5 Comparing wood density databases

5.1 Introduction

This script compares biomass estimates derived from different wood density databases. We use the Global Wood Database (Chave et al. 2009), available through the BIOMASS package, and the Brazilian Woods Database (https://lpf.florestal.gov.br/en-us/brazilian-woods) from Brazilian Forest Service.

5.2 Setup

Code

library(dplyr)
library(sf)
library(ggplot2)
library(ggrepel)
library(readr)
library(rnaturalearth)
library(rnaturalearthdata)
library(BIOMASS)
library(kableExtra)

5.3 Comparing different wood databases

BWD has many samples collected in Jamari Forest, as we can see below. We obtained the basic wood density information (g/m³) and the sample collection location for each species from the BWD website. There is no specific coordinate for the sampling location; therefore, we assigned a central coordinate to each collection location in order to determine the geographic region of origin of the sample.

Code

bwd <- read.csv("data/wood_density/tabela_densidade_coordenadas.csv")

bwd_clean <- bwd %>%
  filter(!is.na(long), !is.na(lat))

pts_bwd <- st_as_sf(
  bwd_clean,
  coords = c("long", "lat"),
  crs = 4326   # WGS84 (graus decimais)
)

pts_bwd_count <- pts_bwd %>%
  st_coordinates() %>%
  as.data.frame() %>%
  group_by(X, Y) %>%
  summarise(n = n()) %>%
  ungroup() %>%
  st_as_sf(coords = c("X", "Y"), crs = st_crs(pts_bwd))

brasil <- ne_countries(
  scale = "medium",
  country = "Brazil",
  returnclass = "sf"
)

ggplot() +
  geom_sf(data = brasil, 
          fill = "gray95", 
          color = "gray40") +
  geom_sf(data = pts_bwd_count, 
          aes(size = n), 
          color = "red", 
          alpha = 0.7) +
  geom_text_repel(
    data = pts_bwd_count,
    aes(label = n, 
        geometry = geometry),
    stat = "sf_coordinates",
    size = 3,
    min.segment.length = 0
  ) +
  scale_size_continuous(name = "Number of samples") +
  coord_sf() +
  theme_minimal() +
  labs(
    title = paste("Brazilian woods database samples locations (",
                  nrow(pts_bwd),
                  "samples )."),
            x = "Longitude",
            y = "Latitude"
  )

5.4 Estimating biomass with BWD

We will use permanent plots measurements from Jamari National Forest. The Wood density estimates presented here are only for species the species present in the permanent plots.

In BWD there are multiple samples for the same species. We calculated mean basic wood density for each species with multiple samples. Then we converted bwd table to BIOMASS wood density table format.

We retrieved wood density from Gobal Wood Database (Chave et al. 2009) using BIOMASS.

Code

pps <- read.csv("output/pps/tabelao_pps.csv")

message("Permanent plots of APUs: \n",paste(unique(paste(pps$UMF,
                                                         pps$UPA)),
                                                         collapse = "\n"
                                                         ))

Permanent plots of APUs: 
UMF_I UPA_1
UMF_I UPA_10
UMF_I UPA_13
UMF_I UPA_2
UMF_I UPA_3
UMF_I UPA_4
UMF_I UPA_5
UMF_I UPA_6
UMF_I UPA_7
UMF_I UPA_8
UMF_I UPA_9
UMF_IV UPA_15

Code

message("Years of measurement:\n",paste(unique(pps$p23_cdmedicao),
                                         collapse = "\n"))

Years of measurement:
2010
2013
2016
2018
2021
2015
2017
2023
2020
2022
2011
2012
2014
2019

Code

bwd <- bwd %>%
  group_by(nome_cientifico) %>%
  summarise(mean_wd = mean(densidade_basica), 
            .groups = "drop")

bwd_biomass <- bwd %>%
  tidyr::separate("nome_cientifico",
                  c("genero","epiteto"),
                  " ")
names(bwd_biomass) <- c("genus","species","wd")

taxo <- data.frame(
  genusCorrected   = pps$genero,
  speciesCorrected = pps$epiteto,
  stringsAsFactors = FALSE
)

w_density <- getWoodDensity(genus = taxo$genusCorrected,
                            species = taxo$speciesCorrected)

The reference dataset contains 16467 wood density values

Your taxonomic table contains 104 taxa

Code

densidades <- bwd_biomass %>%
                  inner_join(w_density,
                  by = c("genus",
                         "species")) %>%
                  select(genus,
                         species,
                         WD_global = meanWD,
                         WD_brazil = wd)

densidades <- unique(densidades)

ggplot(densidades, aes(x = WD_global, 
                       y = WD_brazil)) +
  geom_point(alpha = 0.7) +
  geom_abline(slope = 1, 
              intercept = 0, 
              linetype = "dashed") +
  labs(
    x = "Global Wood density database",
    y = "Brazilian Woods density database",
    title = paste("Comparison of wood density estimates for",
                  nrow(densidades),
                  "species between BWD and GWD")
  ) +
  theme_minimal()

Code

text <- paste("BWD has wood density of",
              length(unique(bwd$nome_cientifico)),
              "species.",
              "BWD and GWD share", 
              nrow(densidades),
              "species in common.")

Note

BWD has wood density of 275 species. BWD and GWD share 55 species in common.

We can use Bland-Altman graphic to see differences between wood density databases. I will highlight species that are out of limits of agreement.

