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Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them

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Abstract

Forest biomass monitoring is at the core of the research agenda due to the critical importance of forest dynamics in the carbon cycle. However, forest biomass is never directly measured; thus, upscaling it from trees to stand or larger scales (e.g., countries, regions) relies on a series of statistical models that may propagate large errors. Here, we review the main steps usually adopted in forest aboveground biomass mapping, highlighting the major challenges and perspectives. We show that there is room for improvement along the scaling-up chain from field data collection to satellite-based large-scale mapping, which should lead to the adoption of effective practices to better control the propagation of errors. We specifically illustrate how the increasing use of emerging technologies to collect massive amounts of high-quality data may significantly improve the accuracy of forest carbon maps. Furthermore, we discuss how sources of spatially structured biases that directly propagate into remote sensing models need to be better identified and accounted for when extrapolating forest carbon estimates, e.g., through a stratification design. We finally discuss the increasing realism of 3D simulated stands, which, through radiative transfer modelling, may contribute to a better understanding of remote sensing signals and open avenues for the direct calibration of large-scale products, thereby circumventing several current difficulties.

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Courtesy of L. Descroix, unpublished

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Acknowledgements

We gratefully thank the organizers of the Workshop held at ISSI Bern in November 2017 that was at the origin of this Special Issue. This review has been conducted under the project 3DForMod funded by ERA-GAS (ANR-17-EGAS-0002-01, NWO-3DForMod-5160957540) and has also benefited from the “Investissement d’Avenir” programs managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01).

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Authors and Affiliations

  1. AMAP, IRD, CNRS, CIRAD, INRA, Univ Montpellier, Montpellier Cedex 05, France

    Maxime Réjou-Méchain, Nicolas Barbier, Pierre Couteron, Pierre Ploton, Grégoire Vincent, Stéphane Momo Takoudjou & Raphaël Pélissier

  2. Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB, Wageningen, The Netherlands

    Martin Herold

  3. CESBIO, CNES/CNRS, IRD/UPS, Université de Toulouse, Toulouse, France

    Stéphane Mermoz

  4. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA

    Sassan Saatchi

  5. CNRS, ENFA; UMR5174 EDB (Laboratoire Evolution et Diversité Biologique), Université Paul Sabatier, 118 Route de Narbonne, 31062, Toulouse, France

    Jérôme Chave

  6. TETIS, Irstea, AgroParisTech, CIRAD, CNRS, University of Montpellier, 34000, Montpellier, France

    Florian de Boissieu & Jean-Baptiste Féret

  7. Plant Systematic and Ecology Laboratory, Higher Teacher’s Training College, University of Yaoundé I, P.O. Box 047, Yaoundé, Cameroon

    Stéphane Momo Takoudjou

  8. GlobEO, Avenue Saint-Exupery, Toulouse, France

    Stéphane Mermoz

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Réjou-Méchain, M., Barbier, N., Couteron, P. et al. Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them. Surv Geophys 40, 881–911 (2019). https://doi.org/10.1007/s10712-019-09532-0

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