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. 2010 Sep 21;107(38):16738-42.
doi: 10.1073/pnas.1004875107. Epub 2010 Sep 7.

High-resolution forest carbon stocks and emissions in the Amazon

Gregory P Asner  1 , George V N Powell, Joseph Mascaro, David E Knapp, John K Clark, James Jacobson, Ty Kennedy-Bowdoin, Aravindh Balaji, Guayana Paez-Acosta, Eloy Victoria, Laura Secada, Michael Valqui, R Flint Hughes
Affiliations

High-resolution forest carbon stocks and emissions in the Amazon

Gregory P Asner et al. Proc Natl Acad Sci U S A. .

Abstract

Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Sources of variation in forest canopy height detected with high-resolution Carnegie Airborne Observatory LiDAR in the Peruvian Amazon: (A) artisanal gold mining; (B) selective logging; (C) deforestation for cattle ranching; (D) infrastructural development in towns, cities, and supporting land uses; and (E) alluvial and geologic substrate. White bars indicate a distance of 0.5 km in each example image.
Fig. 2.
Fig. 2.
Variation in aboveground carbon storage at 0.1 ha resolution throughout a 4.3 million ha region of the Peruvian Amazon, derived from an integrated use of CLASlite, LiDAR and field-plot data. Examples of (i) artisanal gold mining, (ii) selective logging and other forest disturbances, and (iii) deforestation for cattle ranching, road building, and other infrastructure are indicated.
Fig. 3.
Fig. 3.
(A) Distributions of aboveground carbon storage for the seven common forest types found in the Peruvian Amazon, derived from airborne LiDAR. (B) Annual emissions of carbon from deforestation and degradation mapped from time-series CLASlite imagery and LiDAR data.
See this image and copyright information in PMC

Comment in

  • Implications of allometry.
    Skole DL, Samek JH, Smalligan MJ. Skole DL, et al. Proc Natl Acad Sci U S A. 2011 Jan 25;108(4):E12; author reply E13-4. doi: 10.1073/pnas.1015854108. Epub 2011 Jan 7. Proc Natl Acad Sci U S A. 2011. PMID: 21217056 Free PMC article. No abstract available.

References

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