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Proceedings Paper

Estimating forest biomass using scale linkage from tree to Landsat-TM reflectance data
Author(s): Chhun-Huor Ung; Marie-Claude Lambert; Frédéric Raulier
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Paper Abstract

Estimates of forest biomass are needed to account for carbon at the tree, stand and regional scales. Sample plots of national forest inventories provide the basic database for these estimates. At the tree scale, a common estimation method is the use of an allometric equation that relates a tree's predicted compartment biomass yi (i = foliage, branches, stem wood or stem bark) with easily obtained non-destructive measurements, i.e., diameter at breast height (D): yi=bi1Dbi2 or with both D and tree height (H): yi=bi1Dbi2Hbi3, bik being the parameters estimated. A common paradigm observed in biomass literature considers that parameter values vary between stands and regions. At the regional scale, however, when comparing national biomass equations to regional biomass equations, our results showed no significant differences between both types of equation. These results contribute to strengthening the allometric theory as an organizing principle for quantifying the relationship between tree size and biomass across spatial scales. In tandem with the allometry theory, we used a soil-canopy model based on Li-Strahler's approach for up-scaling biomass from the tree to stand scale in a mixed hardwood-coniferous forest. Our results indicated that the shadow fraction of Landsat TM reflectance was correlated with stand biomass. However, this model is indebted with heteroscedasticity, meaning that its error increases appreciably when stand biomass density is high.

Paper Details

Date Published: 19 October 2005
PDF: 8 pages
Proc. SPIE 5976, Remote Sensing for Agriculture, Ecosystems, and Hydrology VII, 59761C (19 October 2005); doi: 10.1117/12.626416
Show Author Affiliations
Chhun-Huor Ung, Natural Resources Canada (Canada)
Marie-Claude Lambert, Natural Resources Canada (Canada)
Frédéric Raulier, Univ. Laval (Canada)

Published in SPIE Proceedings Vol. 5976:
Remote Sensing for Agriculture, Ecosystems, and Hydrology VII
Manfred Owe; Guido D'Urso, Editor(s)

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