Package 'LandR'

Description

Utilities for 'LandR' suite of landscape simulation models. These models simulate forest vegetation dynamics based on LANDIS-II, and incorporate fire and insect disturbance, as well as other important ecological processes. Models are implemented as 'SpaDES' modules.

Package options

LandR packages use the following options() to configure behaviour:

• LandR.assertions: If TRUE, additional code checks are run during function calls. Default FALSE.

Author(s)

Maintainer: Eliot J B McIntire [email protected] (ORCID)

Authors:

• Eliot J B McIntire [email protected] (ORCID)

• Alex M Chubaty [email protected] (ORCID)

• Ceres Barros [email protected] (ORCID)

• Ian M S Eddy [email protected] (ORCID)

Other contributors:

• His Majesty the King in Right of Canada, as represented by the Minister of Natural Resources Canada [copyright holder]

See Also

Useful links:

• https://landr.predictiveecology.org

• https://github.com/PredictiveEcology/LandR

• Report bugs at https://github.com/PredictiveEcology/LandR/issues

Description

TODO: Move to reproducible

TODO: Move to reproducible TODO: expand to multiple objects

Usage

.compareRas(x, ...)

.compareCRS(x, y)

Arguments

Value

the projected extent

Note

this function internally converts Raster to SpatRaster to allow using compareGeom(), and benefit from its complexity.

Description

Uses likelihood parameter estimation to fit non linear models while attempting several starting values.

Usage

.fitNLMwCovariates(
  data,
  nonLinModelQuoted,
  linModelQuoted,
  mllsOuterPrev,
  model = c("CR", "Logistic"),
  maxCover = 1L,
  starts = NULL,
  lower = NULL,
  upper = NULL,
  nbWorkers = 1L
)

Arguments

Value

a list with entries mll (the maximum likelihood-estimated coefficients) and AICbest (the AIC of the best models generating these coefficients)

See Also

bbmle::mle2()

Description

Extracts the names of linear coefficients for the maximum-biomass-equivalent parameter in the non-linear growth equations

Usage

.getMaxBCoefs(mll, model = c("CR", "Logistic"))

Arguments

Value

a list of two vectors of parameter names one following coefficient names in mll ('mllCoefNames'), the other using the original names as in the data used for model fitting ('origCoefNames')

Description

Find interpolated value of light probability

Usage

.interpolateLightProb(x, x0, x1, y0, y1)

Arguments

Value

vector of the interpolated value

Description

Prepare data for model plotting

Usage

.MLLMaxBPartialPlotData(
  mll,
  nonLinModelQuoted,
  linModelQuoted,
  targetCovar = "cover",
  fixMaxCover = TRUE,
  maxCover = 1,
  data,
  fun = "mean",
  plotCIs = TRUE
)

Arguments

See Also

bbmle::mle2()

Description

Prepare data for model plotting

Usage

.MLLMaxBplotData(
  mll,
  nonLinModelQuoted,
  linModelQuoted,
  maxCover,
  data,
  averageCovariates = TRUE,
  observedAge = FALSE,
  plotCIs = TRUE
)

Arguments

See Also

bbmle::mle2()

Description

These all create a single function that can be used for either Raster or SpatRaster objects.

Usage

.NAvalueFlag(ras, NAval)

.stack(rasList)

.projectExtent(ras, crs)

asInt(ras)

isInt(ras)

reclass(ras, tab)

Arguments

Value

a raster with attributed NA values

a stacked raster

the projected extent

asInt returns a ⁠*Raster⁠ with values converted to integer, if they weren't already.

isInt returns a logical as per is.integer.

reclass returns a ⁠*Raster⁠ with values reclassified as per terra::classify and raster::reclassify.

Description

Add number of pixels per pixelGroup and add it has a new column to cohortData

Usage

addNoPixel2CohortData(
  cohortData,
  pixelGroupMap,
  cohortDefinitionCols = LandR::cohortDefinitionCols(),
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Value

An cohortData dat.table with a new noPixels column

Description

Generate cohortData table per pixel:

Usage

addPixels2CohortData(
  cohortData,
  pixelGroupMap,
  cohortDefinitionCols = LandR::cohortDefinitionCols(),
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Value

An expanded cohortData data.table with a new pixelIndex column.

Description

Reduces the age of cohorts that exceed their ⁠longevity x adjustmentFactor⁠

Usage

adjustAgeToLongevity(pixelCohortData, longevity, adjustmentFactor)

Arguments

Value

A cohortData data.table with corrected ages.

Description

Uses data.table to perform aggregation calculations, which is faster than raster::aggregate.

Usage

aggregateRasByDT(ras, newRas, fn = sum)

Arguments

Value

RasterLayer

Description

Essentially a wrapper around round, rather than truncate, which is what as.integer does. Internally, this is simply as.integer(floor(x + 0.5)).

Usage

asInteger(x)

Arguments

Value

An integer vector of length x, rounded to zero decimal places prior to as.integer

Note

Values ending in .5 will be rounded up, whether positive or negative. This is different than round.

Examples

x <- seq(-2, 2, 0.25)
data.frame(dbl = x, int = asInteger(x))

Description

• assert1: Assert that ecoregionCodes that were replaced, were correctly identified;

This is the full pixelCohortData, not the collapsed one.

Usage

assert1(
  cohortData34to36,
  cohortData,
  rmZeroBiomassQuote,
  classesToReplace = 34:36,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assert2(
  cohortDataNo34to36,
  classesToReplace = 34:36,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertSppMaxBMaxANPP(
  speciesEcoregion,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertUniqueCohortData(
  cohortData,
  columns,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertERGs(
  ecoregionMap,
  cohortData,
  speciesEcoregion,
  minRelativeB,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertColumns(
  obj,
  colClasses,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertCohortData(
  cohortData,
  pixelGroupMap,
  maxExpectedNumDiverge = 1,
  message = "",
  doAssertion = getOption("LandR.assertions", TRUE),
  verbose = getOption("LandR.verbose", TRUE),
  cohortDefinitionCols = LandR::cohortDefinitionCols()
)

assertCohortDataERG(
  cohortData,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertPixelCohortData(
  pixelCohortData,
  pixelGroupMap,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertSpeciesPlotLabels(
  speciesNames,
  sppEquiv,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertFireToleranceDif(
  burnedPixelCohortData,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertSpeciesLayers(
  speciesLayers,
  thresh,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertRstLCChange(
  rstLCChange,
  rasterToMatch,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertSpeciesEcoregionCohortDataMatch(
  cohortData,
  speciesEcoregion,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertPixelCohortDataValid(
  standCohortData,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertRepsAllCohortData(
  allCohortData,
  reps,
  years,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertStandAgeMapAttr(
  standAgeMap,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertCohortDataAttr(
  cohortData,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertSppVectors(
  sppEquiv = NULL,
  sppNameVector = NULL,
  sppColorVect = NULL,
  sppEquivCol = NULL,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertSpeciesTableRaw(
  speciesTableRaw,
  doAssertion = getOption("LandR.assertions", TRUE)
)

assertSpeciesTable(
  speciesTable,
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Description

