R: Tree-Level Variables Estimation for TLS Single-Scan Approach

tree.detection.single.scan {FORTLS}

R Documentation

Tree-Level Variables Estimation for TLS Single-Scan Approach

Description

Detects trees from TLS point clouds corresponding to a single scan. For each tree detected, the function calculates the central coordinates and estimates the diameter at 1.3 m above ground level (which is known as dbh, diameter at breast height) and classifies it as fully visible or partially occluded. Finally, the function obtains the number of points belonging to normal sections of trees (those corresponding to dbh +/- 5 cm) and estimates them for both original and reduced (with point cropping process) point clouds.

Usage

tree.detection.single.scan(data, single.tree = NULL,
                           dbh.min = 4, dbh.max = 200, h.min = 1.3,
                           ncr.threshold = 0.1,
                           tls.resolution = list(),
                           tls.precision = NULL,
                           density.reduction = 2,
                           stem.section = c(0.7, 3.5), stem.range = NULL, breaks = NULL,
                           slice = 0.1, understory = NULL, bark.roughness = 1,
                           den.type = 1, d.top = NULL,
                           plot.attributes = NULL, plot = TRUE,
                           save.result = TRUE, dir.result = NULL)

Arguments

`data`	Data frame with same description and format as indicated for `normalize` ‘Value’.
`single.tree`	Optional argument to indicate if there is only one tree.
`dbh.min`	Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 4 cm.
`dbh.max`	Optional maximum dbh (cm) considered for detecting trees. By default it will be set at 200 cm.
`h.min`	Optional minimum h (m) considered for detecting trees. By default it will be set at 1.3 m.
`ncr.threshold`	Local surface variation (also known as normal change rate, NCR). By default it will be set as 0.1. For better understanding of this argument see ‘Details’.
`tls.resolution`	List containing parameters of TLS resolution. This can be defined by the angle aperture:

horizontal.angle: horizontal angle aperture (degrees).
vertical.angle: vertical angle aperture (degrees).
point.dist: distance (mm) between two consecutive points.
tls.dist: distance (m) from TLS at which two consecutive points are separated by point.dist.

If this argument is not specified by the user, it will be set to NULL by default and, as a consequence the function will stop giving an error message.

`tls.precision`	Average point cloud precision in cm.
`density.reduction`	Density reduction intensity.
`stem.section`	Section free of noise (shurb, branches, etc.) considered to detect trees. If not specified, an automatic internal algorithm will be applied (see ‘Details’).
`stem.range`	Section considered to estimate straightness tree attributes.
`breaks`	Height above ground level (m) of slices considered for detecting trees. By default it will be considered all possible sections from 0.1 m to maximum height by 0.3 m intervals (+/- 5 cm).
`slice`	Slice width considered for detecting trees. By default it will be considered as 0.1 m.
`understory`	Optional argument to indicate if there is dense understory vegetation.
`bark.roughness`	Bark roughness established in 3 degrees (1 < 2 < 3). By default it will be considered as 1.
`den.type`	Numeric argument indicating the dendrometic type used to estimate volumen when there are not sections enough to fit a taper equation. Dendrometrics types available are the following: cylinder = 0, paraboloid = 1 (by default), cone = 2 and neiloid = 3.
`d.top`	Top stem diameter (cm) considered to estimate commercial timber volume.
`plot.attributes`	Data frame with attributes at plot level. It must contain a column named `id` (character string or numeric value) with encoding coinciding with that used in `id` argument of `normalize` for identifying plots. If there are strata, another column named ‘stratum’ (numeric) will be required for other functionalities of FORTLS (see, for instance, `estimation.plot.size` or `metrics.variables`). If this argument is not specified by the user, it will be set to NULL by default and, as a consequence, the function will not add these possible plot attributes.
`plot`	Optional logical which indicates whether or not the normalized point cloud will be plot. If this argument is not specified by the user, it will be set to `TRUE` by default and, as consequence, the normalized point cloud will be plot.
`save.result`	Optional logical which indicates whether or not the output files described in ‘Output Files’ section should be saved in `dir.result`. If this argument is not specified by the user, it will be set to `TRUE` by default and, as a consequence, the output files will be saved.
`dir.result`	Optional character string naming the absolute path of an existing directory where the files described in ‘Output Files’ section will be saved. `.Platform$file.sep` must be used as the path separator in `dir.result`. If this argument is not specified by the user, and the `save.result` is `TRUE`, it will be set to `NULL` by default and, as a consequence, the current working directory of the R process will be assigned to `dir.result` during the execution.

Details

Slices determined by breaks argument are clustered using the DBSCAN algorithm (Ester et al., 1996) on the horizontal plane according to Cartesian coordinates (x, y). Before and after this process, several algorithms are used to remove noisy points and apply classification criteria to select the clusters of trees.

dbh is directly estimated for the section of 1.3 m above ground level, and estimated from other sections using dbh~breaks linear regression. Finally, the mean value of all estimates is provided in ‘Value’ as the dbh of the tree section.

The number of points corresponding to a normal section (+/- 5 cm) is estimated in proportion to dbh, using the average number of points per radius unit as reference. In this respect, only tree sections fully visible at 1.3 m above ground level will be considered for estimating the average number of points.

