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  • 标题:Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy
  • 本地全文:下载
  • 作者:Gregory P. Asner ; Daniel Nepstad ; Gina Cardinot
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2004
  • 卷号:101
  • 期号:16
  • 页码:6039-6044
  • DOI:10.1073/pnas.0400168101
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Amazonia contains vast stores of carbon in high-diversity ecosystems, yet this region undergoes major changes in precipitation affecting land use, carbon dynamics, and climate. The extent and structural complexity of Amazon forests impedes ground studies of ecosystem functions such as net primary production (NPP), water cycling, and carbon sequestration. Traditional modeling and remote-sensing approaches are not well suited to tropical forest studies, because (i) biophysical mechanisms determining drought effects on canopy water and carbon dynamics are poorly known, and (ii) remote-sensing metrics of canopy greenness may be insensitive to small changes in leaf area accompanying drought. New spaceborne imaging spectroscopy may detect drought stress in tropical forests, helping to monitor forest physiology and constrain carbon models. We combined a forest drought experiment in Amazonia with spaceborne imaging spectrometer measurements of this area. With field data on rainfall, soil water, and leaf and canopy responses, we tested whether spaceborne hyperspectral observations quantify differences in canopy water and NPP resulting from drought stress. We found that hyperspectral metrics of canopy water content and light-use efficiency are highly sensitive to drought. Using these observations, forest NPP was estimated with greater sensitivity to drought conditions than with traditional combinations of modeling, remote-sensing, and field measurements. Spaceborne imaging spectroscopy will increase the accuracy of ecological studies in humid tropical forests.
  • 关键词:carbon cycle ; rainforest ; remote sensing ; tropical
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