Assert post-fire disturbance mortality and regeneration

Usage

assertPostPartialDist(
  cohortDataOrig,
  pixelGroupMapOrig,
  cohortDataNew,
  pixelGroupMapNew,
  postDistPixelCohortData,
  distrbdPixelCohortData,
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Description

Assign light probability

Usage

assignLightProb(
  sufficientLight,
  newCohortData,
  interpolate = TRUE,
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Value

newCohortData with a lightProb column

Simple wrapper around biomod2::BIOMOD_EnsembleForecasting()

Description

Simple wrapper around biomod2::BIOMOD_EnsembleForecasting()

Usage

biomodEnsembleFrcstWrapper(
  bm.em,
  bm.proj = NULL,
  proj.name = NULL,
  new.env = NULL,
  new.env.xy = NULL,
  keep.in.memory = TRUE,
  ...
)

Arguments

Description

Make maps from BIOMOD_EnsembleForecasting() results

Usage

biomodEnsembleProjMaps(bm.em.proj, predModel, rasTemplate, origCRS)

Arguments

Value

a SpatRaster object

Description

BIOMOD_EnsembleModeling wrapper

Usage

biomodEnsembleWrapper(bm.mod, metric.select.thresh = NULL, ...)

Arguments

Description

BIOMOD_Modeling wrapper

Usage

biomodModelingWrapper(
  sp,
  responseVar,
  responseVarData,
  predictorVars,
  predictorVarsData,
  dir.name,
  BIOMOD_ModelingArgs = list(models = c("GLM", "MARS"), bm.options = NULL, CV.k = 5,
    CV.perc = 100, metric.eval = c("TSS", "ROC"), modeling.id = "test"),
  ...
)

Arguments

Description

BIOMOD_Projection wrapper

Usage

biomodProjWrapper(
  bm.mod,
  proj.name = "testProj",
  new.env = NULL,
  new.env.xy = NULL,
  ...
)

Arguments

Description

Extract point locations from DEM raster to pass to BioSIM functions

Usage

BioSIM_extractPoints(x)

Arguments

Value

data.table with columns Name, Long, Lat, Elev.

See Also

prepInputsCanDEM()

Description

Raster stacks for all 9 MPB climate indices. See BioSIM::getModelHelp("MPB_SLR").

Usage

BioSIM_getMPBSLR(dem, years, SLR = "R", climModel = "GCM4", rcp = "RCP45")

Arguments

Value

RasterStack

Note

Although the BioSIM MPB_SLR model provides several other indices (see BioSIM::getModelHelp("MPB_SLR")), only 4 are currently used here.

Description

Get annual historic and projected wind maps from BioSIM

Usage

BioSIM_getWindAnnual(dem, years, climModel = "GCM4", rcp = "RCP45")

Arguments

Value

RasterStack

Description

Get monthly historic and projected wind maps from BioSIM

Usage

BioSIM_getWindMonthly(dem, years, months, climModel = "GCM4", rcp = "RCP45")

Arguments

Value

RasterStack

Description

calc_raster_stats iteratively calculates the frequency of raster values within each polygon. Supports user-provided polygons or automatic retrieval of ecoregions/ecozones.

Usage

calc_raster_counts(
  raster,
  polygons = NULL,
  polygon_id = NULL,
  filter_ids = NULL
)

calc_raster_stats(
  raster,
  polygons = NULL,
  polygon_id = NULL,
  filter_ids = NULL
)

plot_raster_stats(
  raster,
  polygons = NULL,
  polygon_id = NULL,
  filter_ids = NULL,
  counts_df = NULL,
  stats_df = NULL,
  aggregate_factor = 1,
  remove_zeros = FALSE,
  raster_label = NULL,
  inset_canada = TRUE,
  bin_width = 10,
  output_dir = ".",
  csv_file = NULL
)

Arguments

Details

plot_raster_stats calculates and returns statistics (min, max, median, mean, 25th percentile, 75th percentile, and proportion of zeroes) for a raster, and creates and saves figures for each polygon in a vector layer, including a histogram of raster values, a map of raster values within the polygon, and a summary of statistics . Optionally, zero values can be removed from the analysis, and the raster can be aggregated to a lower resolution to speed up plotting.

Value

A data.frame with columns: ID, value, count.

A data.frame of computed statistics (via calc_raster_stats()), with side effects of saving summary plots (and optionally a ‘.csv’) to disk.

Note

Large rasters should be processed on disk, where possible, e.g. by setting terraOptions(memfrac = 0.0).

Examples

if (interactive()) {
  if (requireNamespace("dplyr", quietly = TRUE) &&
      requireNamespace("geodata", quietly = TRUE) &&
      requireNamespace("purrr", quietly = TRUE) &&
      requireNamespace("withr", quietly = TRUE) &&
      requireNamespace("zonal", quietly = TRUE)) {
    tmp_pth <- withr::local_tempdir()

    ## work with raster on disk instead of in memory
    terraOptions(memfrac = 0.0)

    age <- prepInputsStandAgeMap(
      dataSource = "KNN",
      dataYear = 2011,
      destinationPath = tmp_pth
    )

    ecozones <- reproducible::prepInputs(
      url = "https://sis.agr.gc.ca/cansis/nsdb/ecostrat/zone/ecozone_shp.zip",
      destinationPath = tmp_pth
    ) |>
     sf::st_make_valid()

    id_col <- "ZONE_NAME"

    ## WARNING: don't use Cache() below -- excessive RAM usage

    ecozone_counts <- calc_raster_counts(
      raster = age,
      polygons = ecozones,
      polygon_id = id_col
    )

    ecozone_stats <- calc_raster_stats(
     raster = age,
     polygons = ecozones,
      polygon_id = id_col
    )

    gc()

    ## below will call calc_raster_counts and calc_raster_stats internally,
    ## so we shortcut this by passing the previously-calculated tables
    stats_ecozone <- plot_raster_stats(
      raster = age,
      polygons = ecozones,
      polygon_id = id_col,
      filter_ids = NULL,
      counts_df = ecozone_counts,
      stats_df = ecozone_stats,
      aggregate_factor = 1,
      remove_zeros = FALSE,
      raster_label = "age",
      inset_canada = TRUE,
      bin_width = 10,
      output_dir = tmp_pth,
      csv_file = NULL
    )

    gc()

    ## review the plots produced in tmp_pth
    list.files(tmp_pth, pattern = "[.]png", full.names = TRUE)

    identical(ecozone_stats, stats_ecozone) ## TRUE
  }
}

Description

Calculates fire severity as the loss of pre-fire to post-fire biomass (in absolute and percentual terms).

