Forest biomass retrieval approaches from earth observation in different biomes

•The widest inter-comparison of regional-to-national AGB maps to date.•Commonalities are observed from the comparison of standardized accuracy assessments.•Retrievals showed RMSE ranging from 10 t ha−1 to 55 t ha−1.•All retrievals present overestimation at low AGB, and underestimation at high AGB. T...

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Published in:International journal of applied earth observation and geoinformation 2019-05, Vol.77, p.53-68
Main Authors: 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, 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
Format: Article
Language:English
Subjects:
Aboveground biomass
Agricultural sciences
Carbon cycle
Engineering Sciences
Environmental Engineering
Environmental Sciences
Forest biomes
Forest plots
LiDAR
Life Sciences
Optical
SAR
Signal and Image processing
Silviculture, forestry
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Summary:•The widest inter-comparison of regional-to-national AGB maps to date.•Commonalities are observed from the comparison of standardized accuracy assessments.•Retrievals showed RMSE ranging from 10 t ha−1 to 55 t ha−1.•All retrievals present overestimation at low AGB, and underestimation at high AGB. 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.
ISSN:1569-8432
1872-826X
DOI:10.1016/j.jag.2018.12.008