Code

densidades$mean_density <- (densidades$WD_global + densidades$WD_brazil) / 2
densidades$diff_density <- densidades$WD_global - densidades$WD_brazil

bias <- mean(densidades$diff_density, 
             na.rm = TRUE)
sd_diff <- sd(densidades$diff_density, 
              na.rm = TRUE)

loa_inf <- bias - 1.96 * sd_diff
loa_sup <- bias + 1.96 * sd_diff

densidades <- densidades %>%
                 mutate(fora_loa = diff_density < loa_inf | diff_density > loa_sup)

densidades$especie <- paste(densidades$genus,
                            densidades$species,
                            sep = " ")

ggplot(densidades, aes(x = mean_density, 
                       y = diff_density,
                       color = especie)) +
  geom_point(alpha = 0.7) +
  geom_hline(yintercept = mean(densidades$diff_density,
                               na.rm = TRUE),
             linetype = "dashed") +
  geom_hline(yintercept = mean(densidades$diff_density,
                               na.rm = TRUE) +
                           1.96 * sd(densidades$diff_density,
                                     na.rm = TRUE),
             linetype = "dotted") +
  geom_hline(yintercept = mean(densidades$diff_density,
                               na.rm = TRUE) -
                           1.96 * sd(densidades$diff_density,
                                     na.rm = TRUE),
             linetype = "dotted") +
    geom_text_repel(
    data = subset(densidades, fora_loa),
    aes(label = especie),
    size = 3,
    show.legend = FALSE
  ) +
  labs(
    x = "Mean wood density",
    y = "Difference between methods",
    title = "Bland–Altman: wood density databases"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

Let’s see the difference in biomass estimation.

Total tree height was obtained using the retrieveH function from the BIOMASS package, based on the central coordinates of the Jamari National Forest (Flona de Jamari). Biomass was estimated using the computeAGB function from the BIOMASS package, using the total height derived from retrieveH and the two wood density databases (the Global Wood Density Database and the Brazilian Wood Density Database). The BWD does not provide estimates of basic wood density for all species. When species-level values are unavailable, estimates from Global Wood Database will be used.

Each point represents one year of measurement for a plot. I will highlight plots with biomass estimation out of limits of agreement.

Code

w_density_BR <- left_join(w_density, 
                          bwd_biomass, 
                          by = c("species",
                                 "genus"), 
                          multiple = "any") %>% 
                
                mutate(wd = ifelse(!is.na(wd), 
                                   wd, 
                                   meanWD)) %>% 
                select(family, 
                       genus, 
                       species, 
                       wd)


altura <- retrieveH(D = pps$diametrocm,
                    coord = c(-62.99,
                                   -9.11
                                   ))


pps$altura <- altura$H

pps$AGB_Global <- computeAGB(D = pps$diametrocm,
                             WD = w_density$meanWD,
                             H = pps$altura)

pps$AGB_Brazilian <- computeAGB(D = pps$diametrocm,
                                WD = w_density_BR$wd,
                                H = pps$altura)

pps$plot <- paste(pps$UMF,pps$UPA,
                  "plot",
                  pps$p23_cdparcela,
                  pps$p23_cdmedicao)


pps_plots <- pps %>% 
              group_by(plot,
                       p23_cdmedicao) %>% 
              summarise("AGB_Global" = sum(AGB_Global),
                        "AGB_Brazilian" = sum(AGB_Brazilian))

pps_plots$mean_density <- (pps_plots$AGB_Global + pps_plots$AGB_Brazilian) / 2
pps_plots$diff_density <- pps_plots$AGB_Global - pps_plots$AGB_Brazilian

bias <- mean(pps_plots$diff_density, 
             na.rm = TRUE)
sd_diff <- sd(pps_plots$diff_density, 
              na.rm = TRUE)

loa_inf <- bias - 1.96 * sd_diff
loa_sup <- bias + 1.96 * sd_diff

pps_plots <- pps_plots %>%
            mutate(fora_loa = diff_density < loa_inf | diff_density > loa_sup)


ggplot(pps_plots, aes(x = mean_density, 
                      y = diff_density,
                      color = plot)) +
  geom_point(alpha = 0.7) +
  geom_hline(yintercept = mean(pps_plots$diff_density,
                               na.rm = TRUE),
             linetype = "dashed") +
  geom_hline(yintercept = mean(pps_plots$diff_density,
                               na.rm = TRUE) +
                           1.96 * sd(pps_plots$diff_density,
                                     na.rm = TRUE),
             linetype = "dotted") +
  geom_hline(yintercept = mean(pps_plots$diff_density,
                               na.rm = TRUE) -
                           1.96 * sd(pps_plots$diff_density,
                                     na.rm = TRUE),
             linetype = "dotted") +
    geom_text_repel(
    data = subset(pps_plots, 
                  fora_loa),
    aes(label = plot),
    size = 3,
    show.legend = FALSE
  ) +
  labs(
    x = "Mean plot AGB",
    y = "Difference between methods",
    title = "Bland-Altman: Plot AGB estimates with different wood density databases"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

Code

dif_AGBs <- pps_plots %>%
  mutate(
    diff = abs(AGB_Global - AGB_Brazilian),
    diff_perc = (diff / AGB_Global) * 100
  ) %>%
  select(plot, AGB_Global, AGB_Brazilian, diff, diff_perc) %>%
  arrange(desc(diff_perc)) %>%
  mutate(diff_perc = round(diff_perc, 2))


  kable(dif_AGBs,
    caption = "Absolute and relative difference between AGBs",
    col.names = c("Plot", "AGB Global", "AGB Brazilian", "Difference", "Difference (%)")
  ) %>%
  kable_styling(full_width = FALSE, position = "center") %>%
    kableExtra::scroll_box(height = "400px", width = "100%")

