Forest biomass retrieval approaches from earth observation in different biomes

Rodríguez-Veiga, Pedro; Quegan, Shaun; Carreiras, Joao; Persson, Henrik J.; Fransson, Johan E.S.; Hoscilo, Agata; Ziółkowski, Dariusz; Stereńczak, Krzysztof; Lohberger, Sandra; Stängel, Matthias; Berninger, Anna; Siegert, Florian; Avitabile, Valerio; Herold, Martin; Mermoz, Stéphane; Bouvet, Alexandre; Toan, Thuy Le; 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


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.