The Value of Determining Global Land Cover for Assessing Climate Change Mitigation Options

  • Steffen Fritz
  • Sabine Fuss
  • Petr Havlík
  • Jana Szolgayová
  • Ian McCallum
  • Michael Obersteiner
  • Linda See


Land cover maps provide critical input data for global models of land use. Urgent questions exist, such as how much land is available for the expansion of agriculture to combat food insecurity, how much land is available for afforestation projects, and whether reducing emissions from deforestation and forest degradation (REDD) is more cost-effective than carbon capture and sequestration. Such questions can be answered only with reliable maps of land cover. However, global land cover datasets currently differ drastically in terms of the spatial extent of cropland distributions. One of the data layers that differ is cropland area. In this study, we evaluate how models designed to help in policy design can be used to quantify the differences in implementation costs. By examining these cost differences, we are able to quantify the benefits, which equal the loss from making a decision under imperfect information. Taking the specific example of choosing between REDD and carbon capture and storage under uncertainty about the available cropland area, we have developed a methodology on how the value derived from reducing uncertainty can be assessed. By implementing a portfolio optimization model to find the optimal mix of mitigation options under different sets of information, we are able to estimate the benefit of improved land cover data and thus determine the value of land cover validation efforts. We illustrate the methodology by comparing portfolio outputs of the different mitigation options modeled within the GLOBIOM economic land use model using cropland data from different databases.


Value of information Land cover maps Land use Mitigation GEOSS 



This research was conducted in the frame of the EU-funded EUROGEOSS (grant no. 226487) and CC-TAME (grant no. 212535) and GEOCARBON (grant no. 283080) projects.


