| Height curve models and corelations between diameter at breast height(DBH) and collar diameter(CD),crown breadth and diameter of Sawtooth Oak (Quercus acutissima) have been studied by establishing 10 belt sample plots ,measuring tree height(H), DBH, CD, height blow branch(HBB),CB of 406 trees,employing multifactor linear regression analysis,stepwise regression analysis and other functions.Using hardwood standard volume table of Huabei region and height-DBH equations,one-way volume table has been established.The results are as follows:Multifactor linear regression equations are established to calculate the height by multifactor linear regression analysis . The significance levels of equations are very high, The average precision is 98.09%, the lowest precision is 95.51%,which indicate that there tree height can be estimated through CD,DBH,HBB and CB.At the 0.15 significance level,the stepwise regression equations for tree height are established by using the stepwise regression analysis.The average precision is 98.01%,the lowest precision is 93.99%.Compared with multifactor linear regression analysis method,this method enhances efficiency of investigation ,although its precision is lower a litter than that of the multifactor linear regression analysis method.The corelations are studied among height,HBB,CB,different regression equations for different DBH groups are established.The average accuracy of non-linear regression equation is 94.86%,hower,the average accuracy of linear regression equation is 90.84%,and the lowest accuracy is 78.84%,the results indicate that non-linear regression equations are better than linear regression equations.The corelations are studied among tree height,DBH,CD,also the different regression equations for different DBH groups are established,,the results indicate that non-linear regression equations (the average precision 98.43%) are better than linear and the changeable linear regression equations(the average precision 97.71%, the lowest precision 94.04%).Regression equations for different DBH groups are establisheded by studying the relations between tree height and DBH.The results indicate that the changeable linear regression equations are better than non-linear regression equations ( the average precision 96.21%, the lowest precision 89.01%). The corelations between tree height and HBB are studied ,different equations are established for different diameter groups. Their relative coefficient squares 0.119-0.3829, the average precision is 96.35%, the lowest precision is 67.39%.The reliability of the equations is lower than that of height-diameter regression equations The corelations between tree height and CB are studied, and the different equations for different DBH groups are established, their relative coefficient squares 0.03-0.29, the average precision is 93.34%, the lowest precision is 69.13%,the reliability of the equations is lower than that of height-DBH regression equations. Through above studies, the results indicate that height-DBH equations are best among the single factor models. In the multifactor models, the better equations are multifactor linear regression equation, stepwise regression equation and binary factor equation. As to equation precision, multifactor linear regression equation can be used to calculate tree height; as to efficiency of investigation, tree height can be calculated by using the single factor (DBH)equation,binary factor (DBH,CB)equation.The corelations between DBH and CB are studied, and the equations for different diameter groups are established.Particular equation precision(72.37%) is slower ,however,precisions of other equations are high up to 93%.Their regression results are more reliable,and the equations can be used to calculate DBH through CB.Different DBH group regression equations are established by studying the relations between CB and DBH. The precision of the equations is above 91%, the results are more reliable.The CB can be calculated through DBH and |
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