Absolute and relative difference between AGBs
Plot	AGB Global	AGB Brazilian	Difference	Difference (%)
UMF_I UPA_2 plot 8 2011	49.78417	45.76349	4.0206756	8.08
UMF_I UPA_2 plot 8 2013	55.63526	51.35464	4.2806251	7.69
UMF_I UPA_2 plot 8 2016	61.40135	56.92133	4.4800279	7.30
UMF_I UPA_5 plot 9 2019	109.08370	101.74244	7.3412640	6.73
UMF_I UPA_2 plot 8 2021	73.91046	69.07685	4.8336139	6.54
UMF_I UPA_7 plot 4 2020	88.43654	93.77966	5.3431237	6.04
UMF_I UPA_7 plot 4 2019	88.39637	93.73296	5.3365841	6.04
UMF_I UPA_5 plot 9 2013	111.49535	104.78220	6.7131586	6.02
UMF_I UPA_5 plot 9 2015	112.98623	106.31004	6.6761913	5.91
UMF_I UPA_8 plot 1 2017	124.77302	117.94769	6.8253253	5.47
UMF_I UPA_9 plot 1 2017	124.77302	117.94769	6.8253253	5.47
UMF_I UPA_8 plot 1 2019	126.01309	119.18066	6.8324362	5.42
UMF_I UPA_9 plot 1 2019	126.01309	119.18066	6.8324362	5.42
UMF_I UPA_4 plot 9 2012	140.66985	133.49095	7.1788979	5.10
UMF_I UPA_6 plot 1 2014	175.80782	166.87583	8.9319891	5.08
UMF_I UPA_10 plot 4 2023	62.14742	65.22646	3.0790489	4.95
UMF_I UPA_4 plot 9 2014	143.46225	136.38440	7.0778559	4.93
UMF_I UPA_2 plot 2 2016	71.63603	75.05387	3.4178394	4.77
UMF_I UPA_1 plot 5 2010	55.47410	58.11771	2.6436094	4.77
UMF_I UPA_2 plot 2 2021	80.44123	84.21434	3.7731018	4.69
UMF_I UPA_5 plot 8 2019	99.86790	104.50618	4.6382782	4.64
UMF_I UPA_10 plot 4 2015	63.31582	66.16888	2.8530553	4.51
UMF_I UPA_10 plot 4 2017	65.12840	68.05299	2.9245835	4.49
UMF_I UPA_10 plot 3 2015	56.63193	59.12236	2.4904342	4.40
UMF_I UPA_10 plot 3 2017	58.55062	61.02998	2.4793597	4.23
UMF_I UPA_13 plot 3 2020	62.05757	64.55164	2.4940658	4.02
UMF_I UPA_13 plot 3 2022	64.53398	67.12730	2.5933256	4.02
UMF_I UPA_5 plot 8 2013	94.08163	97.77032	3.6886900	3.92
UMF_I UPA_5 plot 8 2015	96.84418	100.61929	3.7751154	3.90
UMF_I UPA_6 plot 1 2016	131.47873	126.41455	5.0641793	3.85
UMF_I UPA_2 plot 1 2011	98.18137	94.53309	3.6482772	3.72
UMF_I UPA_2 plot 1 2016	108.69576	104.71439	3.9813761	3.66
UMF_I UPA_2 plot 1 2021	121.96488	117.51318	4.4516999	3.65
UMF_I UPA_2 plot 1 2013	105.35751	101.52762	3.8298872	3.64
UMF_I UPA_5 plot 3 2019	48.23569	49.90397	1.6682865	3.46
UMF_I UPA_6 plot 9 2014	67.70705	70.03345	2.3264038	3.44
UMF_I UPA_8 plot 4 2017	78.54076	75.87703	2.6637348	3.39
UMF_I UPA_9 plot 4 2017	78.54076	75.87703	2.6637348	3.39
UMF_I UPA_6 plot 9 2016	71.71043	74.10109	2.3906627	3.33
UMF_I UPA_8 plot 4 2019	80.51725	77.86603	2.6512117	3.29
UMF_I UPA_9 plot 4 2019	80.51725	77.86603	2.6512117	3.29
UMF_I UPA_5 plot 7 2019	41.30085	42.60685	1.3060015	3.16
UMF_I UPA_8 plot 6 2019	92.84010	95.71768	2.8775724	3.10
UMF_I UPA_9 plot 6 2019	92.84010	95.71768	2.8775724	3.10
UMF_IV UPA_15 plot 3 2020	50.75750	52.32646	1.5689553	3.09
UMF_I UPA_8 plot 6 2017	89.49599	92.23748	2.7414901	3.06
UMF_I UPA_9 plot 6 2017	89.49599	92.23748	2.7414901	3.06
UMF_IV UPA_15 plot 3 2022	52.16722	53.76304	1.5958216	3.06
UMF_I UPA_4 plot 3 2012	40.24498	41.46130	1.2163188	3.02
UMF_I UPA_7 plot 2 2020	46.42593	47.78968	1.3637562	2.94
UMF_I UPA_1 plot 5 2013	60.07885	61.83601	1.7571584	2.92
UMF_I UPA_6 plot 1 2020	117.53119	114.16292	3.3682747	2.87
UMF_I UPA_4 plot 3 2014	43.32220	44.56010	1.2378913	2.86
