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Published November 12, 2020 | Version v1
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Predicting tropical tree mortality with leaf spectroscopy

  • 1. Northern Arizona University
  • 2. James Cook University
  • 3. University of Oxford
  • 4. University of Edinburgh

Description

Do tropical trees close to death have a distinct change to their leaf spectral signature? Tree mortality rates have been increasing in tropical forests globally, reducing the global carbon sink.  Upcoming hyperspectral satellites could be used to predict regions close to experiencing extensive tree mortality during periods of stress, such as drought.  Here we show, for a tropical rainforest in Borneo, how imminent tropical tree mortality impacts leaf physiological traits and reflectance.  We measured leaf reflectance (400-2500 nm), light saturated photosynthesis (Asat), leaf dark respiration (Rdark), leaf mass area (LMA) and % leaf water across five campaigns in a six-month period during which there were two causes of tree mortality: a major natural drought and a co-incident tree stem girdling treatment.  We find that prior to mortality, there were significant (P<0.05) leaf spectral changes in the red (650-700 nm), the NIR (1000 -1400 nm) and SWIR bands (2000-2400 nm) and significant reductions in the potential carbon balance of the leaves (increased Rdark and reduced Asat).  We show that the partial least squares regression technique can predict mortality in tropical trees across different species and functional groups with medium precision but low accuracy (r2 of 0.65 and RMSE/mean of 0.58).   However, most tree death in our study was due to girdling, which is not a natural form of death. More research is needed to determine if this spectroscopy technique can be applied to tropical forests in general.

Notes

To produce all figures in the paper, run Doughty_Biotropica_2020.m in the same folder with the datasets girdle_data1.mat and girdle_data2.mat.  Raw spectral data are in the folder spectral_data.  Raw physiology data are in the folder licro_data.

Funding provided by: John Fell Fund, University of Oxford
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004789
Award Number:

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