1. A new flexible software tool for rapidly counting individual trees using point cloud data from liDAR or photogrammetry
Mitch Bryson1, Lee Stamm2, Amrit Kathuria3 and Christine Stone3
1Australian Centre of Field Robotics, University of Sydney
2HQPlantations, Queensland
3NSW Forest Science, NSW Department of Industry - Lands s:
IFA Conference, 16 August 2017

2. Introduction: PointcloudITD
We have developed a new software tool “pointcloudITD” for performing Individual Tree Detection (ITD) based on aerially-acquired point cloud data
Detects and maps tree locations and counts from pointclouds
Has the capacity to work with large datasets and can work with LiDAR or photogrammetry points
Uses a machine-learning approach to refine a model for tree identification based on plot-based stem maps
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

3. Background
Kathuria et. al, 2016* developed a tree detection algorithm based on maxima detection and logistic regression model development
Accurate performance, RMSE 5.7% on stands simulated from manually segmented lidar pointclouds
Implemented in R scripts: couldn’t work with large pointclouds
Current software application “pointcloudITD”:
Builds on the approach of this work using a first stage maxima detection and second stage machine learning classifier
Provides variations of classification algorithm, features used and provides a computationally-efficient processing tool implemented in a software GUI
* Kathuria, A., Turner, R., Stone, C., Duque-Lazo, J., West, R. Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation. Australian Forestry, 79:2, 2016.
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

4. Background
Kathuria et. al, 2016* developed a tree detection algorithm based on maxima detection and logistic regression model development
Accurate performance, RMSE 5.7% on stands simulated from manually segmented lidar pointclouds
Implemented in R scripts: couldn’t work with large pointclouds
Current software application “pointcloudITD”:
Builds on the approach of this work using a first stage maxima detection and second stage machine learning classifier
Provides variations of classification algorithm features used and a computationally-efficient processing tool implemented in a software GUI
* Kathuria, A., Turner, R., Stone, C., Duque-Lazo, J., West, R. Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation. Australian Forestry, 79:2, 2016.
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

5. Overview Overview of software application Software results
Tree detection methodology
Processing steps
Software results
Computation/running times
Tree counting accuracy
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

6. Overview of PointcloudITD
PointcloudITD uses a two stage process to identify tree crown locations:
Local maxima finding: candidate tree crown locations
Machine-learnt classification of tree crowns from maxima data
Advantages of a machine learning approach to crown classification:
Uses pointcloud data and CHM raster information in the vicinity of each crown to make a binary decision on whether a maxima point is a tree crown or not
Decision algorithm is made using the data itself (and training examples): flexibility to different conditions present: i.e.
Types of pointclouds data (high vs. low resolution ALS, photogrammetric)
Differing stocking densities, tree age and crown shape
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

7. Overview of PointcloudITD
PointcloudITD uses a two stage process to identify tree crown locations:
Local maxima finding: candidate tree crown locations
Machine-learnt classification of tree crowns from maxima data
Advantages of a machine learning approach to crown classification:
Uses pointcloud data and CHM raster information in the vicinity of each crown to make a binary decision on whether a maxima point is a tree crown or not
Decision algorithm is made using the data itself (and training examples): flexibility to different conditions present: i.e.
Types of pointclouds data (high vs. low resolution ALS, photogrammetric)
Differing stocking densities, tree age and crown shape
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

8. Ground-acquired plot-level stem maps
Workflow
Classification model development using pointclouds and stem reference map:
Loads lidar data and plot data containing ground-acquired stem maps
Uses stem maps to train a classification model that distinguishes real crowns/stems from other local maxima points
LiDAR Data
Classification model
PointcloudITD
Ground-acquired plot-level stem maps
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

9. Workflow Individual Tree Detection using pointclouds: PointcloudITD
Loads lidar data and imports classification model to produce a tree map
Tree Map
LiDAR Data
PointcloudITD
Classification model
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

10. Software GUI
Software uses a fairly simple GUI to provide control over sequential processing steps in a “project-based” workflow
Outputs of each step are stored locally using file formats suitable for use with open-source GIS and data analysis tools (.las, .ply, .tif, .csv)
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

11. Overview of PointcloudITD: Processing Steps
Lidar pre-processing, CHM generation
Local maxima finding, focal statistic extraction
Workflow 1
Workflow 2
Associate training data
Import classification model
Produce classification model
Run classification, produce tree map
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

12. Overview of lidarITD: Processing Steps
Lidar pre-processing, CHM generation
Local maxima finding, focal statistic extraction
Workflow 1
Workflow 2
Associate training data
Import classification model
Produce classification model
Run classification, produce tree map
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

13. Local Maxima and Focal Statistic/Feature Extraction
Software computes the locations of local maxima in the canopy height model at user-specified radii
Focal statistics/pointcloud features are then extracted using the local points around each maxima
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

