INFCvpe: Estimate bole volume or tree phytomass for individual stems,...

In ForIT: Functions to Estimate Tree Volume and Phytomass in the Italian Forest Inventory 2005

Estimate bole volume or tree phytomass for individual stems, with associated accuracy info

Description

Using the functions developed for INFC 2005 (the 2005 Italian national forest inventory), stem volume or tree compartment phytomass are estimated for each (EPPOcode, dbh.cm, htot.m) input tuple. Accompaining the main value, accuracy estimates are returned, as attributes. The functions are documented in Tabacchi et al. (2011a)

Usage

INFCvpe(EPPOcode, dbh.cm, htot.m, quantity = "vol")

Arguments

EPPOcode	Character vector of tree species code, as defined in EPPO database, (See INFCspecies() and https://gd.eppo.int )
dbh.cm	Numeric vector of stem/s breast height diameter (in cm)
htot.m	Numeric vector of tree total height/s (in m). Length equal to dbh.cm vector or one. In this case same value will be replicated for all dbh.cm entries
quantity	(default = vol) Character vector specifying required quantity, one of c("vol", "dw1" : "dw4"). Use qantities() to retrieve codes definitions. Length equal to dbh.cm vector or one. In this case same value will be replicated for all dbh.cm entries

Details

Output value will have following added attributes with estimates accuracy evaluations for each stem:

1. pag - page number, referred to original source

2. wrv - weighted residual variance

3. Var_ea - variance for an estimated average
   or variance for 'confidence interval' estimation, see prediction.lm(.., interval = "confidence")

4. Var_ie - variance for an individual estimate
   or 'prediction variance', (see prediction.lm(.., interval = "prediction") and Freese, 1964 - in:Tabacchi, 2011

5. InDomain - logical indicating whether the (dbh, htot) point lies out of the domain explored by the experimental data (see 'INFCtabulate()')

Value

The functions returns a Numeric vector of the same length of the dbh.cm argument, with accuracy info as attributes

See Also

INFCvpe_summarise() and functions related to INFCvpe_sum() to produce estimates of aggregates with better accuracy evaluation

Examples

# COMPARE WITH Tabacchi (2011a) page 25 ----
(v <- INFCvpe("ACRCA", dbh.cm = 22, htot.m = 14))
# [1] 252.9581
# attr(,"pag")
# [1] 231
# attr(,"wrv")
# [1] 2.271e-05
# attr(,"Var_ea")
# [1] 33.17182
# attr(,"Var_ie")
# [1] 1075.883
# attr(,"InDomain")
# [1] TRUE

# Standard Error of the Estimate
see <- sqrt(attr(v, "Var_ie"))
# Degrees of freedom
df <- INFCcatalog$n_oss[INFCcatalog$pag == attr(v, "pag")] -
      INFCcatalog$n_par[INFCcatalog$pag == attr(v, "pag")]
# confidence level
p <- 95/100
# Confidence Interval Half Width
cihw <- qt(1-(1-p)/2, df) * see
cat(" *** Volume confidence interval (p = ", p*100, "%) is [", round(v, 1),
    " +/- ", round(cihw, 1), "] dm^3\n", sep = "")

# ESTIMATION OF PHYTOMASS ----
Quantities[5,] %>% as.data.frame()
#   quantity         quantity_definition
# 1      dw4 phytomass of the whole tree [kg]
tree_phy <- INFCvpe(c("ACRCA", "ALUCO"),
                    dbh.cm = c(22, 15),
                    htot.m = c(14, 16),
                    quantity = "dw4")
tree_phy
# [1] 185.1291  87.7970
# attr(,"pag")
# [1] 231 245
# attr(,"wrv")
# [1] 3.142e-05 2.104e-05
# attr(,"Var_ea")
# [1] 45.89002  9.12407
# attr(,"Var_ie")
# [1] 1488.5135  281.8072
# attr(,"InDomain")
# [1] TRUE TRUE

# PROCESSING A TALLY DATA-FRAME ----
tst_vol <- ForIT_test_data %>%
  dplyr::mutate(vol = INFCvpe(specie, d130, h_dendro),
                OutOfDomain = !attr(vol, "InDomain"))
tst_vol %>%
  dplyr::filter(OutOfDomain)
tst_vol %>%
  dplyr::filter(UC == "U1")

# SUMS AND direct ACCUARACY AGGREGATION (instead of via ?INFCvpeSUM) ----
df <- function(pag) return(
  INFCcatalog %>%
    dplyr::right_join(tibble::tibble(pag = !!pag), by = "pag") %>%
    dplyr::transmute(df = n_oss - n_par) %>%
    purrr::pluck(1)
)
p <- 95/100
tst_vol %>%
  dplyr::mutate(cihw = qt(1-(1-p)/2,
                          df(attr(vol, "pag"))) *
                  sqrt(attr(vol, "Var_ie"))
  ) %>%
  dplyr::filter(!OutOfDomain) %>%
  dplyr::group_by(specie) %>%
  dplyr::summarise(.groups = "drop",
                   est = sum(vol),
                   cihw = sqrt(sum(cihw^2)),
  ) %>%
  dplyr::left_join(INFCspecies %>% dplyr::select(EPPOcode, pag),
                   by = c("specie" = "EPPOcode")) %>%
  dplyr::left_join(INFCcatalog %>% dplyr::select(pag, section),
                   by = "pag") %>% 
  dplyr::select(-c(specie, pag)) %>% 
  dplyr::rename(specie = section) %>% 
  dplyr::mutate(dplyr::across(c("est", "cihw"), ~round(.x, 1))) %>% 
  dplyr::arrange(specie) %>%
  dplyr::select(specie, est, cihw) -> tab
tab[c(2,1,3),] %>% 
  t()
rm(tst_vol, tab, df)

ForIT documentation built on Sept. 27, 2023, 1:07 a.m.