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Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps

Edward TA Mitchard1*, Sassan S Saatchi2, Alessandro Baccini3, Gregory P Asner4, Scott J Goetz3, Nancy L Harris5 and Sandra Brown5

Author Affiliations

1 School of GeoSciences, University of Edinburgh, Crew Building, The King’s Buildings, Edinburgh EH9 3JN, UK

2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA

3 Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540-1644, USA

4 Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA

5 Ecosystem Services Unit, Winrock International, 2121 Crystal Drive, Suite 500, Arlington, VA 22202, USA

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Carbon Balance and Management 2013, 8:10  doi:10.1186/1750-0680-8-10

Published: 26 October 2013

Abstract

Background

Mapping the aboveground biomass of tropical forests is essential both for implementing conservation policy and reducing uncertainties in the global carbon cycle. Two medium resolution (500 m – 1000 m) pantropical maps of vegetation biomass have been recently published, and have been widely used by sub-national and national-level activities in relation to Reducing Emissions from Deforestation and forest Degradation (REDD+). Both maps use similar input data layers, and are driven by the same spaceborne LiDAR dataset providing systematic forest height and canopy structure estimates, but use different ground datasets for calibration and different spatial modelling methodologies. Here, we compare these two maps to each other, to the FAO’s Forest Resource Assessment (FRA) 2010 country-level data, and to a high resolution (100 m) biomass map generated for a portion of the Colombian Amazon.

Results

We find substantial differences between the two maps, in particular in central Amazonia, the Congo basin, the south of Papua New Guinea, the Miombo woodlands of Africa, and the dry forests and savannas of South America. There is little consistency in the direction of the difference. However, when the maps are aggregated to the country or biome scale there is greater agreement, with differences cancelling out to a certain extent. When comparing country level biomass stocks, the two maps agree with each other to a much greater extent than to the FRA 2010 estimates. In the Colombian Amazon, both pantropical maps estimate higher biomass than the independent high resolution map, but show a similar spatial distribution of this biomass.

Conclusions

Biomass mapping has progressed enormously over the past decade, to the stage where we can produce globally consistent maps of aboveground biomass. We show that there are still large uncertainties in these maps, in particular in areas with little field data. However, when used at a regional scale, different maps appear to converge, suggesting we can provide reasonable stock estimates when aggregated over large regions. Therefore we believe the largest uncertainties for REDD+ activities relate to the spatial distribution of biomass and to the spatial pattern of forest cover change, rather than to total globally or nationally summed carbon density.

Keywords:
Aboveground biomass; Carbon; Data inter-comparison; Maxent; Random forest; REDD; REDD+; Remote sensing; Tropical forests; UNFCCC