UMF_I UPA_4 plot 7 2012	83.91347	81.52543	2.3880359	2.85
UMF_I UPA_1 plot 1 2010	141.59067	137.59897	3.9916962	2.82
UMF_I UPA_7 plot 2 2019	46.62393	47.92261	1.2986812	2.79
UMF_I UPA_10 plot 5 2015	38.72413	37.64722	1.0769117	2.78
UMF_I UPA_1 plot 6 2010	65.29853	67.08520	1.7866696	2.74
UMF_I UPA_5 plot 7 2015	70.60840	72.53069	1.9222901	2.72
UMF_I UPA_1 plot 1 2013	148.99734	144.96700	4.0303350	2.70
UMF_I UPA_4 plot 7 2014	87.17371	84.84377	2.3299357	2.67
UMF_I UPA_5 plot 7 2013	68.70582	70.53507	1.8292548	2.66
UMF_I UPA_3 plot 6 2012	69.67803	67.83784	1.8401950	2.64
UMF_I UPA_10 plot 5 2017	40.46484	39.40227	1.0625623	2.63
UMF_I UPA_13 plot 2 2022	70.29663	72.12250	1.8258754	2.60
UMF_I UPA_3 plot 6 2013	73.11365	71.21718	1.8964629	2.59
UMF_I UPA_3 plot 6 2023	77.08552	75.09241	1.9931084	2.59
UMF_I UPA_4 plot 3 2018	48.82881	50.08417	1.2553580	2.57
UMF_I UPA_13 plot 2 2020	68.37757	70.09773	1.7201612	2.52
UMF_I UPA_3 plot 6 2017	73.11969	71.28398	1.8357061	2.51
UMF_I UPA_4 plot 5 2018	78.34285	80.30077	1.9579185	2.50
UMF_I UPA_6 plot 9 2020	65.41002	67.04035	1.6303362	2.49
UMF_I UPA_1 plot 4 2010	78.28289	80.22775	1.9448626	2.48
UMF_I UPA_4 plot 3 2023	53.59144	54.91902	1.3275800	2.48
UMF_I UPA_4 plot 5 2014	71.53291	73.28962	1.7567086	2.46
UMF_I UPA_4 plot 7 2018	93.20928	90.92654	2.2827417	2.45
UMF_I UPA_5 plot 3 2015	63.98371	65.54751	1.5638015	2.44
UMF_I UPA_2 plot 2 2011	138.44420	141.81538	3.3711764	2.44
UMF_I UPA_4 plot 5 2012	68.39018	70.05366	1.6634802	2.43
UMF_I UPA_1 plot 3 2016	103.81550	106.34005	2.5245486	2.43
UMF_I UPA_1 plot 4 2018	73.86184	75.63370	1.7718551	2.40
UMF_I UPA_6 plot 3 2020	56.57611	57.92632	1.3502119	2.39
UMF_I UPA_2 plot 2 2013	144.59019	147.97494	3.3847444	2.34
UMF_I UPA_1 plot 4 2021	74.88661	76.63932	1.7527131	2.34
UMF_I UPA_5 plot 3 2013	61.97244	63.41857	1.4461294	2.33
UMF_I UPA_3 plot 7 2023	89.55115	87.47834	2.0728058	2.31
UMF_I UPA_3 plot 7 2017	83.44802	81.54640	1.9016255	2.28
UMF_I UPA_4 plot 9 2023	64.19795	62.73879	1.4591629	2.27
UMF_I UPA_1 plot 2 2010	53.79504	55.01628	1.2212372	2.27
UMF_I UPA_1 plot 8 2021	61.87745	63.27444	1.3969969	2.26
UMF_I UPA_3 plot 8 2023	83.69963	85.58468	1.8850439	2.25
UMF_I UPA_8 plot 5 2019	34.47048	35.23482	0.7643388	2.22
UMF_I UPA_9 plot 5 2019	34.47048	35.23482	0.7643388	2.22
UMF_I UPA_1 plot 3 2010	99.65737	101.84870	2.1913247	2.20
UMF_I UPA_1 plot 8 2016	56.88342	58.12381	1.2403869	2.18
UMF_I UPA_1 plot 8 2018	59.28152	60.56873	1.2872083	2.17
UMF_I UPA_3 plot 4 2023	79.33470	77.61907	1.7156268	2.16
UMF_I UPA_1 plot 3 2013	105.75754	108.03683	2.2792864	2.16
UMF_I UPA_4 plot 6 2018	62.57151	63.91201	1.3404997	2.14
UMF_I UPA_1 plot 4 2016	76.24741	77.87986	1.6324443	2.14
UMF_I UPA_1 plot 9 2010	90.62348	92.55793	1.9344442	2.13
UMF_I UPA_3 plot 7 2012	82.01588	80.26592	1.7499671	2.13
UMF_I UPA_3 plot 7 2013	85.42467	83.60381	1.8208562	2.13
UMF_I UPA_1 plot 3 2018	105.37675	107.62059	2.2438437	2.13
UMF_I UPA_1 plot 3 2021	110.20383	112.51870	2.3148729	2.10
UMF_I UPA_3 plot 4 2017	77.02731	75.41305	1.6142586	2.10
UMF_I UPA_8 plot 3 2017	91.84758	93.75445	1.9068729	2.08
UMF_I UPA_9 plot 3 2017	91.84758	93.75445	1.9068729	2.08
