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Remote Sens. 2014, 6(4), 3387-3408; doi:10.3390/rs6043387

A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China

Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), No.9 Dengzhuang South Road, Beijing 100094, China
German Remote Sensing Data Centre (DFD), German Aerospace Centre (DLR), D-82234 Wessling, Germany
Author to whom correspondence should be addressed.
Received: 10 February 2014 / Revised: 23 March 2014 / Accepted: 31 March 2014 / Published: 17 April 2014
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Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR) in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS) surface reflectance and the enhanced vegetation index (EVI) time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region. View Full-Text
Keywords: land cover classification; MODIS; phenology; Xinjiang Uygur Autonomous Region land cover classification; MODIS; phenology; Xinjiang Uygur Autonomous Region

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Lu, L.; Kuenzer, C.; Guo, H.; Li, Q.; Long, T.; Li, X. A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China. Remote Sens. 2014, 6, 3387-3408.

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