Detecting Cutleaf Teasel (Dipsacus laciniatus) along a Missouri Highway with Hyperspectral Imagery

Abstract

AbstractCutleaf teasel is an invasive, biennial plant that poses a significant threat to native species along roadsides in Missouri. Flowering plants, together with understory rosettes, often grow in dense patches. Detection of cutleaf teasel patches and accurate assessment of the infested area can enable targeted management along highways. Few studies have been conducted to identify specific species among a complex of vegetation composition along roadsides. In this study, hyperspectral images (63 bands in visible to near-infrared spectral region) with high spatial resolution (1 m) were analyzed to detect cutleaf teasel in two areas along a 6.44-km (4-mi) section of Interstate I-70 in mid Missouri. The identified classes included cutleaf teasel, bare soil, tree/shrub, grass/other broadleaf plants, and water. Classification of cutleaf teasel reached a user's accuracy of 82 to 84% and a producer's accuracy of 89% in the two sites. The conditional κ value was around 0.9 in both sites. The image-classified cutleaf teasel map provides a practical mechanism for identifying locations and extents of cutleaf teasel infestation so that specific cutleaf teasel management techniques can be implemented.Cutleaf teasel is an exotic weed that infests roadside environments in Missouri. As a growing biennial, the plant develops as a rosette during the first year and bolts during the second. Dense patches contain flowering plants with understory rosettes. The objective of this work was to develop approaches for detecting cutleaf teasel patches with accurate assessment in a complex of species along a roadside. Thus, management of cutleaf teasel could be located at specific sites. Two hyperspectral images (63 bands with 1-m spatial resolution) were analyzed to detect cutleaf teasel along the Interstate Highway I-70 in mid Missouri. Classification of cutleaf teasel reached a user's accuracy of 82 to 84% and a producer's accuracy of 89% at the two sites. The image-classified teasel map provides a practical mechanism for identifying the locations and extents of cutleaf teasel infestation so that specific management techniques can be implemented.