Package 'BerkeleyForestsAnalytics' reference manual

Title:	Compute and Summarize Core Forest Metrics from Field Data
Description:	A suite of open-source R functions designed to produce standard metrics for forest management and ecology from forest inventory data. The overarching goal is to minimize potential inconsistencies introduced by the algorithms used to compute and summarize core forest metrics. Learn more about the purpose of the package and the specific algorithms used in the package at <https://github.com/kearutherford/BerkeleyForestsAnalytics>.
Authors:	Kea Rutherford [aut, cre], John Battles [aut], Danny Foster [aut], The Regents of the University of California (Regents) [cph]
Maintainer:	Kea Rutherford <[email protected]>
License:	file LICENSE
Version:	2.0.4
Built:	2025-01-10 23:07:31 UTC
Source:	CRAN

Data for biomass demonstrations

Description

A fake dataset created for biomass demonstration purposes only

Usage

bio_demo_data
bio_demo_data

Format

A dataframe with 9 rows and 7 columns:

Forest: Broader location or forest where the data were collected
Plot_id: Plot in which the individual tree was measured
SPH: Stems per hectare
Live: Live (1) or dead (0)
Decay: 1-5 for standing dead trees. NA for live trees.
SPP: Species of the individual tree, using four-letter species codes
DBH_CM: Diameter at breast height in centimeters
HT_M: Tree height in meters

Source

Created by Kea Rutherford for demonstration purposes

Data for biomass demonstrations

Description

A fake dataset created for biomass demonstration purposes only. Includes a plot without trees.

Usage

bio_NT_demo
bio_NT_demo

Format

A dataframe with 9 rows and 7 columns:

Forest: Broader location or forest where the data were collected
Plot_id: Plot in which the individual tree was measured
SPH: Stems per hectare
Live: Live (1) or dead (0)
Decay: 1-5 for standing dead trees. NA for live trees.
SPP: Species of the individual tree, using four-letter species codes
DBH_CM: Diameter at breast height in centimeters
HT_M: Tree height in meters

Source

Created by Kea Rutherford for demonstration purposes

BiomassNSVB

Description

Uses the national-scale volume and biomass (NSVB) framework, from GTR-WO-104, to estimate above-ground tree biomass and carbon. The package will summarize to the tree or plot level, with options to additionally summarize by species and/or status. The package is specifically designed for use in California ecosystems, and, therefore, only covers the ecodivisions found in California (260, M260, 320, and 340).

Usage

BiomassNSVB(
  data,
  sp_codes = "4letter",
  input_units = "metric",
  output_units = "metric",
  results = "by_plot"
)
BiomassNSVB(
  data,
  sp_codes = "4letter",
  input_units = "metric",
  output_units = "metric",
  results = "by_plot"
)

Arguments

`data`	A dataframe or tibble with the following columns: division, province, site, plot, exp_factor, status, decay_class, species, dbh, ht1, ht2, crown_ratio, top, and cull. Each row must be an observation of an individual tree.
`sp_codes`	Not a variable (column) in the provided dataframe or tibble. Specifies whether the species variable follows the four-letter code or FIA naming convention (see README file for more detail). Must be set to either "4letter" or "fia". The default is set to "4letter".
`input_units`	Not a variable (column) in the provided dataframe or tibble. Specifies (1) whether the input dbh, ht1, and ht2 variables were measured using metric (centimeters and meters) or imperial (inches and feet) units; and (2) whether the input expansion factor is in metric (stems per hectare) or imperial (stems per acre) units. Must be set to either "metric" or "imperial". The default is set to "metric".
`output_units`	Not a variable (column) in the provided dataframe or tibble. Specifies whether results will be given in metric (kilograms or megagrams per hectare) or imperial (US tons or US tons per acre) units. Must be set to either "metric" or "imperial". The default is set to "metric".
`results`	Not a variable (column) in the provided dataframe or tibble. Specifies whether the results will be summarized by tree, by plot, by plot as well as species, by plot as well as status (live/dead), or by plot as well as species and status. Must be set to either "by_tree", "by_plot", "by_species", "by_status", or "by_sp_st". The default is set to "by_plot".

Value

Depends on the results setting:

by_tree: a list with two components: (1) total run time for the function and (2) a dataframe with tree-level biomass and carbon estimates (reported in kilograms or US tons).
by_plot: a list with two components: (1) total run time for the function and (2) a dataframe with plot-level biomass and carbon estimates (reported in megagrams per hectare or US tons per acre).
by_species: a list with two components: (1) total run time for the function and (2) a dataframe with plot-level biomass and carbon estimates, further summarized by species (reported in megagrams per hectare or US tons per acre).
by_status: a list with two components: (1) total run time for the function and (2) a dataframe with plot-level biomass and carbon estimates, further summarized by status (live/dead; reported in megagrams per hectare or US tons per acre).
by_sp_st: a list with two components: (1) total run time for the function and (2) a dataframe with plot-level biomass and carbon estimates, further summarized by species as well as by status (reported in megagrams per hectare or US tons per acre).

Examples

BiomassNSVB(data = nsvb_demo,
            sp_codes = "4letter",
            input_units = "metric",
            output_units = "metric",
            results = "by_plot")

BiomassNSVB(data = nsvb_demo,
            sp_codes = "4letter",
            input_units = "metric",
            output_units = "metric",
            results = "by_status")

BiomassNSVB(data = nsvb_demo,
            sp_codes = "4letter",
            input_units = "metric",
            output_units = "metric",
            results = "by_plot")

BiomassNSVB(data = nsvb_demo,
            sp_codes = "4letter",
            input_units = "metric",
            output_units = "metric",
            results = "by_status")

CoarseFuels

Description

Estimates coarse woody (1000-hour) fuel loads from line-intercept transects. See README for details.

Usage

CoarseFuels(
  tree_data,
  fuel_data,
  sp_codes = "4letter",
  units = "metric",
  summed = "no"
)
CoarseFuels(
  tree_data,
  fuel_data,
  sp_codes = "4letter",
  units = "metric",
  summed = "no"
)

Arguments

`tree_data`	A dataframe or tibble with the following columns: time, site, plot, exp_factor, species, and dbh. Each row must be an observation of an individual tree.
`fuel_data`	A dataframe or tibble. If the summed parameter is set to "no" the following columns are required: time, site, plot, transect, length_1000h, diameter, and status. If the summed parameter is set to "yes" the following columns are required: time, site, plot, transect, length_1000h, ssd_R, and ssd_S.
`sp_codes`	Specifies whether the species column in tree_data follows the four-letter code or FIA naming convention. Must be set to either "4letter" or "fia." The default is set to "4letter".
`units`	Specifies whether the input data are in metric (centimeters, meters, and trees per hectare) or imperial (inches, feet, and trees per acre) units. Inputs must be all metric or all imperial (do not mix-and-match units). The output units will match the input units (i.e., if inputs are in metric then outputs will be in metric). Must be set to either “metric” or “imperial”. The default is set to “metric”.
`summed`	Specifies whether the sum-of-squared-diameters for sound and rotten 1000-hour fuels has already been calculated by the user. Must be set to either "yes" or "no". The default is set to "no".

