제주도 일대 동백나무의 간재적 모델 개발 및 검증
Development and Validation of Tree Volume Models for Camellia japonica in Jeju Island
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초록
본 연구는 동백나무의 간재적 추정식을 개발하고자 하였다. 제주지역에 자생하고 있는 동백나무 200본을 표준목으로 선정하여 분석 자료로 사용하였다. 재적모델 개발을 위해 80% 자료와 모델 타당성 검정(Validation)을 위해 20% 자료를 무작위로(Randomly) 구분하여 분석하였고, 본 연구에서 제시된 최종모형의 추정된 계수는 100% 자료를 이용하였다. 동백나무에 대한 재적 모델의 적합성 검정은 결정계수(R2), 추정치의 오차(RMSE), 평균편의(MD), 절대평균편의(AMD)와 직경급별 평균편의(MD), 변동계수(CV%)를 비교 분석하였다. 본 연구 결과, 결정계수는 0.7571에서 0.9381로 나타났으며, V=aDbHc에서 결정계수가 가장 높은 것으로 나타났다. 추정치의 오차는 0.0326으로 V=aD2H에서 가장 높았으며, 반면에 0.0258로 V=aDbHc에서 가장 낮았다. 평균편의는 V=a+bD2H에서 0에 값으로 나타났으며, V=aDbHc에서 0.0012로 나타났다. 절대평균편의는 V=D2/(a+b/H)에서 0.0186으로 가장 낮았고, 0.0223으로 V=aD2H에서 가장 높게 나타났다. 변동계수는 V=aDbHc에서 30.4348로 가장 적합한 계수로 나타났다. 5개의 모델식 중 최적의 식은 V=aDbHc에서 나타났으며 V=aD2H에서 적합성이 가장 낮은 것으로 나타났다.
Abstract
This study was carried out to develop volume equations for Camellia japonica trees in JejuExperimental Forests using the five commonly used volume models. A total of 200 trees weremeasured in the three experimental forests of Jeju, namely Hannam, Seogwipo and Gotjawalforests. The collected data were divided into two dataset. The majority(80%) of the dataset wereused in the initial modeling while the remaining dataset(20%) were used for validation of thecandidate models. To evaluate these models, coefficient of determination(R2), root mean squareerror(RMSE), mean difference(MD), absolute mean difference(AMD), and coefficient ofvariation(CV%) were used as evaluation statistics. In the model validation, the MD in eachdiameter class were determine for the five models. The two dataset were then combined for thefinal model development. The R2 ranged from 0.7571 to 0.9381 with V=aDbHc having thehighest value. For the RMSE, V=aD2H had the highest with 0.0326, whereas V=aDbHc hadthe best with 0.0258. V=a+bD2H had the best MD with zero(0), whereas V=aDbHc had thehighest with 0.0012. On the other hand, V=D2/(a+b/H) had the best AMD with 0.0186,whereas V=aD2H had the highest. The CV(%) of V=aDbHc was the best with 30.4348. Overall, results of the model evaluation and validation showed that V=aDbHc having DBH andtotal height as predicting variables was considered best based on the rank analysis whileV=aD2H had the poorest performance among the candidate models.
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