Comparative Analysis of Simple Volume Models for Japanese Larch(Larix kaempferi) Species in the Central Region of South Korea
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참고문헌
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초록
This study was conducted to develop a simple volume model for the Larix kaempferi speciesin the Central Region of South Korea. General volume model forms were used and validatedusing a subset of the data collected for the L. kaempferi species. A total of 550 trees werecollected in the different L. kaempferi stands through destructive sampling. The dataset wasrandomly divided into two: 80% for initial model development(seven volume models) and 20%for model validation. The 100% dataset was used for the final model fitting. Evaluationstatistics including bias(Ē), absolute mean deviation(AMD), root mean square error(RMSE),coefficient of determination(R2), and the Akaike Information Criterion values(AIC) and weight(AICw) were used to assess performance of the different volume models. Rank analysis wasemployed and the first five best volume models were chosen for the model validation and finalmodel fitting. The evaluations showed that volume model 4(V=aDBHb*Hc) had the bestperformance, while volume model 3(V=aDBH+bDBH2), which has a single variable, had thepoorest performance.
Abstract
This study was conducted to develop a simple volume model for the Larix kaempferi speciesin the Central Region of South Korea. General volume model forms were used and validatedusing a subset of the data collected for the L. kaempferi species. A total of 550 trees werecollected in the different L. kaempferi stands through destructive sampling. The dataset wasrandomly divided into two: 80% for initial model development(seven volume models) and 20%for model validation. The 100% dataset was used for the final model fitting. Evaluationstatistics including bias(Ē), absolute mean deviation(AMD), root mean square error(RMSE),coefficient of determination(R2), and the Akaike Information Criterion values(AIC) and weight(AICw) were used to assess performance of the different volume models. Rank analysis wasemployed and the first five best volume models were chosen for the model validation and finalmodel fitting. The evaluations showed that volume model 4(V=aDBHb*Hc) had the bestperformance, while volume model 3(V=aDBH+bDBH2), which has a single variable, had thepoorest performance.
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