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

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.