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Journal of Applied Remote Sensing

Object-based approach to national land cover mapping using HJ satellite imagery
Author(s): Lei Zhang; Xiaosong Li; Quanzhi Yuan; Yu Liu
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Paper Abstract

To meet the carbon storage estimate in ecosystems for a national carbon strategy, we introduce a consistent database of China land cover. The Chinese Huan Jing (HJ) satellite is proven efficient in the cloud-free acquisition of seasonal image series in a monsoon region and in vegetation identification for mesoscale land cover mapping. Thirty-eight classes of level II land cover are generated based on the Land Cover Classification System of the United Nations Food and Agriculture Organization that follows a standard and quantitative definition. Twenty-four layers of derivative spectral, environmental, and spatial features compose the classification database. Object-based approach characterizing additional nonspectral features is conducted through mapping, and multiscale segmentations are applied on object boundary match to target real-world conditions. This method sufficiently employs spatial information, in addition to spectral characteristics, to improve classification accuracy. The algorithm of hierarchical classification is employed to follow step-by-step procedures that effectively control classification quality. This algorithm divides the dual structures of universal and local trees. Consistent universal trees suitable to most regions are performed first, followed by local trees that depend on specific features of nine climate stratifications. The independent validation indicates the overall accuracy reaches 86%.

Paper Details

Date Published: 29 January 2014
PDF: 19 pages
J. Appl. Rem. Sens. 8(1) 083686 doi: 10.1117/1.JRS.8.083686
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Lei Zhang, Institute of Remote Sensing and Digital Earth (China)
Xiaosong Li, Institute of Remote Sensing and Digital Earth (China)
Quanzhi Yuan, Institute of Remote Sensing and Digital Earth (China)
Yu Liu, Institute of Remote Sensing and Digital Earth (China)

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