UMF_I UPA_8 plot 3 2019	93.68835	95.62978	1.9414310	2.07
UMF_I UPA_9 plot 3 2019	93.68835	95.62978	1.9414310	2.07
UMF_I UPA_4 plot 7 2023	85.31005	83.55186	1.7581920	2.06
UMF_I UPA_8 plot 5 2017	30.83416	31.46883	0.6346720	2.06
UMF_I UPA_9 plot 5 2017	30.83416	31.46883	0.6346720	2.06
UMF_I UPA_13 plot 6 2022	86.40409	88.18142	1.7773310	2.06
UMF_I UPA_13 plot 6 2020	84.81513	86.55165	1.7365152	2.05
UMF_I UPA_3 plot 1 2017	93.58411	91.68251	1.9015995	2.03
UMF_I UPA_4 plot 5 2023	82.27265	83.93260	1.6599519	2.02
UMF_I UPA_6 plot 7 2020	64.48849	65.78338	1.2948954	2.01
UMF_I UPA_3 plot 1 2013	92.12634	90.34460	1.7817429	1.93
UMF_I UPA_2 plot 6 2021	88.92697	87.20792	1.7190527	1.93
UMF_I UPA_3 plot 1 2012	89.74763	88.01856	1.7290652	1.93
UMF_I UPA_3 plot 1 2023	98.74674	96.84718	1.8995586	1.92
UMF_I UPA_4 plot 8 2023	62.91237	64.11419	1.2018205	1.91
UMF_I UPA_1 plot 4 2013	84.71152	86.32938	1.6178511	1.91
UMF_I UPA_2 plot 6 2013	72.39286	71.01508	1.3777797	1.90
UMF_I UPA_2 plot 6 2016	79.88236	78.36775	1.5146101	1.90
UMF_I UPA_7 plot 3 2020	37.75361	38.46851	0.7148984	1.89
UMF_I UPA_7 plot 3 2019	37.58997	38.30057	0.7106029	1.89
UMF_I UPA_1 plot 6 2018	79.22584	77.73220	1.4936418	1.89
UMF_I UPA_7 plot 6 2020	76.84243	78.26924	1.4268055	1.86
UMF_I UPA_7 plot 6 2019	76.45739	77.87579	1.4183963	1.86
UMF_I UPA_6 plot 4 2020	55.27042	56.29516	1.0247416	1.85
UMF_I UPA_4 plot 6 2014	57.15729	58.20641	1.0491145	1.84
UMF_I UPA_6 plot 8 2014	80.01950	81.47478	1.4552807	1.82
UMF_I UPA_2 plot 4 2011	96.16023	94.41840	1.7418342	1.81
UMF_I UPA_2 plot 6 2011	66.75033	65.54787	1.2024664	1.80
UMF_I UPA_2 plot 4 2013	100.56273	98.77820	1.7845297	1.77
UMF_I UPA_1 plot 9 2021	108.15476	110.06666	1.9118998	1.77
UMF_I UPA_4 plot 6 2012	53.90106	54.85001	0.9489575	1.76
UMF_I UPA_10 plot 8 2015	76.60485	77.95310	1.3482513	1.76
UMF_I UPA_1 plot 6 2016	77.12099	75.76869	1.3523052	1.75
UMF_I UPA_10 plot 8 2017	78.26024	79.62969	1.3694572	1.75
UMF_I UPA_1 plot 9 2018	103.01691	104.81830	1.8013842	1.75
UMF_I UPA_6 plot 8 2016	82.73919	84.18556	1.4463757	1.75
UMF_I UPA_3 plot 2 2023	87.55731	86.05718	1.5001349	1.71
UMF_I UPA_4 plot 9 2018	63.08077	62.01486	1.0659035	1.69
UMF_I UPA_3 plot 8 2012	76.48495	77.77480	1.2898512	1.69
UMF_I UPA_4 plot 8 2018	61.43267	62.45680	1.0241310	1.67
UMF_I UPA_3 plot 2 2017	84.48085	83.08389	1.3969600	1.65
UMF_I UPA_1 plot 2 2016	65.67454	66.75800	1.0834597	1.65
UMF_I UPA_1 plot 9 2013	95.79155	97.35707	1.5655202	1.63
UMF_I UPA_5 plot 5 2015	64.84191	65.89449	1.0525843	1.62
UMF_I UPA_5 plot 5 2013	63.19198	64.21549	1.0235017	1.62
UMF_I UPA_5 plot 10 2015	69.19741	70.31497	1.1175542	1.62
UMF_I UPA_2 plot 7 2016	70.90359	69.76314	1.1404425	1.61
UMF_I UPA_3 plot 8 2013	79.42413	80.69837	1.2742404	1.60
UMF_I UPA_13 plot 1 2022	78.01820	79.26652	1.2483240	1.60
UMF_I UPA_4 plot 6 2023	64.34666	65.37483	1.0281715	1.60
UMF_I UPA_1 plot 2 2021	67.56413	68.64286	1.0787234	1.60
UMF_I UPA_13 plot 5 2020	45.56140	46.28546	0.7240602	1.59
UMF_I UPA_13 plot 8 2022	52.23611	53.06279	0.8266777	1.58
UMF_I UPA_5 plot 10 2013	66.57363	67.62568	1.0520450	1.58
UMF_I UPA_13 plot 1 2020	76.25764	77.45406	1.1964251	1.57