Usage

calcSeverityB(cohortData, burnedPixelCohortData)

Arguments

Value

data.table with columns pixelIndex, pixelGroup and severityB (absolute biomass lost) and severityPropB (proportion of biomass lost)

Note

if burnedPixelCohortData does not have a B column, the fire is assumed to be stand replacing (i.e. we assume B to be 0 across all pixels/cohorts in burnedPixelCohortData)

Description

Calculate site shade

Usage

calcSiteShade(currentTime, cohortData, speciesEcoregion, minRelativeB)

Arguments

Value

cohortData table with a siteShade column

See Also

makeMinRelativeB()

Description

Default climate effects function in the case where no climate effect is simulated

Usage

calculateClimateEffect(cohortData, ...)

Arguments

Details

the cohortData object is used to calculate the \ reduction/increase of mortality and growth biomasses per cohort.

Value

data.table with pixelGroup, age and speciesCode, as well as mortPred and growthPred columns with \ growth biomasses resulting from a climate effect. These percentages are later multiplied by the by baseline biomasses of mortality and growth (e.g. 0\ This default, no climate effect, function outputs 100\ mortPred and growthPred.

Description

Extract CASFRI data.table values and create species layer rasters

Usage

CASFRItoSpRasts(
  CASFRIRas,
  CASFRIattrLong,
  CASFRIdt,
  sppEquiv,
  sppEquivCol,
  destinationPath
)

Arguments

Value

RasterStack (or equivalent)

Description

Check if all species in have trait values

Usage

checkSpeciesTraits(speciesLayers, species, sppColorVect)

Arguments

Value

A list with the speciesLayers and sppColorVect containing only the species that have trait values in species

Description

Default columns that define cohorts

Usage

cohortDefinitionCols()

Note

because the name cohortDefinitionCols is also used as a function argument, be sure to use LandR::cohortDefinitionCols() in those functions or you'll get a "promise already under evaluation" error.

Description

This is a wrapper to help with migration to terra. Currently can only be used for a single layer SpatRaster or a RasterLayer.

Usage

Colors(ras, cols, n = NULL)

Arguments

Examples

cols <- colorRampPalette(c("blue", "red"))(12)
ras <- terra::rast(matrix(1:100, 10, 10))
ras <- Colors(ras, cols)
terra::plot(ras)

ras <- raster::raster(matrix(1:100, 10, 10))
ras <- Colors(ras, cols)
raster::plot(ras)

Description

Default columns that define pixel groups

Usage

columnsForPixelGroups()

Note

because the name columnsForPixelGroups is also used as a function argument, be sure to use LandR::columnsForPixelGroups() in those functions or you'll get a "promise already under evaluation" error.

Description

This function has been run previously to create layers with the correct codes which are available on Google Drive. These corrected layers are available here: https://drive.google.com/drive/folders/1zLYV-wcDjJfSflH1VkXG6sosqZZF4SYc In the respective folder for each year, under the file name ⁠SCANFI_att_nfiLandCover_CanadaLCCclassCodes_S_<YEAR>_v1_1.tif⁠

Usage

convert_SCANFI_LCC_codes(year = 2000, dataVersion = "V2", ...)

Arguments

Value

a SpatRaster with corrected classification codes

Description

This will search around the pixels on rstLCC that have classesToReplace, and search in iteratively increasing radii outwards for other Land Cover Classes than the those indicated in classesToReplace. This will constrain It will then take the cohorts that were in pixels with classesToReplace and assign them new values in the output object. This function will also check that it must be an ecoregionCode that already exists in cohortData, i.e., not create new ecoregionCode values. See Details.

Usage

convertUnwantedLCC(
  classesToReplace = 34:36,
  rstLCC,
  availableERC_by_Sp,
  theUnwantedPixels,
  ecoregionGroupVec,
  speciesEcoregion,
  pixelClassesToReplace,
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Details

This function is designed to be used in highly constrained situations, where it is not just replacing a Land Cover Class by a neighbouring Land Cover Class. But it can be used for the simpler cases of simply replacing a Land Cover Class.

Value

A data.table with two columns, pixelIndex and ecoregionGroup. This represents the new codes to used in the pixelIndex locations. These should have no values overlapping with classesToReplace.

Author(s)

Eliot McIntire

Description

This will search around the pixels on rstLCC that have classesToReplace, and search in iteratively increasing radii outwards for other Land Cover Classes than the those indicated in classesToReplace. .

Usage

convertUnwantedLCC2(
  classesToReplace = 34:36,
  rstLCC,
  nIterations = 6,
  defaultNewValue = NA,
  invalidClasses = NA
)

Arguments

Value

A rasterLayer with values in classesToReplace converted to adjacent values or NA.

Author(s)

Eliot McIntire Ian Eddy

Description

Define flammability map

Usage

defineFlammable(
  LandCoverClassifiedMap = NULL,
  nonFlammClasses = c(0L, 25L, 30L, 33L, 36L, 37L, 38L, 39L),
  mask = NULL,
  to = NULL,
  writeTo = NULL,
  ...
)

Arguments

Description

Summary for cohortData

Usage

describeCohortData(cohortData)

Arguments

Description

Activate resprouting after a (fire) disturbance

Usage

doResprouting(
  burnedPixelCohortData,
  postFirePixelCohortData,
  postFireRegenSummary = NULL,
  serotinyPixel,
  treedFirePixelTableSinceLastDisp,
  currentTime,
  species,
  sufficientLight,
  calibrate = FALSE
)

Arguments

Value

A list of objects: postFirePixelCohortData, a data.table with the cohorts that undergo serotiny; serotinyPixel, a vector of pixels where serotiny was activated; postFireRegenSummary, the updated postFireRegenSummary, if calibrate = TRUE.

Description

Activate serotiny after a (fire) disturbance

Usage

doSerotiny(
  burnedPixelCohortData,
  postFirePixelCohortData,
  postFireRegenSummary = NULL,
  species,
  sufficientLight,
  speciesEcoregion,
  currentTime,
  treedFirePixelTableSinceLastDisp,
  calibrate = FALSE
)

Arguments

Value

A list of objects: postFirePixelCohortData, a data.table with the cohorts that undergo serotiny; serotinyPixel, a vector of pixels where serotiny was activated; postFireRegenSummary, the updated postFireRegenSummary, if calibrate = TRUE.

