为满足全国可燃物类型制图的需要，在国内外前人的研究工作基础上，建立了全国尺度的可燃物分类体系，并基于MODIS数据产品和全国植被区划数据，结合地理信息空间分析技术，对全国森林、灌木和草本等3类可燃物进行精细划分和成图。利用实地调查数据和其他数据产品，采用直接验证和交叉验证相结合方式对可燃物类型分类结果进行精度评价。评价结果表明，一级可燃物类型总精度为90.89%，Kappa系数为0.81；二级可燃物类型总精度84.14%，Kappa系数为0.74；三级可燃物类型总精度为68.16%，Kappa系数0.6。利用多源数据和地理信息空间分析技术相结合，有效地实现了在全国尺度上的森林、灌木和草本等3类可燃物类型的精细划分，为森林草原火灾的预防管理提供技术支持。

The rate and intensity of forest fire spread can be predicted, and forest fire prevention measures can be formulated according to the classifying results of fuel types. Accurately exploring the types and spatial distribution of fuel is crucial for predicting the occurrence of forest fires, predicting forest fire behavior, commanding fire-fighting, and biological fire prevention. At present, most researches on fuel types classification based on remote sensing in China are carried out in local areas, but research based on the national scale will become one of the trends in this field. To meet the needs of China’s national-scale fuel types mapping, a fuel type classification system was developed combined with the characteristics of vegetation distribution and phenology in Chinabased on the previous research. The fuels in China, including forest, shrub, and grass, were classified and mapped based on MODIS products and the Chinese national vegetation regionalization map using geographical spatial analysis technology. A method for national fuel types classification in forests, shrubs and grasses based on remote sensing and geospatial analysis was explored. Non-tree cover, average vegetation canopy height and area occupied by each fuel types were calculated, using the product datasets of MODIS VCF（Vegetation Continuous Fields）and forest canopy height. The classification results were validated using field survey data and other data products. The results show that the total accuracies of the classification result at levels 1, 2, and 3 are 90.89%, 84.14%, and 68.16%, respectively; the Kappa coefficients of the classification result at levels 1, 2, and 3 are 0.81, 0.74, and 0.6, respectively. The national-scale fuel types, including forest, shrub, and grass, were classified and mapped by using the multi-source data and geographical spatial analysis technology. The study will provide technical support for the prevention and management of forest and grassland fire in China.