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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Veröffentlicht in:Australian forestry Jg. 79; H. 2; S. 126 - 136
Hauptverfasser: Kathuria, Amrit, Turner, Russell, Stone, Christine, Duque-Lazo, Joaqin, West, Ron
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 02.04.2016
Schlagworte:
age structure
algorithms
canopy
crown detection
forestry
Inventories
LiDAR
Pinus radiata
plantations
regression analysis
remote sensing
trees
ISSN:0004-9158, 2325-6087, 2325-6087
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Abstract 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.
AbstractList 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.
Author Stone, Christine
Kathuria, Amrit
Turner, Russell
West, Ron
Duque-Lazo, Joaqin
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  surname: Kathuria
  fullname: Kathuria, Amrit
  email: amrit.kathuria@dpi.nsw.gov.au
  organization: Forest Science, NSW Department of Primary Industries
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  surname: Turner
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  organization: Forest Science, NSW Department of Primary Industries
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  givenname: Joaqin
  surname: Duque-Lazo
  fullname: Duque-Lazo, Joaqin
  organization: Department of Forestry, University of Córdoba
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  givenname: Ron
  surname: West
  fullname: West, Ron
  organization: Australian National Centre for Ocean Resources and Security (ANCORS), University of Wollongong
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Snippet We develop and evaluate a new individual tree detection (ITD) algorithm to automatically locate and estimate the number of individual trees within a Pinus...
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SubjectTerms age structure
algorithms
canopy
crown detection
forestry
Inventories
LiDAR
Pinus radiata
plantations
regression analysis
remote sensing
trees
Title Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation
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