Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation in Xilin River Basin, Inner Mongolia, China

Abstract

Community ecologists and vegetation scientists in grassland research have a strong interest in quantifying biotic communities in detail. However, a satisfactory classification with fine biotic details has been challenged by the coarse resolutions of Landsat images, although they are easily accessible. In this paper, a hybrid fuzzy classifier (HFC) for vegetation classification with Landsat ETM+ imagery on the typical grassland in Xilinhe River Basin, Inner Mongolia, China has been developed. Three vegetation classification systems were created from different aspects: the botanical system (Bio‐classes, also as the final mapping units for vegetation cover), the combined botanical and spectral system (Bio‐S classes), and the spectral system (Spec‐classes). The HFC designed a fuzzy logic to measure the similarity between Spec‐classes, extracted by the unsupervised classification, and Bio‐S classes, built from the field samples, when considering the spectral variations of samples within the same Bio‐class. Then, Bio‐S classes, which served as a bridge for assigning Spec‐classes to the target Bio‐classes, were merged to restore Bio‐classes for the final mapping. To assess the classification accuracy, the HFC was compared with a conventional supervised classification (CSC). The overall result of the HFC was much better than that of the CSC, with an accuracy percentage of 80.2% as compared to 69.0% for the CSC.

Acknowledgements

The authors wish to thank The Center for Ecological Research, Institute of Botany, Chinese Academy of Sciences (CAS) for the financial support through The One Hundred Scholars – Distinguished Overseas Scholar Funds. The authors are also grateful to the research staff and graduate assistants at CAS – Inner Mongolia Grassland Research Station (IMGERS) who assisted in collecting the field samples for this research.