Canadian Forest Service Publications
Modeling and mapping forest biomass using forest inventory and Landsat TM data: results from the Foothills Model Forest, Alberta. 2002. Hall, R.J.; Case, B.S.; Arsenault, E.; Price, D.T.; Luther, J.E.; Piercey, D.E.; Guindon, L.; Fournier, R.A. Pages 1320-1323 (Vol. 3) in Proceedings IGARSS 2002. IEEE International Geoscience and Remote Sensing Symposium/24th Canadian Symposium on Remote Sensing (CD-ROM), June 24-28, 2002, Toronto, Ontario. IEEE, Piscataway, New Jersey, USA. 4 p.
Available from: Northern Forestry Centre
Catalog ID: 20570
Forest biomass information is needed for reporting of selected indicators of sustainable forest management and for models that estimate carbon budgets and forest productivity, particularly within the context of a changing climate. In collaboration with the Canadian Space Agency, a strategy for mapping Canada's forest biomass has been developed as part of the Earth Observation for Sustainable Development of Forests (EOSD) project. This paper reports on the results derived from an application of this strategy to a pilot study area in the Foothills Model Forest, Alberta. Methods to estimate forest biomass have been developed using tree-level inventory plot data that is then extrapolated to the stand level by statistical relationships between biomass density and stand structural characteristics. These ground-based biomass estimates serve as source data that are related to stand structure derived from classified Landsat TM data. Models developed from inventory data to estimate biomass density attained adjusted R2 values that ranged from 0.60 to 0.77 for 5 species groups, and tests with an independent validation sample compared favourably for all species (deciduous, lodgepole pine, mixed species, white spruce/fir), except black spruce/larch. Landsat-derived forest biomass was statistically and moderately correlated to the inventory-derived biomass with values of 0.63, 0.68, and 0.70 for conifer, deciduous, and mixed species, respectively. Research areas were identified from both inventory and remote sensing perspectives that will lead to incremental improvements in biomass estimation.
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