System and method for analyzing trees in LiDAR data using views
A method and apparatus for identifying individual trees in LiDAR data based on the view of a LiDAR data point. In one embodiment, those LiDAR data points that do not have neighboring LiDAR data points that block the local view at a defined angle are considered to be in a central part of a tree canopy. In one embodiment, those LiDAR data points that are in a central part of a tree canopy are added to an output file that stores clusters of data points for each tree canopy detected. The central part of the tree canopy area can be analyzed to predict one or more characteristics of the tree.
1. A computer system for identifying individual trees in Light Detection and Ranging (LiDAR) data, comprising:
a memory that stores a sequence of programmed instructions that are executable by a processor; and
a processor that is configured to execute the instructions to identify individual trees in a set of LiDAR data by:
for each LiDAR data point in the set of LiDAR data:
determining if the LiDAR data point has one or more neighboring LiDAR data points with a height value that is above a height defined by an angle from the LiDAR data point;
determining whether to add the LIDAR data point to a cluster based on a number of neighboring LiDAR data points that have heights above the height defined by the angle; and
classifying the cluster of LiDAR data points as representing a central area of a tree canopy .
2. The system of claim 1 , wherein the instructions cause the processor to determine a viewing window for the LiDAR data point and to determine if the LiDAR data point has one or more neighboring LiDAR data points with a height value that is above the height defined by the angle in an area of the viewing window.
3. The system of claim 2 , wherein the viewing window has a size that is a function of a height value of the LiDAR data point being analyzed.
4. The system of claim 1 , wherein the instructions cause the processor to analyze the clusters of LiDAR data to remove LiDAR data that lacks sufficient neighbors to represent a tree.
5. The system of claim 1 , wherein the instructions cause the processor to analyze the LiDAR data to add LiDAR data to fill in gaps in a cluster of LiDAR data that represents a central area of a tree canopy.
6. The system of claim 1 , wherein the instructions cause the processor to estimate a characteristic of a tree based on the LiDAR data in a cluster that are classified as representing a central area in the canopy of a tree.
7. The system of claim 6 , wherein the characteristic of the tree is whether the tree is a broadleaf tree or a conifer.
8. The system of claim 6 , wherein the characteristic is a quality of the lumber in a tree.
9. A non-transitory computer-readable medium containing instructions that are executable by a processor to identify individual trees in a set of Light Detection and Ranging (LiDAR) data by:
analyzing each LiDAR data point in the set of LiDAR data to determine if the LiDAR data point has one or more neighboring LiDAR data points with a height value that is above a height defined by an angle from the LiDAR data point;
adding the LiDAR data point to a cluster depending on the number of neighboring LiDAR data points with heights above the height defined by the angle; and
classifying the cluster of LiDAR data points as representing a central area of a tree canopy.
10. The non-transitory computer-readable medium of claim 9 , wherein the instructions are executable by the processor to determine a viewing window for the LiDAR data point and wherein the instructions are executable by the processor to determine if the LiDAR data point has one or more neighboring LiDAR data points with a height value that is above the height determined by the angle in an area of the viewing window.
11. The non-transitory computer-readable medium of claim 10 , wherein the instructions are executable by the processor to determine a size for the viewing window as a function of a height value of the LiDAR data point being analyzed.
12. The non-transitory computer-readable medium of claim 9 , wherein the instructions are executable to cause the processor to add LiDAR data to an output file for each LiDAR data point that is classified as representing a central area of a tree canopy.
13. The non-transitory computer-readable medium of claim 12 , wherein the instructions are executable to cause the processor to analyze the LiDAR data in the output file to remove LiDAR data that lack sufficient neighbors to represent a tree.
14. The non-transitory computer-readable medium of claim 12 , wherein the instructions are executable to cause the processor to analyze the LiDAR data in the output file to add LiDAR data to fill in gaps in the LiDAR data that represent a central area of a tree canopy.
15. The non-transitory computer-readable medium of claim 9 , wherein the instructions cause the processor to identify a characteristic of a tree based on the LiDAR data that are classified as representing a central area in the canopy of a tree.
16. The non-transitory computer-readable medium of claim 15 , wherein the characteristic of the tree is whether the tree is a broadleaf tree or a conifer.
17. The non-transitory computer-readable medium of claim 15 , wherein the characteristic is an age of the tree.
18. The non-transitory computer-readable medium of claim 15 , wherein the characteristic is a quality of the lumber of the tree.