Value

A dataframe with the following columns:

time: as described above
site: as described above
plot: as described above
load_1000s_Mg_ha (or load_1000s_ton_ac): fuel load of sound 1000-hour fuels in megagrams per hectare (or US tons per acre)
load_1000r_Mg_ha (or load_1000r_ton_ac): fuel load of rotten 1000-hour fuels in megagrams per hectare (or US tons per acre)
load_cwd_Mg_ha (or load_cwd_ton_ac): total coarse woody debris fuel load (1000-hour sound + 1000-hour rotten) in megagrams per hectare (or US tons per acre)
sc_length_1000s: slope-corrected transect length (i.e., horizontal transect length) for sound 1000-hour fuels in either meters or feet. This is the total horizontal length of transect sampled for sound 1000-hour fuels at the specific time:site:plot.
sc_length_1000r: slope-corrected transect length (i.e., horizontal transect length) for rotten 1000-hour fuels in either meters or feet. This is the total horizontal length of transect sampled for rotten 1000-hour fuels at the specific time:site:plot.

Examples

CoarseFuels(tree_data = overstory_demo,
            fuel_data = cwd_YS_demo,
            sp_codes = "4letter",
            units = "metric",
            summed = "yes")

CoarseFuels(tree_data = overstory_demo,
            fuel_data = cwd_NS_demo)

CoarseFuels(tree_data = overstory_demo,
            fuel_data = cwd_YS_demo,
            sp_codes = "4letter",
            units = "metric",
            summed = "yes")

CoarseFuels(tree_data = overstory_demo,
            fuel_data = cwd_NS_demo)

Coarse woody debris data for compilation demonstrations

Description

A fake dataset created for coarse woody debris compilation demonstration purposes only.

Usage

compilation_cwd_demo
compilation_cwd_demo

Format

A dataframe with 9 rows and 9 columns:

time: year in which the data were collected
site: broader location or forest where the data were collected
stratum: stratum within site
plot: plot within stratum
load_1000s_Mg_ha: fuel load of sound 1000-hour fuels in megagrams per hectare
load_1000r_Mg_ha: fuel load of rotten 1000-hour fuels in megagrams per hectare
load_cwd_Mg_ha: total coarse woody debris fuel load in megagrams per hectare
sc_length_1000s: slope-corrected transect length for sound 1000-hour fuels in meters
sc_length_1000r: slope-corrected transect length for rotten 1000-hour fuels in meters

Source

Created by Kea Rutherford for demonstration purposes.

Data for general Fire and Fire Surrogate demonstrations

Description

A fake dataset created for general compilation demonstration purposes only.

Usage

compilation_ffs_demo
compilation_ffs_demo

Format

A dataframe with 9 rows and 8 columns:

time: year in which the data were collected
trt_type: treatment type (burn, thin, thin + burn, control)
site: compartment
plot: plot within compartment
sph: stems per hectare
ba_m2_ha: basal area in meters squared per hectare
qmd_cm: quadratic mean diameter in centimeters
dbh_cm: average diameter at breast height in centimeters

Source

Created by Kea Rutherford for demonstration purposes.

FPC data for general simple random sampling demonstrations

Description

A fake dataset created for general compilation demonstration purposes only.

Usage

compilation_fpc_demo
compilation_fpc_demo

Format

A dataframe with 2 rows and 3 columns:

site: broader location or forest where the data were collected
N: number of possible plots for the site
n: number of plots measured

Source

Created by Kea Rutherford for demonstration purposes.

Fine woody debris data for compilation demonstrations

Description

A fake dataset created for fine woody debris compilation demonstration purposes only.

Usage

compilation_fwd_demo
compilation_fwd_demo

Format

A dataframe with 9 rows and 11 columns:

time: year in which the data were collected
site: broader location or forest where the data were collected
stratum: stratum within site
plot: plot within stratum
load_1h_Mg_ha: fuel load of 1-hour fuels in megagrams per hectare
load_10h_Mg_ha: fuel load of 10-hour fuels in megagrams per hectare
load_100h_Mg_ha: fuel load of 100-hour fuels in megagrams per hectare
load_fwd_Mg_ha: total fine woody debris fuel load in megagrams per hectare
sc_length_1h: slope-corrected transect length for 1-hour fuels in meters
sc_length_10h: slope-corrected transect length for 10-hour fuels in meters
sc_length_100h: slope-corrected transect length for 100-hour fuels in meters

Source

Created by Kea Rutherford for demonstration purposes.

Data for general simple random sampling demonstrations

Description

A fake dataset created for general compilation demonstration purposes only.

Usage

compilation_srs_demo
compilation_srs_demo

Format

A dataframe with 9 rows and 7 columns:

time: year in which the data were collected
site: broader location or forest where the data were collected
plot: plot within site
sph: stems per hectare
ba_m2_ha: basal area in meters squared per hectare
qmd_cm: quadratic mean diameter in centimeters
dbh_cm: average diameter at breast height in centimeters

Source

Created by Kea Rutherford for demonstration purposes.

Data for general simple random sampling demonstrations

Description

A fake dataset created for general compilation demonstration purposes only.

Usage

compilation_srs_sp_demo
compilation_srs_sp_demo

Format

A dataframe with 8 rows and 8 columns:

time: year in which the data were collected
site: broader location or forest where the data were collected
plot: plot within site
species: tree species
dominance: relative basal area in percent

Source

Created by Kea Rutherford for demonstration purposes.

Data for general stratified random sampling demonstrations

Description

A fake dataset created for general compilation demonstration purposes only.

Usage

compilation_strs_demo
compilation_strs_demo

Format

A dataframe with 9 rows and 8 columns:

time: year in which the data were collected
site: broader location or forest where the data were collected
stratum: stratum within site
plot: plot within stratum
sph: stems per hectare
ba_m2_ha: basal area in meters squared per hectare
qmd_cm: quadratic mean diameter in centimeters
dbh_cm: average diameter at breast height in centimeters

Source

Created by Kea Rutherford for demonstration purposes.

Weight data for stratified random sampling demonstrations

Description

A fake dataset created for general compilation demonstration purposes only.

