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Continental-Scale Living Forest Biomass and Carbon Stock: A Robust Fuzzy Ensemble of IPCC Tier 1 Maps for Europe

  • Daniele de Rigo
  • José I. Barredo
  • Lorenzo Busetto
  • Giovanni Caudullo
  • Jesús San-Miguel-Ayanz
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 413)

Abstract

Forest ecosystems play a key role in the global carbon cycle. Spatially explicit data and assessments of forest biomass and carbon are therefore crucial for designing and implementing effective sustainable forest management options and forest related policies. In this contribution, we present European-wide maps of forest biomass and carbon stock spatially disaggregated at 1km x 1km. The maps originated from a spatialisation improvement of the IPCC methodology for estimating the forest biomass at IPCC Tier 1 level (IPCC-T1). Using a categorical map of ecological zones within the mapping technique may originate boundary effects between the ecological zones. This may induce undue artifacts in the outcomes, as evident in previously published maps generated with the IPCC-T1 methodology. Here we present a novel method for IPCC-T1 biomass mapping which mitigates these artifacts. We propose the use of a fuzzy similarity map of the FAO ecological zones computed by estimating the relative distance similarity (RDS) of each grid-cells climate and geography with respect to the FAO ecological zones. A robust ensemble approach was used to merge an array of simple models with spatially distributed fuzzy set-membership. This allowed the boundary artifacts to be reduced, while mitigating the impact of model semantic extrapolation. The chain of semantically enhanced data-transformations is described following the semantic array programming paradigm. Preliminary results obtained from the application of this novel approach are presented along with a discussion of its impact on the derived maps.

Keywords

Ecological Zones Living Forest Biomass Living Forest Carbon Stock IPCC Tier 1 Relative Distance Similarity Robust Fuzzy Ensemble Semantic Array Programming 

