R/tpaStarter.R

In rFIA: Estimation of Forest Variables using the FIA Database

tpaStarter <- function(x, db, grpBy_quo = NULL, polys = NULL, 
                       returnSpatial = FALSE, bySpecies = FALSE, 
                       bySizeClass = FALSE, landType = 'forest', 
                       treeType = 'live', method = 'TI', lambda = 0.5, 
                       treeDomain = NULL, areaDomain = NULL, totals = FALSE, 
                       byPlot = FALSE, treeList = FALSE, nCores = 1, remote, mr) {

  # Read required data and prep the database ------------------------------
  reqTables <- c('PLOT', 'TREE', 'COND', 'POP_PLOT_STRATUM_ASSGN', 
                 'POP_ESTN_UNIT', 'POP_EVAL', 'POP_STRATUM', 'POP_EVAL_TYP', 
                 'POP_EVAL_GRP', 'PLOTGEOM')

  # If remote, read in state by state. Otherwise, drop all unnecessary tables.
  db <- readRemoteHelper(x, db, remote, reqTables, nCores)

  # If the object was clipped
  if ('prev' %in% names(db$PLOT)) {
    # Filter to only include the most recent plot measurements 
    db$PLOT <- dplyr::filter(db$PLOT, prev == 0)
  }

  # Handle TX issues: we only keep inventory years that are present in 
  # BOTH EAST AND WEST TX
  db <- handleTX(db)

  # Check some of the inputs ----------------------------------------------
  # polys -----------------------------
  if (!is.null(polys) & 
      dplyr::first(class(polys)) %in% 
      c('sf', 'SpatialPolygons', 'SpatialPolygonsDataFame') == FALSE) {
    stop("polys must be a spatial polygons object of class sp or sf.")
  }
  # landType --------------------------
  if (landType %in% c('timber', 'forest') == FALSE) {
    stop('landType must be one of: "forest" or "timber".')
  }
  # treeType --------------------------
  if (treeType %in% c('live', 'dead', 'gs', 'all') == FALSE) {
    stop('treeType must be one of: "live", "dead", "gs", or "all".')
  }
  # db required tables ----------------
  if (any(reqTables %in% names(db) == FALSE)) {
    missT <- reqTables[reqTables %in% names(db) == FALSE]
    stop(paste('Tables', paste(as.character(missT), collapse = ', '),
               'not found in object db.'))
  }
  # method ----------------------------
  if (stringr::str_to_upper(method) %in% c('TI', 'SMA', 'LMA', 'EMA', 
                                           'ANNUAL') == FALSE) {
    warning(paste('Method', method, 'unknown. Defaulting to Temporally Indifferent (TI).'))
  }

  # Other basic variable prep ---------------------------------------------
  # Join PLOT with PLOTGEOM to allow plot-level geographic attributes to be used
  # in grpBy statements
  # First need to get rid of other columns in PLOT in PLOTGEOM. 
  db$PLOTGEOM <- db$PLOTGEOM %>%
    dplyr::select(-STATECD, -INVYR, -UNITCD, -COUNTYCD, -PLOT, -LAT, -LON, 
                  -dplyr::starts_with('CREATED'), -dplyr::starts_with('MODIFIED'))
  db$PLOT <- db$PLOT %>%
    dplyr::left_join(db$PLOTGEOM, by = 'CN')
  # Get a plot CN and a new pltID that gives a unique ID to each plot
  # PLT_CN is UNITCD, STATECD, COUNTYCD, PLOT, and INVYR
  db$PLOT <- db$PLOT %>% 
    dplyr::mutate(PLT_CN = CN, 
                  pltID = stringr::str_c(UNITCD, STATECD, COUNTYCD, PLOT, sep = '_'))

  # Convert grpBy to character
  grpBy <- grpByToChar(db, grpBy_quo)

  # Unique plot ID through time (pltID)
  if (byPlot | treeList) {
    grpBy <- c('pltID', grpBy)
  }

  # Intersect plots with polygons if polygons are given
  if (!is.null(polys)) {
    # Add shapefile names to grpBy
    # Determine the name of the sf column. This assumes the user has not 
    # manually changed the "sf_column" attribute. By default, this is the 
    # same as the sf column. This is needed, because there is no single
    # name for the sf column. It is usually geom or geometry, but that
    # is not always the case.
    sfCol <- attr(polys, 'sf_column')
    grpBy <- c(grpBy, names(polys)[names(polys) != sfCol])

