Ou Jiande, Ou Jialin, Kang Yongwu. Single Tree Biomass Simulation of Taxus yunnanensis Plantation Based on Crown Morphological Index[J]. Journal of Southwest Forestry University, 2022, 42(4): 117-124. DOI: 10.11929/j.swfu.202105033
Citation: Ou Jiande, Ou Jialin, Kang Yongwu. Single Tree Biomass Simulation of Taxus yunnanensis Plantation Based on Crown Morphological Index[J]. Journal of Southwest Forestry University, 2022, 42(4): 117-124. DOI: 10.11929/j.swfu.202105033

Single Tree Biomass Simulation of Taxus yunnanensis Plantation Based on Crown Morphological Index

  • Taking 3-year-old Taxus yunnanensis raw material forest as research object, based on the independent variables of ground diameter and tree height, the independent variables of crown morphological characteristics such as crown width, crown shape rate and crown ratio were introduced, and a series ideal mathematical models of single tree biomass were optimized by stepwise regression method. The t-test, deviation test and precision estimation were carried out on the optimal biomass model of T. yunnanensis by using the independent samples test method. The results showed that the decision coefficient of those single tree biomass modeles was obviously higher after introducted the independent variables of crown morphological characteristics, and the model has better deviation and prediction accuracy than that of crown morphological index unrelated. The crown morphological structure significantly effected the single plant biomass of T. yunnanensis. The crown width and crown shape rate have significant effected on the branches and leaves biomass, stem biomass and aboveground biomass, the crown rate and crown width could significantly promote the total biomass. It is necessary to introduce canopy morphological characteristics factors into those single tree biomass models of T. yunnanensis, which can improve the prediction accuracy. Those optimal biomass models passed the t-test and the deviation test, those optimal models prediction accuracy was above 92.36%, which can be used to predict biomass in production.
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