, Volume 17, Issue 1, pp 142–157 | Cite as

Observation of Trends in Biomass Loss as a Result of Disturbance in the Conterminous U.S.: 1986–2004

  • Scott L. Powell
  • Warren B. Cohen
  • Robert E. Kennedy
  • Sean P. Healey
  • Chengquan Huang


The critical role of forests in the global carbon cycle is well known, but significant uncertainties remain about the specific role of disturbance, in part because of the challenge of incorporating spatial and temporal detail in the characterization of disturbance processes. In this study, we link forest inventory data to remote sensing data to derive estimates of pre- and post-disturbance biomass, and then use near-annual remote sensing observations of forest disturbance to characterize biomass loss associated with disturbance across the conterminous U.S. between 1986 and 2004. Nationally, year-to-year variability in the amount of live aboveground carbon lost as a result of disturbance ranged from a low of 61 Tg C (±16) in 1991 to a high of 84 Tg C (±33) in 2003. Eastern and western forest strata were relatively balanced in terms of their proportional contribution to national-level trends, despite eastern forests having more than twice the area of western forests. In the eastern forest stratum, annual biomass loss tracked closely with the area of disturbance, whereas in the western forest stratum, annual biomass loss showed more year-to-year variability that did not directly correspond to the area of disturbance, suggesting that the biomass density of forests affected by disturbance in the west was more spatially and temporally variable. Eastern and western forest strata exhibited somewhat opposing trends in biomass loss, potentially corresponding to the implementation of the Northwest Forest Plan in the mid 1990s that resulted in a shift of timber harvesting from public lands in the northwest to private lands in the south. Overall, these observations document modest increases in disturbance rates and associated carbon consequences over the 18-year period. These changes are likely not significant enough to weaken a growing forest carbon sink in the conterminous U.S. based largely on increased forest growth rates and biomass densities.