    # Do the intersection
    db <- arealSumPrep2(db, grpBy, polys, nCores, remote)

    # If there's nothing there, skip the state
    if (is.null(db)) return('no plots in polys')
  }

  # Update grpBy if returning spatial points.
  if (byPlot & returnSpatial) {
    grpBy <- c(grpBy, 'LON', 'LAT')
  }

  # Build a domain indicator for each observation (1 or 0) ----------------
  # Land type
  db$COND$landD <- landTypeDomain(landType, db$COND$COND_STATUS_CD, 
                                  db$COND$SITECLCD, db$COND$RESERVCD)
  # Tree type
  db$TREE$typeD <- treeTypeDomain(treeType, db$TREE$STATUSCD, db$TREE$DIA, 
                                  db$TREE$TREECLCD)

  # Spatial boundary (determine which of the plots fall within the polygons
  # supplied in polys)
  if (!is.null(polys)) {
    db$PLOT$sp <- ifelse(!is.na(db$PLOT$polyID), 1, 0)
  } else {
    # If no polys, use all plots.
    db$PLOT$sp <- 1
  }

  # User defined domain indicator for area (ex. specific forest type)
  db <- udAreaDomain(db, areaDomain)

  # User defined domain indicator for trees (ex. trees > 20 ft tall)
  db <- udTreeDomain(db, treeDomain)


  # Handle population tables ----------------------------------------------
  # Filtering out all inventories that are not relevant to the current 
  # estimation type. If using estimator other than TI, handle the differences in 
  # P2POINTCNT and in assigning YEAR column (YEAR = END_INVYR if method = 'TI')
  pops <- handlePops(db, evalType = c('VOL'), method, mr)

  # A lot of states do their stratification in such a way that makes it impossible
  # to estimate variance of annual panels with the post-stratified estimator. That is, 
  # the number of plots within a panel within a stratum is less than 2. When this happens
  # merge strata so that all have at least two observations.
  if (stringr::str_to_upper(method) %in% c('SMA', 'LMA', 'EMA', 'ANNUAL') & !byPlot) {
    pops <- mergeSmallStrata(db, pops)
  }

  # Handle canned groups --------------------------------------------------
  # Add species to groups
  # Note that intData is an internal data object. 
  if (bySpecies) {
    db$TREE <- db$TREE %>%
      dplyr::left_join(dplyr::select(intData$REF_SPECIES_DEC_2024, 
                                     c('SPCD', 'COMMON_NAME', 'GENUS', 'SPECIES')), 
                       by = 'SPCD') %>%
      dplyr::mutate(SCIENTIFIC_NAME = paste(GENUS, SPECIES, sep = ' ')) %>% 
      dplyr::mutate_if(is.factor, character)
    grpBy <- c(grpBy, 'SPCD', 'COMMON_NAME', 'SCIENTIFIC_NAME')
  }

  # Break into size classes
  if (bySizeClass) {
    grpBy <- c(grpBy, 'sizeClass')
    db$TREE$sizeClass <- makeClasses(db$TREE$DIA, interval = 2, numLabs = TRUE)
  }

  # Prep the tree list ----------------------------------------------------
  # Narrow the tables to the necessary variables. 
  # Which grpByNames are in which table? Helps us subset below. 
  # grpBy names in PLOT
  grpP <- names(db$PLOT)[names(db$PLOT) %in% grpBy]
  # grpBy names in COND
  grpC <- names(db$COND)[names(db$COND) %in% grpBy & 
                         !c(names(db$COND) %in% grpP)]
  # grpBy names in TREE                         
  grpT <- names(db$TREE)[names(db$TREE) %in% grpBy & 
                         !c(names(db$TREE) %in% c(grpP, grpC))]