Usage

compilation_wt_demo
compilation_wt_demo

Format

A dataframe with 4 rows and 3 columns:

site: broader location or forest where the data were collected
stratum: stratum within site
wh: stratum weight

Source

Created by Kea Rutherford for demonstration purposes.

CompilePlots

Description

Compiles data beyond the plot level. Recognizes simple random sampling and stratified random sampling designs. Also recognizes the design of the Fire and Fire Surrogate. See README for details.

Usage

CompilePlots(data, design, wt_data = "not_needed", fpc_data = "not_needed")
CompilePlots(data, design, wt_data = "not_needed", fpc_data = "not_needed")

Arguments

`data`	A dataframe or tibble. Data must already be summarized to the plot-level. Required columns depend on the sampling design: Simple random sampling: must have time, site, and plot columns. A species column is optional. Other columns can be any numerical variables of interest (any column names, any number of variables). Stratified random sampling: must have time, site, stratum, and plot columns. A species column is optional. Other columns can be any numerical variables of interest (any column names, any number of variables). Fire and Fire Surrogate: must have time, trt_type, site, and plot columns. A species column is optional. Other columns can be any numerical variables of interest (any column names, any number of variables).
`design`	Specifies the sampling design. Must be set to "SRS" (simple random sample), "STRS" (stratified random sample), or "FFS" (Fire and Fire Surrogate). There is no default.
`wt_data`	Only required for stratified random sampling designs. A dataframe or tibble with the following columns: time (optional), site, stratum, and wh (stratum weight). The default is set to "not_needed", and should be left as such for design = "SRS" or design = "FFS".
`fpc_data`	An optional dataframe or tibble. Incorporates the finite population correction factor (FPC) when samples were taken without replacement. The default is set to "not_needed". Required columns depend on the sampling design: Simple random sampling: must have site, N, and n columns. A time column is optional. Stratified random sampling: must have site, stratum, N, and n columns. A time column is optional. Fire and Fire Surrogate: must have trt_type, site, N and n columns. A time column in optional.

Value

Depends on the sampling design:

Simple random sampling: a dataframe with site-level summaries.
Stratified random sampling: a list with two components: (1) a dataframe with stratum-level summaries and (2) a dataframe with site-level summaries.
Fire and Fire Surrogate: a list with two components: (1) a dataframe with site-level (i.e., compartment-level) summaries and (2) a dataframe with treatment-level summaries.

Examples

CompilePlots(data = compilation_srs_demo,
             design = "SRS",
             wt_data = "not_needed",
             fpc_data = "not_needed")

CompilePlots(data = compilation_strs_demo,
             design = "STRS",
             wt_data = compilation_wt_demo,
             fpc_data = "not_needed")

CompilePlots(data = compilation_srs_demo,
             design = "SRS",
             wt_data = "not_needed",
             fpc_data = "not_needed")

CompilePlots(data = compilation_strs_demo,
             design = "STRS",
             wt_data = compilation_wt_demo,
             fpc_data = "not_needed")

CompileSurfaceFuels

Description

Compiles surface fuel data beyond the plot level. Specifically designed to further summarize outputs from the FineFuels and/or CoarseFuels functions. Recognizes simple random sampling and stratified random sampling designs. Also recognizes the design of the Fire and Fire Surrogate. See README for details.

Usage

CompileSurfaceFuels(
  fwd_data = "none",
  cwd_data = "none",
  design,
  wt_data = "not_needed",
  fpc_data = "not_needed",
  units = "metric"
)
CompileSurfaceFuels(
  fwd_data = "none",
  cwd_data = "none",
  design,
  wt_data = "not_needed",
  fpc_data = "not_needed",
  units = "metric"
)

Arguments

`fwd_data`	A dataframe or tibble. Fine woody debris (FWD) loads must already be calculated at the plot-level using the FineFuels function. Required columns depend on the sampling design: Simple random sampling: must have time, site, plot, sc_length_1h, sc_length_10h, and sc_length_100h columns. Must also have load_1h_Mg_ha, load_10h_Mg_ha, and load_100h_Mg_ha (OR load_1h_ton_ac, load_10h_ton_ac, and load_100h_ton_ac). Stratified random sampling: must have time, site, stratum, plot, sc_length_1h, sc_length_10h, and sc_length_100h columns. Must also have load_1h_Mg_ha, load_10h_Mg_ha, and load_100h_Mg_ha (OR load_1h_ton_ac, load_10h_ton_ac, and load_100h_ton_ac). Fire and Fire Surrogate: must have time, trt_type, site, plot, sc_length_1h, sc_length_10h, and sc_length_100h columns. Must also have load_1h_Mg_ha, load_10h_Mg_ha, and load_100h_Mg_ha (OR load_1h_ton_ac, load_10h_ton_ac, and load_100h_ton_ac).
`cwd_data`	A dataframe or tibble. Coarse woody debris (CWD) loads must already be calculated at the plot-level using the CoarseFuels function. Required columns depend on the sampling design: Simple random sampling: must have time, site, plot, sc_length_1000s, and sc_length_1000r columns. Must also have load_1000s_Mg_ha, load_1000r_Mg_ha, and load_cwd_Mg_ha (OR load_1000s_Mg_ha, load_1000r_Mg_ha, and load_cwd_Mg_ha). Stratified random sampling: must have time, site, stratum, plot, sc_length_1000s, and sc_length_1000r columns. Must also have load_1000s_Mg_ha, load_1000r_Mg_ha, and load_cwd_Mg_ha (OR load_1000s_Mg_ha, load_1000r_Mg_ha, and load_cwd_Mg_ha). Fire and Fire Surrogate: must have time, trt_type, site, plot, sc_length_1000s, and sc_length_1000r columns. Must also have load_1000s_Mg_ha, load_1000r_Mg_ha, and load_cwd_Mg_ha (OR load_1000s_Mg_ha, load_1000r_Mg_ha, and load_cwd_Mg_ha).
`design`	Specifies the sampling design. Must be set to "SRS" (simple random sample), "STRS" (stratified ransom sample), or "FFS" (Fire and Fire Surrogate). There is no default.
`wt_data`	Only required for stratified random sampling designs. A dataframe or tibble with the following columns: time (optional), site, stratum, and wh (stratum weight). The default is set to "not_needed", and should be left as such for design = "SRS" or design = "FFS".
`fpc_data`	An optional dataframe or tibble. Incorporates the finite population correction factor (FPC) when samples were taken without replacement. The default is set to "not_needed". Required columns depend on the sampling design: Simple random sampling: must have site, N, and n columns. A time column is optional. Stratified random sampling: must have site, stratum, N, and n columns. A time column is optional. Fire and Fire Surrogate: must have trt_type, site, N and n columns. A time column in optional.
`units`	Specifies whether the input data are in metric (megagrams per hectare) or imperial (US tons per acre) units. Inputs must be all metric or all imperial (do not mix-and-match units). The output units will match the input units (i.e., if inputs are in metric then outputs will be in metric). Must be set to either “metric” or “imperial”. The default is set to “metric”.