Description

Based on https://stackoverflow.com/a/23382097/1380598.

Usage

dropTerm(form, term, dropRanEff = TRUE)

Arguments

Value

An updated model formula.

Description

This function combines an ecoregion map and a land cover map (e.g. ecodistricts and LCC) and creates a map and table of containing their combined values and pixel IDs. Used internally in LandR modules to prepare maps for to make cohortData.

Usage

ecoregionProducer(
  ecoregionMaps,
  ecoregionName = NULL,
  rasterToMatch,
  ecoregionTable
)

Arguments

Value

A list with two objects: the ecoregionMap and a table summarizing its information per pixelID

Description

This is simply a wrapper around match or ⁠\%in\%⁠ for a specific data.frame of values.

Usage

equivalentName(value, df, column, multi = FALSE, searchColumn = NULL)

equivalentNameColumn(value, df, column, multi = FALSE, searchColumn = NULL)

Arguments

Description

Estimation of maximum biomass as the A parameter in the Chapman-Richards and Logistic growth equations. Since A is modelled as linear term, it is a matrix product of its linear coefficients. It is assumed that all coefficients are related additively.

Usage

extractMaxB(mll, newdata, average = FALSE, model = c("CR", "Logistic"))

Arguments

Description

Rasterize polygons using fasterize

Usage

fasterizeFromSp(sp, raster, fieldName)

Arguments

Value

RasterLayer

Description

Disturbance functions

Usage

FireDisturbance(
  cohortData = copy(sim$cohortData),
  cohortDefinitionCols = c("pixelGroup", "age", "speciesCode"),
  calibrate = FALSE,
  postFireRegenSummary = copy(sim$postFireRegenSummary),
  treedFirePixelTableSinceLastDisp = sim$treedFirePixelTableSinceLastDisp,
  rstCurrentBurn = sim$rstCurrentBurn,
  inactivePixelIndex = sim$inactivePixelIndex,
  pixelGroupMap = sim$pixelGroupMap,
  currentTime = NULL,
  rasterToMatch = sim$rasterToMatch,
  species = copy(sim$species),
  sufficientLight = copy(sim$sufficientLight),
  speciesEcoregion = copy(sim$speciesEcoregion),
  initialB = 10,
  successionTimestep = 10L,
  doAssertion = getOption("LandR.assertions", TRUE),
  verbose = getOption("LandR.verbose", TRUE)
)

FireDisturbancePM(
  cohortData = copy(sim$cohortData),
  cohortDefinitionCols = c("pixelGroup", "age", "speciesCode"),
  colsForPixelGroups = LandR::columnsForPixelGroups(),
  calibrate = FALSE,
  LANDISPM = TRUE,
  postFireRegenSummary = copy(sim$postFireRegenSummary),
  treedFirePixelTableSinceLastDisp = copy(sim$treedFirePixelTableSinceLastDisp),
  rstCurrentBurn = sim$rstCurrentBurn,
  inactivePixelIndex = sim$inactivePixelIndex,
  pixelGroupMap = sim$pixelGroupMap,
  currentTime = NULL,
  rasterToMatch = sim$rasterToMatch,
  fireDamageTable = copy(sim$fireDamageTable),
  fireRSORas = sim$fireRSORas,
  fireROSRas = sim$fireROSRas,
  fireCFBRas = sim$fireCFBRas,
  species = copy(sim$species),
  sufficientLight = copy(sim$sufficientLight),
  speciesEcoregion = copy(sim$speciesEcoregion),
  initialB = 10,
  minRelativeB = copy(sim$minRelativeB),
  successionTimestep = 10L,
  doAssertion = getOption("LandR.assertions", TRUE),
  verbose = getOption("LandR.verbose", TRUE)
)

PeatlandThermokarst(
  thawedPixIDs = copy(sim$thawedPixIDs),
  treedThawedPixelTableSinceLastDisp = copy(sim$treedThawedPixelTableSinceLastDisp),
  wetlands = sim$wetlands,
  cohortData = copy(sim$cohortData),
  pixelGroupMap = sim$pixelGroupMap,
  rasterToMatch = sim$rasterToMatch,
  species = copy(sim$species),
  speciesEcoregion = copy(sim$speciesEcoregion),
  cohortDefinitionCols = LandR::cohortDefinitionCols(),
  initialB = 10L,
  inactivePixelIndex = sim$inactivePixelIndex,
  currentTime = NULL,
  successionTimestep = 10L
)

Arguments

initialB = asInteger(pmin(maxANPP, asInteger(pmax(1, maxANPP \* exp(-1.6 \* sumB / maxB_eco)))))

Value

a list of objects to be exported to sim:

• cohortData

• pixelGroupMap

• lastFireYear

• treedFirePixelTableSinceLastDisp

• serotinyResproutSuccessPixels

• severityBMap

• severityData

• postFireRegenSummary (if calibrate == TRUE and !is.null(postFireRegenSummary))

Stand-replacing fire disturbances

FireDisturbance() simulates post-fire mortality, serotiny and regeneration sequentially occurring after a fire. Post-fire mortality is assumed to be 100% (stand-replacement). The serotiny and regeneration algorithms are based on those in LANDIS-II Biomass Succession extension, v3.2.1, with modifications Requires the following objects in sim (passed as ⁠sim$*⁠): @template FireDisturbance. For any given burnt pixel, the function begins by killing all cohorts (i.e. removing them from cohortData). Then it activates serotiny for serotinous species that had been present pre-fire, and reprouting for reprouter species. Whether a species successfully regenerates via serotiny or resprouting depends on its traits (whether it is serotinous, resprouter, both or none; species table), pre-fire cohort age (in cohortData table), its light/shade requirements (species table) and its local maxB (speciesEcoregion) table (see Scheller & Miranda, 2015 and Scheller & Mladenoff, 2004 for details). Unlike in LANDIS-II Biomass Succession extension, v3.2.1, here we allow serotiny and resprouting to occur in the same pixel to reflect the competitive advantage of reprouters. However, for a given species only serotiny (takes precedence) or resprouting can be activated. The species table must contain the columns:

• sexualmature – age at sexual maturity

• postfireregen – post-fire regeneration strategy ("serotiny", "resprout" or "none")

• shadetolerance – shade tolerance value relative to other species.

