http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
MODIS NDVI와 기상자료를 이용한 미국 일리노이, 아이오와주 옥수수, 콩 수량 추정
이경도,나상일,홍석영,박찬원,소규호,박재문,Lee, Kyung-Do,Na, Sang-Il,Hong, Suk-Young,Park, Chan-Won,So, Kyu-Ho,Park, Jae-Moon 대한원격탐사학회 2017 大韓遠隔探査學會誌 Vol.33 No.5
본 연구는 대표적인 곡물 생산, 수출국인 미국의 일리노이주와 아이오와주에 대하여 카운티별 MODIS 위성영상 식생지수 및 기상자료를 활용하여 수량을 추정할 수 있는 다중회귀 모형을 구축하고 그 결과를 평가하였다. 2002년부터 2012년까지의 MODIS 위성영상 식생지수 및 기상자료로 옥수수와 콩 수량 추정 모형을 구축하고 2013년 수량을 추정한 결과, 일리노이, 아이오와 2개주에 대하여 약 1~16% 내외의 오차 결과를 얻었다. 모형의 수량 추정 정확도 향상을 위해 추후에는 지대 구분 및 다양한 지표면 생물리 모수를 함께 활용하여 수량 추정 모형의 성과를 높여나가야 할 것으로 판단된다. The objective of this study was to estimate corn and soybean yield in Illinois and Iowa in USA using satellite and meteorological data. MODIS products for NDVI were downloaded from a NASA website. Each layer was processed to convert projection and extract layers for NDVI. Relations of NDVI from 2002 to 2012 with corn and soybean yield were investigated to find informative days for rice yield estimation. Weather data for the county of study state duration from 2002 to 2012 to correlate crop yield. Multiple regression models based on MODIS NDVI and rainfall were made to estimate corn and soybean yields in study site. Corn yields estimated for 2013 were $10.17ton\;ha^{-1}$ in Illinois, $10.21ton\;ha^{-1}$ in Iowa and soybean yields estimated were $3.11ton\;ha^{-1}$ in Illinois, $2.58ton\;ha^{-1}$ in Iowa, respectively. Corn and Soybean yield distributions in 2013 were mapped to show spatial variability of crop yields of the Illinois and Iowa state.
이경도,박찬원,나상일,정명표,김준환,Lee, Kyung-do,Park, Chan-won,Na, Sang-il,Jung, Myung-Pyo,Kim, Junhwan 대한원격탐사학회 2017 大韓遠隔探査學會誌 Vol.33 No.5
농작물 작황 추정은 생산량 예측을 통한 수급 조절, 가격 예측, 농가 소득 보전을 위한 정책 수립 등에 중요한 판단자료로 활용된다. 급변하는 국내외 여건에서 작물의 안정생산과 식량안보, 생태계 지속성 평가를 위해 원격탐사 등 국가차원의 미래기술 개발 노력이 요구되고 있다. 농촌진흥청은 2010년부터 국내외 주요 곡물생산지대 작황 평가를 위한 원격탐사, 작물모형, 농업기상 분야 원천기술 개발을 위해 노력해왔다. 본 특별호는 농촌진흥청에서 지난 8년간 국내외 작황 평가를 위해 수행해 온 원격탐사, 작물모형, 농업기상 분야의 연구개발 성과 및 연계된 이들 분야 간 융복합 연구 수행 현황을 정리하고 향후 연구 방향을 제시하고자 발간하게 되었다. The periodic monitoring of crop conditions and timely estimation of crop yield are of great importance for supporting agricultural decision-makings, as well as for effectively coping with food security issues. Remote sensing has been regarded as one of effective tools for crop condition monitoring and crop type classification. Since 2010, RDA (Rural Development Administration) has been developing technology for monitoring on crop condition using remote sensing and model. These special papers address recent state-of-the-art of remote sensing and geospatial technologies for providing operational agricultural information, such as, crop yield estimation methods using remote sensing data and process-oriented model, crop classification algorithm, monitoring and prediction of weather and climate based on remote sensing data,system design and architecture of crop monitoring system, history on rice yield forecasting method.
이경도,박찬원,소규호,김기덕,나상일,Lee, Kyung Do,Park, Chan Won,So, Kyu Ho,Kim, Ki Deog,Na, Sang Il 한국농공학회 2017 한국농공학회논문집 Vol.59 No.6
Growth monitoring of highland Kimchi cabbage is very important to respond the fluctuations in supply and demand from middle of August to early September in Korea. For evaluating Kimchi cabbage growth, it needs to classify the transplanting period of Kimchi cabbage, preferentially. This study was conducted to estimate the transplanting period of highland Kimchi cabbage from 2015 to 2016 in the main production area of highland Kimchi cabbage, Anbandegi, Maebongsan, and Gwinemi. Correlation between NDVI (Normalized Difference Vegetation Index) from UAV images and days after transplanting of Kimchi cabbage was high in early transplanting period. But because the growth curve of Kimchi cabbage showed S-type, joint use of multi-temporal linear regression equation for estimation of transplanting period was more suitable. Using application of these equations at Anbandegi, Maebongsan, and Gwinemi, we made the map of transplanting periods of highland Kimchi cabbage. Generally, highland Kimchi cabbage is harvested in sixty days later since transplanting. As a result, we could estimate the harvest time and area of highland Kimchi cabbage.
댐 유역 가뭄 관리를 위한 강수량 임계수준 결정에 관한 연구
이경도,손경환,이병주,Lee, Kyoung Do,Son, Kyung Hwan,Lee, Byong Ju 한국수자원학회 2020 한국수자원학회논문집 Vol.53 No.4
This study determined appropriate threshold level (cumulative period and percentage) of precipitation for drought management in dam basin. The 5 dam basins were selected, the daily dam storage level and daily precipitation data were collected. MAP (Mean Areal Precipitation was calculated by using Thiessen polygon method, and MAP were converted to accumulated values for 6 cumulative periods (30-, 60-, 90-, 180-, 270-, and 360-day). The correlation coefficient and ratio of variation coefficient between storage level and MAP for 6 cumulative periods were used to determine the appropriate cumulative period. Correlation of cumulative precipitation below 90-day was low, and that of 270-day was high. Correlation was high when the past precipitation during the flood period was included within the cumulative period. The ratio of variation coefficient was higher for the shorter cumulative period and lower for the longer in all dam, and that of 270-day precipitation was closed to 1.0 in every month. ROC (Receiver Operating Characteristics) analysis with TLWSA (Threshold Line of Water Supply Adjustment) was used to determine the percentage of precipitation shortages. It is showed that the percentage of 270-day cumulative precipitation on Boryung dam and other 4-dam were less than 90% and 80% as threshold level respectively, when the storage was below the attention level. The relationship between storage and percentage of dam outflow and precipitation were analyzed to evaluate the impact of artificial dam operations on drought analysis, and the magnitude of dam outflow caused uncertainty in the analysis between precipitation and storage data. It is concluded that threshold level should be considered for dam drought analysis using based on precipitation.