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      드론 원격탐사를 활용한 벼 성장 모니터링 연구 = A Study on Rice Growth Monitoring Using Drone Remote Sensing

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      https://www.riss.kr/link?id=A108767752

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      다국어 초록 (Multilingual Abstract)

      Rice paddy is one of the key crops of Korean agriculture and it has long been regarded as an important crop. Due to the high self-sufficiency of rice in Korea, the need to establish systematic rice paddy field data is increasing, and rice paddy resear...

      Rice paddy is one of the key crops of Korean agriculture and it has long been regarded as an important crop. Due to the high self-sufficiency of rice in Korea, the need to establish systematic rice paddy field data is increasing, and rice paddy research using remote sensing is being conducted several times in Korea. The optical satellite remote sensing method faces difficulties in data acquisition due to the abundance of clouds caused by the summer monsoon season, a characteristic of the domestic climate. However, the drone remote sensing method equipped with an optical sensor has the advantage of being able to freely acquire data, avoiding periods with high cloud cover. In this study, a drone equipped with a multispectral optical sensor was utilized to measure the height of rice paddy. Compared to satellites, drones provide the advantage of flexible timing for observations, allowing for the acquisition of high-frequency time-series data according to specific preferences. In this study, we seek to obtain values related to rice growth rates by analyzing optical data acquired through drone remote sensing. The DJI MAVIC 2 PRO drone was employed, and the Metashape program was utilized to generate a highresolution digital elevation model (DEM) from optical data. After the harvest period, rice-free rice paddy data were assumed to be digital terrain model (DTM), the height of the rice paddies. During the rice growth period, a digital surface model (DSM) was generated from drone imagery, and by calculating the difference between DSM and DTM, the height and growth of the rice plants were observed. Using network GPS measurement data, we validated monthly DEM models. This allowed us to anticipate the acquisition of precise rice growth data throughout the year, from planting to harvest.

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