UMF_I UPA_1 plot 6 2013	71.46023	70.34148	1.1187464	1.57
UMF_I UPA_13 plot 5 2022	47.39914	48.13703	0.7378831	1.56
UMF_I UPA_4 plot 4 2014	59.50858	60.43472	0.9261397	1.56
UMF_I UPA_13 plot 8 2020	50.98947	51.78075	0.7912733	1.55
UMF_I UPA_3 plot 8 2017	81.09508	82.33227	1.2371862	1.53
UMF_I UPA_4 plot 4 2012	56.87086	57.73384	0.8629793	1.52
UMF_I UPA_5 plot 10 2019	72.95890	74.05974	1.1008397	1.51
UMF_I UPA_10 plot 8 2023	73.56025	74.66892	1.1086706	1.51
UMF_I UPA_1 plot 9 2016	104.66734	106.23096	1.5636226	1.49
UMF_I UPA_2 plot 3 2016	70.50813	69.46436	1.0437755	1.48
UMF_I UPA_2 plot 7 2021	76.13251	75.00818	1.1243280	1.48
UMF_IV UPA_15 plot 2 2022	98.74045	97.28317	1.4572871	1.48
UMF_IV UPA_15 plot 2 2020	97.45259	96.01660	1.4359886	1.47
UMF_I UPA_1 plot 2 2018	68.20128	69.20472	1.0034468	1.47
UMF_I UPA_2 plot 3 2021	81.37186	80.18205	1.1898072	1.46
UMF_I UPA_2 plot 3 2013	69.79904	68.78026	1.0187847	1.46
UMF_I UPA_1 plot 2 2013	61.88458	62.78657	0.9019917	1.46
UMF_I UPA_6 plot 7 2016	68.70774	69.70901	1.0012759	1.46
UMF_I UPA_2 plot 7 2013	70.89011	69.85988	1.0302270	1.45
UMF_I UPA_6 plot 7 2014	67.47663	68.44021	0.9635778	1.43
UMF_I UPA_5 plot 1 2019	90.86566	92.16302	1.2973597	1.43
UMF_I UPA_2 plot 7 2011	65.49202	64.56168	0.9303363	1.42
UMF_I UPA_6 plot 4 2016	62.23487	63.11545	0.8805732	1.41
UMF_I UPA_6 plot 4 2014	57.61442	58.41779	0.8033667	1.39
UMF_I UPA_10 plot 7 2017	64.17284	65.06394	0.8910982	1.39
UMF_I UPA_4 plot 4 2018	65.74774	66.65737	0.9096273	1.38
UMF_I UPA_2 plot 3 2011	61.81673	60.96325	0.8534875	1.38
UMF_I UPA_10 plot 7 2015	62.10232	62.94780	0.8454744	1.36
UMF_I UPA_10 plot 7 2023	66.00121	66.89919	0.8979759	1.36
UMF_I UPA_3 plot 4 2013	88.96555	87.76129	1.2042664	1.35
UMF_IV UPA_15 plot 4 2020	49.51833	48.84890	0.6694356	1.35
UMF_I UPA_5 plot 4 2019	57.98692	58.76763	0.7807015	1.35
UMF_I UPA_3 plot 4 2012	86.21513	85.06654	1.1485848	1.33
UMF_I UPA_4 plot 8 2014	60.99722	61.79394	0.7967169	1.31
UMF_I UPA_5 plot 1 2015	96.62997	97.86797	1.2379950	1.28
UMF_IV UPA_15 plot 4 2022	51.02614	50.37884	0.6472966	1.27
UMF_I UPA_4 plot 1 2014	99.55331	100.78863	1.2353146	1.24
UMF_I UPA_4 plot 8 2012	56.95336	57.65442	0.7010619	1.23
UMF_I UPA_5 plot 1 2013	94.39891	95.55004	1.1511343	1.22
UMF_I UPA_13 plot 4 2022	82.50755	83.49894	0.9913893	1.20
UMF_I UPA_13 plot 4 2020	80.37445	81.33181	0.9573547	1.19
UMF_I UPA_4 plot 1 2012	96.85337	98.00653	1.1531593	1.19
UMF_I UPA_10 plot 3 2023	53.80592	54.44106	0.6351407	1.18
UMF_I UPA_6 plot 2 2020	43.71303	44.20093	0.4879055	1.12
UMF_I UPA_10 plot 2 2023	63.50069	62.79932	0.7013696	1.10
UMF_I UPA_6 plot 3 2016	68.01510	68.74598	0.7308836	1.07
UMF_I UPA_3 plot 5 2012	46.09271	45.59783	0.4948804	1.07
UMF_I UPA_3 plot 2 2013	90.47815	89.51823	0.9599262	1.06
UMF_I UPA_5 plot 4 2013	68.88582	69.60691	0.7210914	1.05
UMF_I UPA_3 plot 2 2012	86.64463	85.74108	0.9035441	1.04
UMF_I UPA_5 plot 4 2015	70.69376	71.42920	0.7354401	1.04
UMF_I UPA_1 plot 10 2010	78.02907	78.83287	0.8038040	1.03
UMF_I UPA_3 plot 5 2013	48.58948	48.10361	0.4858700	1.00
UMF_I UPA_7 plot 1 2020	44.15510	44.58483	0.4297336	0.97
UMF_I UPA_7 plot 1 2019	43.97294	44.39937	0.4264318	0.97
UMF_I UPA_6 plot 10 2016	42.77583	43.18735	0.4115190	0.96
