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.

Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

“…Sha et al (2008) at the Xilinhe River Basin in China employed a hybrid fuzzy classifier (HFC) for mapping vegetation on typical grassland using Landsat ETM? imagery.…”

Characterization of a Highly Biodiverse Floodplain Meadow Using Hyperspectral Remote Sensing within a Plant Functional Trait Framework

“…These communities, in general, are very challenging for spectroscopy research [38] due to the relatively small plant size and the presence of an often diverse assemblage of species and multiple plants in one sampling unit. There have been applications of remote sensing to herbaceous communities, such as those monitoring species diversity (e.g., [32,[40][41][42]) involving retrieval of properties such as LAI and biomass using empirical methods or radiative transfer model inversion [43][44][45][46]. However, there is a recognized need for further research on spectroscopy-based vegetation biophysical and functional characterization [47,48].…”

Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority

“…Studies have generally used either a popular approach for testing the statistical significance of a difference, such as the comparison of kappa coefficients (Congalton et al, 1983;Sha et al, 2008) or proportion of correctly allocated cases (Gao and Liu, 2008), or recently promoted approaches such as the McNemar test (Foody, 2004;De Leeuw et al, 2006;Demir and Ertürk, 2008).…”