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https://github.com/forestgeo/fgeo.biomass

Calculate biomass with allometric equations from the allodb package and ForestGEO data
https://github.com/forestgeo/fgeo.biomass

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Calculate biomass with allometric equations from the allodb package and ForestGEO data

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README 

        
---

output: github_document 

editor_options: 

  chunk_output_type: inline

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  cache = TRUE,

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-"

)



set.seed(1)

```



#  Calculate biomass



[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)

[![Travis build status](https://travis-ci.org/forestgeo/fgeo.biomass.svg?branch=master)](https://travis-ci.org/forestgeo/fgeo.biomass)

[![Coverage status](https://coveralls.io/repos/github/forestgeo/fgeo.biomass/badge.svg)](https://coveralls.io/r/forestgeo/fgeo.biomass?branch=master)

[![CRAN status](https://www.r-pkg.org/badges/version/fgeo.biomass)](https://cran.r-project.org/package=fgeo.biomass)



The goal of fgeo.biomass is to calculate biomass using [ForestGEO](https://forestgeo.si.edu/) data and equations from either the [BIOMASS package](https://CRAN.R-project.org/package=BIOMASS) or the [allodb package](https://forestgeo.github.io/allodb/). 



* The BIOMASS package is applicable to tropical forests. It was first [published on CRAN in 2016](https://cran.r-project.org/) and on [Methods on Ecology and Evolution in 2017](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12753). fgeo.biomass provides the main features of BIOMASS with a simpler interface, consistent with all [fgeo packages](https://forestgeo.github.io/fgeo/).



* The allodb package is work in progress, and aims to provide expert-selected allometric equations, both for tropical and temperate forests. fgeo.biomass provides a simple interface to automate the process of finding the right equation(s) for each stem and computing biomass. 



## Installation



Install the development version of **fgeo.biomass** with:



```

# install.packages("devtools")

devtools::install_github("forestgeo/fgeo.biomass")

```



## Setup



In addition to the fgeo.biomass package we will use dplyr and ggplot2 for data wrangling and plotting.



```{r, message=FALSE}

library(ggplot2)

library(dplyr)

library(fgeo.biomass)

```



## fgeo.biomass wrapping BIOMASS



We'll use data from the [Barro Colorado Island, Panama](https://forestgeo.si.edu/sites/neotropics/barro-colorado-island) (BCI). We first pick alive trees and drop missing `dbh` values as we can't calculate biomass for them.



```{r}

if (!requireNamespace("bciex", quietly = TRUE)) {

  stop(

    "For this example, you must first install the bciex package with:\n",

    "devtools::install_github('forestgeo/bciex')"

  )

}



bci_tree <- as_tibble(bciex::bci12t7mini) %>% 

  filter(status == "A", !is.na(dbh))

bci_tree

```



We also need species data.



```{r}

bci_species <- as_tibble(bciex::bci_species)

bci_species

```



`add_tropical_biomass()` adds biomass to your census data.



```{r}

biomass <- add_tropical_biomass(bci_tree, bci_species)

biomass

```



You may also provide a specific `region` or `latitude` and `longitude`.



```{r}

biomass <- add_tropical_biomass(

  bci_tree, 

  bci_species,

  latitude = 9.154965, 

  longitude = -79.845884

)



biomass %>% 

  select(biomass, everything())

```



`propagate_errors()` allows you to propagate errors.



```{r}

str(

  propagate_errors(biomass)

)

```



`model_height()` allows you to create a height model, which you can use to propagate height errors. This is what the entire pipeline looks like:



```{r}

model <- model_height(bci_tree)



errors <- bci_tree %>% 

  add_tropical_biomass(bci_species) %>% 

  propagate_errors(height_model = model)



str(errors)

```



If you pass `latitude` and `longitude` to `add_tropical_biomass(), and then you pass a `height_model` to `propagate_errors()`, then you will need to ignore the coordinates. On an interactive session, you should see something like this:



![](https://i.imgur.com/dhHCYJN.png)



```{r}

if (interactive()) {

  errors <- bci_tree %>% 

    add_tropical_biomass(

      bci_species, 

      latitude = 9.154965, 

      longitude = -79.845884

    ) %>% 

    propagate_errors(height_model = model)

  

  str(errors)

}

```



`add_wood_density()` adds wood density to your census data. It is not limited to tropical forests, and has support for all of these regions: `r glue::glue_collapse(fgeo.biomass:::wd_regions(), sep = ", ", last = ", and ")`.



