camtrapR (version 2.3.0)

checkSpeciesIdentification: Consistency check on species image identification

Description

This function serves 2 purposes: 1) it assesses possible misidentification of species and 2) compares double observer species identification (only if metadata tagging was used for species identification).

Usage

checkSpeciesIdentification(
  inDir,
  IDfrom,
  hasCameraFolders,
  metadataSpeciesTag,
  metadataSpeciesTagToCompare,
  metadataHierarchyDelimitor = "|",
  maxDeltaTime,
  excludeSpecies,
  stationsToCheck,
  writecsv = FALSE
)

Value

A list containing 2 data frames. The first contains a data frame with images file names, directories, time stamp and species ID that were taken within maxDeltaTime seconds of another species image at a particular station. The second data frame contains images with conflicting species IDs (if IDfrom = "metadata" and metadataSpeciesTagToCompare is defined)

Arguments

inDir

character. Directory containing identified camera trap images sorted into station subdirectories (e.g. inDir/StationA/)

IDfrom

character. Read species ID from image metadata ("metadata") of from species directory names ("directory")?

hasCameraFolders

logical. Do the station directories in inDir have camera subdirectories (e.g. "inDir/StationA/Camera1" or "inDir/StationA/Camera1/Species1")?

metadataSpeciesTag

character. The species ID tag name in image metadata (if IDfrom = "metadata").

metadataSpeciesTagToCompare

character. A second species ID tag name in image metadata (if IDfrom = "metadata"). For comparing double observer species identification.

metadataHierarchyDelimitor

character. The character delimiting hierarchy levels in image metadata tags in field "HierarchicalSubject". Either "|" or ":"

maxDeltaTime

numeric. Maximum time interval between images to be returned (in seconds)

excludeSpecies

character. vector of species to exclude from checks

stationsToCheck

character. vector of stations to be checked (optionally)

writecsv

logical. Should the resulting data.frame be saved as a .csv?

Author

Juergen Niedballa

Details

Within each station, it assesses whether there are images of a species taken within a given time interval of another species. Often, it is unlikely that different species are encountered within a very short time intervals at the same location. This type of misidentification can arise easily if some images belonging to a sequence of images were accidentally moved into different species directories or tagged incorrectly.

Double observer identification may be desirable to increase reliability of species identification. The function returns conflicts in species identification between 2 observers. These conflicts can then be corrected.

Images may accidentally be misidentified by assigning wrong species tags or by moving them into wrong species directories. Imagine your cameras take sequences of images each time they are triggered and one image of the sequence is misidentified. The time difference between these images (that have different species assigned to them) will be very small, usually a few seconds. This function will return all these images for you to check if they were identified correctly.

If multiple observers identify images independently using metadata tagging, their identifications can be compared by setting metadataSpeciesTagToCompare. Conflicting or missing identifications will be reported. This feature is only available if images were identified by metadata tagging.

Species like "blank" or "team" can be ignored using excludeSpecies. If only specific stations are to be checked, stationsToCheck can be set.

Examples

# \donttest{
wd_images_ID <- system.file("pictures/sample_images_species_dir", package = "camtrapR")

if (Sys.which("exiftool") != ""){        # only run this example if ExifTool is available
check.folders <- checkSpeciesIdentification(inDir             = wd_images_ID,
                                            IDfrom            = "directory",
                                            hasCameraFolders  = FALSE,
                                            maxDeltaTime      = 120,
                                            writecsv          = FALSE)
                                     
check.folders   # In the example, 2 different species were photographed within 2 minutes. 
}
# }

if (FALSE) {
# now exclude one of these 2 species 
check.folders2 <- checkSpeciesIdentification(inDir             = wd_images_ID,
                                             IDfrom            = "directory",
                                             hasCameraFolders  = FALSE,
                                             maxDeltaTime      = 120,
                                             excludeSpecies    = "EGY",
                                             writecsv          = FALSE)           
  
check.folders2   # the data frame is empty

# now we check only one station
check.folders3 <- checkSpeciesIdentification(inDir             = wd_images_ID,
                                             IDfrom            = "directory",
                                             hasCameraFolders  = FALSE,
                                             maxDeltaTime      = 120,
                                             stationsToCheck   = "StationB",
                                             writecsv          = FALSE)
check.folders3   # the data frame is empty
}