Value

Depends on the sampling design:

Simple random sampling: a dataframe with site-level summaries.
Stratified random sampling: a list with two components: (1) a dataframe with stratum-level summaries and (2) a dataframe with site-level summaries.
Fire and Fire Surrogate: a list with two components: (1) a dataframe with site-level (i.e., compartment-level) summaries and (2) a dataframe with treatment-level summaries.

Examples

CompileSurfaceFuels(fwd_data = compilation_fwd_demo,
                    cwd_data = compilation_cwd_demo,
                    design = "STRS",
                    wt_data = compilation_wt_demo,
                    fpc_data = "not_needed",
                    units = "metric")

CompileSurfaceFuels(fwd_data = compilation_fwd_demo,
                    cwd_data = "none",
                    design = "STRS",
                    wt_data = compilation_wt_demo,
                    fpc_data = "not_needed",
                    units = "metric")

CompileSurfaceFuels(fwd_data = compilation_fwd_demo,
                    cwd_data = compilation_cwd_demo,
                    design = "STRS",
                    wt_data = compilation_wt_demo,
                    fpc_data = "not_needed",
                    units = "metric")

CompileSurfaceFuels(fwd_data = compilation_fwd_demo,
                    cwd_data = "none",
                    design = "STRS",
                    wt_data = compilation_wt_demo,
                    fpc_data = "not_needed",
                    units = "metric")

Data for coarse woody debris demonstrations

Description

A fake dataset created for coarse surface fuel demonstration purposes only. Sum-of-squared-diameters for sound and rotten 1000-hour fuels NOT already calculated.

Usage

cwd_NS_demo
cwd_NS_demo

Format

A dataframe with 16 rows and 8 columns:

time: Year in which the data were collected
site: Broader location or forest where the data were collected
plot: Plot in which the individual transect was measured
transect: Transect on which the 1000-hour fuel measurements were taken
length_1000h: Length of the sampling transect for 1000-hour fuels in meters
slope: Slope along the transect in percent
diameter: Diameter of 1000-hour fuel in centimeters
status: Rotten (R) or sound (S)

Source

Created by Kea Rutherford for demonstration purposes

Data for coarse woody debris demonstrations

Description

A fake dataset created for coarse surface fuel demonstration purposes only. Sum-of-squared-diameters for sound and rotten 1000-hour fuels already calculated.

Usage

cwd_YS_demo
cwd_YS_demo

Format

A dataframe with 12 rows and 8 columns:

time: Year in which the data were collected
site: Broader location or forest where the data were collected
plot: Plot in which the individual transect was measured
transect: Transect on which the 1000-hour fuel measurements were taken
length_1000h: Length of the sampling transect for 1000-hour fuels in meters
slope: Slope along the transect in percent
ssd_S: Sum-of-squared-diameters for sound 1000-hour fuels
ssd_R: Sum-of-squared-diameters for rotten 1000-hour fuels

Source

Created by Kea Rutherford for demonstration purposes.

FineFuels

Description

Estimates fine woody (1-hour, 10-hour, and 100-hour) fuel loads from line-intercept transects. See README for details.

Usage

FineFuels(fuel_data, tree_data, sp_codes = "4letter", units = "metric")
FineFuels(fuel_data, tree_data, sp_codes = "4letter", units = "metric")

Arguments

`fuel_data`	A dataframe or tibble with the following columns: time, site, plot, transect, count_1h, count_10h, count_100h, length_1h, length_10h, and length_100h. A slope column is optional. Each row must be an observation of an individual transect at a specific time/site/plot.
`tree_data`	A dataframe or tibble with the following columns: time, site, plot, exp_factor, species, and dbh. Each row must be an observation of an individual tree.
`sp_codes`	Specifies whether the species column in tree_data follows the four-letter code or FIA naming convention. Must be set to either "4letter" or "fia." The default is set to "4letter".
`units`	Specifies whether the input data are in metric (centimeters, meters, and trees per hectare) or imperial (inches, feet, and trees per acre) units. Inputs must be all metric or all imperial (do not mix-and-match units). The output units will match the input units (i.e., if inputs are in metric then outputs will be in metric). Must be set to either “metric” or “imperial”. The default is set to “metric”.

Value

A dataframe with the following columns:

time: as described above
site: as described above
plot: as described above
load_1h_Mg_ha (or load_1h_ton_ac): fuel load of 1-hour fuels in megagrams per hectare (or US tons per acre)
load_10h_Mg_ha (or load_10h_ton_ac): fuel load of 10-hour fuels in megagrams per hectare (or US tons per acre)
load_100h_Mg_ha (or load_100h_ton_ac): fuel load of 100-hour fuels in megagrams per hectare (or US tons per acre)
load_fwd_Mg_ha (or load_fwd_ton_ac): total fine woody debris fuel load (1-hour + 10-hour + 100-hour) in megagrams per hectare (or US tons per acre)
sc_length_1h: slope-corrected transect length (i.e., horizontal transect length) for 1-hour fuels in either meters or feet. This is the total horizontal length of transect sampled for 1-hour fuels at the specific time:site:plot.
sc_length_10h: slope-corrected transect length (i.e., horizontal transect length) for 10-hour fuels in either meters or feet. This is the total horizontal length of transect sampled for 10-hour fuels at the specific time:site:plot.
sc_length_100h: slope-corrected transect length (i.e., horizontal transect length) for 100-hour fuels in either meters or feet. This is the total horizontal length of transect sampled for 100-hour fuels at the specific time:site:plot.

Examples

FineFuels(tree_data = overstory_demo,
          fuel_data = fwd_demo)

FineFuels(tree_data = overstory_demo,
          fuel_data = fwd_demo)

Data for forest composition and structure demonstrations

Description

A fake dataset created for composition and structure demonstration purposes only

Usage

for_demo_data
for_demo_data

Format

A dataframe with 9 rows and 7 columns:

Forest: Broader location or forest where the data were collected
Plot_id: Plot in which the individual tree was measured
SPH: Stems per hectare
Live: Live (1) or dead (0)
SPP: Species of the individual tree, using four-letter species codes
DBH_CM: Diameter at breast height in centimeters
HT_M: Tree height in meters

Source

Created by Kea Rutherford for demonstration purposes

Data for forest composition and structure demonstrations

Description

A fake dataset created for composition and structure demonstration purposes only. Includes a plot without trees.