biomass carbon disturbance Landsat time series LandTrendr FIA 


  1. Blackard JA, Finco MV, Helmer EH, Holden GR, Hoppus ML, Jacobs DM, Lister AJ, Moisen GG, Nelson MD, Riemann R, Ruefenacht B, Salanjanu D, Weyermann DL, Winterberger KC, Brandeis TJ, Czaplewski RL, McRoberts RE, Patterson PL, Tymcio RP. 2008. Mapping US forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens Environ 112:1658–77.CrossRefGoogle Scholar
  2. Breiman L. 2001. Random Forests. Mach Learn 45:5–32.CrossRefGoogle Scholar
  3. Campbell JL, Kennedy RE, Cohen WB, Miller R. 2012. Assessing the carbon consequences of western juniper encroachment across Oregon, USA. Range Ecol Manag 65(3):223–31.CrossRefGoogle Scholar
  4. Canty MJ, Nielson AA, Schmidt M. 2004. Automatic radiometric normalization of multitemporal satellite imagery. Remote Sens Environ 91(3–4):441–51.CrossRefGoogle Scholar
  5. Chambers JQ, Fisher JI, Zeng H, Chapman EL, Baker DB, Hurtt GC. 2007. Hurricane Katrina’s carbon footprint on U.S. Gulf Coast forests. Science 318:1107.PubMedCrossRefGoogle Scholar
  6. Climate Change Science Program. 2007. The first State of the Carbon Cycle Report (SOCCR): the North American carbon budget and implications for the global carbon cycle. In: King AW, Dilling L, Zimmerman GP, Fairman DM, Houghton RA, Marland G, Rose RA, Wilbanks TJ, Eds. A report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Asheville, NC: National Oceanic and Atmospheric Administration, National Climate Data Center. 242 pp.Google Scholar
  7. Cohen WB, Goward SN. 2004. Landsat’s role in ecological applications of remote sensing. Bioscience 54:535–45.CrossRefGoogle Scholar
  8. Cohen WB, Harmon ME, Wallin DO, Fiorella M. 1996. Two decades of carbon flux from forests of the Pacific Northwest. Bioscience 46:836–44.CrossRefGoogle Scholar
  9. Coops NC, Wulder MA, Iwanicka D. 2009. Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations. Remote Sens Environ 113:1250–61.CrossRefGoogle Scholar
  10. Domke GM, Woodall CW, Smith JE, Westfall JA, McRoberts RE. 2012. Consequences of alternative tree-level biomass estimation procedures on U.S. forest carbon stock estimates. For Ecol Manage 270:108–16.CrossRefGoogle Scholar
  11. Eidenshink J, Schwind B, Brewer K, Zhu Z, Quayle B, Howard S. 2007. A project for monitoring trends in burn severity. Fire Ecol Spec Issue 3(1):3–21.CrossRefGoogle Scholar
  12. Freeman EA, Frescino TA. 2009. ModelMap: an R package for modeling and map production using Random Forest and Stochastic Gradient Boosting. Ogden, UT: USDA Forest Service, Rocky Mountain Research Station.
  13. Gao F, Masek J, Wolfe R. 2009. An automated registration and orthorectification package for Landsat and Landsat-like data processing. J Appl Remote Sens 3:033515. doi:10.1117/1.3104620.CrossRefGoogle Scholar
  14. Goward SN, Masek JG, Cohen W, Moisen G, Collatz GJ, Healey S, Houghton RA, Huang C, Kennedy R, Law B, Powell S, Turner D, Wulder MA. 2008. Forest disturbance and North American carbon flux. EOS 89(11):105–6.CrossRefGoogle Scholar
  15. Healey SP, Yang Z, Cohen WB, Pierce DJ. 2006. Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sens Environ 101:115–26.CrossRefGoogle Scholar
  16. Healey SP, Cohen WB, Spies TA, Moeur M, Pflugmacher D, Whitley MG, Lefsky M. 2008. The relative impact of harvest and fire upon landscape-level dynamics of older forests: lessons from the Northwest Forest Plan. Ecosystems 11(7):1106–19.CrossRefGoogle Scholar
  17. Healey SP, Blackard JA, Morgan TA, Loeffler D, Jones G, Songster J, Brandt JP, Moisen GG, DeBlander LT. 2009. Changes in timber haul emissions in the context of shifting forest management and infrastructure. Carbon Balance Manage 4:9.CrossRefGoogle Scholar
  18. Horvitz DG, Thompson DJ. 1952. A generalization of sampling without replacement from a finite universe. J Am Stat Assoc 47:663.CrossRefGoogle Scholar
  19. Houghton RA. 2005. Aboveground forest biomass and the global carbon balance. Glob Change Biol 11:945–58.CrossRefGoogle Scholar
  20. Houghton RA, Hall F, Goetz SJ. 2009. Importance of biomass in the global carbon cycle. J Geophys Res 114:G00E03. doi:10.1029/2009JG000935.CrossRefGoogle Scholar
  21. Huang C, Goward SN, Masek JG, Gao F, Vermote EF, Thomas N, Schleeweis K, Kennedy RE, Zhu Z, Eidenshink JC, Townshend JRG. 2009. Development of time series stacks of Landsat images for reconstructing forest disturbance history. Int J Digit Earth 2:195–218.CrossRefGoogle Scholar
  22. Huang C, Goward SN, Masek JG, Thomas N, Zhu Z, Vogelmann JE. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ 114:183–98.CrossRefGoogle Scholar
  23. Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA. 2003. National-scale biomass equations for United States tree species. For Sci 49(1):12–35.Google Scholar
  24. Kellndorfer JM, Walker WS, Pierce LE, Dobson MC, Fites J, Hunsaker C, Vona J, Clutter M. 2004. Vegetation height derivation from Shuttle Radar Topography Mission and National Elevation data sets. Remote Sens Environ 93(3):339–58.CrossRefGoogle Scholar
  25. Kellndorfer JM, Walker WS, LaPoint E, Kirsch K, Bishop J, Fiske G. 2010. Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: a regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets. J Geophys Res 115:G00E08. doi:10.1029/2009JG000997.CrossRefGoogle Scholar
  26. Kennedy RE, Yang Z, Cohen WB. 2010. Detecting trends in disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation. Remote Sens Environ 114:2897–910.CrossRefGoogle Scholar
  27. Kennedy RE, Yang Z, Cohen WB, Pfaff E, Braaten J, Nelson P. 2012. Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sens Environ 122:117–33.CrossRefGoogle Scholar
