Réjou-Méchain Maxime, Barbier Nicolas, Couteron Pierre, Ploton Pierre, Vincent Grégoire, Herold M., Mermoz S., Saatchi S., Chave J., de Boissieu F., Feret J. B., Takoudjou S. M., Pélissier Raphaël. (2019). Upscaling forest biomass from field to satellite measurements : sources of errors and ways to reduce them. Surveys in Geophysics, 40 (4), p. 881-911. ISSN 0169-3298.
Titre du document
Upscaling forest biomass from field to satellite measurements : sources of errors and ways to reduce them
Année de publication
2019
Auteurs
Réjou-Méchain Maxime, Barbier Nicolas, Couteron Pierre, Ploton Pierre, Vincent Grégoire, Herold M., Mermoz S., Saatchi S., Chave J., de Boissieu F., Feret J. B., Takoudjou S. M., Pélissier Raphaël
Source
Surveys in Geophysics, 2019,
40 (4), p. 881-911 ISSN 0169-3298
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.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Etudes, transformation, conservation du milieu naturel [082]
;
Télédétection [126]
Localisation
Fonds IRD [F B010076479]
Identifiant IRD
fdi:010076479
