tree_detection {lidaRtRee}

Tree detection

Description

Performs tree detection by applying the functions tree_segmentation and tree_extraction to objects of class SpatRaster-class, LAS-class or LAScatalog-class

Usage

tree_detection(las, res = 1, ROI = NULL, normalize = FALSE, crown = FALSE, ...)

Arguments

las

An object of class SpatRaster-class, LAS-class or LAScatalog-class

res

numeric. The size of a grid cell in point cloud coordinates units, used to rasterize the point cloud. In case the las argument is a SpatRaster res is not used.

ROI

spatial polygons in sf/sfc format, in the same CRS as argument las. geometric object that defines the region where tree detection has to be performed. In case the input is of class LAScatalog-class, the chunk buffer set with engine_options is applied to the point cloud to prevent border effects, but only treetops lying within the ROI are returned.

normalize

boolean. Should the point cloud be normalized before detection (not applicable if las argument is a SpatRaster) ?

crown

Parameter passed to tree_extraction

...

Parameters passed to tree_segmentation

Value

A sf collection of POINTs with 7 fields: tree id, local maximum stats (height, dominance radius), segment stats (surface and volume), coordinates (x and y). In case argument crown is TRUE, a crown field containing the WKT geometry of the 2D crown is also present.

References

Monnet, J.-M. 2011. Using airborne laser scanning for mountain forests mapping: Support vector regression for stand parameters estimation and unsupervised training for treetop detection. Ph.D. thesis. University of Grenoble, France. Section 6.2 https://theses.hal.science/tel-00652698/document

Monnet, J.-M., Mermin, E., Chanussot, J., Berger, F. 2010. Tree top detection using local maxima filtering: a parameter sensitivity analysis. Silvilaser 2010, the 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems, September 14-17, Freiburg, Germany, 9 p. https://hal.science/hal-00523245/document

See Also

tree_segmentation, tree_extraction

Examples

# load canopy height model
data(chm_chablais3)
chm_chablais3 <- terra::rast(chm_chablais3)
# create polygon of region of interest
ROI <- sf::st_polygon(list(cbind(
 c(974360, 974360, 974380, 974380, 974360),
 c(6581640, 6581680, 6581680, 6581640, 6581640)
)))
# convert to sfc and set projection
ROI = sf::st_sfc(ROI)
sf::st_crs(ROI) <- terra::crs(chm_chablais3)
#
# tree detection
trees <- tree_detection(chm_chablais3)
# plot results
# canopy height model background
terra::plot(chm_chablais3)
# detected trees
plot(trees["h"], add = TRUE, cex = trees$h/20, col = "black")
#
# tree detection in ROI and minimum tree height set to 10
trees_ROI <- tree_detection(chm_chablais3, ROI = ROI, hmin = 10, crown = TRUE)
# create polygons from WKT field
trees_ROI_crowns <- sf::st_as_sf(sf::st_drop_geometry(trees_ROI), wkt = "crown")
# plot results
# canopy height model background 
terra::plot(chm_chablais3)
# detected trees
plot(trees_ROI["h"], add = TRUE, cex = trees_ROI$h/20, col = "black")
# corresponding crowns
plot(sf::st_geometry(trees_ROI_crowns), add = TRUE, border = "black", col = NA)
# add ROI
plot(ROI, add = TRUE, border = "red", col = NA)