Tree Detection and Health Monitoring in Multispectral Aerial Imagery and Photogrammetric Pointclouds Using Machine Learning

Lloyd Windrim; Angus J. Carnegie; Murray Webster; Mitch Bryson

doi:10.1109/JSTARS.2020.2995391

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Tree Detection and Health Monitoring in Multispectral Aerial Imagery and Photogrammetric Pointclouds Using Machine Learning

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Tree Detection and Health Monitoring in Multispectral Aerial Imagery and Photogrammetric Pointclouds Using Machine Learning

Lloyd Windrim, Angus J. Carnegie, Murray Webster and Mitch Bryson

IEEE journal of selected topics in applied earth observations and remote sensing, Vol.13, pp.2554-2572

27/05/2020

DOI: https://doi.org/10.1109/JSTARS.2020.2995391

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Abstract

Feature extraction

Forestry

Image color analysis

Machine learning

Monitoring

multispectral imaging

photogrammetry

Task analysis

Three-dimensional displays

Vegetation

A machine learning methodology is developed for the detection of individual trees, classification of health, and detection of dead/dying trees in 125 mm resolution aerial multispectral orthoimagery and photogrammetric pointcloud data. The novelty of the proposed method lies in its flexible utilization of features from different georegistered data sources. The methodology is evaluated on a commercial Pinus radiata plantation with a known outbreak of Sirex noctilio. An analysis was carried out to determine the value of the different sensors/data sources and the influence of the spatial resolution of the data on performance. Using the proposed methodology, trees were detected with approximate commission and omission errors of 5% and 22%, respectively, whilst dead or dying trees could be detected with commission and omission errors of approximately 26% and 9%. It was found that the multispectral imagery was the most informative source of data for the given tasks. Tree detection in general was found to be sensitive to the spatial resolution of the data, whilst diseased tree detection was found to be more robust.

Details

Title: Tree Detection and Health Monitoring in Multispectral Aerial Imagery and Photogrammetric Pointclouds Using Machine Learning
Creators: Lloyd Windrim - University of Sydney
Angus J. Carnegie - NSW Department of Primary Industries
Murray Webster - ForeSense (Gosford, NSW)
Mitch Bryson - University of Sydney
Publication Details: IEEE journal of selected topics in applied earth observations and remote sensing, Vol.13, pp.2554-2572
Publisher: IEEE
Grant note: National Sirex Coordination Committee
Identifiers: 991013176691402368
Copyright: This work is licensed under a Creative Commons Attribution 4.0 License
Academic Unit: Faculty of Science and Engineering
Language: English
Resource Type: Journal article

Tree Detection and Health Monitoring in Multispectral Aerial Imagery and Photogrammetric Pointclouds Using Machine Learning

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UN Sustainable Development Goals (SDGs)

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