  # PLOT ------------------------------
  db$PLOT <- db$PLOT %>%
    # Dropping irrelevant rows and columns
    dplyr::select(PLT_CN, STATECD, MACRO_BREAKPOINT_DIA, INVYR, MEASYEAR, 
                  PLOT_STATUS_CD, dplyr::all_of(grpP), sp, COUNTYCD) %>% 
    # Drop non-forested plots, and those otherwise outside our domain of interest.
    dplyr::filter(PLOT_STATUS_CD == 1 & sp == 1) %>% 
    # Drop visits not used in our eval of interest
    dplyr::filter(PLT_CN %in% pops$PLT_CN)
  
  # COND ------------------------------
  db$COND <- db$COND %>% 
    dplyr::select(PLT_CN, CONDPROP_UNADJ, PROP_BASIS, COND_STATUS_CD, CONDID, 
                  dplyr::all_of(grpC), aD, landD) %>% 
    # Drop non-forested plots, and those otherwise outside our domain of interest
    dplyr::filter(aD == 1 & landD == 1) %>% 
    # Drop visits not used in our eval of interest
    dplyr::filter(PLT_CN %in% pops$PLT_CN)

  # TREE ------------------------------
  db$TREE <- db$TREE %>%
    dplyr::select(PLT_CN, CONDID, DIA, SPCD, TPA_UNADJ, SUBP, TREE, 
                  dplyr::all_of(grpT), tD, typeD) %>% 
    # Drop plots outside our domain of interest
    dplyr::filter(!is.na(DIA) & TPA_UNADJ > 0 & tD == 1 & typeD == 1) %>%
    # Drop visits not used in our eval of interest
    dplyr::filter(PLT_CN %in% db$PLOT$PLT_CN)

  # Separate area grouping names from tree grouping names
  if (!is.null(polys)) {
    aGrpBy <- grpBy[grpBy %in% c(names(db$PLOT), names(db$COND), names(polys))]
  } else {
    aGrpBy <- grpBy[grpBy %in% c(names(db$PLOT), names(db$COND))]
  }

  # Full tree list
  data <- db$PLOT %>% 
    dplyr::left_join(db$COND, by = c('PLT_CN')) %>% 
    dplyr::left_join(db$TREE, by = c('PLT_CN', 'CONDID'))

  # Comprehensive indicator function
  data$aDI <- data$landD * data$aD * data$sp
  data$tDI <- data$landD * data$aD * data$tD * data$typeD * data$sp
  data$pDI <- data$landD * data$aD * data$tD * data$sp

  # Plot-level summaries --------------------------------------------------
  if (byPlot & !treeList) {
    grpBy <- c('YEAR', grpBy)
    # Create a list of symbols for the grpBy statements
    grpSyms <- rlang::syms(grpBy)
    aGrpSyms <- rlang::syms(aGrpBy)

    # Plot-level estimates
    # Area
    a <- data %>% 
      dplyr::distinct(PLT_CN, CONDID, .keep_all = TRUE) %>%
      # Convert to a lazy data table for quicker analysis.
      dtplyr::lazy_dt() %>% 
      # Note the use of !!! to inject aGrpSyms back into an evaluation context.
      dplyr::group_by(PLT_CN, !!!aGrpSyms) %>%
      # Calculate proportion of area in the plot that meets the current area domain 
      dplyr::summarize(PROP_FOREST = sum(CONDPROP_UNADJ * aDI, na.rm = TRUE)) %>%
      # Convert to data frame
      as.data.frame()

    # TPA and BAA for each plot. 
    t <- data %>% 
      # Set the YEAR to the measurement year for plot-level estimates. 
      dplyr::mutate(YEAR = MEASYEAR) %>% 
      dplyr::distinct(PLT_CN, SUBP, TREE, .keep_all = TRUE) %>% 
      dtplyr::lazy_dt() %>% 
      dplyr::group_by(!!!grpSyms, PLT_CN) %>%  
      dplyr::summarize(TPA = sum(TPA_UNADJ * tDI, na.rm = TRUE), 
                       BAA = sum(basalArea(DIA) * TPA_UNADJ * tDI, na.rm = TRUE)) %>% 
      as.data.frame() %>% 
      dplyr::left_join(a, by = c('PLT_CN', aGrpBy)) %>% 
      dplyr::distinct()