UMF_I UPA_7 plot 7 2019	71.38729	70.71808	0.6692170	0.94
UMF_I UPA_10 plot 5 2023	39.22463	38.85817	0.3664532	0.93
UMF_I UPA_6 plot 10 2014	38.23924	38.59431	0.3550766	0.93
UMF_I UPA_7 plot 7 2020	71.77288	71.10683	0.6660560	0.93
UMF_I UPA_6 plot 8 2020	64.27557	64.86683	0.5912558	0.92
UMF_IV UPA_15 plot 1 2020	85.66747	84.89497	0.7725050	0.90
UMF_I UPA_4 plot 4 2023	63.04084	63.60904	0.5682029	0.90
UMF_I UPA_2 plot 4 2021	77.42662	76.74579	0.6808313	0.88
UMF_I UPA_6 plot 3 2014	66.90394	67.49047	0.5865315	0.88
UMF_IV UPA_15 plot 1 2022	87.35465	86.59366	0.7609898	0.87
UMF_I UPA_3 plot 3 2013	84.61047	83.87583	0.7346432	0.87
UMF_I UPA_1 plot 6 2021	64.89678	65.45626	0.5594741	0.86
UMF_I UPA_1 plot 5 2021	52.16414	51.71444	0.4496935	0.86
UMF_I UPA_3 plot 5 2017	50.05510	49.62707	0.4280239	0.86
UMF_I UPA_3 plot 3 2012	81.53275	80.83752	0.6952286	0.85
UMF_I UPA_3 plot 5 2023	51.86898	51.42948	0.4395053	0.85
UMF_I UPA_7 plot 8 2020	89.54130	90.29225	0.7509533	0.84
UMF_I UPA_7 plot 8 2019	89.34384	90.09226	0.7484156	0.84
UMF_I UPA_3 plot 3 2017	82.47439	81.78563	0.6887610	0.84
UMF_I UPA_3 plot 3 2023	87.47240	86.75832	0.7140807	0.82
UMF_I UPA_8 plot 7 2019	58.83860	59.30061	0.4620051	0.79
UMF_I UPA_9 plot 7 2019	58.83860	59.30061	0.4620051	0.79
UMF_I UPA_5 plot 6 2013	65.40087	64.90076	0.5001170	0.76
UMF_I UPA_6 plot 2 2014	42.16957	41.85613	0.3134444	0.74
UMF_I UPA_6 plot 5 2014	74.23097	73.68984	0.5411267	0.73
UMF_I UPA_5 plot 6 2015	67.84900	67.36446	0.4845461	0.71
UMF_I UPA_1 plot 5 2018	49.43925	49.08748	0.3517625	0.71
UMF_I UPA_5 plot 5 2019	45.13145	45.45032	0.3188698	0.71
UMF_I UPA_13 plot 7 2022	79.77982	80.33656	0.5567359	0.70
UMF_I UPA_13 plot 7 2020	77.39105	77.91421	0.5231639	0.68
UMF_I UPA_5 plot 6 2019	67.57095	67.12176	0.4491889	0.66
UMF_I UPA_6 plot 5 2016	73.14978	72.66495	0.4848302	0.66
UMF_I UPA_10 plot 1 2023	84.99371	85.53011	0.5363965	0.63
UMF_I UPA_1 plot 7 2010	48.37900	48.68136	0.3023567	0.62
UMF_I UPA_4 plot 10 2023	116.53078	115.80563	0.7251568	0.62
UMF_I UPA_8 plot 7 2017	55.68702	56.03201	0.3449874	0.62
UMF_I UPA_9 plot 7 2017	55.68702	56.03201	0.3449874	0.62
UMF_I UPA_4 plot 2 2018	54.00996	54.34087	0.3309095	0.61
UMF_I UPA_6 plot 2 2016	44.53567	44.26407	0.2715974	0.61
UMF_I UPA_4 plot 10 2012	112.79100	112.10639	0.6846067	0.61
UMF_I UPA_1 plot 10 2013	85.05974	85.56967	0.5099252	0.60
UMF_I UPA_6 plot 5 2020	83.75145	83.27018	0.4812609	0.57
UMF_I UPA_2 plot 9 2016	95.03303	94.53889	0.4941441	0.52
UMF_I UPA_4 plot 10 2014	116.95538	116.35725	0.5981315	0.51
UMF_I UPA_8 plot 2 2019	56.53209	56.81470	0.2826127	0.50
UMF_I UPA_9 plot 2 2019	56.53209	56.81470	0.2826127	0.50
UMF_I UPA_2 plot 4 2016	63.55154	63.23934	0.3121983	0.49
UMF_I UPA_3 plot 9 2023	78.08600	77.71997	0.3660287	0.47
UMF_I UPA_2 plot 9 2021	112.67888	112.15249	0.5263914	0.47
UMF_I UPA_1 plot 10 2016	93.31957	93.75176	0.4321846	0.46
UMF_I UPA_6 plot 10 2020	49.06983	49.29309	0.2232562	0.45
UMF_I UPA_2 plot 5 2021	104.99960	104.53147	0.4681390	0.45
UMF_I UPA_3 plot 10 2013	71.59298	71.27629	0.3166953	0.44
UMF_I UPA_10 plot 1 2017	91.30645	91.70979	0.4033415	0.44
UMF_I UPA_4 plot 2 2012	108.80715	108.32791	0.4792397	0.44
