The Effects of Land Use Change on Terrestrial Carbon Dynamics in the Black Sea Region

  • Pontus Olofsson
  • Curtis E. Woodcock
  • Alessandro Baccini
  • Richard A. Houghton
  • Mutlu Ozdogan
  • Vladimir Gancz
  • Viorel Blujdea
  • Paata Torchinava
  • Aydin Tufekcioglu
  • Emin Zeki Baskent
Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

The effects of land use change on terrestrial carbon budgets for the Black Sea Region were investigated using remote sensing, forest inventory data, and a carbon model. We focus on three countries in the region: Romania, Georgia and Turkey. Rates of land use change between circa-1990 and circa-2000 were quantified by analyzing Landsat imagery. A carbon book-keeping model was used to quantify these effects in Romania. In Georgia, illegal logging and state-controlled forest harvest are the main sources of land use change. Our analysis shows a small amount of land use change — in the relatively populous Ajdara region, 2.5% of the forested area in 1990 had been converted to non-forest in 2000. Even less land use change was found in Turkey — for the Northeastern part of the country bordering Georgia, 0.28% of the forested land (1,113 ha) had been converted to non forest over the period 1990–2000. For the whole country of Romania, the corresponding number was 2.4%. Integrating this harvest rate with forest inventory data in the carbon book-keeping model indicates that Romanian forests are currently a carbon sink and will remain so until about 2080 if current harvesting rates persist. The current carbon sink of 2.54 TgC/year is approximately 10% of the anthropogenic emission from fossil fuels in Romania.

Keywords

land use carbon dynamics Black Sea region remote sensing 

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References

  1. Card DH (1982) Using map categorical marginal frequencies to improve estimates of thematic map accuracy. Photogramm. Eng. Remote Sens. 48, 431–439.Google Scholar
  2. Carpenter G, Gjaja M, Gopal S, and Woodcock CE (1997) ART neural networks for remote sensing: Vegetation classification from Landsat TM and terrain data. IEEE Trans. Geosci. Remote Sens. 35, 308–325.CrossRefGoogle Scholar
  3. Collins JB, and Woodcock CE (1996) An assessment of several linear change detection techniques for mapping forest mortality using Multitemporal Landsat TM data. Remote Sens Environ 56, 66–77.CrossRefGoogle Scholar
  4. Crist EP, and Cicone RC (1984) A physically-based transformation of Thematic mapper data-the TM Tasseled Cap. IEEE Trans. Geosci. Remote Sens. 22, 256–263.CrossRefGoogle Scholar
  5. DeFries R, Houghton RA, Hansen M, et al. (2002) Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 90s. Proc. Natl. Acad. Sci. 99(22), 14256–14261.CrossRefGoogle Scholar
  6. GLCF. University of Maryland global land cover facility (http://glcf.umiacs.umd..edu).
  7. Goodale CL, Apps MJ, Birdsey RA, et al. (2002) Forest carbon sinks in the northern hemisphere. Ecol. Appl. 12, 891–899.CrossRefGoogle Scholar
  8. Houghton RA (1987) The flux of carbon from terrestrial ecosystems to the atmosphere in 1980 due to changes in land use: Geographic distribution of the global flux. Tellus 39B, 122–139.CrossRefGoogle Scholar
  9. Houghton RA, and Hackler JL (2003) Revised estimates of the annual net flux of carbon to the atmosphere from changes in land use and land management 1850–2000. Tellus 55B, 378– 390.Google Scholar
  10. Houghton RA, Hobbie JE, Melillo JM, et al. (1983) Changes in carbon content of terrestrial biota and soils between 1860 and 1980: A net release of CO2 to the atmosphere. Ecol. Monogr. 53, 235–262.CrossRefGoogle Scholar
  11. Janssens IA, Freibauer P, Ciais P, et al. (2003) Europe's terrestrial biosphere absorbs 7–12% of European anthropogenic CO2 emissions. Science 300, 1538–1542.CrossRefGoogle Scholar
  12. Johnson D, and Curtis P (2001) Effects of forest management on soil C and N storage: Meta analysis. Forest Ecol. Manage. 140, 227–238.CrossRefGoogle Scholar
  13. Moore B, Boone RD, Hobbie JE, et al. (1981) A simple model for analysis of the role of terrestrial ecosystems in the global carbon budget. In Bolin, B. (ed.) Modelling the Global Carbon Cycle, SCOPE Report No. 16, New York, Wiley.Google Scholar
  14. Nabuurs, GJ, Päivinen R, Sikkema R, et al. (1997) The role of European forests in the global carbon cycle — review. Biomass Bioenerg. 13, 345–358.CrossRefGoogle Scholar
  15. Pacala, SW, Hurtt GC, Baker D, et al. (2001) Consistent land- and atmosphere-based U.S. carbon sink estimates. Science 292, 2316–2320.CrossRefGoogle Scholar
  16. Tucker C, Grant D, and Dykstra J (2004) NASAs global orthorectified Landsat data set. Photogramm. Eng. Remote Sens. 55B, 378–390.Google Scholar
  17. Woodcock CE, Macomber SA, Pax-Lenney M (2001) Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors. Remote Sens. Environ. 78, 194–203.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pontus Olofsson
    • 1
  • Curtis E. Woodcock
    • 1
  • Alessandro Baccini
    • 2
  • Richard A. Houghton
    • 2
  • Mutlu Ozdogan
    • 3
  • Vladimir Gancz
    • 4
  • Viorel Blujdea
    • 4
  • Paata Torchinava
    • 5
  • Aydin Tufekcioglu
    • 6
  • Emin Zeki Baskent
    • 7
  1. 1.Department of Geography and EnvironmentBoston UniversityBostonUSA
  2. 2.Woods Hole Research CenterFalmouthUSA
  3. 3.Department of Forest and Wildlife Ecology, 285 Enzyme InstituteUniversity of Wisconsin — MadisonMadisonUSA
  4. 4.Forest Research and Management Institute (I.C.A.S)Judetul IlfovRomania
  5. 5.V. Gulisashvili Forest InstituteTbilisiGeorgia
  6. 6.Department of ForestryKafkas UniversityArtvinTurkey
  7. 7.Karadeniz Teknik UniversitesiTrabzonTurkey

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