DOI: 10.1002/2013GL058373
论文题名: Imaging spectroscopy- and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystem dynamics
作者: Antonarakis A.S. ; Munger J.W. ; Moorcroft P.R.
刊名: Geophysical Research Letters
ISSN: 0094-10198
EISSN: 1944-9929
出版年: 2014
卷: 41, 期: 7 起始页码: 2535
结束页码: 2542
语种: 英语
英文关键词: Carbon Fluxes
; Ecosystem Dynamics
; Forest Structure and Composition
; Harvard Forest
; Hyperspectral
; Lidar
Scopus关键词: Biospherics
; Carbon
; Computer simulation
; Ecosystems
; Forecasting
; Optical radar
; Remote sensing
; Structure (composition)
; Vegetation
; Carbon fluxes
; Ecosystem dynamics
; Forest structure
; Harvard forests
; HyperSpectral
; Forestry
; carbon flux
; ecosystem dynamics
; energy flux
; forest ecosystem
; ground-based measurement
; lidar
; prediction
; remote sensing
; satellite data
; spectroscopy
; water flow
; Carbon
; Ecosystems
; Forest Canopy
; Plants
; Radar
; Remote Sensing
; Harvard Forest
; Massachusetts
; United States
英文摘要: The composition and structure of vegetation are key attributes of ecosystems, affecting their current and future carbon, water, and energy fluxes. Information on these attributes has traditionally come from ground-based inventories of the plant canopy within small sample plots. Here we show how imaging spectrometry and waveform lidar can be used to provide spatially comprehensive estimates of forest canopy composition and structure that can improve the accuracy of the carbon flux predictions of a size-structured terrestrial biosphere model, reducing its root-mean-square errors from 85%-104% to 37%-57% the improvements are qualitatively and quantitatively similar to those obtained from simulations initialized with ground measurements and approximately double the estimated rate of ecosystem carbon uptake as compared to a potential vegetation simulation these results suggest that terrestrial biosphere model simulations can utilize modern remote-sensing data on vegetation composition and structure to improve their predictions of the current and near-term future functioning of the terrestrial biosphere. Key Points Predictions of forest change hampered by errors in current model formulations Remote Sensing can derive fine-scale information on the current ecosystem state Regional carbon fluxes can be constrained using remote sensing derived info © 2014. American Geophysical Union. All Rights Reserved. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897324429&doi=10.1002%2f2013GL058373&partnerID=40&md5=45ffc6609c62cfac0762002126e7a827
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/7462
Appears in Collections: 气候减缓与适应
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作者单位: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States
Recommended Citation:
Antonarakis A.S.,Munger J.W.,Moorcroft P.R.. Imaging spectroscopy- and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystem dynamics[J]. Geophysical Research Letters,2014-01-01,41(7).