Tropical mangrove species discrimination using hyperspectral data: A laboratory study

C. Vaiphasa, S. Ongsomwang, T. Vaiphasa, A.K. Skidmore

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129 Citations (Scopus)

Abstract

The aim of this study is to test whether spectra of crown canopy leaves of various tropical mangrove species measured under laboratory conditions contain sufficient spectral information for discriminating mangroves at the species level. This laboratory-level study is one of the most important prerequisites to the future use of airborne and satellite hyperspectral sensors for mangrove studies. First, spectral responses of 16 Thai tropical mangrove species (2151 spectral bands between 350 nm and 2500 nm) were recorded from the leaves, using a spectrometer under laboratory conditions. Next, the mangrove spectra were statistically tested using one-way ANOVA to see whether they significantly differ at every spectral location. Finally, the spectral separability between each pair of mangrove species was quantified using the Jeffries¿Matusita (J¿M) distance measure. It turned out that the 16 mangrove species under study were statistically different at most spectral locations, with a 95% confidence level (p <0.05). The total number of spectral bands that had p-values less than 0.05 was 1941, of which 477 bands had a 99% confidence level (p <0.01). Moreover, the J¿M distance indices calculated for all pairs of the mangrove species illustrated that the mangroves were spectrally separable except the pairs that comprised the members of Rhizophoraceae. Although the difficulties of discriminating the members of Rhizophoraceae are expected, the overall result encourages further investigations into the use of on-board hyperspectral sensors to see whether mangrove species can be separated when the difficulties of the field conditions are taken into account.
Original languageEnglish
Pages (from-to)371-379
JournalEstuarine Coastal and Shelf Science
Volume65
Issue number1-2
DOIs
Publication statusPublished - 2005

Keywords

  • feature-selection
  • reflectance variability
  • chlorophyll
  • vegetation
  • canopy
  • classification
  • ecosystems
  • airborne
  • wetland
  • leaves

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