Individual tree segmentation using Li et al. (2012) algorithm (see reference). This method is deprecated. Use lastrees_li2 instead which is a slighly closer implementation of the original paper.
lastrees_li(las, dt1 = 1.5, dt2 = 2, Zu = 15, hmin = 2, R = 10, ...)An object of the class LAS.
numeric. Threshold number 1. See reference page 79 in Li et al. (2012). Default 1.5.
numeric. Threshold number 2. See reference page 79 in Li et al. (2012). Default 2.
numeric. If point elvation is greater than Zu, dt2 is used otherwise dt1 is used.
See reference page 79 in Li et al. (2012). Default 15.
numeric. Minimum height of a detected tree. Default 2.
numeric. Maximum radius of a crown. Any value greater than a crown is good because this parameter does not affect the result. However, it greatly affects the computation speed. The lower the value, the faster the method. Default is 10.
Supplementary options. Currently field is supported to change the default name of
the new column.
Nothing (NULL), the point cloud is updated by reference. The original point cloud
has a new column named treeID containing an ID for each point that refer to a segmented tree.
This is a growing region method working at the point cloud level. It is an implementation of the
Li et al. 2012 (see references) algorithm made by the lidR author but with the addition of
a parameter hmin to stop the over-segmentation for objects that are too low. The classification
is done at the point cloud level and the function always returns nothing (NULL). The original
point cloud is updated in place with an ID for each point in a new column treeID. The user
is free to post-process this output the way he want.
Li, W., Guo, Q., Jakubowski, M. K., & Kelly, M. (2012). A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering & Remote Sensing, 78(1), 75-84.