Performance of one-class classifiers for invasive species mapping using airborne imaging spectroscopy

Sandra Skowronek, Gregory P. Asner, Hannes Feilhauer

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Most remote sensing approaches for mapping invasive plant species focus on species in a prominent phenological stage, such as during flowering, and do not systematically evaluate the performance for mapping lower cover fractions. In this study, we used airborne imaging spectroscopy (also known as hyperspectral imaging) to detect the invasive grass Phalaris aquatica and the invasive herb Centaurea solstitialis in a pre-flowering stage in the Jasper Ridge Biological Preserve, California, and compared the performance of three different one-class classifiers: Maxent, biased support vector machines and boosted regression trees. We collected presence data for C. solstitialis and P. aquatica to calibrate each approach and additional presence-absence data to validate model performance on 3 m × 3 m plots. The imaging spectroscopy data were acquired using the Carnegie Airborne Observatory Visible-to-Shortwave Infrared (VSWIR) imaging spectrometer (400–2500 nm range) with a pixel size of 1 m × 1 m. The resulting overall accuracies were 72–74% for C. solstitialis, and 83–88% for P. aquatica. For both species, the overall performance was slightly better for Maxent and BRT than for biased SVM. The detection rates for low cover plots were considerably higher for C. solstitialis than for P. aquatica. The models relied on different areas of the reflectance spectrum, but still produced the same general pattern of predicted species occurrences. We conclude that the different one-class classifiers allow for the detection and monitoring of target species with similar success rates.

Original languageEnglish (US)
Pages (from-to)66-76
Number of pages11
JournalEcological Informatics
Volume37
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Fingerprint

Centaurea solstitialis
Imaging Spectroscopy
Phalaris aquatica
invasive species
spectroscopy
Classifiers
Classifier
image analysis
Spectroscopy
Imaging techniques
flowering
Infrared imaging
Observatories
Flowering
Support vector machines
Spectrometers
Maximum Entropy
Remote sensing
species occurrence
Pixels

Keywords

  • Biased support vector machine
  • Boosted regression trees
  • Carnegie airborne observatory
  • Centaurea solstitialis
  • Hyperspectral
  • Imaging spectroscopy
  • Maxent
  • Phalaris aquatica
  • Species distribution model
  • Vegetation
  • Yellow starthistle

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Modeling and Simulation
  • Ecological Modeling
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

Performance of one-class classifiers for invasive species mapping using airborne imaging spectroscopy. / Skowronek, Sandra; Asner, Gregory P.; Feilhauer, Hannes.

In: Ecological Informatics, Vol. 37, 01.01.2017, p. 66-76.

Research output: Contribution to journalArticle

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