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https://github.com/adokter/bioRad

R package for analysis and visualisation of biological signals in weather radar data
https://github.com/adokter/bioRad

aeroecology enram eumetnet-opera lifewatch movement-ecology nexrad oscibio package r radar weather-radar wsr-88d

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R package for analysis and visualisation of biological signals in weather radar data

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README 

        
---

output: github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

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

  out.width = "100%"

)

```



# bioRad 



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

[![R-CMD-check](https://github.com/adokter/bioRad/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/adokter/bioRad/actions/workflows/R-CMD-check.yaml)

[![repo status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)

[![codecov](https://codecov.io/gh/adokter/bioRad/branch/master/graph/badge.svg?token=pDmyO4JVJu)](https://app.codecov.io/gh/adokter/bioRad)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3370004.svg)](https://doi.org/10.5281/zenodo.3370004)



bioRad provides standardized methods for extracting and reporting biological signals from weather radars. It includes functionality to inspect low-level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics.



To get started, see:



* [Dokter et al. (2019)](https://doi.org/10.1111/ecog.04028): a paper describing the package.

* [bioRad vignette](https://adriaandokter.com/bioRad/articles/bioRad.html): an introduction to bioRad's main functionalities.

* [Function reference](https://adriaandokter.com/bioRad/reference/index.html): an overview of all bioRad functions.

* [Introductory exercises](https://adriaandokter.com/bioRad/articles/rad_aero_19.html): a tutorial with code examples and exercises.



More vignettes:



* [Range correction](https://adriaandokter.com/bioRad/articles/range_correction.html): estimate spatial images of vertically integrated density corrected for range effects.



Documentation for the latest development version can be found [here](https://adriaandokter.com/bioRad/dev/).



## Installation



### Install system libraries

For OS X and Linux the GNU Scientific Library (GSL), PROJ and HDF5 libraries need to be installed as system libraries prior to installation, which are required by dependency package **[vol2birdR](https://adriaandokter.com/vol2birdR/)**:



| System                                    | Command

|:------------------------------------------|:---------------------------------------------|

|**OS X (using Homebrew)**                  | `brew install hdf5 proj gsl`

|**Debian-based systems (including Ubuntu)**| `sudo apt-get install libhdf5-dev libproj-dev gsl-bin libgsl-dev`

|**Systems supporting yum and RPMs**        | `sudo yum install hdf5-devel proj-devel gsl gsl-devel`



Additional required system libraries on Linux (Ubuntu)



The following system libraries are required before installing bioRad on Linux systems. In terminal, install these with:



```

sudo apt install libcurl4-openssl-dev

sudo apt install libssl-dev

sudo apt install libgdal-dev

```







### Install bioRad

You can install the released version of bioRad from [CRAN](https://CRAN.R-project.org) with:



```{r eval = FALSE}

install.packages("bioRad")

```



Alternatively, you can install the latest development version from [GitHub](https://github.com/adokter/bioRad) with:



```{r eval = FALSE}

# install.packages("devtools")

devtools::install_github("adokter/bioRad")

```



Then load the package with:



```{r load_bioRad}

library(bioRad)

```



### (optional) Enable MistNet

To enable MistNet, the following vol2birdR commands should be executed:

```{r, eval=FALSE}

vol2birdR::install_mistnet()

vol2birdR::install_mistnet_model()

```

Read the [vol2birdR documentation](https://adriaandokter.com/vol2birdR/articles/vol2birdR.html) for more details.



## Usage



### Radar data example



bioRad can read weather radar data (= polar volumes) in the [`ODIM`](http://eumetnet.eu/wp-content/uploads/2017/01/OPERA_hdf_description_2014.pdf) format and formats supported by the [RSL library](https://trmm-fc.gsfc.nasa.gov/trmm_gv/software/rsl/), such as NEXRAD data. NEXRAD data (US) are [available as open data](https://www.ncdc.noaa.gov/nexradinv/) and on [AWS](https://registry.opendata.aws/noaa-nexrad/).



Here we read an example polar volume data file with `read_pvolfile()`, extract the scan/sweep at elevation angle 3 with `get_scan()`, project the data to a plan position indicator with `project_as_ppi()` and plot the _radial velocity_ of detected targets with `plot()`:



```{r plot_ppi, warning = FALSE, message = FALSE}

library(tidyverse) # To pipe %>% the steps below

system.file("extdata", "volume.h5", package = "bioRad") %>%

  read_pvolfile() %>%

  get_scan(3) %>%

  project_as_ppi() %>%

  plot(param = "VRADH") # VRADH = radial velocity in m/s

```



_Radial velocities towards the radar are negative, while radial velocities away from the radar are positive, so in this plot there is movement from the top right to the bottom left._



### Vertical profile data example



Weather radar data can be processed into vertical profiles of biological targets using `calculate_vp()`. This type of data is [available as open data](https://aloftdata.eu) for over 100 European weather radars.



Once vertical profile data are loaded into bioRad, these can be bound into time series using `bind_into_vpts()`. Here we read an example time series, project it on a regular time grid with `regularize_vpts()` and plot it with `plot()`:



```{r plot_vpts, warning = FALSE, message = FALSE}

example_vpts %>%

  regularize_vpts() %>%

  plot()

```



_The gray bars in the plot indicate gaps in the data._



The altitudes in the profile can be integrated with `integrate_profile()` resulting in a dataframe with rows for datetimes and columns for quantities. Here we plot the quantity _migration traffic rate_ (column `mtr`) with `plot()`:



```{r plot_vpi, warning = FALSE, message = FALSE}

my_vpi <- integrate_profile(example_vpts)



plot(my_vpi, quantity = "mtr") # mtr = migration traffic rate

```



To know the total number of birds passing over the radar during the full time series, we use the last value of the _cumulative migration traffic_ (column `mt`):



```{r mt_value, warning = FALSE, message = FALSE}

my_vpi %>%

  pull(mt) %>% # Extract column mt as a vector

  last()

```



For more exercises, see [this tutorial](https://adriaandokter.com/bioRad/articles/rad_aero_19.html).



## Meta



* We welcome [contributions](https://adriaandokter.com/bioRad/CONTRIBUTING.html) including bug reports.

* License: MIT

* Get citation information for `bioRad` in R doing `citation("bioRad")`.

* Please note that this project is released with a [Contributor Code of Conduct](https://adriaandokter.com/bioRad/CODE_OF_CONDUCT.html). By participating in this project you agree to abide by its terms.