http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Toshiharu Kojima (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Vegetation Classification Using Seasonal Variation MODIS Data
최현아,이우균,손요환,Toshiharu Kojima,Hiroyuki Muraoka 대한원격탐사학회 2010 大韓遠隔探査學會誌 Vol.26 No.6
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.
-
Vegetation Classification Using Seasonal Variation MODIS Data
Choi, Hyun-Ah,Lee, Woo-Kyun,Son, Yo-Whan,Kojima, Toshiharu,Muraoka, Hiroyuki The Korean Society of Remote Sensing 2010 大韓遠隔探査學會誌 Vol.26 No.6
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.
-
Vegetation Classification Using Seasonal Variation MODIS Data
Hyun Ah Choi,Woo Kyun Lee,Yo Whan Son,Toshiharu Kojima,Hiroyuki Muraoka 大韓遠隔探査學會 2010 大韓遠隔探査學會誌 Vol.26 No.6
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.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
