Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks

Jiarui Dong, Robert K. Kaufmann, Ranga B. Myneni, Compton J. Tucker, Pekka E. Kauppi, Jari Liski, Wolfgang Buermann, V. Alexeyev, Malcolm K. Hughes

Research output: Contribution to journalArticle

269 Scopus citations

Abstract

The relation between satellite measurements of the normalized difference vegetation index (NDVI), cumulated over the growing season, and inventory estimates of forest woody biomass carbon is estimated statistically with data from 167 provinces and states in six countries (Canada, Finland, Norway, Russia and the USA for a single time period and Sweden for two periods). Statistical tests indicate that the regression model can be used to represent the relation between forest biomass and NDVI across spatial, temporal and ecological scales for relatively long time scales. For the 1.42 billion ha of boreal and temperate forests in the Northern Hemisphere, the woody biomass carbon pools and sinks are estimated at a relatively high spatial resolution (8 x 8 km). We estimate the carbon pool to be 61 ± 20 gigatons (109) carbon (Gt C) during the late 1990s and the biomass sink to be 0.68 ± 0.34 Gt C/year between the 1982 and 1999. The geographic detail of carbon sinks provided here can contribute to a potential monitoring program for greenhouse gas emission reduction commitments under the Kyoto Protocol.

Original languageEnglish (US)
Pages (from-to)393-410
Number of pages18
JournalRemote Sensing of Environment
Volume84
Issue number3
DOIs
StatePublished - Mar 1 2003

Keywords

  • Forest biomass pools
  • Forest inventory
  • Kyoto Protocol
  • NDVI
  • Remote sensing
  • Sources and sinks

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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    Dong, J., Kaufmann, R. K., Myneni, R. B., Tucker, C. J., Kauppi, P. E., Liski, J., Buermann, W., Alexeyev, V., & Hughes, M. K. (2003). Remote sensing estimates of boreal and temperate forest woody biomass: Carbon pools, sources, and sinks. Remote Sensing of Environment, 84(3), 393-410. https://doi.org/10.1016/S0034-4257(02)00130-X