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Influence of carbon mapping and land change modelling on the prediction of carbon emissions from deforestation

Published online by Cambridge University Press:  15 June 2012

VICTOR HUGO GUTIERREZ-VELEZ  and
ROBERT GILMORE PONTIUS JR
VICTOR HUGO GUTIERREZ-VELEZ*
Affiliation:
Department of Ecology, Evolution, and Environmental Biology, Columbia University, 10th floor Schermerhorn, 1200 Amsterdam Avenue, New York, NY 10027, USA Research Centre on Ecosystems and Global Change (Carbono and Bosques), Calle 51A # 72-23, Interior 601, Medellin, Colombia
ROBERT GILMORE PONTIUS JR
Affiliation:
Graduate School of Geography, Clark University, 950 Main Street, Worcester MA 01610-1477, USA
*
*Correspondence: Victor Hugo Gutierrez-Velez e-mail vhg2103@columbia.edu
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Summary

The implementation of an international programme for reducing carbon emissions from deforestation and degradation (REDD) can help to mitigate climate change and bring numerous benefits to environmental conservation. Information on land change modelling and carbon mapping can contribute to quantify future carbon emissions from deforestation. However limitations in data availability and technical capabilities may constitute an obstacle for countries interested in participating in the REDD programme. This paper evaluates the influence of quantity and allocation of mapped carbon stocks and expected deforestation on the prediction of carbon emissions from deforestation. The paper introduces the conceptual space where quantity and allocation are involved in predicting carbon emissions, and then uses the concepts to predict carbon emissions in the Brazilian Amazon, using previously published information about carbon mapping and deforestation modelling. Results showed that variation in quantity of carbon among carbon maps was the most influential component of uncertainty, followed by quantity of predicted deforestation. Spatial allocation of carbon within carbon maps was less influential than quantity of carbon in the maps. For most of the carbon maps, spatial allocation of deforestation had a minor but variable effect on the prediction of carbon emissions relative to the other components. The influence of spatial carbon allocation reaches its maximum when 50% of the initial forest area is deforested. The method can be applied to other case studies to evaluate the interacting effects of quantity and allocation of carbon with future deforestation on the prediction of carbon emissions from deforestation.

Keywords

accuracyallocationAmazoniaclimate change mitigationforest conservationquantityREDDreference emissions levelsscenariosuncertainty
Type
Papers
Information
Environmental Conservation , Volume 39 , Issue 4 , December 2012 , pp. 325 - 336
Copyright
Copyright © Foundation for Environmental Conservation 2012

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