빅데이터 기반 낙엽송 초기 생육예측모델 개발

노남진1*, 송규홍1, 김형섭2, 심상래1, 최한석1, 한승현3, 김현섭3

(1강원대학교 산림과학부, 2코넬대학교 융합식물과학대학,

3국립산림과학원 산림기술경영연구소)

Model Development of Early Growth Prediction in Larix kaempferi Plantations based on Big Data

Nam Jin Noh1*, Kyuhong Song1, Hyung-sub Kim2, Sangrea Shim1, Han Suk Choi1,

Seung Hyun Han3, Hyun Seob Kim3

(1Division of Forest Science, Kangwon National University,

2School of Integrative Plant Sciences, Cornell University,

3Forest Technology and Management Research Center, National Institute of Forest Science)

요약: 본 연구는 조림 3년차 낙엽송을 대상으로 기후, 입지, 토양환경 빅데이터 기반하여 조림목 초기 생육예측모델을 개발하고자 수행되었다. 2020년 전국단위 낙엽송 조림지를 기후 및 공간 분포를 고려하여 총 120개소의 영구조사지를 조성하였다. 조림지 기후입지환경(기온, 강수량, 해발고, 사면, 경사, 토심 등) 및 토양 물리화학적 특성(토성, 산성도, 총탄소, 전질소, 유효태인산, 양이온 농도 등)을 조사하였고, 조사지별 생존율과 조사지 내 묘목의 수고, 근원경, 재적 등의 생육특성을 측정하였다(n≒7,200). 조림목 생육에 영향을 미치는 주요 인자 도출과 생육예측모델을 기계학습회귀인 랜덤포레스트모델을 이용하였다. 조림 3년차 조림목 평균 생존율은 76%로 나타났으며, 노암율(r=-0.28), 누적강수량(r=0.26)과 상관관계를 보였다. 근원경생장률은 위도(r=-0.45), H/D율(r=0.45), 유효태인산농도(r=0.39)가, 수고생장률은 위도(r=-0.33), 유효토심(r=0.31), 유효태인산농도(r=0.28)가 주요 영향 인자로 나타났다. 모델에 의한 생육예측 검증 결과, 생존율은 낮은 설명력을 보였으나, 근원경과 수고 생장률의 결정계수(r2)는 각각 0.59, 0.57로 높은 설명력을 보였다. 본 연구는 낙엽송의 초기 생육 특성을 보다 정확하게 예측함으로써 주요 용재 수종의 조림기술 개발 및 적정입지를 선정하는 데 기여할 것이며, 나아가 생성형 AI를 활용한 생육예측모델의 활용 방안을 제시하고자 한다.

Abstract: This study was conducted to develop a prediction model for early growth performance, specifically targeting larch (Larix kaempferi) seedlings three years after planting, based on big data on climate, location, and soil environment. In 2020, a total of 120 permanent sites were established nationwide in larch plantations across South Korea, taking into account the climatic and spatial distributions. The climatic and site conditions of the plantations (temperature, precipitation, altitude, aspect, slope, soil depth, etc.) and soil physical and checmical properties (soil texture, acidity, total carbon, total nitrogen, available phosphorus, cation concentration, etc.) were investigated. Growth characteristics such as survival rate, height, root collar diameter (RCD), and volume were measured (n≒7,200). Using the machine learning regression technique, the random forest model, the main factors affecting tree growth were determined, and early growth prediction models for larch trees was developed. The average survival rate across the sites was 76% in the third year. It showed significant correlations with rock content (r=-0.28) and cumulative precipitation (r=0.26). The growth rate of RCD was mainly influenced by latitude, H/D ratio, and available phosphorus, while the height growth rate was mainly influenced by latitude, soil depth, and available phosphorus. The model verification for growth prediction showed that the R-squred value for the survival rate was low, but the growth rates of RCD (0.59) and height (0.57) had high explanatory powers. This study aims to contribute to the development of sivilcultural techniques for major timber species and the selection of optimal sites by more accurately predicting the early growth performance of larch trees. Furthermore, it seeks to propose utilization methods for growth prediction models using generative AI.

Keyword: larch, phosphorus, random forest model, silviculture, soil property

사사: 본 연구는 국립산림과학원 일반연구사업(SC0500-2020-01-2024)의 지원으로 수행되었음.