References

  1. 1.
    Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E.: A Large and Persistent Carbon Sink in the Worlds Forests. Science 33(6045), 988–993 (2011)CrossRefGoogle Scholar
  2. 2.
    Nabuurs, G.J., van Putten, B., Knippers, T.S., Mohren, G.M.J.: Comparison of uncertainties in carbon sequestration estimates for a tropical and a temperate forest. Forest Ecology and Management 256(3), 237–245 (2008)CrossRefGoogle Scholar
  3. 3.
    Bonan, G.B.: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320(5882), 1444–1449 (2008)CrossRefGoogle Scholar
  4. 4.
    Eurostat: Forestry in the EU and the world: a statistical portraitGoogle Scholar
  5. 5.
    Seebach, L., McCallum, I., Fritz, S., Kindermann, G., Leduc, S., et al.: Choice of forest map has implications for policy analysis: A case study on the EU biofuel target. Env. Sci. & Policy 22, 13–24 (2012)CrossRefGoogle Scholar
  6. 6.
    de Rigo, D.: Behind the horizon of reproducible integrated environmental modelling at European scale: ethics and practice of scientific knowledge freedom. Submitted to F1000 Research (2013)Google Scholar
  7. 7.
    Food and Agriculture Organization of the United Nations: Global forest resources assessment 2010: Main report. (2010) ISBN: 9789251066546Google Scholar
  8. 8.
    Barredo, J.I., San-Miguel-Ayanz, J., Caudullo, G., Busetto, L.: A European map of living forest biomass and carbon stock. Reference Report by the Joint Research Centre of the European Commission. EUR – Sci. Tech. Res. 25730 (2012)Google Scholar
  9. 9.
    Ruesch, A., Gibbs, H. K.: New IPCC tier-1 global biomass carbon map for the year 2000. Carbon Dioxide Information Analysis Center, Oak Ridge National Lab (2008)Google Scholar
  10. 10.
    Koh, L.P., Ghazoul, J.: Spatially explicit scenario analysis for reconciling agricultural expansion, forest protection, and carbon conservation in indonesia. Proc. Natl. Acad. Sci. U.S.A. 107(24), 11140–11144 (2010)CrossRefGoogle Scholar
  11. 11.
    West, P.C., Gibbs, H.K., Monfreda, C., Wagner, J., Barford, C.C., et al.: Trading carbon for food: Global comparison of carbon stocks vs. crop yields on agricultural land. Proc. Natl. Acad. Sci. U.S.A. 107(46), 19645–19648 (2010)CrossRefGoogle Scholar
  12. 12.
    Nelson, E., Sander, H., Hawthorne, P., Conte, M., Ennaanay, D., et al.: Projecting global Land-Use change and its effect on ecosystem service provision and biodiversity with simple models. PLoS ONE 5(12), e14327+ (2010)Google Scholar
  13. 13.
    Kempeneers, P., Sedano, F., Seebach, L.M., Strobl, P., San-Miguel-Ayanz, J.: Data fusion of different spatial resolution remote sensing images applied to Forest-Type mapping. IEEE Trans. Geosci. Remote Sens. 49(12), 4977–4986 (2011)CrossRefGoogle Scholar
  14. 14.
    EEA: CORINE land cover technical guide Addendum 2000. European Environment Agency, Technical report No 40, Copenhagen (2000)Google Scholar
  15. 15.
    Food and Agriculture Organization of the United Nations: Global Ecological Zoning for the Global Forest Resources Assessment 2000 - Final Report. Food and Agriculture Organization of the United Nations, Forestry Department, Rome, Italy (2001)Google Scholar
  16. 16.
    Food and Agriculture Organization of the United Nations: Global ecological Zones for FAO forest reporting: 2010 update. For. Resour. Assess. Work. Paper 179 (2012)Google Scholar
  17. 17.
    Aalde, H., Gonzalez, P., Gytarsky, M., Krug, T., Kurz, W. A., et al.: Forest Land. IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. The Intergovernmental Panel on Climate Change (IPCC), vol. 4, ch. 4, p. 83 (2006)Google Scholar
  18. 18.
    de Rigo, D.: Relative distance similarity as multivariate supervised or unsupervised ensemble interpolation (in prep. 2013)Google Scholar
  19. 19.
    de Rigo, D.: Semantic Array Programming for Environmental Modelling: Application of the Mastrave Library. In: Int. Congress on Environmental Modelling and Software. Managing Resources of a Limited Plant, Pathways and Visions under Uncertainty, Sixth Biennial Meeting, pp. 1167–1176 (2012)Google Scholar
  20. 20.
    de Rigo, D.: Semantic array programming with Mastrave - introduction to semantic computational modelling (2012)Google Scholar
  21. 21.
    European Parliament: Directive 2007/2/EC of the European Parliament and of the Council of 14 march 2007 establishing an infrastructure for spatial information in the european community (INSPIRE). Official J. Eur. Union 50(L 108), 1–14 (2007)Google Scholar
  22. 22.
    European Commission: Commission regulation (EC) no 1205/2008 of 3 december 2008 implementing directive 2007/2/EC of the European Parliament and of the Council as regards metadata. Official J. Eur. Union 51(L 326), 12–30 (2008)Google Scholar
  23. 23.
    Iverson, K.E.: Notation as a tool of thought. Commun. ACM 23(8), 444–465 (1980)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Eaton, J.W.: GNU Octave and reproducible research. J. Process Contr. 22(8), 1433–1438 (2012)CrossRefGoogle Scholar
  25. 25.
    Holdridge, L. R.: Life zone ecology. Tropical Science Center, Costa Rica. 206p. (1967)Google Scholar
  26. 26.
    Hofierka, J., Suri, M., Huld, T.: GRASS GIS manual: r.sun. In: GRASS Development Team, 2013. GRASS GIS 6.4.3svn Ref. Manual. Open Source Geospat. Found. (2007)Google Scholar
  27. 27.
    Nordenskiold, O., Mecking, L.: The geography of the polar regions. Amer. Geogr. SOC. Spec. Publ. 8, 359 (1928)Google Scholar
  28. 28.
    Guo, Z.-Y., Zhu, H.-Y., Liang, X.-G.: Entransy A physical quantity describing heat transfer ability. Int. J. Heat Mass Transfer 50(13-14), 2545–2556 (2007)CrossRefzbMATHGoogle Scholar
  29. 29.
    Xu, M.: The thermodynamic basis of entransy and entransy dissipation. Energy 36(7), 4272–4277 (2011)CrossRefGoogle Scholar
  30. 30.
