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Remote Sens. 2016, 8(1), 33; doi:10.3390/rs8010033

Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy

1
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
2
Institute of Pacific Islands Forestry, Pacific Southwest Research Station, USDA Forest Service, Hilo, HI 96720, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Susan L. Ustin, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 15 October 2015 / Revised: 8 December 2015 / Accepted: 29 December 2015 / Published: 5 January 2016
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
View Full-Text   |   Download PDF [6334 KB, uploaded 5 January 2016]   |  

Abstract

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. View Full-Text
Keywords: invasive species; strawberry guava; single-class classification; mixture tuned matched filtering; biased support vector machine; Carnegie Airborne Observatory invasive species; strawberry guava; single-class classification; mixture tuned matched filtering; biased support vector machine; Carnegie Airborne Observatory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Barbosa, J.M.; Asner, G.P.; Martin, R.E.; Baldeck, C.A.; Hughes, F.; Johnson, T. Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy. Remote Sens. 2016, 8, 33.

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