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http://dx.doi.org/10.7780/kjrs.2010.26.6.665

Vegetation Classification Using Seasonal Variation MODIS Data  

Choi, Hyun-Ah (Korea Adaptation Center for Climate Change, Korea Environment Institute)
Lee, Woo-Kyun (Department of Environmental Science and Ecological Engineering, Korea University)
Son, Yo-Whan (Department of Environmental Science and Ecological Engineering, Korea University)
Kojima, Toshiharu (Institute for Basin Ecosystem Studies, Gifu University)
Muraoka, Hiroyuki (Institute for Basin Ecosystem Studies, Gifu University)
Publication Information
Korean Journal of Remote Sensing / v.26, no.6, 2010 , pp. 665-673 More about this Journal
Abstract
The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.
Keywords
Vegetation classification; seasonal variation; MODIS; Best Index Slope Extraction (BISE); fuzzy classification;
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Times Cited By KSCI : 2  (Citation Analysis)
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