Usage

for_NT_demo
for_NT_demo

Format

A dataframe with 9 rows and 7 columns:

Forest: Broader location or forest where the data were collected
Plot_id: Plot in which the individual tree was measured
SPH: Stems per hectare
Live: Live (1) or dead (0)
SPP: Species of the individual tree, using four-letter species codes
DBH_CM: Diameter at breast height in centimeters
HT_M: Tree height in meters

Source

Created by Kea Rutherford for demonstration purposes.

ForestComp

Description

Compiles forest composition at the plot level. Measured as relative basal area or relative density for live trees.

Usage

ForestComp(
  data,
  site,
  plot,
  exp_factor,
  status,
  species,
  dbh,
  relative = "BA",
  units = "metric"
)
ForestComp(
  data,
  site,
  plot,
  exp_factor,
  status,
  species,
  dbh,
  relative = "BA",
  units = "metric"
)

Arguments

`data`	A dataframe or tibble. Each row must be an observation of an individual tree.
`site`	Must be a character variable (column) in the provided dataframe or tibble. Describes the broader location or forest where the data were collected.
`plot`	Must be a character variable (column) in the provided dataframe or tibble. Identifies the plot in which the individual tree was measured.
`exp_factor`	Must be a numeric variable (column) in the provided dataframe or tibble. The expansion factor specifies the number of trees per hectare (or per acre) that a given plot tree represents.
`status`	Must be a character variable (column) in the provided dataframe or tibble. Specifies whether the individual tree is alive (1) or dead (0).
`species`	Must be a character variable (column) in the provided dataframe or tibble. Specifies the species of the individual tree.
`dbh`	Must be a numeric variable (column) in the provided dataframe or tibble. Provides the diameter at breast height (DBH) of the individual tree in either centimeters or inches.
`relative`	Not a variable (column) in the provided dataframe or tibble. Specifies whether forest composition should be measured as relative basal area or relative density. Must be set to either "BA" or "density". The default is set to "BA".
`units`	Not a variable (column) in the provided dataframe or tibble. Specifies whether the dbh variable was measured using metric (centimeters) or imperial (inches) units. Must be set to either "metric" or "imperial". The default is set to "metric".

Value

A dataframe with the following columns:

site: as described above
plot: as described above
species: as described above
dominance: relative basal area (or relative density) in percent (%)

Examples

ForestComp(data = for_demo_data,
           site = "Forest",
           plot = "Plot_id",
           exp_factor = "SPH",
           status = "Live",
           species = "SPP",
           dbh = "DBH_CM",
           relative = "BA",
           units = "metric")

ForestComp(data = for_demo_data,
           site = "Forest",
           plot = "Plot_id",
           exp_factor = "SPH",
           status = "Live",
           species = "SPP",
           dbh = "DBH_CM",
           relative = "BA",
           units = "metric")

ForestStr

Description

Compiles forest structure at the plot level.

Usage

ForestStr(data, site, plot, exp_factor, dbh, ht = "ignore", units = "metric")
ForestStr(data, site, plot, exp_factor, dbh, ht = "ignore", units = "metric")

Arguments

`data`	A dataframe or tibble. Each row must be an observation of an individual tree.
`site`	Must be a character variable (column) in the provided dataframe or tibble. Describes the broader location or forest where the data were collected.
`plot`	Must be a character variable (column) in the provided dataframe or tibble. Identifies the plot in which the individual tree was measured.
`exp_factor`	Must be a numeric variable (column) in the provided dataframe or tibble. The expansion factor specifies the number of trees per hectare (or per acre) that a given plot tree represents.
`dbh`	Must be a numeric variable (column) in the provided dataframe or tibble. Provides the diameter at breast height (DBH) of the individual tree in either centimeters or inches.
`ht`	Default is set to "ignore", which indicates that tree heights were not taken. If heights were taken, it can be set to a numeric variable (column) in the provided dataframe or tibble, providing the height of the individual tree in either meters or feet.
`units`	Not a variable (column) in the provided dataframe or tibble. Specifies (1) whether the dbh and ht variables were measured using metric (centimeters and meters) or imperial (inches and feet) units; (2) whether the expansion factor is in metric (stems per hectare) or imperial (stems per acre) units; and (3) whether results will be given in metric or imperial units. Must be set to either "metric" or "imperial". The default is set to "metric".

Value

A dataframe with the following columns:

site: as described above
plot: as described above
sph (or spa): stems per hectare (or stems per acre)
ba_m2_ha (or ba_ft2_ac): basal area in meters squared per hectare (or feet squared per acre).
qmd_cm (or qmd_in): quadratic mean diameter in centimeters (or inches). Weighted by the expansion factor.
dbh_cm (or dbh_in): average diameter at breast height in centimeters (or inches). Weighted by the expansion factor.
ht_m (or ht_ft): average height in meters (or feet) if ht argument was set. Weighted by the expansion factor.

Examples

ForestStr(data = for_demo_data,
          site = "Forest",
          plot = "Plot_id",
          exp_factor = "SPH",
          dbh = "DBH_CM",
          ht = "HT_M",
          units = "metric")

ForestStr(data = for_demo_data,
          site = "Forest",
          plot = "Plot_id",
          exp_factor = "SPH",
          dbh = "DBH_CM",
          ht = "HT_M",
          units = "metric")

Data for fine woody debris demonstrations

Description

A fake dataset created for fine surface fuel demonstration purposes only

Usage

fwd_demo
fwd_demo

Format

A dataframe with 12 rows and 11 columns:

time: Year in which the data were collected
site: Broader location or forest where the data were collected
plot: Plot in which the individual transect was measured
transect: Transect on which the fuel tallies were collected
count_1h: Count of 1-hour fuels
count_10h: Count of 10-hour fuels
count_100h: Count of 100-hour fuels
length_1h: Length of the sampling transect for 1-hour fuels in meters
length_10h: Length of the sampling transect for 10-hour fuels in meters
length_100h: Length of the sampling transect for 100-hour fuels in meters
slope: Slope along the transect in percent

Source

Created by Kea Rutherford for demonstration purposes

Data for duff and litter demonstrations

Description

A fake dataset created for duff and litter fuel demonstration purposes only. Depths already averaged together for each transect.

Usage

lit_duff_avg_demo
lit_duff_avg_demo

Format

A dataframe with 24 rows and 6 columns:

time: Year in which the data were collected
site: Broader location or forest where the data were collected
plot: Plot in which the individual transect was measured
transect: Transect on which the duff/litter depths were measured
litter_depth: Litter depth in centimeters
duff_depth: Duff depth in centimeters

Source

Created by Kea Rutherford for demonstration purposes.