    Milly, P.C.D., Dunne, K.A.: On the hydrologic adjustment of Climate-Model projections: The potential pitfall of potential evapotranspiration. Earth Interact 15(1), 1–14 (2011)CrossRefGoogle Scholar
  31. 31.
    Knuth, D.E.: Two notes on notation. Amer. Math. Monthly 99(5), 403–422 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    de Rigo, D., Barredo, J.I., Busetto, L., Caudullo, G., San Miguel-Ayanz, J.: Extending FAO Ecological Zones with Pan-European Fuzzy Geoclimatic Similarity.Environmental Data and Data Transformation Models Review ( in prep., 2013)Google Scholar
  33. 33.
    Mesirov, J.P.: Accessible reproducible research. Science 327(5964), 415–416 (2010)CrossRefGoogle Scholar
  34. 34.
    Nature: Devil in the details. Nature 470(7334), 305–306 (2011)Google Scholar
  35. 35.
    Kleiner, K.: Data on demand. Nature Climate Change 1(1), 10–12 (2011)CrossRefGoogle Scholar
  36. 36.
    Stallman, R.M.: Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52(6), 31–33 (2009)CrossRefGoogle Scholar
  37. 37.
    Murray-Rust, P.: Open data in science. Serials Review 34(1), 52–64 (2008)CrossRefGoogle Scholar
  38. 38.
    Stodden, V.: Trust your science? open your data and code. Amstat News 21–22 (2011)Google Scholar
  39. 39.
    de Rigo, D., Guariso, G.: Rewarding Open Science: A Collaborative Review System for Semantically-Enhanced Free Software and Environmental Data Modelling ( in prep., 2013)Google Scholar
  40. 40.
    Bosco, C., de Rigo, D., Dijkstra, T., Sander, G., Wasowski, J.: Multi-Scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble. In: Hřebíček, J., Schimak, G., Kubásek, M., Rizzoli, A. (eds.) ISESS 2013. IFIP AICT, vol. 413, pp. 321–335. Springer, Heidelberg (2013)Google Scholar
  41. 41.
    de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Di Leo, M., San-Miguel-Ayanz, J.: An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty. In: Hřebíček, J., Schimak, G., Kubásek, M., Rizzoli, A. (eds.) ISESS 2013. IFIP AICT, vol. 413, pp. 367–380. Springer, Heidelberg (2013)Google Scholar
  42. 42.
    Di Leo, M., de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Petroliagkis, T., Camia, A., San-Miguel-Ayanz, J.: Dynamic Data Driven Ensemble for Wildfire Behaviour Assessment: A Case Study. In: Hřebíček, J., Schimak, G., Kubásek, M., Rizzoli, A. (eds.) ISESS 2013. IFIP AICT, vol. 413, pp. 11–22. Springer, Heidelberg (2013)Google Scholar
  43. 43.
    Mitchard, E.: A Comparison of Tropical Carbon Maps. Ecometrica, Natural Environment Research Council (NERC) (2013)Google Scholar
  44. 44.
    Ciais, P., Schelhaas, M.J., Zaehle, S., Piao, S.L., Cescatti, A., et al.: Carbon accumulation in european forests. Nature Geosci. 1(7), 425–429 (2008)CrossRefGoogle Scholar
  45. 45.
    Mascarelli, A.: Earth’s carbon sink downsized. Nature (2012)Google Scholar
  46. 46.
    Fiorese, G., Guariso, G.: Modeling the role of forests in a regional carbon mitigation plan. Renewable Energy 52, 175–182 (2013)CrossRefGoogle Scholar
  47. 47.
    de Rigo, D., Caudullo, G. Amatulli, G., Strobl, P., San-Miguel-Ayanz, J.: Modelling tree species distribution in Europe with constrained spatial multi-frequency analysis (in prep.)Google Scholar
  48. 48.
    Estreguil, C., Caudullo, G., de Rigo, D., Whitmore, C., San-Miguel-Ayanz, J.: Reporting on European forest fragmentation: Standardized indices and web map services. IEEE Earthzine 5(2), 384031+ (2012); 2nd quarter theme: Forest Resource InformationGoogle Scholar
  49. 49.
    Estreguil, C., Caudullo, G., de Rigo, D., San-Miguel-Ayanz, J.: Forest landscape in Europe: Pattern, fragmentation and connectivity. EUR – Sci. Tech. Res. (JRC 77295) (2013)Google Scholar
  50. 50.
    Malhi, Y., Baldocchi, D.D., Jarvis, P.G.: The carbon balance of tropical, temperate and boreal forests. Plant, Cell and Environment 22(6), 715–740 (1999)CrossRefGoogle Scholar
  51. 51.
    Houghton, R.: Aboveground forest biomass and the global carbon balance. Global Change Biology 11(6), 945–958 (2005)CrossRefGoogle Scholar
  52. 52.
    Myneni, R.B., Dong, J., Tucker, C.J., Kaufmann, R.K., Kauppi, P.E., et al.: A large carbon sink in the woody biomass of northern forests. Proc. Natl. Acad. Sci. U.S.A. 98(26), 14784–14789 (2001)CrossRefGoogle Scholar
  53. 53.
    Rodriguez-Aseretto, D., de Rigo, D., Di Leo, M., Cortés, A., San-Miguel-Ayanz, J.: A data-driven model for large wildfire behaviour prediction in Europe. Procedia Computer Science 18, 1861–1870 (2013)CrossRefGoogle Scholar
  54. 54.
    Lal, R.: Soil erosion and the global carbon budget. Env. Intl. 29(4), 437–450 (2003)CrossRefGoogle Scholar
  55. 55.
    Lal, R.: Forest soils and carbon sequestration. For. Ecol. Manage. 220, 242–258 (2005)CrossRefGoogle Scholar
  56. 56.
    Hurteau, M.D., Brooks, M.L.: Short- and long-term effects of fire on carbon in US dry temperate forest systems. BioScience 61(2), 139–146 (2011)CrossRefGoogle Scholar
  57. 57.
    Panagos, P., Jones, A., Bosco, C., Senthil Kumar, P.S.: European digital archive on soil maps (EuDASM): Preserving important soil data for public free access. Int. J. of Digital Earth 4(5) (2011)Google Scholar
  58. 58.
    de Rigo, D., Bosco, C.: Architecture of a pan-european framework for integrated soil water erosion assessment. In: Hřebíček, J., Schimak, G., Denzer, R. (eds.) ISESS 2011. IFIP AICT, vol. 359, pp. 310–318. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  59. 59.
    Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility (in prep.)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Daniele de Rigo
    • 1
    • 2
  • José I. Barredo
    • 1
  • Lorenzo Busetto
    • 1
  • Giovanni Caudullo
    • 1
  • Jesús San-Miguel-Ayanz
    • 1
  1. 1.Joint Research Centre, Institute for Environment and SustainabilityEuropean CommissionIspraItaly
  2. 2.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

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