14. Local Maxima and Focal Statistic/Feature Extraction
Software computes the locations of local maxima in the canopy height model at user-specified radii
Focal statistics/pointcloud features are then extracted using the local points around each maxima
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

15. Local Maxima and Focal Statistic/Feature Extraction
Software computes the locations of local maxima in the canopy height model at user-specified radii
Focal statistics/pointcloud features are then extracted using the local points around each maxima
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

16. Overview of PointcloudITD: Processing Steps
Lidar pre-processing, CHM generation
Local maxima finding, focal statistic extraction
Workflow 1
Workflow 2
Associate training data
Import classification model
Produce classification model
Run classification, produce tree map
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

17. Local Maxima and Focal Statistic/Feature Extraction
Training data (ground acquired stem maps over plots, and plot boundaries) are loaded into the software and associated to detected local maxima
Associated detections become positive training examples, unassociated detections become negative training examples
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

18. Overview of lidarITD: Processing Steps
Lidar pre-processing, CHM generation
Local maxima finding, focal statistic extraction
Workflow 1
Workflow 2
Associate training data
Import classification model
Produce classification model
Run classification, produce tree map
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

19. Produce Classification Model
Positive/negative training examples are used via machine learning to build a classifier that refines a set of detected local maximas (and associated focal statistics) into positive tree detections
lidarITD uses Scikit-learn ( a powerful, open-source machine learning library under-the-hood to build and run classification models
Currently uses Support Vector Machine (SVM) as the default classification algorithm: future version will support nearest neighbours, logistic regression, naïve bayes and decision tree algorithms
Point cloud ITD performs cross-validation model optimisation under-the-hood to optimise classification parameters and provide estimates of model accuracy
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

20. Overview of lidarITD: Processing Steps
Lidar pre-processing, CHM generation
Local maxima finding, focal statistic extraction
Workflow 1
Workflow 2
Associate training data
Import classification model
Produce classification model
Run classification, produce tree map
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

21. Import and Run Classification Model
Classification models can be imported into any other project and used to classify detected local maximas into tree locations
Tree maps are then exported as point coordinates in .shp and .csv formats
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

22. Overview Overview of software application Software results
tree detection methodology
processing steps
Software results
Computation/running times
Tree counting accuracy
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

23. Processing Time Performance
Benchmarked on 2.9GHz Intel Core i7 dual-core laptop for 100 hectare lidar tile with 13.2 Million points containing approximately 40,000 stems
Forest resource unit of 10,000 hectares to be processed:
30 minutes using a four core computer (typical laptop computer) or
15 minutes on a eight core computer (typical high-powered desktop computer).
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

24. Tree Counting Accuracy: LiDAR
Data acquired by HQPlantations over a 47 ha, 1982 Age Class compartment in a Pinus carribea var. Honduras plantation:
Airborne lidar, approx. 27 points/m2
Stem maps for twenty 0.06 ha plots collected on the ground using Trimble GEO7X with an attached Rangefinder
Used ten randomly selected plots for training/model development, ten plots for testing/validation
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

25. Tree Counting Accuracy: LiDAR
RMSE: 7.61% Bias: 0.4%
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

26. Use of Aerial Photogrammetry (AP) Pointclouds
P. radiata plantation managed by Timberlands Pacific PL, located near Springfield, North east Tasmania
Mixed age classes: PHI, MRI and EAI
Airborne LiDAR, approx. 6 points/m2
Aerial Photogrammetry, approx. 70 points/m2
~250 circular plots ( ha), stem counts (no stem locations)
Used PointcloudITD to count stems using both AP and ALS data (first-stage processing only)
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

27. Maxima Detection Only (ALS and AP)
Initial results just using maxima detection alone for tree counting:
Heterogeneity in stand and topography means peak counts alone are not very accurate for stocking
Lack of stem maps means we haven’t run second-stage classification in Pointcloud ITD: future work looking at dealing with plot data that is count-only
ALS RMSE (%)
AP RMSE (%)
All Plots
48.69
53.05
PHI
33.23
35.80
MRI
51.21
50.31
EAI
26.23
30.17
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data

28. Conclusions
Individual Tree Detection/Counting software application developed
Allows the user to exploit a machine-learning strategy to tailor stem detection and counting using reference data (stem maps) collected in the field
Initial version released as part of a recently completed FWPA project
This version will be available on the FWPA website with the associated project Final Report “Deployment and integration of cost-effective high resolution remotely sensed data for the Australian forest industry”
If companies have any AP data and coincidence stem maps, Mitch would be keen to further test and refine the point cloud ITD App.
Dr. Mitch Bryson | Software tool for rapidly counting individual trees using point cloud data