landscapemetrics

Landscape Metrics for Categorical Map Patterns 🗺️ in R

https://github.com/r-spatialecology/landscapemetrics

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
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Found 5 DOI reference(s) in README
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2 of 15 committers (13.3%) from academic institutions
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Low similarity (19.4%) to scientific vocabulary

Keywords

landscape-ecology landscape-metrics r raster spatial

Keywords from Contributors

geo agent-based-modeling individual-based-modelling netlogo neutral-landscape-model geography geospatial-data spatial-analysis point-pattern-analysis habitat-association

Last synced: 4 months ago · JSON representation

Repository

Landscape Metrics for Categorical Map Patterns 🗺️ in R

Basic Info

Host: GitHub
Owner: r-spatialecology
License: gpl-3.0
Language: R
Default Branch: main
Homepage: https://r-spatialecology.github.io/landscapemetrics
Size: 75.6 MB

Statistics

Stars: 250
Watchers: 13
Forks: 46
Open Issues: 8
Releases: 30

Topics

landscape-ecology landscape-metrics r raster spatial

Created almost 8 years ago · Last pushed 8 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Codemeta

README.Rmd

---
output: 
    github_document:
      html_preview: false
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
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  comment = "#>",
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  out.width = "100%"
)
```

# landscapemetrics 



README last updated: `r Sys.Date()`



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> Starting from v2.0.0, **landscapemetrics** uses `terra` and `sf` internally. More information about the `terra` package can be found here: .

## Overview

**landscapemetrics** is a `R` package for calculating landscape metrics for categorical landscape patterns in a tidy workflow. The package can be used as a drop-in replacement for FRAGSTATS (McGarigal *et al.* 2023), as it offers a reproducible workflow for landscape analysis in a single environment. It also allows for calculations of four theoretical metrics of landscape complexity: a marginal entropy, a conditional entropy, a joint entropy, and a mutual information (Nowosad and Stepinski 2019).

**landscapemetrics** supports **terra**, and **stars** and takes `SpatRaster` or `stars` spatial objects as input arguments. Every function can be used in a piped workflow, as it always takes the data as the first argument and returns a `tibble`. 

#### Citation

To cite **landscapemetrics** or acknowledge its use, please cite the following Software note, substituting the version of the application that you used for 'v0.0':


 
Hesselbarth, M.H.K., Sciaini, M., With, K.A., Wiegand, K., Nowosad, J. 2019.
landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography, 42: 1648-1657 (v0.0). 



For more information see [Publication record](https://r-spatialecology.github.io/landscapemetrics/articles/articles/publication_record.html) vignette. The get a BibTex entry, please use `citation("landscapemetrics")`.

## Installation

There are several ways to install **landscapemetrics**:

```{r, eval = FALSE, echo = TRUE}
# Get the stable version from CRAN
install.packages("landscapemetrics")

# Alternatively, you can install the development version from Github
# install.packages("remotes")
remotes::install_github("r-spatialecology/landscapemetrics")
```

#### Announcement

Due to an improved connected-component labelling algorithm (**landscapemetrics** v1.4 and higher), patches are labeled in a different order than before and therefore different patch IDs might be used compared to previous versions. However, results for all metrics are identical.

## Using landscapemetrics

The resolution of a raster cell has to be in **meters**, as the package converts units internally and returns results in either meters, square meters or hectares. Before using **landscapemetrics**, be sure to check your raster (see `check_landscape()`).

All functions in **landscapemetrics** start with `lsm_` (for **l**and**s**cape **m**etrics). The second part of the name specifies the level (patch - `p`, class - `c` or landscape - `l`). The last part of the function name is the abbreviation of the corresponding metric (e.g. `enn` for the euclidean nearest-neighbor distance):   

```
# general structure
lsm_"level"_"metric"

# Patch level
## lsm_p_"metric"
lsm_p_enn()

# Class level
## lsm_c_"metric"
lsm_c_enn()

# Landscape level
## lsm_p_"metric"
lsm_l_enn()
```

All functions return an identical structured tibble:

| layer | level     | class | id | metric           | value |
| ----- | --------- | ----- | -- | ---------------- | ----- |
| 1     | patch     | 1     | 1  | landscape metric | x     |
| 1     | class     | 1     | NA | landscape metric | x     |
| 1     | landscape | NA    | NA | landscape metric | x     |

### Using metric functions

Every function follows the same implementation design, so the usage is quite straightforward:

```{r, message = FALSE, warning = FALSE, fig.align = "center", out.width = "75%"}
library(landscapemetrics)
library(terra)

# internal data needs to be read
landscape <- terra::rast(landscapemetrics::landscape)

# landscape raster
plot(landscape)

# calculate for example the Euclidean nearest-neighbor distance on patch level
lsm_p_enn(landscape)

# calculate the total area and total class edge length
lsm_l_ta(landscape)
lsm_c_te(landscape)
```

There is also a wrapper around every metric in the package to quickly calculate a bunch of metrics:

