Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 551212
Title Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them
Author(s) Réjou-Méchain, Maxime; Barbier, Nicolas; Couteron, Pierre; Ploton, Pierre; Vincent, Grégoire; Herold, Martin; Mermoz, Stéphane; Saatchi, Sassan; Chave, Jérôme; Boissieu, Florian de; Féret, Jean-Baptiste; Takoudjou, Stéphane Momo; Pélissier, Raphaël
Source Surveys in Geophysics 40 (2019)4. - ISSN 0169-3298 - p. 881 - 911.
DOI https://doi.org/10.1007/s10712-019-09532-0
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2019
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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