Forest biomass retrieval approaches from earth observation in different biomes

Avitabile, Valerio ¹Herold, Martin ¹Mermoz, Stephane ²Bouvet, Alexandre ²Thuy Le Toan ²Carvalhais, Nuno ³Santoro, Maurizio ⁴Cartus, Oliver ⁴Rauste, Yrjo ⁵Mathieu, Renaud ⁶Asner, Gregory P. ⁷Thiel, Christian ⁸Pathe, Carsten ⁹Schmullius, Chris ⁹Seifert, Frank Martin ¹⁰Tansey, Kevin ¹¹Balzter, Heiko ¹¹Rodriguez-Veiga, Pedro ¹¹Quegan, Shaun ¹²Carreiras, Joao ¹²Persson, Henrik J. ¹³Fransson, Johan E. S. ¹³Hoscilo, Agata ¹⁴Ziolkowski, Dariusz ¹⁴Sterenczak, Krzysztof ¹⁵Lohberger, Sandra ¹⁶Siegert, Florian ¹⁶Staengel, Matthias ¹⁶Berninger, Anna¹⁶

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作者信息

1. Wageningen Univ & Res, Wageningen, Netherlands
2. Univ Toulouse, CESBIO, CNES, CNRS,IRD,UPS, Toulouse, France
3. Max Planck Inst Biogeochem, Jena, Germany
4. GAMMA Remote Sensing, Gumlingen, Switzerland
5. VTT Tech Res Ctr Finland Ltd, Espoo, Finland
6. CSIR, Pretoria, South Africa
7. Carnegie Inst Sci, Washington, DC 20005 USA
8. German Aerosp Agcy, Jena, Germany
9. Friedrich Schiller Univ Jena, Jena, Germany
10. European Space Agcy ESRIN, Frascati, Italy
11. Univ Leicester, Ctr Landscape & Climate Res, Leicester, Leics, England
12. Natl Ctr Earth Observat, Leicester, Leics, England
13. Sveriges Lantbruksuniv Sweden, Uppsala, Sweden
14. Inst Geodesy & Cartog, Warsaw, Poland
15. Forest Res Inst, Sekocin Stary, Poland
16. Remote Sensing Solut, Munich, Germany
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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.

Key words

Aboveground biomass/Forest biomes/Forest plots/Carbon cycle/Optical/SAR/LiDAR

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出版年

2019

International journal of applied earth observation and geoinformation

SCI

ISSN：0303-2434

被引量20

参考文献量123

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