An article of Pedro Rodríguez-Veiga, Shaun Quegan, Joao Carreiras, Henrik Persson, Johan Fransson, Agata Hoscilo, Dariusz Ziółkowski, Krzysztof Stereńczak, Sandra Lohberger, Matthias Stängel, Anna Berningerg, Florian, Siegert. Valerio Avitabile, Martin Herold, Stéphane Mermoz, Alexandre Bouvet, Thuy Le Toan, Nuno Carvalhais, Maurizio Santoro, Oliver Cartus, Yrjö Rausten, Renaud Mathieu, Gregory Asner, Christian Thiel, Carsten Pathe, Chris Schmullius, Frank Martin Seifert, Kevin Tanseya and Heiko Balzter: Forest biomass retrieval approaches from earth observation in different biomes, hase been published in International Journal of Applied Earth Observation and Geoinformation, Volume 77, May 2019, Pages 53-68.
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: Aboveground biomass; Forest biomes; Forest plots; Carbon cycle; Optical; SARLiDAR