HallCaseArsenaultEtAl2002

Reference

Hall, R.J., Case, B.S., Arsenault, E., Price, D.T., Luther, J.E., Piercey, D.E., Guindon, L., Fournier, R.A. (2002) Modeling and mapping forest biomass using forest inventory and Landsat TM data: Results from the Foothills Model Forest, Alberta. In International Geoscience and Remote Sensing Symposium (IGARSS). Pages 1320-1323. (Scopus )

Abstract

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 R<sup>2</sup> 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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@INPROCEEDINGS { HallCaseArsenaultEtAl2002,
    AUTHOR = { Hall, R.J. and Case, B.S. and Arsenault, E. and Price, D.T. and Luther, J.E. and Piercey, D.E. and Guindon, L. and Fournier, R.A. },
    TITLE = { Modeling and mapping forest biomass using forest inventory and Landsat TM data: Results from the Foothills Model Forest, Alberta },
    BOOKTITLE = { International Geoscience and Remote Sensing Symposium (IGARSS) },
    YEAR = { 2002 },
    VOLUME = { 3 },
    PAGES = { 1320--1323 },
    ABSTRACT = { 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 R<sup>2</sup> 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. },
    COMMENT = { Cited By (since 1996): 3 Export Date: 10 February 2010 Source: Scopus CODEN: IGRSE },
    KEYWORDS = { Biomass, Forestry, Mapping, Mathematical models, Sustainable development, Forest biomass, Forest inventory, Sustainable forest management, Remote sensing },
    OWNER = { Luc },
    TIMESTAMP = { 2010.02.10 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-0036401368&partnerID=40&md5=720ef8c38bc65b0d37c320b56822ec7e },
}

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