지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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In this paper, we developed a detection and correction method of noisy pixels embedded in the time series of normalized difference vegetation index (NDVI) data based on the spatio-temporal continuity of vegetation conditions. For the application of the method, 25-year (1982-2006) GIMMS (Global Inventory Modeling and Mapping Study) NDVI dataset over the Korean peninsula were used. The spatial resolution and temporal frequency of this dataset are 8 × 8 ㎢ and 15-day, respectively. Also the land cover map over East Asia is used. The noisy pixels are detected by the temporal continuity check with the reference values and dynamic threshold values according to season and location. In general, the number of noisy pixels are especially larger during summer than other seasons. And the detected noisy pixels are corrected by the iterative method until the noisy pixels are completely corrected. At first, the noisy pixels are replaced by the arithmetic weighted mean of two adjacent NDVIs when the two NDVI are normal. After that the remnant noisy pixels are corrected by the weighted average of NDVI of the same land cover according to the distance. After correction, the NDVI values and their variances are increased and decreased by 5% and 50%, respectively. Comparing to the other correction method, this correction method shows a better result especially when the noisy pixels are occurred more than 2 times consistently and the temporal change rates of NDVI are very high. It means that the correction method developed in this study is superior in the reconstruction of maximum NDVI and NDVI at the starting and falling season.
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Abstract
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2. 자료
3. 연구방법
4. 연구결과
5. 요약 및 결론
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UCI(KEPA) : I410-ECN-0101-2013-453-001204877