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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# belg
<!-- badges: start -->
[](https://cran.r-project.org/package=belg)
[](https://github.com/r-spatialecology/belg/actions)
[](https://app.codecov.io/gh/r-spatialecology/belg)
[](https://cran.r-project.org/package=belg)
[](https://doi.org/10.5281/zenodo.1209419)
<!-- badges: end -->
Boltzmann entropy (also called configurational entropy) has been recently adopted to analyze entropy of landscape gradients (Gao et al. (2017, 2018, 2019)).
The goal of **belg** is to provide an efficient C++ implementation of this method in R.
It also extend the original idea by allowing calculations on data with missing values (Nowosad and Gao (2020)).
## Installation
You can install the released version of belg from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("belg")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("remotes")
remotes::install_github("r-spatialecology/belg")
```
## Examples
As an example, we use two rasters - `land_gradient1` representing a complex landscape and `land_gradient2` representing a simple landscape:
```{r, message=FALSE, fig.height=3}
library(raster)
library(belg)
land_gradient1 = raster(system.file("raster/land_gradient1.tif", package = "belg"))
land_gradient2 = raster(system.file("raster/land_gradient2.tif", package = "belg"))
plot(stack(land_gradient1, land_gradient2))
```
The main function in this package, `get_boltzmann()`, calculates the Boltzmann entropy of a landscape gradient:
```{r}
get_boltzmann(land_gradient1)
get_boltzmann(land_gradient2)
```
This function accepts a `SpatRaster`, `stars`, `RasterLayer`, `RasterStack`, `RasterBrick`, `matrix`, or `array` object as an input.
It allows for calculation of the relative (the `relative` argument equal to `TRUE`) and absolute Boltzmann entropy of a landscape gradient.
As a default, it uses a logarithm of base 10 (`log10`), however `log` and `log2` are also available options for the `base` argument.
```{r}
get_boltzmann(land_gradient1, base = "log")
get_boltzmann(land_gradient1, relative = TRUE)
get_boltzmann(land_gradient1, base = "log2", relative = TRUE)
```
Two methods of calculating the Boltzmann entropy of a landscape gradient are available: `"hierarchy"` (default) for the hierarchy-based method (Gao et al., 2017) or `"aggregation"` for the aggregation-based method (Gao et al., 2019).
The aggregation-based method requires that the number of rows and columns in the input data must be a multiple of 2.
```{r}
get_boltzmann(land_gradient1, method = "aggregation")
get_boltzmann(land_gradient1, relative = TRUE, method = "aggregation")
```
More examples can be find at https://github.com/Nowosad/belg-examples.
## References
- Gao, Peichao, Hong Zhang, and Zhilin Li. "A hierarchy-based solution to calculate the configurational entropy of landscape gradients." Landscape Ecology 32(6) (2017): 1133-1146.
- Gao, Peichao, Hong Zhang, and Zhilin Li. "An efficient analytical method for computing the Boltzmann entropy of a landscape gradient." Transactions in GIS (2018).
- Gao, Peichao and Zhilin Li. "Aggregation-based method for computing absolute Boltzmann entropy of landscape gradient with full thermodynamic consistency." Landscape Ecology (2019).
- Nowosad, J.; Gao, P. belg: A Tool for Calculating Boltzmann Entropy of Landscape Gradients. Entropy 2020, 22, 937. https://doi.org/10.3390/e22090937