Method for identifying individual trees in airborne lidar data and corresponding computer program product
View Patent ↗The invention relates to a method for identifying individual trees in airborne lidar data and a corresponding computer program product. The method comprises: a. obtaining lidar data points of a group of one or more trees; b. define voxels in a regular 3D grid on the basis of the data points; c. applying an image segmentation algorithm to obtain at least one segment; and, if at least two segments are obtained: d. find the root voxel and branch voxels of a first segment and a second neighbouring segment; and e. merging the first and second segment if the distance between the first and second root voxel is less than a first threshold, the distance between the first root voxel and the closest second branch voxel is less than a second threshold; and the distance between the first branch voxels and the second branch voxels is less than a third threshold.
1. A method for identifying individual trees in airborne lidar data, comprising the steps of:
a. obtaining lidar data of a group of one or more trees to be separated into individual trees, the lidar data comprising a plurality of lidar data points;
b. define voxels in a regular 3D grid on the basis of the lidar data points;
c. applying an image segmentation algorithm to obtain at least one segment comprising a subset of the 3D voxels;
wherein the following steps are performed if at least two segments are obtained in step c: d. for each of a first segment and a second neighbouring segment of said at least two segments:
I. find the root voxel of said segment, the root voxel being a voxel having the lowest height of the voxels of said segment;
II. find the branch voxels of said segment, wherein a branch voxel is a voxel connected directly or indirectly to the root voxel;
e. merging the first segment and the neighbouring second segment if:
I. the distance between the root voxel of the first segment and the root voxel of the second segment is less than a first threshold; and
II. the distance between the root voxel of the first segment and the closest branch voxel of the second segment is less than a second threshold; and
III. the distance between each of the branch voxels of the first segment and the corresponding closest branch voxels of the second segment is less than a third threshold.
2. The method according to claim 1 , wherein step c. comprises applying watershed segmentation to obtain the at least one segment.
3. The method according to claim 2 , comprising projecting the 3D voxels on a 2D grid corresponding to the horizontal plane, wherein each grid cell is assigned a value on the basis of the height coordinates of the 3D voxels projected onto said grid cell.
4. The method according to claim 3 , wherein each grid cell is assigned a value corresponding to an average, a mode, a median or a maximum of the height coordinates of the 3D voxels projected onto said grid cell.
5. The method according to claim 1 , wherein steps d. and e. are iterated over all segments.
6. The method according to claim 1 , wherein step a. comprises obtaining lidar data, separating lidar data in ground data and non-ground data and separating non-ground data in said lidar data of a group of one or more trees and non-tree data.
7. The method according to claim 1 , further comprising modelling the individual trees on the basis of the obtained individual tree segments to produce a set of 3D models for each individual tree.
8. The method according to claim 1 , further comprising extracting parameters from the segments corresponding to individual trees.
9. The method according to claim 8 , the parameters comprising at least one of: height of the tree, volume of the tree and canopy area of the tree.
10. A computer program product comprising non-transitory computer-executable instructions configured to, when executed, perform the steps of the method of claim 1 .