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Proceedings Paper

Land cover classification based on typical indices combinations of MODIS NDVI time series
Author(s): Zitao Du; Yulin Zhan; Changyao Wang
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Paper Abstract

MODIS data has a high temporal and spectral resolution, and it can provide vegetation indices of high quality. By using MODIS NDVI time series with 250 m spatial resolution which were composite of 16 days in 2005, this work chose annual modulus of vector, maximum and minimum NDVI three indices to do classification. Training and validation samples were selected based on TM images and the 1:1,000,000 vegetation atlas of China. Then the land coverage map was generated using maximum likelihood classification (MLC) method. After post-classification process of the original classification result, the final land classification map of Keerqin sandy land was got in the end. The classification accuracy was assessed using validation samples and the result indicates that 250 m MODIS NDVI time series has advantage and potential in regional land coverage mapping. Also the classification method used in the paper could not only reduce the data amount and quicken the speed of classification, but also could reduce the disturbance of other invalidation information to classification and get better classification accuracy.

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, 714705 (7 November 2008); doi: 10.1117/12.813205
Show Author Affiliations
Zitao Du, Institute of Remote Sensing Applications (China)
Yulin Zhan, Institute of Remote Sensing Applications (China)
Changyao Wang, Institute of Remote Sensing Applications (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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