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Mapping global forest biomass and its changes over the first decade of the 21st century

Abstract

Forests played an important role in carbon sequestration during the past two decades. Using a model tree ensemble method (MTE) to regress the seven reflectance bands of EOS-Terra-MODIS satellite data against country level forest biomass carbon density (BCD) of 2001–2005 provided by United Nations’s Forest Resource Assessment (FRA), we developed a global map of forest BCD at 1 km×1 km resolution for both 2001–2005 and 2006–2010. For 2006–2010, the total global forest biomass carbon stock is estimated as 279.6±7.1 Pg C, and the tropical forest biomass carbon stock is estimated as 174.4±5.4 Pg C. During the first decade of the 21st century, we estimated an increase of global forest biomass of 0.28±0.75 Pg C yr−1. Tropical forest biomass carbon stock slightly decreased (−0.31±0.60 Pg C yr−1); by contrast, temperate and boreal forest biomass increased (0.58±0.28 Pg C yr−1) during the same period. Our estimation of the global forest biomass carbon stock and its changes is subject to uncertainties due to lack of extensive ground measurements in the tropics, spatial heterogeneity in large countries, and different definitions of forest. The continuously monitoring of forest biomass carbon stock with MODIS satellite data will provide useful information for detecting forest changes.

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Acknowledgements

We thank Dr. Cleo Chou for her comments on an early version of this manuscript. The work was supported by the Purdue University Forestry and Natural Resources research scholarship and the U. S. Forest Services contract grant to the Woods Hole Research Center.

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Correspondence to Anping Chen.

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Chen, A., Peng, S. & Fei, S. Mapping global forest biomass and its changes over the first decade of the 21st century. Sci. China Earth Sci. 62, 585–594 (2019). https://doi.org/10.1007/s11430-018-9277-6

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  • DOI: https://doi.org/10.1007/s11430-018-9277-6

Keywords

  • Forest biomass
  • Carbon stock
  • Model tree ensemble (MTE)
  • MODIS
  • Remote sensing