• resproutage_min, resproutage_max – minimum and maximum age at which species can repsrout

• resproutprob – probability of resporuting success (before light/shade suitability is assessed)

Partial severity (i.e. mortality) fire disturbances

FireDisturbancePM() simulates partial post-fire mortality, serotiny and regeneration sequentially after a fire. The level of mortality depends of fire severity, and, by default, follows the mechanisms in LANDIS-II Dynamic Fire System v3.0. Serotiny and regeneration algorithms algorithms are based on those in LANDIS-II Biomass Succession extension, v3.2.1, with modifications (see FireDisturbance()). Requires the following objects in sim (and passed as ⁠sim$*⁠): @template FireDisturbance

• fireDamageTable

• fireRSORas (Critical spread rate for crowning)

• fireROSRas (Equilibrium Head Fire Rate of Spread)

• fireCFBRas (Crown Fraction Burnt)

• minRelativeB Rasters of fire behaviour properties (fireRSORas, fireROSRas and fireCFBRas) can be calculated using the cffdrs package. The species table must contain the columns:

• firetolerance – fire tolerance value relative to other species.

• longevity – maximum species age

• sexualmature – age at sexual maturity

• postfireregen – post-fire regeneration strategy ("serotiny", "resprout" or "none")

• shadetolerance – shade tolerance value relative to other species.

• resproutage_min, resproutage_max – minimum and maximum age at which species can repsrout

• resproutprob – probability of resporuting success (before light/shade suitability is assessed)

Peatland permafrost degradation (thermokarst) disturbances

PeatlandThermokarst() simulates tree cohort survival/mortality after peatland permafrost thermokarst. The level of mortality depends on species tolerance to thermokarst, determined by the thermokarsttol trait column in the species traits table. At the moment, this level of tolerance is used as the proportion of a cohort biomass that survives (is kept) when a pixel undergoes thermokarst. This is similar to the partial disturbance effects used in LANDIS-II Biomass Harvest v4.0. Requires the following objects in sim (and passed as ⁠sim$*⁠):

• thawedPixIDs

• treedThawedPixelTableSinceLastDisp

• wetlands

• cohortData

• pixelGroupMap

• rasterToMatch

• species

• speciesEcoregion

• inactivePixelIndex The species table must contain the columns:

• thermokarsttol – proportion of cohort biomass (B) that survives thermokarst

References

Scheller, R.M. & Miranda, B.R. (2015). LANDIS-II Biomass Succession v3.2 Extension – User Guide.

Scheller, R.M. & Mladenoff, D.J. (2004). A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecological Modelling, 180, 211–229.

Sturtevant, B.R., Miranda, B.R., Scheller, R.M. & Shinneman, D. (2018). LANDIS-II Dynamic Fire System Extension v3.0 – User Guide.

Scheller, R.M. & Domingo, J.B. (2021). LANDIS-II Biomass Harvest v4.4 Extension – User Guide

Description

The function will create sensible parameter ranges for A, k and p parameters of the Chapman-Richards and Logistic growth curves and attempt to run a forward step-AIC procedure to add covariates to the linear component of the model (on the A parameter – the asymptote). The maximum number of covariates to add is determined by maxNoCoefs.

Usage

fitNLMModels(
  sp = NULL,
  predictorVarsData,
  sppVarsB,
  predictorVars,
  predictorVarsCombos = NULL,
  maxNoCoefs = 4,
  doFwdSelection = FALSE,
  sampleSize = 3000,
  Ntries = 2000,
  maxCover = 1L,
  models = c("CR", "Logistic"),
  modelOutputsPrev = NULL,
  randomStarts = FALSE,
  lowerBounds = TRUE,
  upperBounds = TRUE,
  nbWorkers = 1L
)

Arguments

See Also

bbmle::mle2()

Description

Canada administrative boundaries

Usage

gadm_canada(src = "stats_can", dst_path = tempdir())

Arguments

Description

Generates unique groupings of a data.table object where one or more rows can all belong to the same pixelIndex. Pixel groups will be identical pixels based on unique combinations of columns.

Usage

generatePixelGroups(
  pixelDataTable,
  maxPixelGroup,
  columns = c("ecoregionGroup", "speciesCode", "age", "B")
)

Arguments

Value

Returns a vector of pixelGroup in the original order of the input pixelDataTable. This should likely be added to the pixelDataTable object immediately.

Description

Extracts points from raster layers using the original raster layer projection.

Usage

genericExtract(x, y, field = NULL, ...)

Arguments

Details

If x and y are both polygons, extract often outputs NA due to misalignments (this can happen even when x == y), even after snap(y, x). To circumvent this problem, intersect is used internally and, if the extract argument fun is passed, it is applied to values of y per polygon ID of x.

Value

a data.table with extracted values and an ID column of y point IDs

Description

Re-generate new pixelGroups in partially disturbed pixels.

Usage

genPGsPostDisturbance(
  cohortData,
  pixelGroupMap,
  disturbedPixelTable,
  disturbedPixelCohortData,
  colsForPixelGroups = LandR::columnsForPixelGroups(),
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Details

This function regenerates pixelGroups in situations where disturbances are not stand-replacing and create survivor and dead cohorts in some, but potentially not all, pixels of a pixelGroup. This is necessary to prevent reintroducing dead cohorts that were not affected in the other pixels of the same, original, pixelGroup. ATTENTION This function alone will not generate final pixelGroups, and will likely need to be followed by an updateCohortData run. The function:

1. creates a table of undisturbed pixels, and disturbed pixels with dead and surviving cohorts but not new cohorts (e.g. from serotiny/resprouting) – these are added by updateCohortData;

2. generates pixelGroups considering these dead and surviving cohorts – this ensures that we account for cohorts that died in some but not all pixels of a given pixelGroup;

3. then removes dead cohorts (as they should not be in tables for downstream functions like updateCohortData) and recalculates pixelGroups – this ensures that pixels that became similar/dissimilar after the death of some cohorts can form new pixelGroups.

Value

a named list with:

• a cohortData table with the updated pixelGroups, as well as survivor cohorts, but not dead cohorts.

• a pixelGroupMap with the updated pixelGroups in disturbed pixels

Description

prepSpeciesTable

Usage

getSpeciesTable(url = NULL, dPath = tempdir(), cacheTags = NULL)

prepSpeciesTable(
  speciesTable,
  speciesLayers = NULL,
  sppEquiv = NULL,
  sppEquivCol = "LandR",
  areas = c("BSW", "BP", "MC")
)

Arguments

Value

getSpeciesTable() returns a data.table with columns LandisCode, Area, Longevity, Maturity, Shade, Fire, SeedEffDist, SeedMaxDist, VegProb, MinAgeVeg, MaxAgeVeg, PostFireRegen, LeafLongevity, WoodDecayRate, MortalityCurve, GrowthCurve, LeafLignin, HardSoft. See assertSpeciesTableRaw() for expected column data types.

prepSpeciesTable() returns a data.table with columns species, Area, longevity, sexualmature, shadetolerance, firetolerance, seeddistance_eff, seeddistance_max, resproutprob, resproutage_min, resproutage_max, postfireregen, leaflongevity, wooddecayrate, mortalityshape, growthcurve, leafLignin, hardsoft. See assertSpeciesTable() for expected column data types.