Data for duff and litter demonstrations

Description

A fake dataset created for duff and litter fuel demonstration purposes only. Depths NOT already averaged together for each transect.

Usage

lit_duff_demo
lit_duff_demo

Format

A dataframe with 24 rows and 6 columns:

time: Year in which the data were collected
site: Broader location or forest where the data were collected
plot: Plot in which the individual transect was measured
transect: Transect on which the duff/litter depths were measured
litter_depth: Litter depth in centimeters
duff_depth: Duff depth in centimeters

Source

Created by Kea Rutherford for demonstration purposes.

LitterDuff

Description

Estimates duff and litter fuel loads. See README for details.

Usage

LitterDuff(
  fuel_data,
  tree_data,
  sp_codes = "4letter",
  units = "metric",
  measurement = "separate"
)
LitterDuff(
  fuel_data,
  tree_data,
  sp_codes = "4letter",
  units = "metric",
  measurement = "separate"
)

Arguments

`fuel_data`	A dataframe or tibble. If the measurement parameter is set to "separate" the following columns are required: time, site, plot, transect, litter_depth, and duff_depth. If the measurement parameter is set to "combined" the following columns are required: time, site, plot, transect, and lit_duff_depth.
`tree_data`	A dataframe or tibble with the following columns: time, site, plot, exp_factor, species, and dbh. Each row must be an observation of an individual tree.
`sp_codes`	Specifies whether the species column in tree_data follows the four-letter code or FIA naming convention. Must be set to either “4letter” or “fia”. The default is set to “4letter”.
`units`	Specifies whether the input data are in metric (centimeters, meters, and trees per hectare) or imperial (inches, feet, and trees per acre) units. Inputs must be all metric or all imperial (do not mix-and-match units). The output units will match the input units (i.e., if inputs are in metric then outputs will be in metric). Must be set to either “metric” or “imperial”. The default is set to “metric”.
`measurement`	Specifies whether duff and litter were measured together or separately. Must be set to "combined" or "separate". The default is set to "separate".

Value

A dataframe with the following columns:

If measurement is set to "separate"
- time: as described above
- site: as described above
- plot: as described above
- litter_Mg_ha (or litter_ton_ac): litter load in megagrams per hectare (or US tons per acre)
- duff_Mg_ha (or duff_ton_ac): duff load in megagrams per hectare (or US tons per acre)
If measurement is set to "combined"
- time: as described above
- site: as described above
- plot: as described above
- lit_duff_Mg_ha (or lit_duff_ton_ac): combined litter and duff load in megagrams per hectare (or US tons per acre)

Examples

LitterDuff(tree_data = overstory_demo,
           fuel_data = lit_duff_demo)

LitterDuff(tree_data = overstory_demo,
           fuel_data = lit_duff_demo)

Data for NSVB framework biomass and carbon demonstrations

Description

A fake dataset created for NSVB framework biomass and carbon demonstration purposes only

Usage

nsvb_demo
nsvb_demo

Format

A dataframe with 16 rows and 14 columns:

division: Ecodivision in which the data were collected
province: Province in which the data were collected
site: Forest where the data were collected
plot: Plot in which the individual tree was measured
exp_factor: Stems per hectare
status: Live (1) or dead (0)
decay_class: 1-5 for standing dead trees. NA for live trees.
Species: Species of the individual tree, using four-letter species codes
dbh: Diameter at breast height in centimeters
ht1: Tree height 1 in meters
ht2: Tree height 2 in meters
crown_ratio: Live crown ratio
top: Yes top (Y) or no top (N)
cull: Percent wood cull

Source

Created by Kea Rutherford for demonstration purposes

Overstory data for surface and ground fuel demonstrations

Description

A fake dataset created for surface and ground fuel demonstration purposes only

Usage

overstory_demo
overstory_demo

Format

A dataframe with 14 rows and 6 columns:

time: Year in which the data were collected
site: Broader location or forest where the data were collected
plot: Plot in which the individual tree was measured
exp_factor: Stems per hectare
species: Species of the individual tree, using four-letter species codes
dbh: Diameter at breast height in centimeters

Source

Created by Kea Rutherford for demonstration purposes

SummaryBiomass

Description

Uses Forest Inventory and Analysis (FIA) Regional Biomass Equations to estimate above-ground stem, bark, and branch tree biomass. The package will summarize by plot or by plot as well as species. The package uses the California equation set and should not be used for data from other regions.

Usage

SummaryBiomass(
  data,
  site,
  plot,
  exp_factor,
  status,
  decay_class,
  species,
  dbh,
  ht,
  sp_codes = "4letter",
  units = "metric",
  results = "by_plot"
)
SummaryBiomass(
  data,
  site,
  plot,
  exp_factor,
  status,
  decay_class,
  species,
  dbh,
  ht,
  sp_codes = "4letter",
  units = "metric",
  results = "by_plot"
)

Arguments

`data`	A dataframe or tibble. Each row must be an observation of an individual tree.
`site`	Must be a character variable (column) in the provided dataframe or tibble. Describes the broader location or forest where the data were collected.
`plot`	Must be a character variable (column) in the provided dataframe or tibble. Identifies the plot in which the individual tree was measured.
`exp_factor`	Must be a numeric variable (column) in the provided dataframe or tibble. The expansion factor specifies the number of trees per hectare (or per acre) that a given plot tree represents.
`status`	Must be a character variable (column) in the provided dataframe or tibble. Specifies whether the individual tree is alive (1) or dead (0).
`decay_class`	Must be a character variable (column) in the provided dataframe or tibble. For standing dead trees, the decay class should be 1, 2, 3, 4, or 5 (see README file for more detail). For live trees, the decay class should be NA or 0.
`species`	Must be a character variable (column) in the provided dataframe or tibble. Specifies the species of the individual tree. Must follow four-letter species code or FIA naming conventions (see README file for more detail).
`dbh`	Must be a numeric variable (column) in the provided dataframe or tibble. Provides the diameter at breast height (DBH) of the individual tree in either centimeters or inches.
`ht`	Must be a numeric variable (column) in the provided dataframe or tibble. Provides the height of the individual tree in either meters or feet.
`sp_codes`	Not a variable (column) in the provided dataframe or tibble. Specifies whether the species variable follows the four-letter code or FIA naming convention (see README file for more detail). Must be set to either "4letter" or "fia". The default is set to "4letter".
`units`	Not a variable (column) in the provided dataframe or tibble. Specifies (1) whether the dbh and ht variables were measured using metric (centimeters and meters) or imperial (inches and feet) units; (2) whether the expansion factor is in metric (stems per hectare) or imperial (stems per acre) units; and (3) whether results will be given in metric (megagrams per hectare) or imperial (US tons per acre) units. Must be set to either "metric" or "imperial". The default is set to "metric".
`results`	Not a variable (column) in the provided dataframe or tibble. Specifies whether the results will be summarized by plot or by plot as well as species. Must be set to either "by_plot" or "by_species." The default is set to "by_plot".

