Crown size is an important tree variable included in growth and yield models, which are commonly used as decision鄄support tools in forest management. Crown size is usually described as tree crown width or live crown ratio. Tree crown width and live crown ratio have been found to be a useful measure for tree vigor, and allow one to predict tree growth, mortality and aboveground biomass. In this study, using 103 plots of Cunninghamia lanceolata stand in Huangfengqiao forest farm in Hunan Province as an example, each plot was divided into five classes by stand canopy density (CD) following the rules of CD 逸 0. 8 (Type 1) , 0. 7 臆 CD <0. 8 (Type 2) , 0. 6 臆 CD <0. 7(Type 3) , 0. 5 臆 CD <0. 6 (Type 4) , and CD <0. 5 (Type 5). We analyzed the distribution structure of tree crown width and live crown ratio of each tree for each type, and also analyzed the relationships of tree crown width and diameter at breast height (DBH) , live crown ratio and DBH, respectively. In addition, nonlinear mixed effects models approach was used to describe the random effects of canopy density on tree crown width and live crown ratio. The commonly used logistic model with predictors of DBH and mean diameter at breast height in larger trees (MDL) was selected as a based model to construct mixed effects crown width and live crown ratio models. The prediction accuracy of the developed nonlinear mixed effects crown width and live crown

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Individual tree biomass equations have been frequently used in ecological and forestry research over the last 60 years. They represent a powerful tool to understand forest productivity, nutrient cycling, and carbon sequestration, and they are used to estimate other structural and functional characteristics of forest ecosystems. Current attempts to develop above鄄 ground biomass equations for Larix forests in northeast China have been mainly focused on only one species or applied to the genus Larix as a whole. However, generalized above鄄ground biomass equations for Larix could be used to estimate the average relationship between above鄄ground biomass and different independent variables and also variations among different Larix species. We developed generalized biomass equations for different Larix species by using Larix olgensis and Larix gmelinii. In this study, a total of nine tree variables that were able to predict above鄄ground biomass in Larix species were examined using biomass equations. The results show that D, H, and CW contributed significantly to predict above鄄ground