Seasonal variation in spectral signatures of five genera of rainforest trees

Monica Papes, Raul Tupayachi, Paola Martinez, A. Townsend Peterson, Gregory P. Asner, George V.N. Powell

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Recent technological and methodological advances in the field of imaging spectroscopy (or hyperspectral imaging) make possible new approaches to studying regional ecosystem processes and structure. We use Earth Observing-1 Hyperion satellite hyperspectral imagery to test our ability to identify tree species in a lowland Peruvian Amazon forest, and to investigate seasonal variation in species detections related to phenology. We obtained four images from 2006-2008, and used them to spectrally differentiate crowns of 42 individual trees of 5 taxa using linear discriminant analysis. Temporal variation of tree spectra was assessed using three methods, based on 1) position of spectra in a two-dimensional canonical variable space, 2) a broadband, multispectral dataset derived from sets of narrow bands identified as informative to spectrally separate taxa, and 3) narrow band vegetation indices (photochemical reflectance index and anthocyanin reflectance index) sensitive to leaf pigments. We obtained high classification success with a reduced set of trees (28 individuals) whose crowns were well represented on Hyperion 30 m resolution pixels. Temporal variability of spectra was confirmed by each of the three methods employed. Understanding seasonality of spectral characteristics of tropical tree crowns has implications in spectral based multi-seasonal species mapping and studying ecosystem processes.

Original languageEnglish (US)
Article number6516076
Pages (from-to)339-350
Number of pages12
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number2
StatePublished - 2013
Externally publishedYes


  • EO-1 Hyperion
  • hyperspectral
  • narrow bands
  • remote sensing
  • seasonality
  • tropical tree

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science


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