Abstract
The accuracy of different coarse-resolution land cover products is an important consideration for product users at the regional or global scale, and different evaluation methods inevitably result in discrepancies in accuracy for the same land cover product. The remote sensing community has responded to this increased interest by improving methodologies for more accurately evaluating the correctness of land cover information. In this study, a pixel-based hierarchical classification strategy followed by an object-based classification method was applied to compact airborne spectrographic imager (CASI) hyperspectral data in order to produce highly accurate, high spatial resolution classification reference data. Some aspects of the fuzzy/conventional evaluation of MODIS land cover (MODISLC) (500 m) and GlobCover (300 m) data based on sub-pixel class fractions derived from high spatial resolution reference data at different thematic resolutions are also discussed. Relationships between homogeneity and fuzzy accuracy for two land cover products were obtained at different thematic resolutions. Additionally, the influences on the relationship resulting from the thematic resolution were also studied, and these are reported in this paper. Attempts were made to establish fuzzy/conventional evaluation rules for fuzzy classes, and the different performances of the fuzzy and conventional evaluations for hard/fuzzy labels were compared. The adjusted GlobCover accuracy after theoretical removal of the effect caused by spatial resolution was calculated based on the relationship between homogeneity and accuracy; the result was a higher accuracy than for MODISLC at the same thematic resolution. In addition, the different performance characteristics of the relationships between homogeneity and adjusted GlobCover accuracy/MODISLC accuracy at different thematic resolutions were compared and analyzed over the area where the CASI transects were obtained.
Highlights
Global land cover maps provide thematic characterizations of the Earth’s surface, which are indispensable parameters required for the analysis of the state and dynamics of terrestrial ecosystems [1,2]
Since global land cover datasets derived from AVHRR data became available in the 1990s, numerous additional global land cover datasets have been produced based on different remote sensing data sources and mapping initiatives, such as the International Geosphere-Biosphere Program (IGBP)
We show how a hyperspatial classification map derived from compact airborne spectrographic imager (CASI) hyperspectral imagery was employed as a reference for the evaluation of MODIS land cover (MODISLC) with hard classes and GlobCover, which includes fuzzy classes, based on the obtained sub-pixel classes and fractions
Summary
Global land cover maps provide thematic characterizations of the Earth’s surface, which are indispensable parameters required for the analysis of the state and dynamics of terrestrial ecosystems [1,2]. The assessment of the accuracy of land cover maps derived from satellite data has been an important preoccupation of the remote sensing community, and a variety of approaches, including direct [21,22,23,24,25,26,27,28,29] and indirect [30,31,32,33,34] methods, have been developed. The accuracy derived using a sub-pixel fraction error matrix is more representative of the confidence-based agreement between the classification map and reference data, since a pixel composed of several land cover classes cannot be 100% correct for a given class; the predicted class was considered to be either 100% correct or 100% incorrect using the conventional evaluation. Four land cover products (MODISLC, GLC2000, IGBP-DIScover and UMD)
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