微钻阻力法间接测量木材密度

doi:10.11707/j.1001-7488.20220914

Abstract

Abstract:

Objective: At present, scholars mainly use linear model to study the relationship between drill resistance and wood density. There are great differences among the models, and the generality of the models is poor. This paper discusses the model form between drill resistance and wood density, and whether different tree species or similar tree species can share a mathematical model, in order to provide a basis for indirect measurement of wood density by micro drill resistance method. Method: 1) The resistance of 8 tree species including softwood and hardwood was measured by Resistograph 650-S. Two thirds of the measured data of each tree species was used as the modeling data set, and the remaining one third of the measured data was used as the test data set. 2)Using the total modeling data set, the linear model, logarithmic model and the mixed model of linear model and logarithmic model between the average drill resistance and wood absolute dry density were established respectively. The model with the highest determination coefficient was selected as the total model between the average drill resistance and wood absolute dry density, and the model form was selected to represent the relationship between drill needle resistance and wood absolute dry density. 3)The 2 class models of softwood and hardwood between drill resistance and absolute dry density were established by using the selected model form. 4)The sub models of each tree species between the average resistance of drill needles and absolute dry density were established respectively using the selected model form. 5)The test data set was used to test the estimation standard error and average estimation accuracy of the total model, class model and sub model. Result: 1) The determination coefficients of linear model, logarithmic model and mixed model of linear model and logarithmic model established using the total modeling data set are 0.942, 0.952 and 0.961 respectively. 2)The determination coefficients of softwood class model and hardwood class model are 0.780 and 0.864 respectively, and the determination coefficients of the sub model of each tree species are 0.397-0.943. 3)The total estimation standard errors of total model, class model and sub model are 32.222, 31.635 and 27.121 kg ·m^-3 respectively, and the average estimation accuracy is 95.554%, 95.636% and 96.292% respectively. 4)There was no significant difference in the estimation standard error and average estimation accuracy among the total model, the class models and the sub models at the significance level of 0.1. Conclusion: 1) The mixed model of linear model and logarithmic model has the highest determination coefficient. Therefore, it is more appropriate to use the mixed model of linear model and logarithmic model to study the relationship between drill resistance and wood absolute dry density. 2)The estimation standard error of sub model is the smallest and the average estimation accuracy is the highest. Therefore, it is appropriate to use the sub model to predict the absolute dry density of wood. 3)When the measurement accuracy is not high, the total model can be used to estimate the absolute dry density of wood to reduce the modeling workload.

Key words: wood density, micro drill resistance method, micro destructive measurement, linear model, logarithmic model

CLC Number:

S758.7

Jianfeng Yao,Xuzhan Guo,Liyong Fu,Xuefeng Wang,Xiangdong Lei,Jun Lu,Yili Zheng,Xinyu Song. Indirect Measurement of Wood Density by Micro Drill Resistance Method[J]. Scientia Silvae Sinicae, 2022, 58(9): 138-147.

Figures/Tables 13

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References 0

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树种Tree species	株数Tree number	状态Status	测试样品数Number of test samples
马尾松Pinus massoniana	2	枯立木Dead standing wood	90
麻栎Quercus acutissima	2	风倒木Wind fallen wood	90
樱桃Cerasus pseudocerasus	1	风倒木Wind fallen wood	90
泡桐Paulownia fortunei	1	风倒木Wind fallen wood	90
杉木Cunninghamia lanceolata	1	风倒木Wind fallen wood	90
桃Amygdalus persica	1	风倒木Wind fallen wood	90
李Prunus salicina	1	风倒木Wind fallen wood	90
麻梨Pyrus serrulata	1	风倒木Wind fallen wood	90
总计Total	10	—	720

树种 Species	木材绝干密度Wood absolute dry density			钻针阻力Drill resistance
树种 Species	平均值 Mean /(kg·m^-3)	标准差 SD/(kg·m^-3)	变异系数 CV(%)	平均值 Mean/Resi	标准差 SD/Resi	变异系数 CV(%)
杉木Cunninghamia lanceolata	388.529	30.029	7.729	118.257	28.235	23.876
泡桐Paulownia fortunei	390.544	78.878	20.197	124.841	50.001	40.051
马尾松Pinus massoniana	424.471	75.988	17.902	141.680	33.626	23.734
樱桃Cerasus pseudocerasus	596.227	58.713	9.847	290.093	61.624	21.243
桃Amygdalus persica	669.043	40.156	6.002	418.167	50.783	12.144
麻梨Pyrus serrulata	709.807	48.828	6.879	498.165	59.237	11.891
李Prunus salicina	761.232	36.902	4.741	578.068	52.552	9.091
麻栎Quercus acutissima	832.024	29.135	3.502	623.714	49.389	7.919
总体Total	596.327	172.710	28.962	348.916	201.431	57.730

