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Modelling the forest carbon budget of a Mediterranean region through the integration of ground and satellite data

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Listed:
  • Maselli, F.
  • Chiesi, M.
  • Moriondo, M.
  • Fibbi, L.
  • Bindi, M.
  • Running, S.W.

Abstract

This paper introduces an innovative modelling strategy aimed at simulating the main terms of net forest carbon budget (net primary production, NPP and net ecosystem exchange, NEE) in Tuscany (Central Italy). The strategy is based on the preliminary calibration and application of parametric and bio-geochemical models (C-Fix and BIOME-BGC, respectively), which simulate the behaviour of forest ecosystems close to equilibrium condition (climax). Next, the ratio of actual over-potential tree volume is computed as an indicator of ecosystem distance from climax and is combined with the model outputs to estimate the NPP and NEE of real forests. The per-pixel application of the new modelling strategy was made possible by the collection of several data layers (maps of forest type and volume, daily meteorological data and monthly normalized difference vegetation index (NDVI) images for the years 1999–2003) which served to characterize the eco-climatic and forest features of the region. The obtained estimates of forest NPP and NEE were evaluated against ground measurements of accumulated woody biomass and net carbon exchange. The results of these experiments testify the good potential of the proposed strategy and indicate some problem areas which should be the subject of future research.

Suggested Citation

  • Maselli, F. & Chiesi, M. & Moriondo, M. & Fibbi, L. & Bindi, M. & Running, S.W., 2009. "Modelling the forest carbon budget of a Mediterranean region through the integration of ground and satellite data," Ecological Modelling, Elsevier, vol. 220(3), pages 330-342.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:3:p:330-342
    DOI: 10.1016/j.ecolmodel.2008.10.002
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    1. Chiesi, M. & Maselli, F. & Moriondo, M. & Fibbi, L. & Bindi, M. & Running, S.W., 2007. "Application of BIOME-BGC to simulate Mediterranean forest processes," Ecological Modelling, Elsevier, vol. 206(1), pages 179-190.
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    1. Ruiz-Pérez, G. & González-Sanchis, M. & Del Campo, A.D. & Francés, F., 2016. "Can a parsimonious model implemented with satellite data be used for modelling the vegetation dynamics and water cycle in water-controlled environments?," Ecological Modelling, Elsevier, vol. 324(C), pages 45-53.
    2. Maselli, F. & Vaccari, F.P. & Chiesi, M. & Romanelli, S. & D’Acqui, L.P., 2017. "Modelling and analyzing the water and carbon dynamics of Mediterranean macchia by the use of ground and remote sensing data," Ecological Modelling, Elsevier, vol. 351(C), pages 1-13.
    3. González-Sanchis, Marí a & Del Campo, Antonio D. & Molina, Antonio J. & Fernandes, Tarcí sio J.G., 2015. "Modeling adaptive forest management of a semi-arid Mediterranean Aleppo pine plantation," Ecological Modelling, Elsevier, vol. 308(C), pages 34-44.
    4. Maselli, Fabio & Chiesi, Marta & Brilli, Lorenzo & Moriondo, Marco, 2012. "Simulation of olive fruit yield in Tuscany through the integration of remote sensing and ground data," Ecological Modelling, Elsevier, vol. 244(C), pages 1-12.
    5. Chaobin Zhang & Ying Zhang & Jianlong Li, 2019. "Grassland Productivity Response to Climate Change in the Hulunbuir Steppes of China," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    6. Kai Yin & Dengsheng Lu & Yichen Tian & Qianjun Zhao & Chao Yuan, 2014. "Evaluation of Carbon and Oxygen Balances in Urban Ecosystems Using Land Use/Land Cover and Statistical Data," Sustainability, MDPI, vol. 7(1), pages 1-27, December.
    7. Collalti, Alessio & Perugini, Lucia & Santini, Monia & Chiti, Tommaso & Nolè, Angelo & Matteucci, Giorgio & Valentini, Riccardo, 2014. "A process-based model to simulate growth in forests with complex structure: Evaluation and use of 3D-CMCC Forest Ecosystem Model in a deciduous forest in Central Italy," Ecological Modelling, Elsevier, vol. 272(C), pages 362-378.
    8. Argenti, G. & Chiesi, M. & Fibbi, L. & Maselli, F., 2022. "Use of remote sensing and bio-geochemical models to estimate the net carbon fluxes of managed mountain grasslands," Ecological Modelling, Elsevier, vol. 474(C).

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