Climatic Change

, Volume 90, Issue 3, pp 269–282 | Cite as

Storage of carbon in U.S. forests predicted from satellite data, ecosystem modeling, and inventory summaries

  • Christopher Potter
  • Peggy Gross
  • Steven Klooster
  • Matthew Fladeland
  • Vanessa Genovese


A plant and soil simulation model based on satellite observations of vegetation and climate data was used to estimate the potential carbon pools in standing wood biomass across all forest ecosystems of the conterminous United States up to the year 1997. These modeled estimates of vegetative carbon potential were compared to aggregated measurements of standing wood biomass from the U. S. Forest Service’s national Forest Inventory and Analysis (FIA) data set and the Carbon Online Estimator (COLE) to understand: 1) predominant geographic variations in tree growth rate and 2) local land cover and land use history including the time since the last stand-replacing disturbance (e.g., from wildfire or harvest). Results suggest that although wood appears to be accumulating at high rates in many areas of the U.S. (Northwest and Southeast), there are still extensive areas of relatively low biomass forest in the late 1990s according to FIA records. We attribute these low biomass accumulation levels to the high frequency of disturbances, which can be observed even in high production areas such as the Southeast due to frequent forest harvests. Ecosystem models like the one presented in this study have been coupled with satellite observations of land cover and green plant density to uniquely differentiate areas with a high potential for vegetative carbon storage at relatively fine spatial resolution.