树种 Species	绝干密度Wood absolute dry density /(kg·m^-3)			钻针平均阻力Average drill resistance/Resi
树种 Species	1.3 m	2.3 m	3.3 m	1.3 m	2.3 m	3.3 m
杉木Cunninghamia lanceolata	418.340	373.991	373.255	147.314	111.613	95.843
泡桐Paulownia fortunei	318.336	355.855	497.441	81.247	103.869	189.407
马尾松Pinus massoniana	507.307	403.126	362.980	173.936	133.573	117.531
樱桃Cerasus pseudocerasus	661.468	585.765	541.447	362.205	277.904	230.171
桃Amygdalus persica	719.536	658.732	628.871	472.158	406.923	376.249
麻梨Pyrus serrulata	738.082	703.928	688.355	558.147	488.051	450.230
李Prunus salicina	798.540	767.038	718.117	614.511	585.427	534.266
麻栎Quercus acutissima	853.564	827.220	815.289	677.034	626.316	567.792

模型类型Model type	模型方程Model equation	R²	P
线性模型Linear model	y=304.745+0.832x	0.942	＜0.001
对数模型Logarithmic model	y=-756.876+239.755lnx	0.952	＜0.001
混合模型Mixed model	y=-334.470+0.345x+143.465lnx	0.961	＜0.001

参数Parameter	t	P
常数项Constant	-7.832	＜0.001
线性因子Linear factor	10.360	＜0.001
对数因子Logarithmic factor	15.010	＜0.001

Indirect Measurement of Wood Density by Micro Drill Resistance Method

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树种类型Species type	模型方程Model equation	R²	P
软木类Softwood	y=258.387+1.525x-11.553lnx	0.780	＜0.001
硬木类Hardwood	y=528.160+0.719x-26.334 lnx	0.864	＜0.001

树种Species	模型方程Model equation	R²	P
泡桐Paulownia fortunei	y=-459.072+0.248x+172.348lnx	0.943	＜0.001
杉木Cunninghamia lanceolata	y=217.743+0.629x+20.388lnx	0.603	＜0.001
马尾松Pinus massoniana	y=1 684.559+4.358x-382.553lnx	0.764	＜0.001
樱桃Cerasus pseudocerasus	y=962.354+1.254x-129.767lnx	0.823	＜0.001
桃Amygdalus persica	y=1 827.837+1.324x-284.047lnx	0.755	＜0.001
麻梨Pyrus serrulata	y=5 517.085+2.286x-958.757lnx	0.490	＜0.001
李Prunus salicina	y=-1 437.444-0.117x+356.262lnx	0.480	＜0.001
麻栎Quercus acutissima	y=10 687.485+3.429x-1 864.325lnx	0.397	＜0.001

树种 Species	总模型Total model		类模型Class model		分模型Sub model
树种 Species	估计标准误差 Estimated standard error/(kg·m^-3)	平均估计精度 Mean estimated accuracy (%)	估计标准误差 Estimated standard error/(kg·m^-3)	平均估计精度 Mean estimate daccuracy (%)	估计标准误差 Estimated standard error/(kg·m^-3)	平均估计精度 Mean estimate daccuracy (%)
杉木Cunninghamia lanceolata	28.011	94.290	27.373	94.276	22.513	95.743
泡桐Paulownia fortunei	20.159	95.818	21.112	95.670	20.016	95.916
马尾松Pinus massoniana	54.157	91.087	52.078	91.603	46.394	92.728
麻梨Pyrus serrulata	30.119	96.486	28.206	96.780	29.647	96.616
李Prunus salicina	33.111	96.597	35.095	96.467	29.249	97.095
桃Amygdalus persica	19.390	97.751	18.477	97.844	18.565	97.842
樱桃Cerasus pseudocerasus	33.088	95.811	31.384	96.040	24.406	96.714
麻栎Quercus acutissima	34.956	96.642	35.225	96.446	22.859	97.696
总体Total	32.222	95.554	31.635	95.636	27.121	96.292

模型级别 Model level	估计标准误差 Estimated standard error		平均估计精度 Mean estimated accuracy
模型级别 Model level	t	P	t	P
总模型与类模型 Between total model and class model	0.096	0.925	-0.083	0.935
总模型与分模型 Between total model and sub model	0.994	0.338	-0.794	0.441
类模型与分模型 Between class model and sub model	-0.914	0.377	0.734	0.475

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