Prepare sample data based on published ATPOL data

In our example we will take a published distribution map of Erigeron acris L. subsp. acris published in (Zając and Zając 2019). The image was scanned with resolution 150 dpi and georefenced using QGIS.

par(mar = c(0, 0, 0, 0))
tif <- system.file("extdata/eriacr.tif", package = "atpolR")
r <- terra::rast(tif)
terra::plotRGB(r)

Erigeron acris L. subsp. acris distribution taken from (Zając and Zając 2019)

There is hundreds of records. To get all of them we will use check_atpol_square() function, which takes as the arguments the POINT coordinates and a raster, and checks if the value of raster cell corresponding to some arbitrary buffer around the POINT equals zero. As the points are drawn in centers of ATPOL 10 km grid, we will check the values of atpol10k() centroids with an buffer with default radius of 1200 m. Depending of the quality of scan and precision of georeferencing, it might be useful to adjust a buffer a bit.

Our raster usually consist of 3 layers, one for each R, G, B component. The difference between them are visible on Fig. @ref(fig:rgbraster).

R, G and B layers of a raster

Looking on scan shown on Fig. @ref(fig:eriacr) we can see a lot of green and blue components. For further analysis we will take the first layer only. Please note: depending of the scan quality and further image process, you may find other layer more useful.

The values of the layer are continuous, from 0 to 255. For simplification and easier analysis we will classify the layer, just assigning two values: 0 and 1. We will create a reclassification matrix rclmat and apply classify() function form terra package.

m <- c(0,120, 0,
       120,255, 1)
rclmat <- matrix(m, ncol=3, byrow=TRUE)
rc <- terra::classify(r[[1]], rclmat, include.lowest = TRUE)

Reclasiffied raster with 0 — as black and 1 — as white

Having the raster prepared we can load the package and apply the check_atpol_square() function for all 10k grids:

library(atpolR)

eriacr <- atpol10k()|>
  dplyr::mutate(a = mapply(function(x) check_atpol_square(x, rc), centroid)) |>
  dplyr::filter(a == "YES")

Let’s display the results:

eriacr
#> Simple feature collection with 709 features and 2 fields
#> Active geometry column: geometry
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 170032.6 ymin: 140353.6 xmax: 839912.3 ymax: 778164.1
#> Projected CRS: ETRS89 / Poland CS92
#> First 10 features:
#>    Name                       geometry                  centroid   a
#> 1  AB15 POLYGON ((220203.9 698774.2... POINT (225196.1 693780.2) YES
#> 2  AB21 POLYGON ((180184 688879.5, ... POINT (185177.4 683882.5) YES
#> 3  AB23 POLYGON ((200188.2 688840.5... POINT (205181.3 683844.9) YES
#> 4  AB73 POLYGON ((200121.5 638968.2... POINT (205117.5 633972.6) YES
#> 5  AB83 POLYGON ((200110.3 628991.5...   POINT (205106.9 623996) YES
#> 6  AB94 POLYGON ((210103.7 619001, ... POINT (215100.6 614006.2) YES
#> 7  AB95 POLYGON ((220106.8 618988.3... POINT (225103.5 613994.1) YES
#> 8  AB96 POLYGON ((230109.2 618976, ... POINT (235105.7 613982.3) YES
#> 9  AC05 POLYGON ((220097.7 609011.9... POINT (225094.9 604017.7) YES
#> 10 AC06 POLYGON ((230100.4 609000.3... POINT (235097.4 604006.7) YES

There is 709 observations (grids) in data set.

Let’s have a closer look on BE square:

BE <- atpol100k() |>
  subset(Name == "BE") |>
  sf::st_bbox()
par(pty = "s")
plot(NA, type = "n", xlim = c(BE[1], BE[3]), ylim = c(BE[2], BE[4]), axes = FALSE, xlab = "", ylab = "")
terra::plot(rc, legend = FALSE, add = TRUE)

atpol100k() |>
  subset(Name == "BE") |>
  sf::st_cast("LINESTRING") |>
  terra::plot(add = TRUE, col = "blue", lwd = 1.2)

eriacr |>
  subset(substr(Name, 1, 2) == "BE") |>
  sf::st_set_geometry("centroid") |>
  terra::plot(pch = 16, cex = 1.2, col = "blue", add = TRUE)

So far, so god. Our data set corresponds to published data. In next step we will extend it with our own observations.

Please note, that check_atpol_square() function can give false positive results in case of noisy scans. Also it doesn’t recognize the different shapes used in original publications for different

Extend the data

Let’s assume, we want to extend the data set by adding own observations. If we already know the grid name, we can simply filter out the grid created by atpol10k() function by the Name. If we don’t have the grid, however we have coordinates, we can use latlon_to_grid() function. In below example we are using both methods:

myData <- atpol10k() |>
  dplyr::filter(Name %in% c("BE68", 
                            latlon_to_grid(51.13619, 16.95069, 4))) |>
  dplyr::mutate(a = "myData")

And let’s add them to above BE square plot:

myData |>
  sf::st_set_geometry("centroid") |>
  terra::plot(pch = 16, cex = 1.8, col = "red", add = TRUE)

Data set extended with our observations in grids BE48 and BE68

Plot it

atpolR package provides a function plot_points_on_atpol() which can be used to visualize the data set. Let’s merge our two data sets (eriacr and myData) and plot them together. For removing any duplicates we can use unique.data.frame() function from base R, or distinct(Name) from dplyr package.

eriacr <- eriacr |>
  rbind(myData) |>
  unique.data.frame()

And final plot.

plotPoitsOnAtpol(eriacr$centroid, main = "Erigeron acris subsp. acris", cex = 0.6)

Combined dataset drawn on ATPOL grid