    # Make it spatial if the user wants it.
    if (returnSpatial) {
      t <- t %>% 
        dplyr::filter(!is.na(LAT) & !is.na(LON)) %>% 
        sf::st_as_sf(coords = c('LON', 'LAT'), 
                     crs = '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')
      grpBy <- grpBy[grpBy %in% c('LAT', 'LON') == FALSE]
    }

    out <- list(tEst = t, grpBy = grpBy, aGrpBy = aGrpBy)

  } else {
    # Population estimation or prep for it (treeList) ---------------------
    # Create a list of symbols for the grpBy statements
    aGrpSyms <- rlang::syms(aGrpBy)

    # Condition list
    a <- data %>% 
      # Will be lots of trees here, so CONDPROP is listed multiple times, the 
      # distinct is needed to just get those distinct ones. 
      dplyr::distinct(PLT_CN, CONDID, .keep_all = TRUE) %>% 
      dplyr::mutate(fa = CONDPROP_UNADJ * aDI) %>% 
      dplyr::select(PLT_CN, AREA_BASIS = PROP_BASIS, CONDID, !!!aGrpSyms, fa)

    # Create list of symols for the grpBy statements
    grpSyms <- rlang::syms(grpBy)
    # Tree list
    t <- data %>% 
      dplyr::distinct(PLT_CN, SUBP, TREE, .keep_all = TRUE) %>% 
      dtplyr::lazy_dt() %>% 
      dplyr::mutate(tPlot = TPA_UNADJ * tDI, 
                    bPlot = basalArea(DIA) * TPA_UNADJ * tDI) %>% 
      # Need a code that tells us where the tree was measured 
      # (macroplot, microplot, subplot)
      dplyr::mutate(
        TREE_BASIS = dplyr::case_when(
          # When DIA is NA, adjustment is NA
          is.na(DIA) ~ NA_character_,
          # When DIA is less than 5", use microplot value
          DIA < 5 ~ 'MICR',
          # Use SUBP when macroplot breakpoint DIA is non-positive
          MACRO_BREAKPOINT_DIA <= 0 ~ 'SUBP',
          # When DIA is greater than 5", use subplot value, as long 
          # as diameter is less than the minimum dia on macroplots
          DIA >= 5 & is.na(MACRO_BREAKPOINT_DIA) ~ 'SUBP',
          DIA >= 5 & DIA < MACRO_BREAKPOINT_DIA ~ 'SUBP',
          # Use macroplot if DIA >= the min dbh measured on macroplots
          DIA >= MACRO_BREAKPOINT_DIA ~ 'MACR'
        )
      ) %>% 
      dplyr::filter(!is.na(TREE_BASIS)) %>% 
      dplyr::select(PLT_CN, TREE_BASIS, SUBP, TREE, !!!grpSyms, tPlot, bPlot) %>% 
      as.data.frame()

    # Return a tree/condition list ready to be handed to `customPSE()`
    if (treeList) {
      tEst <- a %>% 
        dplyr::left_join(t, by = c('PLT_CN', aGrpBy)) %>% 
        dplyr::mutate(EVAL_TYP = 'VOL') %>% 
        dplyr::select(PLT_CN, EVAL_TYP, TREE_BASIS, AREA_BASIS, 
                      !!!grpSyms, CONDID, SUBP, TREE, 
                      TPA = tPlot, BAA = bPlot, PROP_FOREST = fa)

      out <- list(tEst = tEst, aEst = NULL, grpBy = grpBy, aGrpBy = aGrpBy)

    } else {
      # If a tree list is not desired, let's move to population estimation.
      # Sum variable(s) up to plot-level and adjust for non-response
      tPlt <- sumToPlot(t, pops, grpBy)
      aPlt <- sumToPlot(a, pops, aGrpBy)

      # Add YEAR to groups
      grpBy <- c('YEAR', grpBy)
      aGrpBy <- c('YEAR', aGrpBy)

      # Sum variable(s) up to strata then estimation unit level
      eu.sums <- sumToEU(db, tPlt, aPlt, pops, grpBy, aGrpBy, method, lambda)
      tEst <- eu.sums$x
      aEst <- eu.sums$y
   
      out <- list(tEst = tEst, aEst = aEst, grpBy = grpBy, aGrpBy = aGrpBy) 
    } # population or tree-level estimation
  } # Population or plot-level estimation

  return(out)
} # tpaStarter