  28. Kurz WA, Dymond CC, Stinson G, Rampley GJ, Neilson ET, Carroll AL, Ebata T, Safranyik L. 2008. Mountain pine beetle and forest carbon feedback to climate change. Nature 452. doi:10.1038/nature06777.
  29. Lefsky MA, Harding DJ, Keller M, Cohen WB, Carabajal CC, Del Bom Espirito-Santo F, Hunter MO, de Oliveira Jr R. 2005. Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett 32. doi:10.1029/2005GL023971.
  30. Masek JG, Healey SP. 2013. Monitoring U.S. forest dynamics with Landsat. In: Achard F, Hansen MH, Eds. Global forest monitoring from earth observation. Boca Raton, FL: CRC Press. Google Scholar
  31. Masek JG, Vermote EF, Saleous N, Wolfe R, Hall FG, Huemmrich F, Gao F, Kutler J, Lim TK. 2006. Landsat surface reflectance data set for North America, 1990–2000. Geosci Remote Sens Lett 3:68–72.CrossRefGoogle Scholar
  32. Masek JG, Goward SN, Kennedy RE, Cohen WB, Moisen GG, Schleweiss K, Huang C. 2013. United States forest disturbance trends observed using Landsat time series. Ecosystems 16:1087–104.Google Scholar
  33. Myneni RB et al. 2001. A large carbon sink in the woody biomass of Northern forests. Proc Natl Acad Sci USA 98(26):14784–9.PubMedCrossRefGoogle Scholar
  34. Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao S, Rautiainen A, Sitch S, Hayes D. 2011a. A large and persistent carbon sink in the world’s forests. Science 333:988–93.PubMedCrossRefGoogle Scholar
  35. Pan Y, Chen JM, Birdsey R, McCullough K, He L, Deng F. 2011b. Age structure and disturbance legacy of North American forests. Biogeosciences 8:715–32.CrossRefGoogle Scholar
  36. Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens Environ 114:1053–68.CrossRefGoogle Scholar
  37. Saatchi SS, Houghton RA, Dos Santos Alvala RC, Soares JV, Yu Y. 2007. Distribution of aboveground live biomass in the Amazon basin. Glob Change Biol 13:816–37.CrossRefGoogle Scholar
  38. Schroeder TA, Cohen WB, Song C, Canty MJ, Yang Z. 2006. Radiometric calibration of Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens Environ 103:16–26.CrossRefGoogle Scholar
  39. Schroeder TA, Wulder MA, Healey SP, Moisen GG. 2011. Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time-series data. Remote Sens Environ 115(6):1421–33.CrossRefGoogle Scholar
  40. Skog KE. 2008. Sequestration of carbon in harvested wood products for the United States. For Prod J 58:56–72.Google Scholar
  41. Smith JE, Heath LS, Jenkins JC. 2003. Forest volume-to-biomass models and estimates of mass for live and standing dead trees of U.S. forests. Gen. Tech. Rep. NE-298. Newtown Square, PA: USDA Forest Service, Northeastern Research Station.Google Scholar
  42. Smith JE, Heath LS, Skog KE, Birdsey RA. 2006. Methods for calculating forest ecosystem and harvested carbon with standard estimates for forest types of the United States (NE-GTR-343). Newtown Square, PA: USDA Forest Service, Northeastern Research Station.Google Scholar
  43. Smith WB, Miles PD, Perry CH, Pugh CH. 2009. Forest resources of the United States, 2007. Gen. Tech. Rep. WO-78. Washington, DC: U.S. Department of Agriculture, Forest Service, Washington Office. 336 pp.Google Scholar
  44. Stewart BP, Wulder MA, McDermid GJ, Nelson T. 2009. Disturbance capture and attribution through the integration of Landsat and IRS-1C imagery. Can J Remote Sens 35:523–33.CrossRefGoogle Scholar
  45. Thomas NE, Huang C, Goward SN, Powell SL, Rishmawi K, Schleeweis K, Hinds A. 2011. Validation of North American forest disturbance dynamics derived from Landsat time series stacks. Remote Sens Environ 115:19–32.CrossRefGoogle Scholar
  46. Toan TL, Quegan S, Woodward I, Lomas M, Delbart N, Picard G. 2004. Relating radar remote sensing of biomass to modeling of forest carbon budgets. Clim Chang 67:379–402.CrossRefGoogle Scholar
  47. U.S. Agriculture and Forestry Greenhouse Gas Inventory. 1990–2008. Climate Change Program Office, Office of the Chief Economist, U.S. Department of Agriculture. Technical Bulletin No. 1930. 159 pp. June, 2011.Google Scholar
  48. U.S. EPA. 2011. Inventory of U.S. greenhouse gas emissions and sinks: 1990–2009. Washington, DC: U.S. Environmental Protection Agency.Google Scholar
  49. USDA Forest Service. 2010. Forest timber product output (TPO) reports. Washington, DC: USDA Forest Service.
  50. van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, Morton DC, Defries RS, Jin Y, van Leewen TT. 2010. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos Chem Phys 10:11707–35.CrossRefGoogle Scholar
  51. Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW. 2006. Warming and earlier spring increase western U.S. forest wildfire activity. Science 313:940–3.PubMedCrossRefGoogle Scholar
  52. Williams CA, Collatz GJ, Masek J, Goward SN. 2012. Carbon consequences of forest disturbance and recovery across the conterminous United States. Global Biogeochem Cy 26:GB1005. doi:10.1029/2010GB003947.CrossRefGoogle Scholar
  53. Zheng D, Heath LS, Ducey MJ, Smith JE. 2011. Carbon changes in conterminous US forests associated with growth and major disturbances: 1992–2001. Environ Res Lett 6. doi:10.1088/1748-9326/6/1/014012.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Scott L. Powell
    • 1
  • Warren B. Cohen
    • 2
  • Robert E. Kennedy
    • 3
  • Sean P. Healey
    • 4
  • Chengquan Huang
    • 5
  1. 1.Department of Land Resources and Environmental SciencesMontana State UniversityBozemanUSA
  2. 2.Pacific Northwest Research StationU.S.D.A. Forest ServiceCorvallisUSA
  3. 3.Department of Earth and EnvironmentBoston UniversityBostonUSA
  4. 4.Rocky Mountain Research StationU.S.D.A. Forest ServiceOgdenUSA
  5. 5.Department of GeographyUniversity of MarylandCollege ParkUSA

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