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

机译：碳测绘和土地变化模型对森林砍伐碳排放预测的影响

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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.
机译：实施减少毁林和退化造成的碳排放的国际计划（REDD）可以帮助减轻气候变化并为环境保护带来诸多好处。有关土地变化建模和碳制图的信息可有助于量化毁林带来的未来碳排放。但是，数据可用性和技术能力的限制可能对有兴趣参加REDD计划的国家构成障碍。本文评估了已规划的碳储量的数量和分配以及预期的森林砍伐对森林砍伐所产生的碳排放量预测的影响。本文介绍了预测碳排放量涉及数量和分配的概念空间，然后使用先前发布的有关碳测绘和森林砍伐建模的信息，使用该概念预测巴西亚马逊地区的碳排放量。结果表明，碳分布图之间的碳量变化是不确定性的最大影响因素，其次是预测的森林砍伐量。碳地图中碳的空间分配影响不如碳地图中的碳数量。对于大多数碳图，森林砍伐的空间分配对碳排放量的预测相对于其他组成部分影响不大，但变化很大。当原始森林面积的50％被砍伐时，空间碳分配的影响达到最大。该方法可用于其他案例研究，以评估碳的数量和分配与未来森林砍伐之间的相互作用，从而预测森林砍伐产生的碳排放量。

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