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    High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest managers there are two main technical challenges that must be addressed: (1) mapping a single focal species amongst a diverse array of other tree species; and (2) detecting early outbreaks of invasive plant species that are often hidden beneath the forest canopy. To address these challenges, we investigated the performance of two single-class classification frameworks—Biased Support Vector Machine (BSVM) and Mixture Tuned Matched Filtering (MTMF)—to estimate the degree of Psidium cattleianum incidence over a range of forest vertical strata (relative canopy density). We demonstrate that both BSVM and MTMF have the ability to detect relative canopy density of a single focal plant species in a vertically stratified forest, but they differ in the degree of user input required. Our results suggest BSVM as a promising method to disentangle spectrally-mixed classifications, as this approach generates decision values from a similarity function (kernel), which optimizes complex comparisons between classes using a dynamic machine learning process.

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    Barbosa, Jomar; Asner, Gregory; Martin, Roberta; Baldeck, Claire; Hughes, Flint; Johnson, Tracy. 2016. Determining subcanopy Psidium cattleianum invasion in Hawaiian forests using imaging spectroscopy. Remote Sensing. 8(1):33.


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    invasive species, strawberry guava, single-class classification, mixture tuned matched filtering, biased support vector machine, Carnegie Airborne Observatory

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