PhD student Auburn university Auburn, Alabama, United States
By leveraging advanced UAV-based multispectral imaging and utilizing machine learning algorithms, we can provide spatially explicit information essential for effective forest management planning. This framework enables accurate delineation of diseased and healthy trees, empowering managers to implement targeted interventions and optimize resource allocation for mitigating BSNB and promoting forest health.
Learning Objectives:
Utilize UAV-borne multispectral imagery with machine learning techniques (Support Vector Machines (SVM) and Artificial Neural Network (ANN)) to detect and map the distribution of healthy and BSNB-infected pine trees.
Assess the performance of point cloud-based metrics derived from UAV-multispectral imagery for individual tree detection (ITD) in quantifying the density of infected trees.
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