A new flexible software tool for rapidly counting individual trees using point cloud data from lidar or photogrammetry

Transcription

1 A new flexible software tool for rapidly counting individual trees using point cloud data from lidar or photogrammetry Mitch Bryson 1, Lee Stamm 2, Amrit Kathuria 3 and Christine Stone 3 1 Australian Centre of Field Robotics, University of Sydney 2 HQPlantations, Queensland 3 NSW Forest Science, NSW Department of Industry - Lands s: christine.stone@industry.nsw.gov.au m.bryson@acfr.usyd.edu.au 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 plotbased stem maps 2

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,

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,

5 Overview Overview of software application Tree detection methodology Processing steps Software results Computation/running times Tree counting accuracy 5

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 6

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 7

8 Workflow 1. 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 PointcloudITD Classification model Ground-acquired plot-level stem maps 8

9 Workflow 2. Individual Tree Detection using pointclouds: Loads lidar data and imports classification model to produce a tree map Tree Map LiDAR Data Classification model PointcloudITD 9

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) 10

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 11

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 12

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 13

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 14

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 Pointcloud Features Raster (Canopy Surface) Features Height max., mean, mode, range, std., var., skew, kurtosis Height percentiles: 0 to 90% Height ranking, distance to highest point Radius to which point is still maxima Point density CHM mean, std. CHM gradient magnitude and direction 15

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 16

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 17

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 18

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 underthe-hood to optimise classification parameters and provide estimates of model accuracy 19

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 20

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 21

22 Overview Overview of software application tree detection methodology processing steps Software results Computation/running times Tree counting accuracy 22

23 Processing Time Performance Processing Step Processing time (sec) Canopy Height Model Generation 35.5 Maxima Finding ( m) 3.5 Focal Statistics Calculation 33.0 Classify Stems 0.8 Total: 72.8 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). 23

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/m 2 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 24

25 Tree Counting Accuracy: LiDAR RMSE: 7.61% Bias: 0.4% 25

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/m 2 Aerial Photogrammetry, approx. 70 points/m 2 ~250 circular plots ( ha), stem counts (no stem locations) Used PointcloudITD to count stems using both AP and ALS data (first-stage processing only) 26

27 Maxima Detection Only (ALS and AP) Initial results just using maxima detection alone for tree counting: ALS RMSE (%) AP RMSE (%) All Plots PHI MRI EAI 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 27

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. (m.bryson@acfr.usyd.edu.au) 28