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Uncertainties in global land cover data and its implications for climate change mitigation policies assessment

机译:全球土地覆盖数据的不确定性及其对减缓气候变化政策评估的影响

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Land cover maps provide critical input data for global models of land use. Urgent questions exist, such as how much land is available for the expansion of agriculture to combat food insecurity, how high will be competition for land between food and bioenergy in the future as well as how much land is there available for afforestation projects? These questions can only be answered if reliable maps of land cover exist.We put this research in the framework of GEOSS, examine how modeling tools can be used for benefit assessment and design an assessment framework.We illustrate the importance of good quality global land cover maps by using cropland extend from the currently best global maps of land cover namely GLC-2000, MODIS, GlobCover and CropLikelyhood as input for the EPIC model (to model crop yields) and global economic land use model GLOBIOM. We use all of the 4 maps and create a maximum crop extend and map. Based on a baseline map and the maximum crop extend map e model effects of climate policies (e.g. the potentials of substitution of fossil fuels with biofuels).
机译:土地覆盖图为全球土地利用模型提供了关键的输入数据。存在紧迫的问题,例如有多少土地可用于农业扩张以应对粮食不安全状况,未来在粮食和生物能源之间争夺土地的竞争将有多高,以及有多少土地可用于造林项目?只有存在可靠的土地覆盖图,才能回答这些问题。 我们将这项研究放在GEOSS的框架中,研究如何将建模工具用于收益评估并设计评估框架。 我们使用耕地从目前最好的全球土地覆盖图(即GLC-2000,MODIS,GlobCover和CropLikelyhood)作为EPIC模型(用于模拟作物产量)和全球经济用地的输入,来说明高质量的全球土地覆盖图的重要性。使用模型GLOBIOM。我们使用所有4张地图,并创建最大的作物延伸图和地图。根据基线图和最大农作物扩展图,e可模拟气候政策的效果(例如用生物燃料替代化石燃料的潜力)。

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