基于模糊集合理论的中国区域土地覆盖数据集融合及精度分析

全球土地覆盖的特征及其空间分布的信息是理解人类活动与全球变化之间复杂相互作用的关键信息来源，对全球变化研究至关重要。目前欧美等国家相继研制了全球范围的土地利用覆盖数据集，并对全球科学界免费提供，这些数据集为人类提供了更加准确的、完整的全球土地覆盖信息。然而，全球范围的土地利用覆盖数据集的种类多，特点不一，因此需要结合其优点，以适用于中国陆面。采用了一种基于模糊集合理论的数据融合方法，对主要数据集进行中国区域土地覆盖数据集融合分类重建，并结合高分辨率卫星分类结果进行了一致性检验分析。检验分析表明该方法能够兼容多种数据集的特点，该项成果可用于全国和区域数值模式以及生态环境评价等进一步研究应用

CHINA LAND COVER CLASSIFICATION FUSION BASED ON EXPERT DECISION AND ACCURACY ANALYSIS

Land cover plays a significant role in the earth system science, which reflects the influence of human activities and environmental changes. Within the past decades, several global land cover datasets derived from satellite observations, free of charge, have become available to the scientific community in Europe and America. They offer valuable information on the current state of the land surface. However, considerable disagreements among their classification legends pose significant errors and uncertainties in the use of such datasets. This paper proposes a fuzzy set theory based method to fuse several land cover classification products for China. First of all, a set of (say, n) target classification legends are defined prior to the fusion process. No doubt that they should be compatible to the classification legends of the original products to be fused. Moreover, each of the target legend must correspond to at least one original legend. Secondly, each original legend is assigned to an affinity score for each target legend, which indicates how much the former agrees with the latter. Scoring, regarded as a technique based on fuzzy theory, is usually performed by experts according to semantic rules. Scoring is followed by the voting and decision making procedure. Among the affinity scores of a pixel, the highest one suggests that the pixel falls into the target class linked by itself. In addition, we have also exploited spatial correlation by weighting the affinity scores of the neighboring pixels. When fusion is completed, a synthetic map (SYNMAP) combining the features of all original classification products is created. Overall consistency of class between SYNMAP and each land cover is engaged to evaluate the fusion method. All the datasets, including SYNMAP, are evaluated after being further categorized into a few of simple classes, each of which include several original or target legends. Note that classification accuracy, which offers an absolute index and is commonly seen, is not presented in the paper since we are short of ground truth data. Nevertheless, the goal of the fuzzy theory based method is to produce a fused map that accommodates all the advantages of different original land cover data sets and reconcile their discrepancy caused by the disagreement of different classification system. Therefore, the index of consistency between two land covers should suffice. In our experiment, ESA, MODIS/IGBP, MODIS/UMD, and MODIS/PFT are employed as the original land covers to be fused. IGBP legends are set as the target. Meanwhile, nine simple classes are used during evaluation. Overall consistencies indicate improved agreement of SYNMAP with all the other land cover products. It means that the proposed fusion method has successfully combined various features of different land cover products. The conclusions can be used for national and regional numerical model and ecological environment evaluation for further research and applications