Automatic detection of tree-ring borders

Source: R/ring_detect.R

This function is used to automatically detect tree ring borders along the user-defined path.

ring_detect(ring.data, seg = 1, auto.path = TRUE, manual = FALSE,
  method = "canny", incline = FALSE, sample.yr = NULL,
  watershed.threshold = "auto", watershed.adjust = 0.8,
  struc.ele1 = NULL, struc.ele2 = NULL, marker.correction = FALSE,
  default.canny = TRUE, canny.t1, canny.t2, canny.smoothing = 2,
  canny.adjust = 1.4, path.dis = 1, origin = 0,
  border.color = "black", border.type = 16, label.color = "black",
  label.cex = 1.2)

Arguments

ring.data

A magick image object produced by ring_read.
seg

An integer specifying the number of image segments.
auto.path

A logical value. If TRUE, a path is automatically created at the center of the image. If FALSE, the function allows the user to create a sub-image and a path by interactive clickings. See details below.
manual

A logical value indicating whether to skip the automatic detection. If TRUE, ring borders are visually identified after creating the path. See ring_modify to learn how to mark tree rings by clicking on the image.
method

A character string specifying how ring borders are detected. It requires one of the following characters: "watershed", "canny", or "lineardetect". See details below.
incline

A logical value indicating whether to correct ring widths. If TRUE, two horizontal paths are added to the image.
sample.yr

NULL or an integer giving the year of formation of the left-most ring. If NULL, use the current year.
watershed.threshold

The threshold used for producing the marker image, either a numeric from 0 to 1, or the character "auto" (using the Otsu algorithm), or a character of the form "XX%" (e.g., "58%").
watershed.adjust

A numeric used to adjust the Otsu threshold. The default is 1 which means that the threshold will not be adjusted. The sizes of early-wood regions in the marker image will reduce along with the decrease of watershed.adjust.
struc.ele1

NULL or a vector of length two specifying the width and height of the first structuring element. If NULL, the size of the structuring element is determined by the argument dpi.
struc.ele2

NULL or a vector of length two specifying the width and height of the second structuring element. If NULL, the size of the structuring element is determined by the argument dpi.
marker.correction

A logical value indicating whether to relabel early-wood regions by comparing the values of their left-side neighbours.
default.canny

A logical value. If TRUE, upper and lower Canny thresholds are determined automatically.
canny.t1

A numeric giving the threshold for weak edges.
canny.t2

A numeric giving the threshold for strong edges.
canny.smoothing

An integer specifying the degree of smoothing.
canny.adjust

A numeric used as a sensitivity control factor for the Canny edge detector. The default is 1 which means that the sensitivity will not be adjusted. The number of detected borders will reduce along with the increase of this value.
path.dis

A numeric specifying the perpendicular distance between two paths when the argument incline = TRUE. The unit is in mm.
origin

A numeric specifying the origin in smoothed gray to find ring borders. See ringBorders from the package measuRing.
border.color

Color for ring borders.
border.type

Symbol for ring borders. See pch in points for possible values and their shapes.
label.color

Color for years and border numbers.
label.cex

The magnification to be used for years and border numbers.

Value

A matrix (grayscale image) or array (color image) representing the tree-ring image.

Details

If auto.path = FALSE, the user can create a rectangular sub-image and a horizontal path by interactively clicking on the tree ring image. The automatic detection will be performed within this rectangular sub-image. To create a sub-image and a path, follow these steps.

  • Step 1. Select the left and right edges of the rectangle

    The user can point the mouse at any desired locations and click the left mouse button to add each edge.

  • Step 2. Select the top and bottom edges of the rectangle

    The user can point the mouse at any desired locations and click the left mouse button to add each edge. The width of the rectangle is defined as the distance between the top and bottom edges, and should not be unnecessarily large to reduce time consumption and memory usage. Creating a long and narrow rectangle if possible.

  • Step 3. Create a path

    After creating the rectangular sub-image, the user can add a horizontal path by left-clicking on the sub-image (generally at the center of the sub-image, try to choose a clean defect-free area). Ring borders and other markers are plotted along this path. If incline = TRUE, two paths are added simultaneously.

After creating the sub-image and the path, this function will open several graphics windows and plot detected ring borders on image segments. The number of image segments is controlled by the argument seg.

Argument method determines how ring borders are identified.

  • If method = "watershed", this function uses the watershed algorithm to obtain ring borders (Soille and Misson, 2001).

  • If method = "canny", this function uses the Canny algorithm to detect borders.

  • If method = "lineardetect", a linear detection algorithm from the package measuRing is used to identify ring borders (Lara et al., 2015). Note that incline = TRUE is not supported in this mode, and path will be automatically created at the center of the image.

If the argument method = "watershed" or "canny", the original image is processed by morphological openings and closings using rectangular structuring elements of increasing size before detecting borders. The first small structuring element is used to remove smaller dark spots in early wood regions, and the second large structuring element is used to remove light strips in late wood regions. More details about morphological processing can be found at Soille and Misson (2001).

Note

This function uses locator to record mouse positions so it only works on "X11", "windows" and "quartz" devices.

References

Soille, P., Misson, L. (2001) Tree ring area measurements using morphological image analysis. Canadian Journal of Forest Research 31, 1074-1083. doi: 10.1139/cjfr-31-6-1074

Lara, W., Bravo, F., Sierra, C.A. (2015) measuRing: An R package to measure tree-ring widths from scanned images. Dendrochronologia 34, 43-50. doi: 10.1016/j.dendro.2015.04.002

Examples

img.path <- system.file("001.png", package = "MtreeRing")

## Read a tree ring image:
t1 <- ring_read(img = img.path, dpi = 1200, plot = FALSE)

## Split a long core sample into 3 pieces to
## get better display performance and use the
## watershed algorithm to detect ring borders:
t2 <- ring_detect(t1, seg = 3, method = 'watershed', border.color = 'green')
#> Warning: The sampling year is set to the current year
#> dev.new(): using pdf(file="Rplots9.pdf")
#> dev.new(): using pdf(file="Rplots10.pdf")
#> dev.new(): using pdf(file="Rplots11.pdf")