Local surface variation (also known as normal change rate ,NCR), is a quantitative measure of curvature feature (Pauly et al., 2002). This is useful for distinguishing points belonging to fine branches and foliage (e.g. leaves, shrubs) and stem points (e.g. Jin et al., 2016; Zhang et al., 2019). Just as we considered 5 cm as suitable for calculating local surface variation for the stem separation in forests, according to other authors (Ma et al., 2015; Xia et al., 2015), we also established the NCR threshold as 0.1, according to Zhang et al. (2019). However, this argument (ncr.threshold) may be modified in order to use more appropriate values.

Value

Data frame with the following columns for every tree detected (each row corresponds to one tree detected):

`id`	Optional plot identification encoded as a character string or numeric. If this argument is not specified by the user, it will be set to NULL by default and, as a consequence, the plot will be encoded as 1.
`file`	Optional file name identification encoded as character string or numeric. If it is null, the file will be encoded as `id` by default.
`tree`	tree numbering
`Coordinates`	Cartesian (according to https://en.wikipedia.org/wiki/Cartesian_coordinate_system notation):

x: distance on x axis (m) of tree centre.
y: distance on y axis (m) of tree centre.

Azimuthal angles:

phi: angular coordinate (rad) of tree centre.
phi.left: angular coordinate (rad) of left border of tree section.
phi.right: angular coordinate (rad) of right border of tree section

`h.dist`	horizontal distance (m) from plot centre to tree centre.
`\emph{dbh}`	estimated tree diameter (cm) at breast height (1.3 m).
`\emph{h}`	estimated tree total height (m).
`\emph{h.com}`	estimated commercial tree height (m) according to the top diameter defined in the argument `d.top`.
`\emph{v}`	estimated tree stem volume (m^3).
`\emph{v.com}`	estimated commercial tree stem volume (m^3) according to the top diameter defined in the argument `d.top`.
`n.pts`	number of points corresponding to a normal section (+/- 5 cm) in the original point cloud.
`n.pts.red`	number of points corresponding to a normal section (+/- 5 cm) in the point cloud reduced by the point cropping process.
`n.pts.est`	number of points estimated for a normal section (+/- 5 cm) in the original point cloud.
`n.pts.red.est`	number of points estimated for a normal section (+/- 5 cm) in the point cloud reduced by the point cropping process.
`partial.occlusion`	yes (1) or no (0)

Output Files

At the end of the tree detection process, if the save.result argument is TRUE, the function will save the data frame described in ‘Value’ as a CSV file named ‘tree.tls.csv’. The data frame will be written without row names in the dir.result directory by using write.csv function from the utils package.

Note

Although tree.detection also works with reduced point clouds, thus reducing the computing time, use of the original point cloud is recommended in order to detect more trees. This will also depend on forest conditions, especially those related to visibility. The more distant the trees are, the lower the density of points will be, and using reduced point clouds will therefore complicate detection of the most distant trees.

Note that dbh.min and dbh.max are important for avoiding outlier values when inventory data are used for reference purposes. Otherwise, knowledge about the autoecology of species could be used for filtering anomalous values of dbh.

The argument breaks = 1.3 could be sufficient for detecting trees visible at dbh, involving lower computational cost. However, those trees not detected at dbh, may be estimated from lower and/or higher sections. Considering the three default sections in the argument breaks = c(1.0, 1.3, 1.6) maintains a good balance in the case study of this package.

Author(s)

Juan Alberto Molina-Valero and Adela Martínez-Calvo.

References

Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231).

Jin, S., Tamura, M., & Susaki, J. (2016). A new approach to retrieve leaf normal distribution using terrestrial laser scanners. J. Journal of Forestry Research, 27(3), 631-638. doi:10.1007/s11676-015-0204-z

Ma, L., Zheng, G., Eitel, J. U., Moskal, L. M., He, W., & Huang, H. (2015). Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial lidar point cloud data of forest canopies. IEEE Transactions Geoscience Remote Sensing, 54(2), 679-696. doi:10.1109/TGRS.2015.2459716

Pauly, M., Gross, M., & Kobbelt, L. P., (2002). Efficient simplification of point-sampled surfaces. In IEEE Conference on Visualization. (pp. 163-170). Boston, USA. doi:10.1109/VISUAL.2002.1183771

Xia, S., Wang, C., Pan, F., Xi, X., Zeng, H., & Liu, H. (2015). Detecting stems in dense and homogeneous forest using single-scan TLS. Forests. 6(11), 3923-3945. doi:10.3390/f6113923

Zhang, W., Wan, P., Wang, T., Cai, S., Chen, Y., Jin, X., & Yan, G. (2019). A novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data. Remote Sens. 11(2), 211. doi:10.3390/rs11020211

Examples




# Establishment of working directories (optional)
# By default here we propose the current working directory of the R process

dir.data <- getwd()
dir.result <- getwd()


# Loading example data (LAZ file) to dir.data

download.file("https://www.dropbox.com/s/17yl25pbrapat52/PinusRadiata.laz?dl=1",
              destfile = file.path(dir.data, "PinusRadiata.laz"),
              mode = "wb")


# Normalizing the whole point cloud data without considering arguments

pcd <- normalize(las = "PinusRadiata.laz",

                 id = "PinusRadiata",

                 dir.data = dir.data, dir.result = dir.result)


# Tree detection without considering arguments
# For this case study, TLS resolution was established as:
# point.dist = 6.34 mm and tls.dist = 10 m

tree.tls <- tree.detection.single.scan(data = pcd,

                                       tls.resolution = list(point.dist = 6.34, tls.dist = 10),

                                       breaks = c(1, 1.3, 1.6),

                                       dir.result = dir.result)

[Package FORTLS version 1.4.0 Index]