Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery

Pathogenic wood decay fungi such as species of are some the most serious forest pathogens in Europe, causing rot tree boles and loss growth, with estimated economic losses eight hundred million euros per year. In conifers low resinous heartwood , these commonly confined to thus external infection signs on bark or foliage trees normally absent. Consequently, determining extent disease presence a stand field surveys is not practical for guiding management decisions optimal rotation time. Remote sensing technologies airborne laser scanning aerial imagery already used reduce reliance fieldwork inventories. This study aimed use remote detect spruce ( L. Karst.) forests Norway. An hyperspectral imager provided information classifying absence single-tree-based framework. Ground reference data showing were collected by harvest machine operators during stands. Random support vector algorithms classify rot. Results indicate 64% overall classification accuracy presence-absence rot, although additional work remains make classifications usable management.HeterobasidionPiceaAbiesPicea abies