Forest biomass retrieval approaches from earth observation in different biomes

Show simple item record Rodriguez-Veiga, Pedro Quegan, Shaun Carreiras, Joao Persson, Henrik J. Fransson, Johan E.S. Hoscilo, Agata Ziolkowski, Dariusz Sterenczak, Krzysztof Lohberger, Sandra Stangel, Matthias Berninger, Anna Siegert, Florian Avitabile, Valerio Herold, Martin Mermoz, Stéphane Bouvet, Alexandre Le Toan, Thuy Carvalhais, Nuno Santoro, Maurizio Cartus, Oliver Rauste, Yrjö Mathieu, Renaud Asner, Gregory P. Thiel, Christian Pathe, Carsten Schmullius, Chris Seifert, Frank Martin Tansey, Kevin Balzter, Heiko 2019-06-24T14:56:34Z 2019-06-24T14:56:34Z 2019-05
dc.description.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. en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.librarian hj2019 en_ZA
dc.description.sponsorship The GlobBiomass project was funded by the European Space Agency under its Data User Element - ITT AO/1-7822/14/I-NB. P. Rodriguez-Veiga, H. Balzter, J. Carreiras, and S. Quegan were supported by the UK’s National Centre for Earth Observation (NCEO). H. Balzter was also supported by the Royal Society Wolfson Research Merit Award, 2011/R3. en_ZA
dc.description.uri en_ZA
dc.identifier.citation Rodriguez-Veiga, P., Quegan, S., Carreiras, J. et al. 2019, 'Forest biomass retrieval approaches from earth observation in different biomes', International Journal of Applied Earth Observation and Geoinformation, vol. 77, pp. 53-68. en_ZA
dc.identifier.issn 1569-8432 (print)
dc.identifier.issn 1872-826X (online)
dc.identifier.other 10.1016/j.jag.2018.12.008
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( en_ZA
dc.subject Forest biomes en_ZA
dc.subject Forest plots en_ZA
dc.subject Carbon cycle en_ZA
dc.subject Aboveground biomass (AGB) en_ZA
dc.subject Optical en_ZA
dc.subject Synthetic aperture radar (SAR) en_ZA
dc.subject Light detection and ranging (LiDAR) en_ZA
dc.title Forest biomass retrieval approaches from earth observation in different biomes en_ZA
dc.type Article en_ZA

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