환경생물 (Korean Journal of Environmental Biology)

  • 제39권4호
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  • Pages.526-535
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  • 2021
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  • 1226-9999(pISSN)
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  • 2287-7851(eISSN)
한국환경생물학회 (Korean Society of Environmental Biology)
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농지 공간격자 자료의 층화랜덤샘플링: 농업시스템 기후변화 영향 공간모델링을 위한 국내 농지 최적 층화 및 샘플 수 최적화 연구

A stratified random sampling design for paddy fields: Optimized stratification and sample allocation for effective spatial modeling and mapping of the impact of climate changes on agricultural system in Korea

  • 이민영 (고려대학교 환경생태공학과) ;
  • 김용은 (고려대학교 오정리질리언스연구원) ;
  • 홍진솔 (고려대학교 환경생태공학과) ;
  • 조기종 (고려대학교 환경생태공학과)
  • Minyoung Lee (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Yongeun Kim (Ojeong Resilience Institute, Korea University) ;
  • Jinsol Hong (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Kijong Cho (Department of Environmental Science and Ecological Engineering, Korea University)
  • 투고 : 2021.11.29
  • 심사 : 2021.12.20
  • 발행 : 2021.12.31
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초록

공간 샘플링은 공간모델링 연구에 활용되어 샘플링 비용을 줄이면서 모델링의 효율성을 높이는 역할을 한다. 농업분야에서는 기후변화 영향을 예측하고 평가하기 위한 고해상도 공간자료 기반 모델링에 대한 연구 수요가 빠르게 증가하고 있으며, 이에 따라 공간 샘플링의 필요성과 중요성이 증가하고 있다. 본 연구는 국내 농지 공간샘플링 연구를 통해 농업분야 기후변화연구의 공간자료 활용의 효율성을 제고하고자 하였다. 본 연구는 층화랜덤샘플링을 기반으로 하였으며, 1 km 해상도의 농지 공간격자자료 모집단(11,386개 격자)에 대해서 RCP 시나리오별(RCP 4.5/8.5) 연대별(2030/2050/2080년대) 공간샘플링을 설계하였다. 국내 농지는 기상 및 토양 특성에 따라 계층화 되었으며, 샘플링 효율 극대화를 위해 최적 층화 및 샘플 배정 최적화를 수행하였다. 최적화는 작물수량, 온실가스 배출량, 해충 분포 확률을 포함하는 16개 목표 변수에 대해 주어진 정밀도 제한 내에서 샘플 수를 최소화하는 방향으로 진행되었다. 샘플링의 정밀도와 정확도 평가는 각각 변동계수(CV)와 상대적 편향을 기반으로 하였다. 국내 농지 공간격자 모집단 계층화 및 샘플 배정 및 샘플 수 최적화 결과, 전체 농지는 5~21개 계층, 46~69개 샘플 수 수준에서 최적화되었다. 본 연구결과물들은 국내 농업시스템 대표 공간격자로써 널리 활용될 수 있을 것으로 기대된다. 또한, 기후변화 영향예측 공간모델링 연구들에 활용되어 샘플링 비용 및 계산 시간을 줄이면서도 모델의 효율성을 높이는 데에 기여할 수 있다.

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.

키워드

과제정보

본 연구는 과학기술정보통신부의 재원으로 한국연구재단의 지원(NRF-2019R1A2C1009812)을 받아 수행된 연구입니다. 자료를 제공해주신 서울대학교 작물생태정보 연구실 김광수 교수님, 현신우 연구원님, 유병현 연구원님, 고려대학교 식물환경학 실험실 김정규 교수님, 민현기 연구원님, 토양환경 및 오염물질 제어 실험실 현승훈 교수님, 황원재 연구원님께 깊은 감사를 드립니다.

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