FORTLS (version 1.0.6)

tree.detection: Tree Detection and Cross Section Estimation

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(data,
               dbh.min = 7.5, dbh.max = 200,
               ncr.threshold = 0.1,
               tls.resolution = list(),
               breaks = c(1.0, 1.3, 1.6),
               plot.attributes = NULL,
               save.result = TRUE, dir.result = NULL)

Arguments

data

Data frame with same description and format as indicated for normalize ‘Value’.

dbh.min

Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 7.5 cm.

dbh.max

Optional maximum dbh (cm) considered for detecting trees. By default it will be set at 200 cm.

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:

or separation between two consecutive points at a certain distance from TLS:

breaks

Height above ground level (m) of slices considered for detecting trees. By default it will be 1.0, 1.3 and 1.6 m (+/- 5 cm).

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.

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.

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

horizontal.distance

horizontal distance (m) from plot centre to tree centre.

dbh

estimated tree diameter (cm) at breast height (1.3 m).

num.points

number of points corresponding to a normal section (+/- 5 cm) in the original point cloud.

num.points.hom

number of points corresponding to a normal section (+/- 5 cm) in the point cloud reduced by the point cropping process.

num.points.est

number of points estimated for a normal section (+/- 5 cm) in the original point cloud.

num.points.hom.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.list.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.

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.

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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/10.3390/rs11020211

See Also

normalize, tree.detection.multiple, distance.sampling, estimation.plot.size, simulations, metrics.variables

Examples

# NOT RUN {
# }
# NOT RUN {
# 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 (LAS file) to dir.data

download.file("https://www.dropbox.com/s/2c3d320o3srcawb/1.las?raw=1",
              destfile = file.path(dir.data, "1.las"),
              mode = "wb")

# Normalizing the point cloud data as a neccesary step for detecting trees

pcd <- normalize(las = "1.las",
                 max.dist = 15, min.height = 0.25, max.height = 25,
                 id = "1", file = "1.txt",
                 dir.data = dir.data, dir.result = dir.result)


# Tree detection without considering arguments
# For this case study, TLS resolution was established as:
# point.dist = 7.67 mm and tls.dist = 10 m

tree.list.tls <- tree.detection(data = pcd,
                                tls.resolution = list(point.dist = 7.67, tls.dist = 10),
                                dir.result = dir.result)



# Tree detection considering several arguments

tree.list.tls <- tree.detection(data = pcd,
                                dbh.min = 7.5, dbh.max = 60,
                                tls.resolution = list(point.dist = 7.67, tls.dist = 10),
                                breaks = 1.3,
                                dir.result = dir.result)

  
# }
# NOT RUN {
# }