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http://dx.doi.org/10.7780/kjrs.2006.22.6.553

Land Cover Classification of the Korean Peninsula Using Linear Spectral Mixture Analysis of MODIS Multi-temporal Data  

Jeong, Seung-Gyu (Environmental Planning Institute, Seoul National University)
Park, Chong-Hwa (Graduate School of Environmental Studies, Seoul National University)
Kim, Sang-Wook (Land and Urban Institute, Korea Land Corporation)
Publication Information
Korean Journal of Remote Sensing / v.22, no.6, 2006 , pp. 553-563 More about this Journal
Abstract
This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).
Keywords
MODIS; Linear Spectral Mixture Analysis (LSMA); Multi-temporal Imagery; Phenology; land cover classification;
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