Forest biomass retrieval approaches from earth observation in different biomes

Yrjö Rauste, Pedro Rodríguez-Veiga (Corresponding Author), Shaun Quegan, Joao Carreiras, Henrik Persson, Johan Fransson, Agata Hoscilo, Dariusz Zielkowski, Krzysztof Sterenczak, Sandra Lohenberger, Matthias Stängel, Anna Berninger, Florian Siegert, Valerio Avitabile, Martin Herold, Stéphane Mermoz, Alexandre Bouvet, Thuy Le Toan, Nuno Carvalhais, Maurizio SantoroOliver Cartus, Renaud Mathieu, Gregory Asner, Christian Thiel, Carsten Pathe, Chris Schmullius, Frank Martin Seifert, Kevin Tansey, Heiko Balzter

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    Abstract

    The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of numerous forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha−1 to 55 t ha−1 (37% to 67% relative RMSE), and an overall bias ranging from −1 t ha−1 to +5 t ha−1 at pixel level. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha−1) in the lower AGB classes, and underestimation (up to 85 t ha−1) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level.
    Original languageEnglish
    Pages (from-to)53-68
    Number of pages16
    JournalInternational Journal of Applied Earth Observation and Geoinformation
    Volume77
    DOIs
    Publication statusPublished - 3 Jan 2019
    MoE publication typeNot Eligible

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    Keywords

    • Remote sensing
    • biomass

    Cite this

    Rauste, Y., Rodríguez-Veiga, P., Quegan, S., Carreiras, J., Persson, H., Fransson, J., Hoscilo, A., Zielkowski, D., Sterenczak, K., Lohenberger, S., Stängel, M., Berninger, A., Siegert, F., Avitabile, V., Herold, M., Mermoz, S., Bouvet, A., Le Toan, T., Carvalhais, N., ... Balzter, H. (2019). Forest biomass retrieval approaches from earth observation in different biomes. International Journal of Applied Earth Observation and Geoinformation, 77, 53-68. https://doi.org/10.1016/j.jag.2018.12.008