UMF_I UPA_4 plot 10 2018	110.48908	110.00539	0.4836951	0.44
UMF_I UPA_3 plot 9 2012	81.29350	81.64255	0.3490532	0.43
UMF_I UPA_1 plot 10 2018	94.52630	94.93131	0.4050089	0.43
UMF_I UPA_8 plot 2 2017	53.73655	53.96239	0.2258434	0.42
UMF_I UPA_9 plot 2 2017	53.73655	53.96239	0.2258434	0.42
UMF_I UPA_3 plot 9 2017	82.17611	82.51290	0.3367915	0.41
UMF_I UPA_3 plot 10 2012	68.93371	68.65249	0.2812111	0.41
UMF_I UPA_10 plot 1 2015	88.26045	88.61520	0.3547438	0.40
UMF_I UPA_2 plot 5 2016	92.29810	91.94119	0.3569125	0.39
UMF_I UPA_10 plot 6 2017	75.02884	74.74635	0.2824919	0.38
UMF_I UPA_3 plot 9 2013	83.69625	84.00952	0.3132760	0.37
UMF_I UPA_10 plot 6 2015	72.87267	72.60231	0.2703594	0.37
UMF_I UPA_1 plot 1 2016	68.44220	68.69547	0.2532788	0.37
UMF_I UPA_4 plot 2 2014	111.15618	110.77485	0.3813256	0.34
UMF_I UPA_2 plot 5 2013	82.59449	82.32679	0.2677017	0.32
UMF_I UPA_4 plot 1 2023	99.61186	99.91806	0.3062021	0.31
UMF_I UPA_2 plot 9 2013	84.28983	84.03138	0.2584545	0.31
UMF_I UPA_4 plot 2 2023	55.26882	55.43507	0.1662474	0.30
UMF_I UPA_8 plot 8 2019	62.52768	62.69680	0.1691232	0.27
UMF_I UPA_9 plot 8 2019	62.52768	62.69680	0.1691232	0.27
UMF_I UPA_1 plot 1 2018	71.14094	71.33313	0.1921936	0.27
UMF_I UPA_10 plot 2 2015	53.74205	53.88480	0.1427504	0.27
UMF_I UPA_3 plot 10 2023	71.49882	71.68077	0.1819545	0.25
UMF_I UPA_1 plot 7 2018	58.71161	58.56780	0.1438099	0.24
UMF_I UPA_6 plot 6 2020	49.78098	49.90245	0.1214775	0.24
UMF_I UPA_8 plot 8 2017	60.54031	60.67592	0.1356164	0.22
UMF_I UPA_9 plot 8 2017	60.54031	60.67592	0.1356164	0.22
UMF_I UPA_1 plot 7 2016	57.77350	57.65079	0.1227096	0.21
UMF_I UPA_2 plot 9 2011	74.85400	74.71161	0.1423887	0.19
UMF_I UPA_5 plot 2 2019	94.52580	94.35240	0.1734021	0.18
UMF_I UPA_5 plot 2 2013	89.07632	88.91380	0.1625188	0.18
UMF_I UPA_6 plot 6 2016	78.62709	78.75516	0.1280660	0.16
UMF_I UPA_5 plot 2 2015	90.92365	90.77653	0.1471140	0.16
UMF_I UPA_2 plot 10 2016	83.98366	83.85487	0.1287838	0.15
UMF_I UPA_2 plot 10 2013	77.00184	76.89262	0.1092198	0.14
UMF_I UPA_1 plot 5 2016	47.68992	47.75724	0.0673127	0.14
UMF_I UPA_1 plot 1 2021	76.89511	77.00218	0.1070777	0.14
UMF_I UPA_1 plot 7 2021	61.43515	61.35126	0.0838844	0.14
UMF_I UPA_2 plot 5 2011	72.96436	72.87122	0.0931383	0.13
UMF_I UPA_10 plot 2 2017	56.43735	56.50327	0.0659212	0.12
UMF_I UPA_1 plot 8 2013	69.36293	69.28518	0.0777496	0.11
UMF_I UPA_2 plot 10 2021	97.49249	97.38766	0.1048290	0.11
UMF_I UPA_2 plot 10 2011	68.19918	68.13211	0.0670619	0.10
UMF_I UPA_4 plot 1 2018	94.11279	94.20081	0.0880225	0.09
UMF_I UPA_6 plot 6 2014	77.67906	77.74176	0.0627007	0.08
UMF_I UPA_1 plot 10 2021	99.37983	99.45315	0.0733164	0.07
UMF_I UPA_1 plot 7 2013	55.56796	55.52756	0.0404060	0.07
UMF_I UPA_7 plot 5 2020	52.46662	52.49355	0.0269255	0.05
UMF_I UPA_10 plot 6 2023	71.15509	71.17819	0.0230965	0.03
UMF_I UPA_1 plot 8 2010	62.20382	62.18935	0.0144732	0.02
UMF_I UPA_7 plot 5 2019	52.01084	52.01619	0.0053426	0.01
UMF_I UPA_3 plot 10 2017	67.96002	67.96002	0.0000056	0.00

Code

text <- max(dif_AGBs$diff_perc)

Important

We can see as much as 8.08 % difference in biomass estimation due to the different wood density databases.