Value

A dataframe with the following columns:

site: as described above
plot: as described above
species: if results argument was set to "by_species"
live_Mg_ha (or live_ton_ac): above-ground live tree biomass in megagrams per hectare (or US tons per acre)
dead_Mg_ha (or dead_ton_ac): above-ground dead tree biomass in megagrams per hectare (or US tons per acre)

Examples

SummaryBiomass(data = bio_demo_data,
               site = "Forest",
               plot = "Plot_id",
               exp_factor = "SPH",
               status = "Live",
               decay_class = "Decay",
               species = "SPP",
               dbh = "DBH_CM",
               ht = "HT_M",
               results = "by_species")

SummaryBiomass(data = bio_demo_data,
               site = "Forest",
               plot = "Plot_id",
               exp_factor = "SPH",
               status = "Live",
               decay_class = "Decay",
               species = "SPP",
               dbh = "DBH_CM",
               ht = "HT_M",
               results = "by_species")

TreeBiomass

Description

Uses Forest Inventory and Analysis (FIA) Regional Biomass Equations to estimate above-ground stem, bark, and branch tree biomass. The package uses the California equation set and should not be used for data from other regions.

Usage

TreeBiomass(
  data,
  status,
  species,
  dbh,
  ht,
  decay_class = "ignore",
  sp_codes = "4letter",
  units = "metric"
)
TreeBiomass(
  data,
  status,
  species,
  dbh,
  ht,
  decay_class = "ignore",
  sp_codes = "4letter",
  units = "metric"
)

Arguments

`data`	A dataframe or tibble. Each row must be an observation of an individual tree.
`status`	Must be a character variable (column) in the provided dataframe or tibble. Specifies whether the individual tree is alive (1) or dead (0).
`species`	Must be a character variable (column) in the provided dataframe or tibble. Specifies the species of the individual tree. Must follow four-letter species code or FIA naming conventions (see README file for more detail).
`dbh`	Must be a numeric variable (column) in the provided dataframe or tibble. Provides the diameter at breast height (DBH) of the individual tree in either centimeters or inches.
`ht`	Must be a numeric variable (column) in the provided dataframe or tibble. Provides the height of the individual tree in either meters or feet.
`decay_class`	Default is set to "ignore", indicating that biomass estimates for standing dead trees will not be adjusted for structural decay. It can be set to a character variable (column) in the provided dataframe or tibble. For standing dead trees, the decay class should be 1, 2, 3, 4, or 5 (see README file for more detail). For live trees, the decay class should be NA or 0.
`sp_codes`	Not a variable (column) in the provided dataframe or tibble. Specifies whether the species variable follows the four-letter code or FIA naming convention (see README file for more detail). Must be set to either "4letter" or "fia". The default is set to "4letter".
`units`	Not a variable (column) in the provided dataframe or tibble. Specifies whether the dbh and ht variables were measured using metric (centimeters and meters) or imperial (inches and feet) units. Also specifies whether the results will be given in metric (kilograms) or imperial (US tons) units. Must be set to either "metric" or "imperial". The default is set to "metric".

Value

The original dataframe, with four new columns. If decay_class is provided, the biomass estimates for standing dead trees will be adjusted for structural decay.

stem_bio_kg (or stem_bio_tons): biomass of stem in kilograms (or tons)
bark_bio_kg (or bark_bio_tons): biomass of bark in kilograms (or tons)
branch_bio_kg (or branch_bio_tons): biomass of branches in kilograms (or tons)
total_bio_kg (or total_bio_kg): biomass of tree (stem + bark + branches) in kilograms (or tons)

Examples

TreeBiomass(data = bio_demo_data,
            status = "Live",
            species = "SPP",
            dbh = "DBH_CM",
            ht = "HT_M",
            sp_codes = "4letter",
            units = "metric")

TreeBiomass(data = bio_demo_data,
            status = "Live",
            species = "SPP",
            dbh = "DBH_CM",
            ht = "HT_M",
            decay_class = "Decay",
            sp_codes = "4letter",
            units = "metric")

TreeBiomass(data = bio_demo_data,
            status = "Live",
            species = "SPP",
            dbh = "DBH_CM",
            ht = "HT_M",
            sp_codes = "4letter",
            units = "metric")

TreeBiomass(data = bio_demo_data,
            status = "Live",
            species = "SPP",
            dbh = "DBH_CM",
            ht = "HT_M",
            decay_class = "Decay",
            sp_codes = "4letter",
            units = "metric")

Fuel data for vignette, version 1

Description

A dataset with intentional errors for demonstration purposes

Usage

vign_fuels_1
vign_fuels_1

Format

A dataframe with 236 rows and 16 columns:

time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual transect was measured
transect: azimuth of transect on which the fuel data were collected
count_1h: count of 1-hour fuels
count_10h: count of 10-hour fuels
count_100h: count of 100-hour fuels
length_1h: length of the sampling transect for 1-hour fuels in meters
length_10h: length of the sampling transect for 10-hour fuels in meters
length_100h: length of the sampling transect for 100-hour fuels in meters
length_1000h: length of the sampling transect for 1000-hour fuels in meters
ssd_S: sum-of-squared-diameters for sound 1000-hour fuels
ssd_R: sum-of-squared-diameters for rotten 1000-hour fuels
litter_depth: litter depth in centimeters
duff_depth: duff depth in centimeters
slope: slope along the transect in percent

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Fuel data for vignette, version 2

Description

A dataset with intentional errors for demonstration purposes

Usage

vign_fuels_2
vign_fuels_2

Format

A dataframe with 236 rows and 16 columns:

time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual transect was measured
transect: azimuth of transect on which the fuel data were collected
count_1h: count of 1-hour fuels
count_10h: count of 10-hour fuels
count_100h: count of 100-hour fuels
length_1h: length of the sampling transect for 1-hour fuels in meters
length_10h: length of the sampling transect for 10-hour fuels in meters
length_100h: length of the sampling transect for 100-hour fuels in meters
length_1000h: length of the sampling transect for 1000-hour fuels in meters
ssd_S: sum-of-squared-diameters for sound 1000-hour fuels
ssd_R: sum-of-squared-diameters for rotten 1000-hour fuels
litter_depth: litter depth in centimeters
duff_depth: duff depth in centimeters
slope: slope along the transect in percent