Note

This one is tailored to Canadian forests

See Also

assertSpeciesTableRaw()

assertSpeciesTable()

Description

Download and prepare raster fire data from NFI

Usage

getWildfire_NFI(dPath, rasterToMatch, url = NULL)

Arguments

Value

a raster with values representing fire year 1985-2015

Description

Plots a maximum biomass estimated at maximum 'cover' (or dominance) levels as a function of age.

Usage

ggplotMLL_maxB(
  mll,
  data,
  maxCover = 1L,
  xCovar = "age",
  plotTitle = NULL,
  nonLinModelQuoted,
  linModelQuoted,
  averageCovariates = TRUE,
  observedAge = FALSE,
  plotCIs = TRUE
)

Arguments

Description

Install BioSIM to retrieve climate and other projections using BioSIM

Usage

installBioSIM(lib)

Arguments

Description

Simulate seed dispersal using user defined function. This is a "receiving pixel" focused dispersal approach. It is the "potentially receiving" cell that looks around itself for potential seed sources. If it finds a single seed source, that passes the probability function described by the dispersalFn. If this passes a comparison to a uniform random draw, then the receiving cell is deemed to have a "successful" dispersal for that species. This function can therefore only be used for a relatively specific situation where there is a yes/no returned for each potential receiving cell, i.e., not abundance. This function is also not cumulative, i.e,. there is no higher abundance of seeds received if a receiving cell has lots of seed sources around it vs. a single seed source. The difference will come with a higher probability of successfully receiving a "seed".

Usage

LANDISDisp(
  dtSrc,
  dtRcv,
  pixelGroupMap,
  speciesTable,
  dispersalFn = Ward,
  b = 0.01,
  k = 0.95,
  plot.it = FALSE,
  successionTimestep,
  verbose = getOption("LandR.verbose", TRUE),
  useCpp = getOption("LandR.LANDISDisp.useCpp", TRUE),
  ...
)

Arguments

Details

dispersalFn (temporarily unused as code is converted to Rcpp – the default dispersalFn is hard coded within the spiralSeedDispersal function that uses C++) must be an expression that returns a probability distribution. Because it is a dispersal kernel, it must be a probability distribution. The expression that can take an argument named "dis" (without quotes) as this will be calculated internally and represents the distance from the initial (receiving) pixel and all active pixels within that cluster of active pixels. LandR includes the Ward() kernel as defined in the LANDIS-II documentation.

The spiral inner loop is implemented in C++ (via Rcpp) and is on by default; it produces bit-identical output to the previous R implementation under a fixed seed, but is typically ~3x faster end-to-end on landscape-scale inputs (and uses dramatically less memory by replacing the per-cell-by-species source matrix with a per-pixelGroup species bitmask). To fall back to the R reference (e.g., for debugging), pass useCpp = FALSE or set options(LandR.LANDISDisp.useCpp = FALSE).

Value

A numeric vector of raster pixel indices, in the same resolution and extent as seedSrc raster.

Author(s)

Eliot McIntire

Examples

if (require("googledrive")) {
  seed <- sample(1e6, 1)
  set.seed(seed)
  library(data.table)

  # keep this here for interactive testing with a larger raster
  rasterTemplate <- reproducible::rasterRead(terra::ext(0, 2500, 0, 2500), res = 100)

  # make a pixelGroupMap
  pgs <- 4 # make even just because of approach below requires even
  pixelGroupMap <- SpaDES.tools::randomPolygons(rasterTemplate, numTypes = pgs)
  pixelGroupMap[1:100] <- NA # emulate a mask at the start

  # Make a receive pixels table -- need pixelGroup and species
  nSpecies <- 3
  maxNSpeciesPerPixel <- min(5, nSpecies)
  rcvSpByPG <- lapply(seq_len(pgs / 2), function(pg) {
    data.table(speciesCode = sample(nSpecies, size = sample(maxNSpeciesPerPixel, 1)))
  })
  seedReceive <- rbindlist(rcvSpByPG, idcol = "pixelGroup")

  # Make a source pixels table -- need pixelGroup and species
  srcSpByPG <- lapply(seq_len(pgs / 2), function(pg) {
    data.table(speciesCode = sample(nSpecies, size = sample(maxNSpeciesPerPixel, 1)))
  })
  seedSource <- rbindlist(srcSpByPG, idcol = "pixelGroup")
  # make source pixels not same pixelGroups as receive
  seedSource[, pixelGroup := pixelGroup + pgs / 2]

  # Get a species table -- if using in Canada, can use this
  destPath <- file.path(tempdir(), "ex_LANDISDisp")
  speciesTable <- getSpeciesTable(dPath = destPath)
  speciesTable <- speciesTable[Area == "BSW"]
  speciesTable[, speciesCode := as.factor(LandisCode)]
  speciesTable[, seeddistance_eff := SeedEffDist]
  speciesTable[, seeddistance_max := SeedMaxDist]

  speciesTable <- speciesTable
  speciesTable <- data.table(speciesTable)[, speciesCode := seq_along(LandisCode)]
  seedReceiveFull <- speciesTable[seedReceive, on = "speciesCode"]
  output <- LANDISDisp(
    dtRcv = seedReceiveFull, plot.it = interactive(),
    dtSrc = seedSource,
    speciesTable = speciesTable,
    pixelGroupMap,
    verbose = TRUE,
    successionTimestep = 10
  )
  # Summarize
  output[, .N, by = speciesCode]

