Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland

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Abstract

Recognising the importance of the timing of image acquisition on the spectral response in remote sensing of vegetated ecosystems is essential. This study used full wavelength, 350-2500. nm, field spectroscopy to establish a spectral library of phenological change for key moorland species, and to investigate suitable temporal windows for monitoring upland peatland systems. Spectral responses over two consecutive growing seasons were recorded at single species plots for key moorland species and species sown to restore eroding peat. This was related to phenological change using narrowband vegetation indices (Red Edge Position, Photochemical Reflectance Index, Plant Senescence Reflection Index and Cellulose Absorption Index); that capture green-up and senescence related changes in absorption features in the visible to near infrared and the shortwave infrared. The selection of indices was confirmed by identifying the regions of maximum variation in the captured reflectance across the full spectrum. The indices show change in the degree of variation between species occurring from April to September, measured for plant functional types. A discriminant function analysis between indices and plant functional types determines how well each index was able to differentiate between the plant functional groups for each month. It identifies April and July as the two months where the species are most separable. What is presented here is not one single recommendation for the optimal temporal window for operational monitoring, but a fuller understanding of how the spectral response changes with the phenological cycle, including recommendations for what indices are important throughout the year. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
Original languageEnglish
Pages (from-to)49-58
Number of pages9
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume90
DOIs
Publication statusPublished - Apr 2014

Keywords

  • Ecology
  • High resolution
  • Hyper spectral
  • Monitoring
  • Spectral
  • Vegetation

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