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 Santoro & 9 others Oliver Cartus, Renaud Mathieu, Gregory Asner, Christian Thiel, Carsten Pathe, Chris Schmullius, Frank Martin Seifert, Kevin Tansey, Heiko Balzter

    Research output: Contribution to journalArticleResearchpeer-review

    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.
    LanguageEnglish
    Pages53-68
    Number of pages16
    JournalInternational Journal of Applied Earth Observation and Geoinformation
    Volume77
    DOIs
    Publication statusPublished - 3 Jan 2019
    MoE publication typeNot Eligible

    Fingerprint

    aboveground biomass
    biome
    Biomass
    Earth (planet)
    biomass
    accuracy assessment
    Mean square error
    forest inventory
    savanna
    tropical forest
    woodland
    Northern Hemisphere
    pixel
    Spatial distribution
    spatial distribution
    Pixels
    method

    Keywords

    • Remote sensing
    • biomass

    OKM Publication Types

    • A1 Refereed journal article

    OKM Open Access Status

    • 0 Not Open Access

    Cite this

    Rauste, Yrjö ; Rodríguez-Veiga, Pedro ; Quegan, Shaun ; Carreiras, Joao ; Persson, Henrik ; Fransson, Johan ; Hoscilo, Agata ; Zielkowski, Dariusz ; Sterenczak, Krzysztof ; Lohenberger, Sandra ; Stängel, Matthias ; Berninger, Anna ; Siegert, Florian ; Avitabile, Valerio ; Herold, Martin ; Mermoz, Stéphane ; Bouvet, Alexandre ; Le Toan, Thuy ; Carvalhais, Nuno ; Santoro, Maurizio ; Cartus, Oliver ; Mathieu, Renaud ; Asner, Gregory ; Thiel, Christian ; Pathe, Carsten ; Schmullius, Chris ; Seifert, Frank Martin ; Tansey, Kevin ; Balzter, Heiko. / Forest biomass retrieval approaches from earth observation in different biomes. In: International Journal of Applied Earth Observation and Geoinformation. 2019 ; Vol. 77. pp. 53-68.
    @article{b651dade06b54d09a0595101c5f53273,
    title = "Forest biomass retrieval approaches from earth observation in different biomes",
    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.",
    keywords = "Remote sensing, biomass",
    author = "Yrj{\"o} Rauste and Pedro Rodr{\'i}guez-Veiga and Shaun Quegan and Joao Carreiras and Henrik Persson and Johan Fransson and Agata Hoscilo and Dariusz Zielkowski and Krzysztof Sterenczak and Sandra Lohenberger and Matthias St{\"a}ngel and Anna Berninger and Florian Siegert and Valerio Avitabile and Martin Herold and St{\'e}phane Mermoz and Alexandre Bouvet and {Le Toan}, Thuy and Nuno Carvalhais and Maurizio Santoro and Oliver Cartus and Renaud Mathieu and Gregory Asner and Christian Thiel and Carsten Pathe and Chris Schmullius and Seifert, {Frank Martin} and Kevin Tansey and Heiko Balzter",
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    language = "English",
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    journal = "International Journal of Applied Earth Observation and Geoinformation",
    issn = "1569-8432",
    publisher = "Elsevier",

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    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, Santoro, M, Cartus, O, Mathieu, R, Asner, G, Thiel, C, Pathe, C, Schmullius, C, Seifert, FM, Tansey, K & Balzter, H 2019, 'Forest biomass retrieval approaches from earth observation in different biomes', International Journal of Applied Earth Observation and Geoinformation, vol. 77, pp. 53-68. https://doi.org/10.1016/j.jag.2018.12.008

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

    In: International Journal of Applied Earth Observation and Geoinformation, Vol. 77, 03.01.2019, p. 53-68.

    Research output: Contribution to journalArticleResearchpeer-review

    TY - JOUR

    T1 - Forest biomass retrieval approaches from earth observation in different biomes

    AU - Rauste, Yrjö

    AU - Rodríguez-Veiga, Pedro

    AU - Quegan, Shaun

    AU - Carreiras, Joao

    AU - Persson, Henrik

    AU - Fransson, Johan

    AU - Hoscilo, Agata

    AU - Zielkowski, Dariusz

    AU - Sterenczak, Krzysztof

    AU - Lohenberger, Sandra

    AU - Stängel, Matthias

    AU - Berninger, Anna

    AU - Siegert, Florian

    AU - Avitabile, Valerio

    AU - Herold, Martin

    AU - Mermoz, Stéphane

    AU - Bouvet, Alexandre

    AU - Le Toan, Thuy

    AU - Carvalhais, Nuno

    AU - Santoro, Maurizio

    AU - Cartus, Oliver

    AU - Mathieu, Renaud

    AU - Asner, Gregory

    AU - Thiel, Christian

    AU - Pathe, Carsten

    AU - Schmullius, Chris

    AU - Seifert, Frank Martin

    AU - Tansey, Kevin

    AU - Balzter, Heiko

    PY - 2019/1/3

    Y1 - 2019/1/3

    N2 - 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.

    AB - 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.

    KW - Remote sensing

    KW - biomass

    U2 - 10.1016/j.jag.2018.12.008

    DO - 10.1016/j.jag.2018.12.008

    M3 - Article

    VL - 77

    SP - 53

    EP - 68

    JO - International Journal of Applied Earth Observation and Geoinformation

    T2 - International Journal of Applied Earth Observation and Geoinformation

    JF - International Journal of Applied Earth Observation and Geoinformation

    SN - 1569-8432

    ER -