In maudqueroue/intercali: Intercalibration Des Différents Types De Suivis Aeriens

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(intercali)

Detection function

The detection_function is use to simulate the probability that a individual could be observed according to the sample design. The detection function could be an uniform detection function with a probability of detection g_zero on the whole strip band until the maximum distance of observation (in m) truncation_m. The detection function could also be a half normal detection function for which we can choose the effective strip width (in km) esw_km i.e. the distance at which there are as much non detected individuals before this distance than detected individuals after this distance. For the half normal detection function it is also possible to choose the proability of detection at 0 meter g_zero and the maximum distance of observation (in m) truncation_m.

Example

Two examples of this function using a dataset dataset_dist created with theintercali package. The first example is a half normal detection hn with a effective strip probability esw_kmof 0.16km, and a truncation truncation_m of 400m. The second exemple use a uniform detection unif with a probability of 0.8 g_zero and a truncation of 250m.

library(ggplot2)

data(dataset_dist)

detected <- detection(dist_obj = dataset_dist,
                   key = "hn",
                   esw_km = 0.16,
                   g_zero = 1,
                   truncation_m = 400)

ggplot(detected, aes(x=distance_m, y=proba)) +
  geom_point(color = "indianred4") +
  xlim(0,500)


detected <- detection(dist_obj = dataset_dist,
                   key = "unif",
                   g_zero = 0.8,
                   truncation_m = 250) 

ggplot(detected, aes(x=distance_m, y=proba)) +
  geom_point(color = "indianred4") +
  xlim(0,500)

Calculate the effective strip width of a half normal function

The esw_hn function comes from the pelaDSM package. This functions allows to calculate the effective strip width corresponding to the sigma of a half normal detection function.

Example

The effective strip width corresponding to a sigma of 0.3 in a half-normal detection function is given by esw_hn.

esw_hn(sigma = 0.3)

# esw of 0.376 km

Calculate the sigma parameter of a half normal function associated to a esw.

The scale_hn function comes from the pelaDSM package. This functions allows to calculate the sigma of a half normal detection function according to the effective strip width esw decided for the detection quality.

Example

The value of sigma corresponding to a effective strip width esw of 160m is given by scale_hn.

scale_hn(esw = 0.16)
# sigma = 0.128

Plot detection

The plot_detection function allows to highligth individuals detected according to the sample design and the detection function. The function represents on the study map, the different transects of the sample design and highligtht in dark blue the detected (simulated) individuals while other non detected (simulated) individuals are in grey.

Example

Example of this function use the dataset_detected containing a dataset of detected individuals created thanks to the detection function. It also use a zigzag transects dataset_segs created in the study area map_obj thanks to the different transect functions. Individuals detected according to the sample design and the detection function are highligthted in dark blue while other non detected individuals are in grey.

data("dataset_detected")
data("dataset_segs")

plot_detect(dist_obj = dataset_detected, 
            transect_obj = dataset_segs, 
            map_obj = dataset_map, 
            title = "Detected individuals")