```{r, message = FALSE}
# calculate all metrics on patch level
calculate_lsm(landscape, level = "patch")
```

### Utility functions

**landscapemetrics** further provides several visualization functions, e.g. show all labeled patches or the core area of all patches. All visualization functions start with the prefix `show_` (e.g. `show_cores()`).

Important building blocks of the package are exported to help facilitate analysis or the development of new metrics. They all start with the prefix `get_`. All of them are implemented with Rcpp and have either memory or performance advantages compared to raster functions.

For more details, see the  `vignette("utility")`.

### Contributing

One of the major motivations behind **landscapemetrics** is the idea to provide an open-source code collection of landscape metrics. This includes, besides bug reports, especially the idea to include new metrics and functions. Therefore, in case you want to suggest new metrics or functions and in the best case even contribute code, we warmly welcome to do so\! For more information see [CONTRIBUTING](CONTRIBUTING.md).

Maintainers and contributors must follow this repository’s [CODE OF CONDUCT](CODE_OF_CONDUCT.md).

### References

McGarigal K., SA Cushman, and E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors; available at the following web site: 

Nowosad J., TF Stepinski. 2019. Information theory as a consistent framework for quantification and classification of landscape patterns. https://doi.org/10.1007/s10980-019-00830-x

Owner

Name: r-spatialecology
Login: r-spatialecology
Kind: organization

Repositories: 9
Profile: https://github.com/r-spatialecology

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GitHub Events

Total

Create event: 3
Release event: 1
Issues event: 12
Watch event: 16
Delete event: 2
Issue comment event: 21
Push event: 25
Pull request event: 5
Fork event: 1

Last Year

Create event: 3
Release event: 1
Issues event: 12
Watch event: 16
Delete event: 2
Issue comment event: 21
Push event: 25
Pull request event: 5
Fork event: 1

Committers

Last synced: 5 months ago

All Time

Total Commits: 1,715
Total Committers: 15
Avg Commits per committer: 114.333
Development Distribution Score (DDS): 0.463

Past Year

Commits: 34
Committers: 2
Avg Commits per committer: 17.0
Development Distribution Score (DDS): 0.088

Top Committers

Name	Email	Commits
Maximilian Hesselbarth	m**h@u**e	921
marco	s**o@g**m	369
jn	t**i@g**m	344
Sebastian Hanss	s**n@h**o	47
GitHub Actions	a**s@g**m	14
GitHub	n**y@g**m	5
mhesselbarth	m**h@g**n	3
DavZim	d**n@h**e	2
Guillermo Ponce	g**e@a**t	2
kalab-oto	k**o@g**m	2
laurajanegraham	l**m@g**m	2
Darío Hereñú	m**a@g**m	1
Matt Strimas-Mackey	m**s@g**m	1
Matthias Grenié	m**e@e**r	1
jsta	s**2@m**u	1

Committer Domains (Top 20 + Academic)

github.com: 2 msu.edu: 1 ens-lyon.fr: 1 arstucson.net: 1 hotmail.de: 1 gmail.con: 1 hanss.info: 1 uni-goettingen.de: 1

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Total pull requests: 122
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Average comments per issue: 5.53
Average comments per pull request: 1.13
Merged pull requests: 114
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 7
Pull requests: 7
Average time to close issues: about 2 months
Average time to close pull requests: about 6 hours
Issue authors: 6
Pull request authors: 2
Average comments per issue: 2.86
Average comments per pull request: 0.0
Merged pull requests: 5
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mhesselbarth (62)
Nowosad (35)
marcosci (21)
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filippo-ferrario (2)

Pull Request Authors

mhesselbarth (80)
Nowosad (27)
marcosci (8)
bitbacchus (7)
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Total packages: 3
Total downloads:
- cran 1,557 last-month
Total docker downloads: 34

Total dependent packages: 6
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Total dependent repositories: 12
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Total versions: 78
Total maintainers: 1

proxy.golang.org: github.com/r-spatialecology/landscapemetrics

Documentation: https://pkg.go.dev/github.com/r-spatialecology/landscapemetrics#section-documentation
License: gpl-3.0
Latest release: v2.2.1+incompatible
published 11 months ago

Versions: 23
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Dependent Repositories: 0

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Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 4 months ago

cran.r-project.org: landscapemetrics

Landscape Metrics for Categorical Map Patterns

Homepage: https://r-spatialecology.github.io/landscapemetrics/
Documentation: http://cran.r-project.org/web/packages/landscapemetrics/landscapemetrics.pdf
License: GPL-3
Latest release: 2.2.1
published 11 months ago

Versions: 34
Dependent Packages: 6
Dependent Repositories: 12
Downloads: 1,557 Last month
Docker Downloads: 34

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Forks count: 1.9%

Stargazers count: 2.0%

Average: 5.7%

Dependent packages count: 8.0%

Downloads: 8.3%

Dependent repos count: 8.4%

Maintainers (1)

mhk.hesselbarth@gmail.com

Last synced: 4 months ago

conda-forge.org: r-landscapemetrics

Homepage: https://r-spatialecology.github.io/landscapemetrics/
License: GPL-3.0-only
Latest release: 1.5.5
published about 3 years ago

Versions: 21
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 24.2%

Forks count: 27.1%

Dependent repos count: 34.0%

Average: 34.1%

Dependent packages count: 51.2%

Last synced: 4 months ago

Dependencies

.github/workflows/Deploy-pkgdown.yaml actions

JamesIves/github-pages-deploy-action 4.1.4 composite
actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/R-CMD-check.yaml actions

actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/Render-README.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/Test-coverage.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/Write-codemeta.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-r v2 composite

DESCRIPTION cran

R >= 3.1 depends
sf * enhances
stars * enhances
Rcpp * imports
cli * imports
ggplot2 * imports
methods * imports
raster * imports
sp * imports
stats * imports
tibble * imports
covr * suggests
dplyr * suggests
knitr * suggests
rgeos * suggests
rmarkdown * suggests
terra * suggests
testthat * suggests