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Fuel data for vignette, version 3

Description

A dataset with intentional errors for demonstration purposes

Usage

vign_fuels_3
vign_fuels_3

Format

A dataframe with 236 rows and 16 columns:

time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual transect was measured
transect: azimuth of transect on which the fuel data were collected
count_1h: count of 1-hour fuels
count_10h: count of 10-hour fuels
count_100h: count of 100-hour fuels
length_1h: length of the sampling transect for 1-hour fuels in meters
length_10h: length of the sampling transect for 10-hour fuels in meters
length_100h: length of the sampling transect for 100-hour fuels in meters
length_1000h: length of the sampling transect for 1000-hour fuels in meters
ssd_S: sum-of-squared-diameters for sound 1000-hour fuels
ssd_R: sum-of-squared-diameters for rotten 1000-hour fuels
litter_depth: litter depth in centimeters
duff_depth: duff depth in centimeters
slope: slope along the transect in percent

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Fuel data for vignette, version 4

Description

A dataset with intentional errors and warnings for demonstration purposes

Usage

vign_fuels_4
vign_fuels_4

Format

A dataframe with 236 rows and 16 columns:

time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual transect was measured
transect: azimuth of transect on which the fuel data were collected
count_1h: count of 1-hour fuels
count_10h: count of 10-hour fuels
count_100h: count of 100-hour fuels
length_1h: length of the sampling transect for 1-hour fuels in meters
length_10h: length of the sampling transect for 10-hour fuels in meters
length_100h: length of the sampling transect for 100-hour fuels in meters
length_1000h: length of the sampling transect for 1000-hour fuels in meters
ssd_S: sum-of-squared-diameters for sound 1000-hour fuels
ssd_R: sum-of-squared-diameters for rotten 1000-hour fuels
litter_depth: litter depth in centimeters
duff_depth: duff depth in centimeters
slope: slope along the transect in percent

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Fuel data for vignette, version 5

Description

A clean dataset for demonstration purposes

Usage

vign_fuels_5
vign_fuels_5

Format

A dataframe with 236 rows and 16 columns:

time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual transect was measured
transect: azimuth of transect on which the fuel data were collected
count_1h: count of 1-hour fuels
count_10h: count of 10-hour fuels
count_100h: count of 100-hour fuels
length_1h: length of the sampling transect for 1-hour fuels in meters
length_10h: length of the sampling transect for 10-hour fuels in meters
length_100h: length of the sampling transect for 100-hour fuels in meters
length_1000h: length of the sampling transect for 1000-hour fuels in meters
ssd_S: sum-of-squared-diameters for sound 1000-hour fuels
ssd_R: sum-of-squared-diameters for rotten 1000-hour fuels
litter_depth: litter depth in centimeters
duff_depth: duff depth in centimeters
slope: slope along the transect in percent

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Tree data for vignette, version 1

Description

A dataset with intentional errors for demonstration purposes

Usage

vign_trees_1
vign_trees_1

Format

A dataframe with 2250 rows and 10 columns:

id: time:site combined
time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual tree was measured
exp_factor: stems per hectare
status: live (1) or dead (0)
decay: 1-5 for standing dead trees. 0 for live trees.
species: Species of the individual tree, using four-letter species codes
dbh: diameter at breast height in centimeters
ht: tree height in meters

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Tree data for vignette, version 2

Description

A dataset with intentional errors for demonstration purposes

Usage

vign_trees_2
vign_trees_2

Format

A dataframe with 2250 rows and 10 columns:

id: time:site combined
time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual tree was measured
exp_factor: stems per hectare
status: live (1) or dead (0)
decay: 1-5 for standing dead trees. 0 for live trees.
species: Species of the individual tree, using four-letter species codes
dbh: diameter at breast height in centimeters
ht: tree height in meters

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Tree data for vignette, version 3

Description

A dataset with intentional warnings for demonstration purposes

Usage

vign_trees_3
vign_trees_3

Format

A dataframe with 2250 rows and 10 columns:

id: time:site combined
time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual tree was measured
exp_factor: stems per hectare
status: live (1) or dead (0)
decay: 1-5 for standing dead trees. 0 for live trees.
species: Species of the individual tree, using four-letter species codes
dbh: diameter at breast height in centimeters
ht: tree height in meters

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Tree data for vignette, version 4

Description

A dataset with intentional warnings for demonstration purposes

Usage

vign_trees_4
vign_trees_4

Format

A dataframe with 2250 rows and 10 columns:

id: time:site combined
time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual tree was measured
exp_factor: stems per hectare
status: live (1) or dead (0)
decay: 1-5 for standing dead trees. 0 for live trees.
species: Species of the individual tree, using four-letter species codes
dbh: diameter at breast height in centimeters
ht: tree height in meters

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Tree data for vignette, version 5

Description

A clean dataset for demonstration purposes

Usage

vign_trees_5
vign_trees_5

Format

A dataframe with 2250 rows and 10 columns:

id: time:site combined
time: pre (pre-burn) or post (post-burn)
site: compartment (60, 340, or 400)
plot: plot in which the individual tree was measured
exp_factor: stems per hectare
status: live (1) or dead (0)
decay: 1-5 for standing dead trees. 0 for live trees.
species: Species of the individual tree, using four-letter species codes
dbh: diameter at breast height in centimeters
ht: tree height in meters

Source

Fire and Fire Surrogate Study, Stephens Lab, University of California, Berkeley

Package 'BerkeleyForestsAnalytics'

Help Index

Data for biomass demonstrations

Description

Usage

Format

Source

Data for biomass demonstrations

Description

Usage

Format

Source

BiomassNSVB

Description

Usage

Arguments

Value

Examples

CoarseFuels

Description

Usage

Arguments

Value

Examples

Coarse woody debris data for compilation demonstrations

Description

Usage

Format

Source

Data for general Fire and Fire Surrogate demonstrations

Description

Usage

Format

Source

FPC data for general simple random sampling demonstrations

Description

Usage

Format

Source

Fine woody debris data for compilation demonstrations

Description

Usage

Format

Source

Data for general simple random sampling demonstrations

Description

Usage

Format

Source

Data for general simple random sampling demonstrations

Description

Usage

Format

Source

Data for general stratified random sampling demonstrations

Description

Usage

Format

Source

Weight data for stratified random sampling demonstrations

Description

Usage

Format

Source

CompilePlots

Description

Usage

Arguments

Value

Examples

CompileSurfaceFuels

Description

Usage

Arguments

Value

Examples

Data for coarse woody debris demonstrations

Description

Usage

Format