A hyperspectral image can predict tropical tree growth rates in single-species stands

T. Trevor Caughlin, Sarah J. Graves, Gregory P. Asner, Michiel Van Breugel, Jefferson S. Hall, Roberta E. Martin, Mark S. Ashton, Stephanie A. Bohlman

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Remote sensing is increasingly needed to meet the critical demand for estimates of forest structure and composition at landscape to continental scales. Hyperspectral images can detect tree canopy properties, including species identity, leaf chemistry and disease. Tree growth rates are related to these measurable canopy properties but whether growth can be directly predicted from hyperspectral data remains unknown. We used a single hyperspectral image and light detection and ranging-derived elevation to predict growth rates for 20 tropical tree species planted in experimental plots. We asked whether a consistent relationship between spectral data and growth rates exists across all species and which spectral regions, associated with different canopy chemical and structural properties, are important for predicting growth rates. We found that a linear combination of narrowband indices and elevation is correlated with standardized growth rates across all 20 tree species (R2 = 53.70%). Although wavelengths from the entire visible-to-shortwave infrared spectrum were involved in our analysis, results point to relatively greater importance of visible and near-infrared regions for relating canopy reflectance to tree growth data. Overall, we demonstrate the potential for hyperspectral data to quantify tree demography over a much larger area than possible with field-based methods in forest inventory plots.

Original languageEnglish (US)
Pages (from-to)2367-2373
Number of pages7
JournalEcological Applications
Volume26
Issue number8
DOIs
StatePublished - Dec 1 2016
Externally publishedYes

Fingerprint

canopy
canopy reflectance
forest inventory
demography
near infrared
wavelength
remote sensing
method
index
demand
chemical
detection
analysis

Keywords

  • canopy biology
  • field planting trials
  • forest dynamics
  • hyperspectral
  • light detection and ranging
  • Panama
  • plantation
  • precision forestry
  • reforestation
  • remote sensing
  • tree demography
  • tropical forest

ASJC Scopus subject areas

  • Ecology

Cite this

A hyperspectral image can predict tropical tree growth rates in single-species stands. / Caughlin, T. Trevor; Graves, Sarah J.; Asner, Gregory P.; Van Breugel, Michiel; Hall, Jefferson S.; Martin, Roberta E.; Ashton, Mark S.; Bohlman, Stephanie A.

In: Ecological Applications, Vol. 26, No. 8, 01.12.2016, p. 2367-2373.

Research output: Contribution to journalArticle

Caughlin, TT, Graves, SJ, Asner, GP, Van Breugel, M, Hall, JS, Martin, RE, Ashton, MS & Bohlman, SA 2016, 'A hyperspectral image can predict tropical tree growth rates in single-species stands', Ecological Applications, vol. 26, no. 8, pp. 2367-2373. https://doi.org/10.1002/eap.1436
Caughlin, T. Trevor ; Graves, Sarah J. ; Asner, Gregory P. ; Van Breugel, Michiel ; Hall, Jefferson S. ; Martin, Roberta E. ; Ashton, Mark S. ; Bohlman, Stephanie A. / A hyperspectral image can predict tropical tree growth rates in single-species stands. In: Ecological Applications. 2016 ; Vol. 26, No. 8. pp. 2367-2373.
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