integrate_to_ppi {bioRad} | R Documentation |

`ppi`

) of vertically integrated density adjusted for range effectsThis function estimates a spatial image (PPI object) of vertically integrated
density (`VID`

) based on all elevation scans of the radar, while
accounting for the changing overlap between the radar beams as a function of
range. The resulting PPI is a vertical integration over the layer of
biological scatterers based on all available elevation scans, corrected for
range effects due to partial beam overlap with the layer of biological echoes
(overshooting) at larger distances from the radar. The methodology is
described in detail in Kranstauber et al. (2020).

integrate_to_ppi( pvol, vp, nx = 100, ny = 100, xlim, ylim, zlim = c(0, 4000), res, quantity = "eta", param = "DBZH", raster = NA, lat, lon, antenna, beam_angle = 1, crs, param_ppi = c("VIR", "VID", "R", "overlap", "eta_sum", "eta_sum_expected"), k = 4/3, re = 6378, rp = 6357 )

`pvol` |
a polar volume of class pvol |

`vp` |
a vertical profile of class vp |

`nx` |
number of raster pixels in the x (longitude) dimension |

`ny` |
number of raster pixels in the y (latitude) dimension |

`xlim` |
x (longitude) range |

`ylim` |
y (latitude) range |

`zlim` |
altitude range in meter, given as a numeric vector of length two. |

`res` |
numeric vector of length 1 or 2 to set the resolution of the raster (see res).
If this argument is used, arguments |

`quantity` |
profile quantity on which to base range corrections, 'eta' or 'dens'. |

`param` |
reflectivity factor scan parameter on which to base range corrections.
Typically the same parameter from which animal densities are estimated for object |

`raster` |
(optional) RasterLayer with a CRS. When specified this raster topology is used for the output, and nx, ny, res arguments are ignored. |

`lat` |
Geodetic latitude of the radar in degrees. If missing taken from |

`lon` |
Geodetic latitude of the radar in degrees. If missing taken from |

`antenna` |
radar antenna height. If missing taken from |

`beam_angle` |
numeric. Beam opening angle in degrees, typically the angle between the half-power (-3 dB) points of the main lobe |

`crs` |
character or object of class CRS. PROJ.4 type description of a Coordinate Reference System (map projection). When 'NA' (default), an azimuthal equidistant projection with origin at the radar location is used. To use a WSG84 (lat,lon) projection, use crs="+proj=longlat +datum=WGS84" |

`param_ppi` |
one or multiple of 'VIR', 'VID', 'R', 'overlap', 'eta_sum', 'eta_sum_expected' |

`k` |
Standard refraction coefficient. |

`re` |
Earth equatorial radius in km. |

`rp` |
Earth polar radius in km. |

The function requires

a polar volume, containing one or multiple scans (

`pvol`

)a vertical profile (of birds) calculated for that same polar volume (

`vp`

)a grid defined on the earth's surface, on which we will calculate the range corrected image (defined by

`raster`

, or a combination of`nx`

,`ny`

,`res`

arguments).

The pixel locations on the ground are easily translated into a corresponding azimuth and range of the various scans (see function beam_range).

For each scan within the polar volume, the function calculates:

the vertical radiation profile for each ground surface pixel for that particular scan, using beam_profile.

the reflectivity expected for each ground surface pixel (

*η_{expected}*), given the vertical profile (of biological scatterers) and the part of the profile radiated by the beam. This*η_{expected}*is simply the average of (linear)`eta`

in the profile, weighted by the vertical radiation profile.the observed eta at each pixel

*η_{observed}*, which is converted form`DBZH`

using function dbz_to_eta, with`DBZH`

the reflectivity factor measured at the pixel's distance from the radar.

For each pixel on the ground, we thus retrieve a set of *η_{expected}*
and a set of *η_{observed}*. From those we can calculate a spatial adjustment factor
`R`

as:

*R=∑{η_{observed}}/∑{η_{expected}}*

, with the sum running over scans.

To arrive at the final PPI image, the function calculates

the vertically integrated density (

`vid`

) and vertically integrated reflectivity (`vir`

) for the profile, using the function integrate_profile.the spatial range-corrected PPI for

`VID`

, defined as the adjustment factor image (`R`

), multiplied by the`vid`

calculated for the profilethe spatial range-corrected PPI for

`VIR`

, defined as the adjustment factor`R`

, multiplied by the`vir`

calculated for the profile.

If one of `lat`

or `lon`

is missing, the extent of the PPI is taken equal to
the extent of the data in the first scan of the polar volume.

As an additional parameter, overlap between vertical profile and vertical radiation
profile is calculated using beam_profile
and stored as quantity `overlap`

.

scans at 90 degree beam elevation (birdbath scans) are ignored.

An object of class 'ppi'.

Kranstauber B, Bouten W, Leijnse H, Wijers B, Verlinden L, Shamoun-Baranes J, Dokter AM (2020) High-Resolution Spatial Distribution of Bird Movements Estimated from a Weather Radar Network. Remote Sensing 12 (4), 635. https://doi.org/10.3390/rs12040635

Buler JJ & Diehl RH (2009) Quantifying bird density during migratory stopover using weather surveillance radar. IEEE Transactions on Geoscience and Remote Sensing 47: 2741-2751. https://doi.org/10.1109/TGRS.2009.2014463

# locate example polar volume file: pvolfile <- system.file("extdata", "volume.h5", package = "bioRad") # load polar volume example_pvol <- read_pvolfile(pvolfile) # load the corresponding vertical profile for this polar volume data(example_vp) # calculate the range-corrected ppi on a 50x50 pixel raster my_ppi <- integrate_to_ppi(example_pvol, example_vp, nx = 50, ny = 50) # plot the vertically integrated reflectivity (VIR) using a 0-2000 cm^2/km^2 color scale: plot(my_ppi, zlim = c(0, 2000)) ## Not run: # calculate the range-corrected ppi on finer 2000m x 2000m pixel raster: my_ppi <- integrate_to_ppi(example_pvol, example_vp, res = 2000) # plot the vertically integrated density (VID) using a 0-200 birds/km^2 color scale: plot(my_ppi, param = "VID", zlim = c(0, 200)) # to overlay ppi objects on a background map, first # download a basemap, and map the ppi: bm <- download_basemap(my_ppi) map(my_ppi, bm) # the ppi can also be projected on a user-defined raster, as follows: # first define the raster: template_raster <- raster::raster(raster::extent(12, 13, 56, 57), crs = sp::CRS("+proj=longlat")) # project the ppi on the defined raster: my_ppi <- integrate_to_ppi(example_pvol, example_vp, raster = template_raster) # extract the raster data from the ppi object: raster::brick(my_ppi$data) # calculate the range-corrected ppi on an even finer 500m x 500m pixel raster, # cropping the area up to 50000 meter from the radar. my_ppi <- integrate_to_ppi(example_pvol, example_vp, res = 500, xlim = c(-50000, 50000), ylim = c(-50000, 50000) ) plot(my_ppi, param = "VID", zlim = c(0, 200)) ## End(Not run)

[Package *bioRad* version 0.5.2 Index]