Research Papers

Object-based approach to national land cover mapping using HJ satellite imagery

[+] Author Affiliations
Lei Zhang

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, P. O. Box 9718, Beijing 100101, China

Xiaosong Li

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, P. O. Box 9718, Beijing 100101, China

Quanzhi Yuan

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, P. O. Box 9718, Beijing 100101, China

Yu Liu

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, P. O. Box 9718, Beijing 100101, China

J. Appl. Remote Sens. 8(1), 083686 (Jan 29, 2014). doi:10.1117/1.JRS.8.083686
History: Received June 18, 2013; Revised December 4, 2013; Accepted December 24, 2013
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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%.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Topics

Land cover

Citation

Lei Zhang ; Xiaosong Li ; Quanzhi Yuan and Yu Liu
"Object-based approach to national land cover mapping using HJ satellite imagery", J. Appl. Remote Sens. 8(1), 083686 (Jan 29, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083686


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