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Proceedings Paper

Endmembers extraction of wheat based on time series of MODIS-NDVI and TM samples data
Author(s): Xulong Liu; Kaiwen Zhong; Wanxia Liu; Yaozhong Pan
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Paper Abstract

Knowledge of the area and distribution of cropland is important for land management and land security. Low spatial resolution imagery is one of the important remote sensing data source in the study of the large extent cropland. There exist many mixed pixels and effective method that should be improved to deal with them. In this paper, linear mixing model was used to unmix the time series of MODIS-NDVI data. The emphasis was the identification and extraction of endmembers, which represent the spectral characteristics of the single pure land cover types. A new endmembers extraction algorithm based on the temporal series of MODIS-NDVI and TM sample data was presented in this paper. We used the effective endmembers to linear spectral mixture model to achieve the wheat area in the study area. Regarding the classification of TM as the reference data, we evaluated the classification results and found wheat distribution's region accuracy and pixel accuracy reach to 92.9% and 0.837 respectively, which were higher than the clarification result based on the endmembers from MODIS-NDVI pixel purity index analysis or from classifications of TM data. This shew that our endmembers extraction algorithmwas available and effective, which helped to improve monitoring accuracy of large scope and distribution of vegetation.

Paper Details

Date Published: 7 November 2008
PDF: 8 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470G (7 November 2008); doi: 10.1117/12.813216
Show Author Affiliations
Xulong Liu, Guangzhou Institute of Geography (China)
Kaiwen Zhong, Guangzhou Institute of Geography (China)
Wanxia Liu, Guangzhou Institute of Geography (China)
Yaozhong Pan, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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