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  1. Ahl DE, Gower ST, Mackay DS, Burrows SN, Normanb JM, Diak GR (2004) Heterogeneity of light use efficiency in a northern Wisconsin forest: implications for modeling net primary production with remote sensing. Remote Sens Environ 93:168–178CrossRefGoogle Scholar
  2. Baccini A, Friedl MA, Woodcock CE, Warbington R (2004) Forest biomass estimation over regional scales using multi-source data. Geophys Res Lett 31:L10501. doi:10.1029/2004GL019782
  3. Birdsey RA (1996) Carbon storage for major forest types and regions in the conterminous United States. In: Sampson RN, Hair D (eds) Forests and global change, volume 2: forest management opportunities for mitigating carbon emissions. American Forests, Washington, DC, pp 1–25. AppendicesGoogle Scholar
  4. Brown SL, Schroeder PE (1999) Spatial patterns of aboveground production and mortality of woody biomass for eastern U.S. forests. Ecol Appl 9:968–980Google Scholar
  5. Brown S, Schroeder P, Birdsey R (1997) Aboveground biomass distribution of US eastern hardwood forests and the use of large trees as an indicator of forest development. For Ecol Manag 96:37–47CrossRefGoogle Scholar
  6. Brown SL, Schroeder P, Kern JS (1999) Spatial distribution of biomass in forests of the eastern USA. For Ecol Manag 123:81–90CrossRefGoogle Scholar
  7. Congalton R, Plourde L (2000) Sampling methodology, sample placement, and other important factors in assessing the accuracy of remotely sensed forest maps. In: Heuvelink GBM, Lemmens MJ (eds) Proceedings of the 4th international symposium on spatial accuracy assessment in natural resources and environmental science. Delft University, Amsterdam, pp 117–124Google Scholar
  8. Fladeland MM, Ashton MS, Lee X (2001) Landscape variations in understory PAR for a mixed deciduous forest in New England, USA. Agric For Meteorol 118:137–141CrossRefGoogle Scholar
  9. Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83:287–302CrossRefGoogle Scholar
  10. Garzuglia M, Saket M (2003) Wood volume and woody biomass: review of FRA 2000 estimates, Forest Resources Assessment WP 68, Food and Agriculture Organization of the United Nations, Rome, p 30Google Scholar
  11. Grigal DF, Homann PS (1994) Nitrogen mineralization, groundwater dynamics, and forest growth on a Minnesota outwash landscape. Biogeochemistry 27:171–185CrossRefGoogle Scholar
  12. Hamilton JEM, Lennon P, O’Donnell B (1988) Objective analysis of monthly climatological fields of temperature, sunshine, rainfall percentage and rainfall amount. J Climatol 8:109–124CrossRefGoogle Scholar
  13. Heath LS, Smith JE, Birdsey RA (2002) Carbon trends in US forest lands: a context for the role of soils in forest carbon sequestration. In: Kimble JM, Heath LS, Birdsey RA, Lal R (eds) The potential of US forest soils to sequester carbon and mitigate the greenhouse effect. CRC, New York, pp 35–46Google Scholar
  14. Hicke JA, Asner GP, Randerson JT, Tucker CJ, Los SO, Birdsey R, Jenkins JC, Field CB, Holland EA (2002) Satellite-derived increases in net primary productivity across North America, 1982–1998. Geophys Res Lett 29(10):1427. doi:10.1029/2001GL013578 CrossRefGoogle Scholar
  15. Jenkins JC, Birdsey RA, Pan Y (2001) Biomass and NPP estimation for the Mid-Atlantic region (USA) using plot-level forest inventory data. Ecol Appl 11:1174–1193CrossRefGoogle Scholar
  16. Knyazikhin Y, Martonchik JV, Myneni RB, Diner DJ, Running SW (1998) Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J Geophys Res 103:32257–32276CrossRefGoogle Scholar
  17. Leak WB, Smith M-L (1996) Sixty years of management and natural disturbance in a New England forested landscape. For Ecol Manag 81:63–73CrossRefGoogle Scholar
  18. Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L, Merchant JW (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21:1303–1365CrossRefGoogle Scholar
  19. McKenzie D, Hessl AE, Peterson DL (2001) Recent growth of conifer species of western North America: assessing spatial patterns of radial growth trends. Can J For Res 31:526–538CrossRefGoogle Scholar
  20. McRoberts RE, Miles PD (2005) Computer-based application tools using forest inventory data from the United States of America. Forest Biometry, Modelling and Information Sciences (FBMIS) 1:11–18Google Scholar
  21. Miles PD (2001) Forest biometry, modeling and information science. Proceedings of IUFRO 4.11 Conference, University of Greenwich, June 2001Google Scholar
  22. Miles PD, Brand GJ, Alerich CL, Bednar LF, Woudenberg SW, Glover JF, Ezzell EN (2001) The forest inventory and analysis database: database description and users manual. USDA For Serv Gen Tech Rep NC-218, p 130Google Scholar
  23. Ollinger SV, Aber JD, Federer CA (1998) Estimating regional forest productivity and water yield using an ecosystem model linked to a GIS. Landsc Ecol 13:323–334CrossRefGoogle Scholar
  24. Perry CA (1999) A regression model for annual streamflow in the Upper Mississippi River Basin based on solar irradiance. In: West GJ, Buffaloe L (eds) Proceedings of the sixteenth annual Pacific Climate Workshop. Santa Catalina Island, California, May 24–27Google Scholar
  25. Phillips DL, Brown SL, Schroeder PE, Birdsey RA (2000) Toward error analysis of large-scale forest carbon budgets. Glob Ecol Biogeogr 9:305–313CrossRefGoogle Scholar
  26. Potter CS (1999) Terrestrial biomass and the effects of deforestation on the global carbon cycle. BioScience 49:769–778CrossRefGoogle Scholar
  27. Potter CS, Randerson JT, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA (1993) Terrestrial ecosystem production: a process model based on global satellite and surface data. Glob Biogeochem Cycles 7(4):811–841CrossRefGoogle Scholar
  28. Potter C, Klooster S, Myneni R, Genovese V, Tan P, Kumar V (2003) Continental scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–98. Glob Planet Change 39:201–213CrossRefGoogle Scholar
  29. Potter C, Klooster S, Hiatt S, Fladeland M, Genovese V, Gross P (2007) Satellite-derived estimates of potential carbon sequestration though afforestation of agricultural lands in the United States. Clim Change 80:323–336CrossRefGoogle Scholar
  30. Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. BioScience 54:547–560CrossRefGoogle Scholar
  31. Thornton PE, Running SW, White MA (1997) Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol 190:214–251CrossRefGoogle Scholar
  32. Urban DL, Miller C, Halpin PN, Stephenson NL (2000) Forest gradient response in Sierran landscapes: the physical template. Landsc Ecol 15:603–620CrossRefGoogle Scholar
  33. VEMAP Participants (2000) The VEMAP Phase I database: an integrated input dataset for ecosystem and vegetation modeling for the conterminous United States. CDROM and World Wide Web (URL =
  34. White D, Kimerling J, Overton S (1992) Cartographic and geometric components of a global sampling design for environmental monitoring. Cartogr Geogr Inf Syst 19(1):5–21CrossRefGoogle Scholar
  35. Zheng D, Heath LS, Ducey MJ (2007) Forest biomass estimated from MODIS and FIA data in the Lake States: MN, WI and MI, USA. Forestry 80(3):265–278CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Christopher Potter
    • 1
  • Peggy Gross
    • 2
  • Steven Klooster
    • 2
  • Matthew Fladeland
    • 1
  • Vanessa Genovese
    • 2
  1. 1.NASA Ames Research CenterMoffett FieldUSA
  2. 2.California State University Monterey BaySeasideUSA

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