Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation

We develop and evaluate a new individual tree detection (ITD) algorithm to automatically locate and estimate the number of individual trees within a Pinus radiata plantation from relatively sparse airborne LiDAR point cloud data. The area of interest comprised stands covering a range of age classes...

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Bibliographic Details
Published in:Australian forestry Vol. 79; no. 2; pp. 126 - 136
Main Authors: Kathuria, Amrit, Turner, Russell, Stone, Christine, Duque-Lazo, Joaqin, West, Ron
Format: Journal Article
Language:English
Published: Taylor & Francis 02.04.2016
Subjects:
age structure
algorithms
canopy
crown detection
forestry
Inventories
LiDAR
Pinus radiata
plantations
regression analysis
remote sensing
trees
ISSN:0004-9158, 2325-6087, 2325-6087
Online Access:Get full text
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Summary:We develop and evaluate a new individual tree detection (ITD) algorithm to automatically locate and estimate the number of individual trees within a Pinus radiata plantation from relatively sparse airborne LiDAR point cloud data. The area of interest comprised stands covering a range of age classes and stocking levels. Our approach is based on local maxima (LM) filtering that tackles the issue of selecting the optimal search radius from the LiDAR point cloud for every potential LM using metrics derived from local neighbourhood data points; thus, it adapts to the local conditions, irrespective of canopy variability. This was achieved through two steps: (i) logistic regression model development using simulated stands composed of individual trees derived from real LiDAR point cloud data and (ii) application testing of the model using real plantation LiDAR point cloud data and geolocated, tree-level reference crowns that were manually identified in the LiDAR imagery. Our ITD algorithm performed well compared with previous studies, producing RMSE of 5.7% and a bias of only −2.4%. Finally, we suggest that the ITD algorithm can be used for accurately estimating stocking and tree mapping, which in turn could be used to derive the plot-level metrics for an area-based approach for enhancing estimates of stand-level inventory attributes based on plot imputation.
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ISSN:0004-9158
2325-6087
2325-6087
DOI:10.1080/00049158.2016.1153770