  1. Anthoff, D., Tol, R. S. J., & Yohe, G. W. (2009). Risk aversion, time preference, and the social cost of carbon. Environmental Research Letters, 4(2), 024002 (7 pp).Google Scholar
  2. Binswanger, H. E. (1980). Attitudes toward risk: Experimental measurement in rural India. American Journal of Agricultural Economics, 62, 395407.CrossRefGoogle Scholar
  3. Birge, J. R., & Louveaux, F. (1997). Introduction to stochastic programming. New York: Springer.Google Scholar
  4. Bouma, J. A., van der Woerd, H. J., & Kuik, O. J. (2009). Assessing the value of information for water quality management in the North Sea. Journal of Environmental Management, 90(2), 1280–1288.CrossRefGoogle Scholar
  5. Center for Science and Technology. (2007). Weather and climate forecast use and value bibliography. University of Colorado. Accessed 20 Aug 2007.
  6. Defourny, P., Schouten, L., Bartalev, S., Cacetta, P., De Witt, A., Di Bella, C., Gerard, B., Heinimann, A., Herold, M., Jaffrain, G., Latifovic, R., Lin, H., Mayaux, P., Mucher, S., Nonguierma, A., Stibig, H-J., Bicheron, P., Brockmann, C., Bontemps, S., Van Bogaert, E., Vancutsem, C., Leroy, M., & Arino, O. (2009, May 4–8). Accuracy assessment of a 300 m global land cover map: the GlobCover experience. In: Proceedings of 33rd international symposium on remote sensing of environment (ISRSE), Stresa, Italy.Google Scholar
  7. Dillon, J. L. (1971). An expository review of Bernouillian decision theory in agriculture: Is utility futility? Review of Marketing and Agricultural Economics, 39, 3–80.Google Scholar
  8. Dillon, J. L., & Scandizzo, E. L. (1978). Risk attitudes of subsistence farmers in Northeast Brazil: A sampling approach. American Journal of Agricultural Economics, 60, 425–435.CrossRefGoogle Scholar
  9. FAO, UNDP, & UNEP. (2008). UN Collaborative Programme on reducing emissions from deforestation and forest degradation in developing countries (UN-REDD). URL:
  10. Freund, R. (1956). The introduction of risk into a programming model. Econometrica, 21, 253–263.CrossRefGoogle Scholar
  11. Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., & Schaaf, C. (2002). Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 287–302.CrossRefGoogle Scholar
  12. Fritz, S., Bartholomé, E., Belward, A., Hartley, A., Stibig H. J., Eva, H., Mayaux, P., Bartalev, S., Latifovic, R., Kolmert, S., Roy, P., Agrawal, S., Bingfang, W., Wenting, X., Ledwith, M., Pekel, F. J., Giri, C., Mücher, S., de Badts, E., Tateishi, R., Champeaux, J.-L., & Defourny, P. (2003). Harmonisation, mosaicing and production of the Global Land Cover 2000 database (Beta Version). Luxembourg: Office for Official Publications of the European Communities, EUR 20849 EN, 41 pp. ISBN 92-894-6332-5*.Google Scholar
  13. Fritz, S., Scholes, R. J., Obersteiner, M., Bouma, J., & Reyers, B. (2008). A conceptual framework for assessing the benefits of a Global Earth Observation System of Systems. Systems Journal, IEEE, 2, 338–348.CrossRefGoogle Scholar
  14. Fuss, S., Johansson, D., Szolgayova, J., & Obersteiner, M. (2008). Impact of climate policy uncertainty on the adoption of electricity generating technologies. Energy Policy, 37(2), 733–743. doi: 10.1016/j.enpol.2008.10.022.CrossRefGoogle Scholar
  15. GEOSS. (2005). The global earth observation system of systems (GEOSS) 10-year implementation plan. See
  16. Havlík, P. Schneider, U. A., Schmid, E., Böttcher, B., Fritz, S., Skalský, R., Aoki, K., De Cara, S., Kindermann, G., Kraxner, F., Leduc, S., McCallum, I., Mosnier, A., Sauer, T., & Obersteiner, M. (2010). Global land-use implications of first and second generation biofuel targets. Energy Policy, Corrected proof available online 7 April 2010.Google Scholar
  17. Katz, R. W., & Murphy, A. H. (Eds.). (1997). Economic value of weather and climate forecasts. Cambridge: Cambridge University Press. 237 pp.Google Scholar
  18. Khabarov, N., Moltchanova, E., & Obersteiner, M. (2008). Valuing weather observation systems for forest fire management. IEEE, 2, 349–357.Google Scholar
  19. Lin, W., Dean, G., & Moore, C. (1974). An empirical test of utility vs. profit maximization in agricultural production. American Journal of Agricultural Economics, 56, 497–508.CrossRefGoogle Scholar
  20. Macauley, M. K. (2006). The value of information: Measuring the contribution of space-derived earth science data to resource management. Space Policy, 22(4), 274–282.CrossRefGoogle Scholar
  21. Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.Google Scholar
  22. Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investments. New York: Wiley.Google Scholar
  23. McCarl, B. A., & Spreen, T. H. (1980). Price endogenous mathematical programming as a tool for sector analysis. American Journal of Agricultural Economics, 62, 87–102.CrossRefGoogle Scholar
  24. McCarl, B.A., & Spreen, T.H. (2007). Applied mathematical programming using algebraic systems. Available at
  25. Nordhaus, W. D., & Popp, D. (1997). What is the value of scientific knowledge? An application to global warming using the PRICE model. The Energy Journal, 18, 1–45.CrossRefGoogle Scholar
  26. Peck, S. C., & Teisberg, T. J. (1993). Global warming uncertainties and the value of information: An analysis using CETA. Resource and Energy Economics, 15, 71–97.CrossRefGoogle Scholar
  27. Raiffa, H., & Schlaifer, R. (1961). Applied statistical decision theory. Boston: Harvard University Graduate School of Business Administration.Google Scholar
  28. Ramankutty, N., Evan, A. T., Monfreda, C., & Foley, J. A. (2008). Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22(1). doi: 10.1029/2007GB002952
  29. Schlamadinger, B., Bird, N., Johns, T., Brown, B., Canadell, J., Ciccarese, L., Dutschke, M., Fiedler, J., Fischlin, A., Fearnside, P., Forner, C., Freibauer, A., Frumhoff, P., Hoehne, N., Kirschbaum, M. U. F., Labat, A., Marland, G., Michaelowa, A., Montanarella, L., Moutinho, P., Murdiyarso, D., Pena, N., Pingoud, K., Rakonczay, Z., Rametsteiner, E., Rock, J., Sanz, M. J., Schneider, U. A., Shvidenko, A., Skutsch, M., Smith, P., Somogyi, Z., Trines, E., Ward, M., & Yamagata, Y. (2007). A synopsis of land use, land-use change and forestry (LULUCF) under the Kyoto Protocol and Marrakech Accords. Environmental Science & Policy, 10, 271–282.CrossRefGoogle Scholar
  30. Springer, U. (2003). International diversification of investments in climate change mitigation. Ecological Economics, 46, 181–193.CrossRefGoogle Scholar
  31. Watson, R. T., Bolin, B., Ravindranath, N. H., Verardo, D. J., & Dokken, D. J. (2000). IPCC special report on land use, Land-use change and forestry, UNEP. URL:
  32. Weyant, J., Davidson, O., Dowlatabadi, H., Edmonds, J., Grubb, M., Parson, E. A., Richels, R., Rotmans, J., Shukla, P. R., Tol, R. S. J., Cline, W. R., & Fankhauser, S. (1996). Integrated assessment of climate change: An overview and comparison of approaches and results. In J. P. Bruce, L. Lee, & E. F. Haites (Eds.), Climate change 1995: Economic and social dimensions of climate change (pp. 367–439). Cambridge: Cambridge University Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Steffen Fritz
    • 1
  • Sabine Fuss
    • 1
  • Petr Havlík
    • 1
  • Jana Szolgayová
    • 1
    • 2
  • Ian McCallum
    • 1
  • Michael Obersteiner
    • 1
  • Linda See
    • 1
    • 3
  1. 1.Ecosystems Services and Management ProgramInternational Institute for Applied Systems AnalysisLaxenburgAustria
  2. 2.Department of Applied Mathematics and Statistics, Faculty of Mathematics, Physics and InformaticsComenius UniversityBratislavaSlovakia
  3. 3.School of GeographyUniversity of LeedsLeedsUK

Personalised recommendations