Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few h...

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Bibliographic Details
Published in:Forests Vol. 13; no. 1; p. 3
Main Authors: Sparks, Aaron M., Smith, Alistair M.S.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2022
Subjects:
Accuracy
Airborne lasers
Airborne sensing
Algorithms
Biomass
Canopies
canopy
Coniferous forests
data collection
Datasets
Diameters
Drones
forest inventory
Forest products
Forestry
Forests
Landowners
Lasers
Lidar
Plant species
Proprietary
Recall
Remote sensing
Resource management
Software
Species classification
supply chain
supply chain management
Supply chains
tree and stand measurements
Trees
Unmanned aerial vehicles
United States > US
Idaho
ISSN:1999-4907, 1999-4907
Online Access:Get full text
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Summary:Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.
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ISSN:1999-4907
1999-4907
DOI:10.3390/f13010003