  ## Plot the maps
  if (interactive()) {
    library(quickPlot)
    clearPlot()
    spMap <- list()
    spMap$pixelGroupMap <- pixelGroupMap
    for (sppp in unique(output$speciesCode)) {
      spppChar <- paste0("Sp_", sppp)
      spMap[[spppChar]] <- reproducible::rasterRead(pixelGroupMap)
      ss <- unique(seedSource[speciesCode == sppp], on = c("pixelGroup", "speciesCode"))
      spMap[[spppChar]][pixelGroupMap[] %in% ss$pixelGroup] <- 1

      receivable <- reproducible::rasterRead(pixelGroupMap)
      srf <- unique(seedReceiveFull[speciesCode == sppp], on = c("pixelGroup", "speciesCode"))
      receivable[pixelGroupMap[] %in% srf$pixelGroup] <- 1

      forest <- which(!is.na(pixelGroupMap[]))
      src <- which(!is.na(spMap[[spppChar]][]))
      recvable <- which(!is.na(receivable[]))
      rcvd <- output[speciesCode == sppp][["pixelIndex"]]

      spMap[[spppChar]][forest] <- 0
      spMap[[spppChar]][recvable] <- 2
      spMap[[spppChar]][src] <- 1
      spMap[[spppChar]][rcvd] <- 3
      spMap[[spppChar]][intersect(src, rcvd)] <- 4

      levels(spMap[[spppChar]]) <- data.frame(
        ID = 0:4,
        type = c(
          "OtherForest", "Source", "Didn't receive",
          "Received", "Src&Rcvd"
        )
      )
    }
    Plot(spMap, cols = "Set2")

    # A summary
    rr <- apply(rast(spMap)[[-1]][] + 1, 2, tabulate)
    rownames(rr) <- raster::levels(spMap[[2]])[[1]][, "type"][1:NROW(rr)]
    # next line only works if there are some places that are both source and potential to receive
    # rr <- rbind(rr, propSrcRcved = round(rr[5,]/ (rr[5,]+rr[2,]), 2))
  }
}

Description

Convert cohortData into landcover class based on leading species and density.

1. Classify each pixel as "broadleaf", "mixedwood", or "conifer";

2. Define the openness of each pixel as "dense", "open", or "sparse";

3. Combine these into cover classes for forested cells.

Usage

lccMapGenerator(
  cohortData,
  pixelGroupMap,
  lccTable = NULL,
  deciduousCoverDiscount = 0.8418911,
  vegLeadingProportion = 0.75,
  decidousSpp = NULL,
  rstLCC
)

Arguments

Details

By default, landcover classes are coded to match the Land Cover Map of Canada 2005 at 250m (Latifovic & Pouliot, 2005).

Code modified from https://github.com/tati-micheletti/caribouRSF_NT/blob/main/R/makeLCCfromCohortData.R.

Value

raster of the same type as pixelGroupMap, containing LCC values

Note

Sparse classes: Even though the default LCC2005 input does not have "mixed sparse" or "broadleaf sparse", these are likely to be produced in simulations, starting from "conifer sparse" sites (i.e. broadleaf trees might grow on these sites, and eventually convert these into mixed or broadleaf sparse sites).

Author(s)

Tati Micheletti and Alex Chubaty

References

Latifovic, R. & Pouliot, D. (2005). Multitemporal land cover mapping for Canada: methodology and products. Canadian Journal of Remote Sensing, 31, 347–363.

Description

Loads CASFRIv4 data specifically supplied for the LandWeb project

Usage

loadCASFRI(
  CASFRIRas,
  attrFile,
  headerFile,
  sppEquiv,
  sppEquivCol,
  type = c("cover", "age")
)

Arguments

Value

list with named elements: CASFRIattrLong and CASFRIdt (both data.tables)

Description

TODO: description needed

Usage

loadkNNSpeciesLayers(
  dPath,
  rasterToMatch = NULL,
  studyArea = NULL,
  sppEquiv,
  year = 2001,
  knnNamesCol = "KNN",
  sppEquivCol = "Boreal",
  thresh = 10,
  url = NULL,
  ...
)

Arguments

Value

A raster stack of percent cover layers by species.

Description

Load SCANFI species layers from online data repository

Usage

loadSCANFISpeciesLayers(
  dPath,
  rasterToMatch = NULL,
  studyArea = NULL,
  sppEquiv,
  year = 2020,
  dataVersion = "V2",
  SCANFINamesCol = "SCANFI",
  sppEquivCol = "SCANFI",
  thresh = 10,
  url = NULL,
  ...
)

Arguments

Value

A raster stack of percent cover layers by species.

Description

Takes a single data.table input, which has the following columns in addition to others that will be labelled with species name, and contain percent cover of each:

Usage

makeAndCleanInitialCohortData(
  inputDataTable,
  sppColumns,
  imputeBadAgeModel = quote(lme4::lmer(age ~ B * speciesCode + cover * speciesCode + (1 |
    initialEcoregionCode))),
  minCoverThreshold,
  doAssertion = getOption("LandR.assertions", TRUE),
  doSubset = TRUE
)

Arguments

Details

• pixelIndex (integer)

• age (integer)

• logAge (numeric)

• initialEcoregionCode (factor)

• totalBiomass (integer)

• lcc (integer)

• rasterToMatch (integer)

• speciesCode (factor)

• cover (integer)

• coverOrig (integer)

• B (integer)

Several data correction/imputation operations are also performed. Namely, age is imputed in pixels where age data is missing (but not cover) and where cover == 0 but age > 0, total biomass is zeroed if age == 0, and age is zeroed if biomass == 0.

Value

a cohortData data.table with attribute "imputedPixID" (a vector of pixel IDs that suffered imputation).

Author(s)

Eliot McIntire

Description

This is a function that creates the biomassMap raster used for simulations in Biomass_core module, using estimated data based on rawBiomassMap contained in pixelCohortData.

Usage

makeBiomassMap(pixelCohortData, rasterToMatch)

Arguments

Value

The biomassMap, a raster of total stand biomass per pixel.

Description

Takes a pixelCohortData table (see makeAndCleanInitialCohortData), the speciesEcoregion list and returns a modified pixelCohortData and the cohortData tables to be used in the simulation. This function mainly removes unnecessary columns from pixelCohortData, subsets pixels with biomass > 0, generates pixelGroups, and adds ecoregionGroup and totalBiomass columns to pixelCohortData. cohortData is then created by subsetting unique combinations of pixelGroup and whatever columns are listed in columnsForPixelGroups. The resulting cohortData table has the following columns:

• speciesCode (factor)

• ecoregionGroup (factor)

• pixelGroup (integer)

• age (integer)

• B (integer)

Usage

makeCohortDataFiles(
  pixelCohortData,
  columnsForPixelGroups,
  speciesEcoregion,
  pixelGroupBiomassClass,
  pixelGroupAgeClass,
  minAgeForGrouping = 0,
  rmImputedPix = FALSE,
  imputedPixID,
  pixelFateDT
)

Arguments

Value

A list with a modified pixelCohortData, cohortData, and pixelFateDT data.tables.

Description

ecoregionMapis a raster of all the unique groupings.

