Predicting tropical tree mortality with leaf spectroscopy
Predicting tropical tree mortality with leaf spectroscopy
复制标题
利用叶子光谱预测热带树木死亡率
DOI:
10.1111/btp.12901
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发表时间:
2020
期刊:
影响因子:
2.1
通讯作者:
Doughty C
中科院分区:
文献类型:
--
作者:
Doughty C
Do tropical trees close to death have a distinct change to their leaf spectral signature? Tree mortality rates have been increasing in tropical forests, 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 (1,000–1,400 nm), and SWIR bands (2,000–2,400 nm) and significant reductions in the potential carbon balance of the leaves (increased Rdarkand 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 (r2of .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.
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DOI:
10.1002/2017jg003883
发表时间:
2017-11
期刊:
Journal of Geophysical Research: Biogeosciences
影响因子:
--
作者:
C. Doughty;P. Santos-Andrade;G. Goldsmith;B. Blonder;A. Shenkin;L. Bentley;C. Chavana-Bryant;
通讯作者:
C. Doughty;P. Santos-Andrade;G. Goldsmith;B. Blonder;A. Shenkin;L. Bentley;C. Chavana-Bryant;
影响因子:
1.4
作者:
E. Miranda;G. Vourlitis;N. P. Filho;Pedro Correto Priante;J. H. Campelo;G. S. Suli;C. L. Fritzen;F. Lobo;Shozo Shiraiwa
通讯作者:
Shozo Shiraiwa
影响因子:
9.4
作者:
Chavana-Bryant, Cecilia;Malhi, Yadvinder;Gerard, France F.
通讯作者:
Gerard, France F.
影响因子:
6.7
作者:
Matheus Henrique Nunes;Sabine Both;Boris Bongalov;Craig C. Brelsford;Sacha Khoury;D. Burslem;Christopher D. Philipson;N. Majalap;T. Riutta;David A. Coomes;Mark Cutler
通讯作者:
Mark Cutler
影响因子:
64.8
作者:
Doughty, Christopher E.;Metcalfe, D. B.;Malhi, Y.
通讯作者:
Malhi, Y.