期刊
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
卷 116, 期 -, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2010JG001567
关键词
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资金
- CFCAS
- NSERC [CRDPJ337273-06]
- BIOCAP Canada
- Climate Research Division of Environment Canada
- National Environment Research Council (NERC) UK [NE/G000360/1]
- Canadian Oilsands Network for Research and Development
- Ducks Unlimited
- Alberta-Pacific Forest Industries Inc.
- Forest Producers Association of Canada
- Canadian Foundation for Innovation (CFI)
- Applied Geomatics Research Group, NS
- Optech Inc., Toronto
- NERC [NE/G000360/1] Funding Source: UKRI
- Natural Environment Research Council [NE/G000360/1] Funding Source: researchfish
In this study, a Boolean classification was applied using novel methods to 3-D vegetation structural and topographic attributes found within flux footprint source/sink areas measured by eddy covariance instrumentation. The purpose was to determine if the spatial frequency of 3-D attributes, such as canopy height, effective leaf area index, etc., found within 1 km resolution Moderate Resolution Imaging Spectroradiometer (MODIS) pixels were significantly different from or similar to attributes sampled by flux footprints originating from prevailing wind directions. A Kolmogorov-Smirnov test was used for the first time to apply confidence limits to individual MODIS pixels based on (1) the spatial distribution of cumulative frequencies of attributes representative of those sampled by eddy covariance and (2) temporal representation of MODIS pixels related to area sampling frequency by eddy covariance based on wind direction. Structural and topographic attributes at homogeneous Southern Old Aspen and heterogeneous Upland Aspen sites are representative of 56% and 69% of a 1 km radius area surrounding the tower and 21% and 47% of a 4 x 4 km area. Attributes found within the MODIS tower pixel compare well with attributes most frequently sampled by eddy covariance instruments at both sites. By classifying pixels using the Boolean approach, correspondence between MODIS pixels and eddy covariance estimates of gross primary production (GPP) explain up to 13% more variance than using pixels proximal to the tower. This study, therefore, provides a method for choosing MODIS pixels that have similar attributes to those found within footprints most frequently sampled by eddy covariance.
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