Usage

makeDummyEcoregionMap(rasterToMatch)

makeDummyRawBiomassMap(rasterToMatch)

makeDummyStandAgeMap(rawBiomassMap)

makeDummyRstLCC(rasterToMatch)

makeDummyEcoregionFiles(ecoregionMap, rstLCC, rasterToMatch)

Arguments

Details

rawBiomassMap is a raster of "raw" total stand biomass per pixel, with values between 100 and 20000 g/m^2.

standAgeMap is a raster of stand age per pixel (where biomass exists) with values between 1 and 300 years.

rstLCC is a raster land-cover class per pixel, with values between 1 and 5 that have no correspondence to any real land-cover classes.

ecoregionFiles uses dummy versions of ecoregionMap and rstLCC to create a list with two objects: the ecoregionMap and a table summarizing its information per pixelID. See ecoregionProducer (it uses ecoregionProducer internally).

Value

a RasterLayer object or, in the case of makeDummyEcoregionFiles, a list.

Description

This function creates a table containing pixel-wise ecoregion codes and whether they are "active" (have biomass > 0) or not for simulation. Unlike ecoregionProducer, this function creates the ecoregion table from pixel information contained in pixelCohortData

Usage

makeEcoregionDT(pixelCohortData, speciesEcoregion)

Arguments

Value

A data.table with ecoregion codes and their active status per pixelID.

Description

Creates a raster of ecoregion codes per pixel. Unlike ecoregionProducer, this fills the raster with pixel information contained in pixelCohortData.

Usage

makeEcoregionMap(ecoregionFiles, pixelCohortData)

Arguments

Value

A raster with ecoregion codes.

Description

The table contains expert-based values for minimum relative biomass of each shade tolerance class (the minimum relative biomass a cohort with a given shade tolerance should have to be able to germinate), in each unique ecoregion group. All ecoregion groups currently have the same values.

Usage

makeMinRelativeB(pixelCohortData)

Arguments

Value

a data.frame of min relative biomass values per ecoregion group.

Description

TODO: description and title needed

Usage

makePickellStack(PickellRaster, sppEquiv, sppEquivCol, destinationPath)

Arguments

Value

TODO: description needed

Description

Create the makePixelGroupMap raster containing pixelGroups in pixelCohortData.

Usage

makePixelGroupMap(pixelCohortData, rasterToMatch)

Arguments

Value

a raster with pixel groups

Description

Make pixelTable from biomass, age, land-cover and species cover data

Usage

makePixelTable(
  speciesLayers,
  standAgeMap,
  ecoregionFiles,
  biomassMap,
  rasterToMatch,
  rstLCC,
  printSummary = TRUE,
  doAssertion = getOption("LandR.assertions", TRUE)
)

Arguments

Value

A data.table as many rows as non-NA pixels in rasterToMath and the columns containing pixel data from the input raster layers, with biomass in g/m^2.

Description

Use statistically estimated maxB, maxANPP and establishment probabilities to generate speciesEcoregion table.

Usage

makeSpeciesEcoregion(
  cohortDataBiomass,
  cohortDataShort,
  cohortDataShortNoCover,
  species,
  modelCover,
  modelBiomass,
  successionTimestep,
  currentYear
)

Arguments

Details

See Details.

establishprob

This section takes the cover as estimated from the mature tree cover and partitions it between resprouting and seeds Unfortunately, establishment by seed is not independent of resprouting, i.e., some pixels would have both Since we don't know the level of independence, we can't correctly assess how much to discount the two. If there is resprouting > 0, then this is the partitioning: establishprob = f(establishprob + resproutprob + jointEstablishProbResproutProb) If jointEstablishProbResproutProb is 0, then these are independent events and the total cover probability can be partitioned easily between seeds and resprout. This is unlikely ever to be the case. We are picking 50% overlap as a number that is better than 0 (totally independent probabilities, meaning no pixel has both seeds and resprout potential) and 100% overlap (totally dependent probabilities, i.e., every pixel where there is seeds will also be a pixel with resprouting) This is expressed with the "* 0.5" in the code.

#' @return A speciesEcoregion data.table with added columns for parameters maxB, maxANPP and establishprob

Description

makeVegTypeMap is a wrapper around vegTypeMapGenerator that works from a species stack of percent cover. These do not have to sum to 100%%

Downloads the 2011 kNN species cover layers from the Canadian Forestry Service, National Inventory System, for validation.

Usage

makeVegTypeMap(speciesStack, vegLeadingProportion, mixed, ...)

loadkNNSpeciesLayersValidation(
  dPath,
  rasterToMatch,
  studyArea,
  sppEquiv,
  knnNamesCol = "KNN",
  sppEquivCol,
  thresh = 1,
  url,
  ...
)

prepSpeciesLayers_KNN2011(
  destinationPath,
  outputPath,
  url = NULL,
  studyArea,
  rasterToMatch,
  sppEquiv,
  sppEquivCol,
  thresh = 10,
  ...
)

Arguments

Value

A factor raster

A raster stack of percent cover layers by species.

Description

minRelativeB defaults for Western Boreal Forest Canada

Usage

minRelativeBDefaults()

Description

Update species and speciesEcoregion tables

Usage

modifySpeciesAndSpeciesEcoregionTable(speciesEcoregion, speciesTable)

Arguments

Value

a list with modified speciesEcoregion and speciesTable, under the names "newSpeciesEcoregion" and "newSpeciesTable", respectively.

Description

Pixels that are NA but are inside rasterToMatch may need to be converted to 0, as they can could still potentially be forested

Usage

NAcover2zero(speciesLayers, rasterToMatch)

Arguments

Value

the speciesLayers with 0 in pixels that had NA

Description

Assess non-forested pixels based on species cover data and land-cover

Usage

nonForestedPixels(
  speciesLayers,
  omitNonTreedPixels,
  forestedLCCClasses,
  rstLCC
)

Arguments

Value

a logical vector of length ncell(rstLCC) where TRUE indicates non-forested pixels where there is no species cover data, or a non-forested land-cover class

Description

Overlay different LCC data sources

Usage

overlayLCCs(
  LCCs,
  forestedList,
  outputLayer,
  NAcondition,
  NNcondition,
  remapTable = NULL,
  classesToReplace,
  availableERC_by_Sp,
  forestEquivalencies = NULL
)

Arguments

Author(s)

Eliot McIntire and Alex Chubaty

Description

Overlays rasters of different data resolution by filling in gaps in the highest resolution raster with data available in lowest resolution one. If only high or low resolution data are available, it will use it without attempting to overlay.

Usage

overlayStacks(
  highQualityStack,
  lowQualityStack,
  outputFilenameSuffix = "overlay",
  destinationPath
)

Arguments