50.3 51.6
## # ℹ 3 more variables: PREV_AREA_SE <dbl>, nPlots_AREA <int>, N <int>
# Estimate component change in forestland area by ownership group
areaChange(ri_mr, grpBy = OWNGRPCD, chngType = 'component')## # A tibble: 8 × 13
## YEAR OWNGRPCD1 OWNGRPCD2 STATUS1 STATUS2 PERC_CHNG AREA_CHNG PREV_AREA
## <dbl> <int> <int> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2022 30 30 Forest Forest 0 0 112728.
## 2 2022 30 40 Forest Forest 14.8 280. 1889.
## 3 2022 30 NA Forest Non-forest 14.9 746. 5002.
## 4 2022 40 30 Forest Forest 16.0 970. 6066.
## 5 2022 40 40 Forest Forest 0 0 237801.
## 6 2022 40 NA Forest Non-forest 16.0 2393. 14954.
## 7 2022 NA 30 Non-forest Forest 16.7 639. 3830.
## 8 2022 NA 40 Non-forest Forest 13.3 670. 5025.
## # ℹ 5 more variables: PERC_CHNG_SE <dbl>, AREA_CHNG_SE <dbl>,
## # PREV_AREA_SE <dbl>, nPlots_AREA <int>, N <int>Note that when, grpBy is specified, change components
are estimated for all shifts in forest land area across the classified
attributes represented by the variables (first and second measurements
again denoted by the suffix 1 and 2). In our case this means
OWNGRPCD1 indicates ownership at initial measurement and
OWNGRPCD2 is ownership at final measurement. Unfortunately,
ownership group is unavailable for non-forest conditions and hence
initial ownership is listed as NA for all non-forest to
forest conversions (and forest to non-forest conversions).
Finally, what if we were interested in understanding change in forest
land that meets some specific criteria? We can easily accomplish this
using the treeDomain argument. For example, below we
calculate net change for forest land that contains live
(STATUSCD == 1) eastern hemlock
(SPCD == 261).
areaChange(ri, treeDomain = STATUSCD == 1 & SPCD == 261) ## # A tibble: 15 × 9
## YEAR PERC_CHNG AREA_CHNG PREV_AREA PERC_CHNG_SE AREA_CHNG_SE PREV_AREA_SE
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2008 0 0 33214. NaN NaN 69.4
## 2 2009 0 0 16605. NaN NaN 71.5
## 3 2010 0 0 21452. NaN NaN 53.1
## 4 2011 0 0 23980. NaN NaN 45.4
## 5 2012 4.36 818. 18780. 102. 89.4 46.5
## 6 2013 4.03 921. 22840. 96.0 84.8 40.6
## 7 2014 4.03 921. 22840. 96.4 85.1 40.8
## 8 2015 4.03 885. 21944. 98.6 87.1 41.8
## 9 2016 3.23 820. 25435. 98.1 88.5 37.6
## 10 2017 2.41 565. 23469. 138. 126. 38.7
## 11 2018 2.41 565. 23469. 138. 126. 38.7
## 12 2019 -0.678 -189. 27889. 84.7 91.7 36.2
## 13 2020 -0.679 -195. 28808. 84.3 91.3 36.0
## 14 2021 -0.689 -214. 31109. 81.7 88.5 35.0
## 15 2022 -3.23 -980. 30341. 60.0 70.6 34.9
## # ℹ 2 more variables: nPlots_AREA <int>, N <int>In this case, the AREA_CHNG column corresponds to the
net change (in acres) of forest land that contains at least one live
eastern hemlock tree, while the PREV_AREA column indicates
the previous area (in acres) of forest land with at least one hemlock
tree. Notice that the PERC_CHNG columns is simply
calculated as AREA_CHNG / PREV_AREA * 100.