```{r}

wood_density <- add_wood_density(bci_tree, bci_species)



wood_density %>% 

  select(starts_with("wd_"), everything())

```



The BIOMASS package provides a tool to correct taxonomic names. fgeo.biomass does not include that feature. You may use BIOMASS directly or the more focused [taxize package](https://cran.r-project.org/web/packages/taxize/taxize.pdf).



##  fgeo.biomass wrapping allodb



## Warning



These features are not ready for research. We are now building a [Minimum Viable Product](https://en.wikipedia.org/wiki/Minimum_viable_product), with just enough features to collect feedback from alpha users and redirect our effort. The resulting biomass is still meaningless.



We'll use the `add_biomass()` with these inputs:



1. A ForestGEO-like _stem_ or _tree_ table.

2. A _species_ table (internally used to look up the Latin species names from the species codes in the `sp` column of the census table).



We'll use data from the [Smithsonian Conservation Biology Institute, USA](https://forestgeo.si.edu/sites/north-america/smithsonian-conservation-biology-institute) (SCBI). We first pick alive trees and drop missing `dbh` values as we can't calculate biomass for them.



```{r}

census <- fgeo.biomass::scbi_tree1 %>% 

  filter(status == "A", !is.na(dbh))



census

```



We now use `add_biomass()` to add biomass to our census dataset. 



```{r}

species <- fgeo.biomass::scbi_species



with_biomass <- census %>% 

  add_biomass(species, site = "SCBI")

```



We are warned that we are using a tree-table (as opposed to a stem-table), and informed about how to interpret the resulting `biomass` values for trees and shrubs.



Some equations couldn't be found. There may be two reasons:



* Some stems in the data belong to species with no matching species in allodb.

* Some stems in the data belong to species that do match species in allodb but the available equations were designed for a dbh range that doesn't include actual dbh values in the data.



Here are the most interesting columns of the result:



```{r}

with_biomass %>% 

  select(treeID, species, biomass)

```



Let's now visualize the relationship between `dbh` and b`biomass` by `species` (black points), in comparison with `agb` (above ground biomass) values calculated with allometric equations for tropical trees (grey points). 



```{r, fig.height=14}

with_biomass %>% 

  # Convert agb from [Mg] to [kg]

  mutate(agb_kg = agb * 1e3) %>% 

  ggplot(aes(x = dbh)) +

  geom_point(aes(y = agb_kg), size = 1.5, color = "grey") +

  geom_point(aes(y = biomass), size = 1, color = "black") +

  facet_wrap("species", ncol = 4) +

  ylab("Reference `agb` (grey) and calculated `biomass` (black) in [kg]") +

  xlab("dbh [mm]") +

  theme_bw()

```



Above, the species for which `biomass` couldn't be calculated show no black points, although they do show grey reference-points.



To better understand the distribution of `biomass` values for each species we can use a box-plot.



```{r}

with_biomass %>% 

  ggplot(aes(species, biomass)) +

  geom_boxplot() +

  ylab("biomass [kg]") +

  coord_flip()

```



For some species the maximum `dbh` for which `biomass` was calculated is much lower than the maximum `dbh` value for which the reference `agb` was calculated. This is because most equations in __allodb__ are defined for a specific range of `dbh` values. Eventually __allodb__ might provide equations beyond the `dbh` limits currently available.



To explore this issue, here we use `add_component_biomass()` which allows us to see intermediary results that `add_biomass()` doesn't show.



```{r}

detailed_biomass <- suppressWarnings(suppressMessages(

  add_component_biomass(census, species, site = "SCBI")

))



# Maximum `dbh` values by species

max_by_species <- detailed_biomass %>% 

  select(species, dbh_max_mm) %>% 

  group_by(species) %>% 

  arrange(desc(dbh_max_mm)) %>% 

  filter(row_number() == 1L) %>% 

  ungroup()



# `dbh` is above the maximum limit, so `biomass` is missing (agb has a value)

detailed_biomass %>% 

  filter(dbh > 1000) %>% 

  select(-dbh_max_mm) %>% 

  left_join(max_by_species) %>% 

  mutate(agb_kg = agb * 1e3) %>%

  select(species, biomass, agb, dbh, dbh_max_mm) %>% 

  arrange(species) %>%

  print(n = Inf)

```



## Biomass via BIOMASS versus allodb



```{r}

temperate_biomass <- add_biomass(census, species, site = "scbi")



# Warning: Aplying tropical equations to a temperate forest for comparison

tropical_biomass <- add_tropical_biomass(census, species)



dbh_biomsss <- tibble(

  dbh = temperate_biomass$dbh,

  species = temperate_biomass$species,

  temperate_biomass = temperate_biomass$biomass, 

  tropical_biomass = tropical_biomass$biomass

)

```



```{r, fig.height=14}

dbh_biomsss %>% 

  ggplot(aes(x = dbh)) +

  geom_point(aes(y = tropical_biomass), size = 1.5, color = "grey") +

  geom_point(aes(y = temperate_biomass), size = 1) +

  facet_wrap("species", ncol = 4) +

  ylab("Biomass [kg] (via the BIOMASS (grey) and allodb (black) packages)") +

  xlab("dbh [mm]") +

  theme_bw()

```



## General information



* [Getting help](SUPPORT.md).

* [Contributing](CONTRIBUTING.md).

* [Contributor Code of Conduct](CODE_OF_CONDUCT.md).