https://github.com/mattmar/rasterdiv
Diversity Indices for Numerical Matrices
https://github.com/mattmar/rasterdiv
Last synced: about 1 month ago
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Diversity Indices for Numerical Matrices
- Host: GitHub
- URL: https://github.com/mattmar/rasterdiv
- Owner: mattmar
- Created: 2020-04-02T16:26:33.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-02-25T08:15:51.000Z (9 months ago)
- Last Synced: 2025-10-22T03:57:37.206Z (about 1 month ago)
- Language: R
- Homepage: https://mattmar.github.io/rasterdiv/
- Size: 35 MB
- Stars: 18
- Watchers: 2
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
Awesome Lists containing this project
- Awesome-Geospatial - rasterdiv - Diversity Indices for Numerical Matrices. (R)
README
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
dpi = 300, # High resolution for sharp plots
fig.width = 7, # Width of the figures
fig.height = 5, # Height of the figures
out.width = "100%", # Adjust output width to be responsive
out.height = "auto", # Adjust output height to maintain aspect ratio
fig.align = 'center' # Center align figures
)
```
[](https://cran.r-project.org/package=rasterdiv)
[](https://cranlogs.r-pkg.org/badges/last-month/rasterdiv)
[](https://www.r-pkg.org/pkg/rasterdiv)
# rasterdiv
## Installation
### Stable version from [CRAN](https://cran.microsoft.com/):
```{r, eval = FALSE}
install.packages("rasterdiv")
```
### Development version from [GitHub](https://github.com/):
```{r, eval = FALSE}
# install.packages("remotes")
remotes::install_github("mattmar/rasterdiv")
```
## What is rasterdiv?
*__rasterdiv__* is an R package that provides functions to apply diversity indexes based on Information Theory on RasterLayer or numerical matrices. Supported indexes include:
- Parametric Rao’s quadratic entropy (classical and multidimensional);
- Shannon's diversity index;
- Pielou's evenness index;
- Hill's generalised entropy;
- Rényi's generalised entropy;
- Berger-Parker index;
- Cumulative Residual Entropy (CRE)
## New Features
* Time Series Integration: The package recently integrated time series of remote sensing data to derive Rao's index, for example considering plant phenology. See the vignette: [Multidimension Rao’s Index](https://mattmar.github.io/rasterdiv/articles/rasterdiv_03_Advanced_multidimension_Rao.html).
* Helical Graphs: Prepare and plot time series with “helical graphs”. See the vignette: `r if(knitr::is_html_output()) {
"[Visualising rasterdiv indexes with Helical Plots](https://mattmar.github.io/rasterdiv/articles/rasterdiv_05_Helical_Plots.html)"
} else {
"\\href{https://mattmar.github.io/rasterdiv/articles/rasterdiv_05_Helical_Plots.html}{Visualising rasterdiv indexes with Helical Plots}"
}`.
## Key Features
* Moving Window and Area-Based Calculations: Derive indexes using a “moving window” approach (all functions) or area-based Parametric Rao’s quadratic entropy via [*paRao()*](https://mattmar.github.io/rasterdiv/articles/rasterdiv_area_based_Rao.html)
## Learn More on **rasterdiv**
- [*__rasterdiv__—An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back*](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13583);
- [*Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective*](https://link.springer.com/article/10.1007/s42974-021-00042-x);
- [*From zero to infinity: Minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy*](https://onlinelibrary.wiley.com/doi/full/10.1111/geb.13270)
- [*Helical graphs to visualize the NDVI temporal variation of forest vegetation in an open source space*](https://www.sciencedirect.com/science/article/abs/pii/S157495412200406X)
- [*Phenological Diversity Trends with Remote Sensing Datacubes*](https://www.researchgate.net/profile/Matteo-Marcantonio/publication/381288639_Phenological_Diversity_Trends_with_Remote_Sensing_Datacubes/links/6666a0fbb769e76919269fa1/Phenological-Diversity-Trends-with-Remote-Sensing-Datacubes.pdf)