Research on Wetland Sub-classification from GlobeLand30

doi:10.13474/j.cnki.11-2246.2017.0310

Abstract

Abstract: Wetland data of the Globaland30 have been classified into 8 subclasses by the improved method, which could meet the application requirements of high resolution and detailed global wetland data. This paper proposed the research framework of wetland classification, formulated the classification method, and analyzed the 8 subclasses at a global scale. The framework was composed of the definition of wetland subclass, the hierarchical classification of multiple knowledge and methods of sub date extraction. The data have been classified by the object filtering of prior knowledge, the parity pixel extraction of forest data and mask method of the maximum likelihood algorithm. The method overcomed the shortcomings of the conventional methods, such as long cycle and low efficiency, and realized the fast and accurate mapping of the global high resolution wetland subclass data,the accuracy for general wetland subclass is 82.6%. This research would be useful for monitoring and studying the land cover information in the further.

Key words: wetland sub-classification, hierarchical classification, global land cover data (GlobeLand30), subclass information

CLC Number:

P237

CHEN Wei, CHEN Lijun, CHEN Jun, CHEN Hao, ZHOU Xiaoguang, XIE Bo. Research on Wetland Sub-classification from GlobeLand30[J]. 测绘通报, 2017, 0(10): 22-28.

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