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

Maxime Réjou-Méchain*, Nicolas Barbier, Pierre Couteron, Pierre Ploton, Grégoire Vincent, Martin Herold, Stéphane Mermoz, Sassan Saatchi, Jérôme Chave, Florian de Boissieu, Jean-Baptiste Féret, Stéphane Momo Takoudjou, Raphaël Pélissier

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

35 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)881-911
JournalSurveys in Geophysics
Volume40
Issue number4
Early online date30 May 2019
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Biomass
  • Calibration
  • Carbon
  • Error propagation
  • Field data
  • Modelling

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