Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91∼0.92, GNDVI were 0.92∼0.94, NGRDI were 0.71∼0.85 and NDREI were 0.84∼0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

ABSTRACT
Ⅰ. 서 론
Ⅱ. 재료 및 방법
    1. 연구대상 지역
    2. 건물수량 조사
    3. 다중분광 무인기 영상 취득
    4. 식생지수 산정
Ⅲ. 결과 및 고찰
    1. 초종별 건물수량 결과
    2. 식생지수 산정
    3. 상관관계 분석
Ⅳ. 요 약
Ⅴ. 사 사
Ⅵ. REFERENCES

• 신재영(GEOMEXSOFT) | Jae-Young Shin (1GEOMEXSOFT, Ltd.)
• 이준민(GEOMEXSOFT) | Jun-Min Lee (1GEOMEXSOFT, Ltd.)
• 양승학(국립축산과학원 초지사료과) | Seung-Hak Yang (Grassland & Forage Division, National Institute of Animal Science)
• 임경재(강원대학교) | Kyoung-Jae Lim (Department of Regional Infrastructure Engineering, Kangwon National University)
• 이효진(GEOMEXSOFT) | Hyo-Jin Lee (GEOMEXSOFT, Ltd.) Corresponding author