Tree Height-Diameter Regression Models Development and One-Variable Tree Volume Tables Examination in Beijing

doi:10.13466/j.cnki.lyzygl.2021.03.010

Abstract

Abstract:

Using the height data of 14 074 sample trees in Beijing,and taking the volume estimates from two-variable tree volume tables as the base,the tree height-diameter regression models and one-variable tree volume tables/equations for 13 tree species were developed,and prediction errors of new and old one-variable tree volume tables were examined and evaluated.The results showed that the relative error (RE) of total volume estimate predicted by the current one-variable tree volume tables for 13 tree species was -1.14%,and the REs of species-specific volume estimates were between -36.59%~47.76%,where the one-variable volume tables for 11 tree species have exceeded the allowable error ±5%;the mean prediction errors (MPE) of new one-variable tree volume equations for 13 tree species were all less than 5%,the total relative errors (TRE) and average systematic errors (ASE) were all within ±1%.The study concluded that only 2 of the 13 old one-variable tree volume tables can be applied,while the other 11 volume tables needed to be revised or updated;and the one-variable tree volume tables developed in this study for 13 tree species met the precision requirement of ministerial standard in forestry.

Key words: one-variable volume table, tree height curve, volume estimation, model examination, Beijing

CLC Number:

S758.5

PU Ying, ZENG Weisheng, YANG Fan. Tree Height-Diameter Regression Models Development and One-Variable Tree Volume Tables Examination in Beijing[J]. FOREST RESOURCES WANAGEMENT, 2021, (3): 62-66.

Figures/Tables 5

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Tab.4

Fig.1

References 11

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序号	树种组	样木数/株	胸径/cm	树高/m
1	落叶松	119	5.0~36.5	2.9~19.7
2	油松	1649	5.0~41.0	2.0~18.3
3	侧柏	1350	5.0~30.2	1.4~14.8
4	栎树	2601	5.0~66.8	2.5~18.5
5	桦木	550	5.0~44.2	2.5~21.0
6	榆树	927	5.0~42.5	2.5~18.2
7	刺槐	998	5.0~45.2	2.0~22.0
8	杨树	968	5.0~71.4	3.1~33.2
9	山杨	192	5.0~51.9	3.1~20.0
10	柳树	232	5.0~48.4	3.0~19.0
11	经济树种	166	5.0~58.3	2.5~ 9.5
12	其他硬阔	2180	5.0~52.2	1.9~18.2
13	其他软阔	2142	5.0~46.1	2.0~23.3

序号	树种组	二元材积公式参数			树高曲线参数
序号	树种组	a	b	c	c₀	c₁	c₂
1	落叶松	0.000053403	1.80807	1.07242	19.61470	0.01409	0.43246
2	油松	0.000066492	1.86556	0.93769	9.28556	0.21929	3.40051
3	侧柏	0.000091972	1.86398	0.83157	4.29895	0.45807	4.32085
4	栎树	0.000057469	1.91556	0.92660	10.01030	0.15553	1.78838
5	桦木	0.000062324	1.82558	0.97749	12.58410	0.12040	1.67084
6	榆树	0.000057469	1.91556	0.92660	28.82340	0.02159	0.75072
7	刺槐	0.000071182	1.94149	0.81487	13.72510	0.13098	1.81821
8	杨树	0.000065768	1.94106	0.84929	32.59870	0.01307	0.65064
9	山杨	0.000065768	1.94106	0.84929	22.12570	0.07204	1.37380
10	柳树	0.000057469	1.91556	0.92660	406.53200	0.00048	0.74104
11	经济树种	0.000057469	1.91556	0.92660	10.01740	0.29215	4.29635
12	其他硬阔	0.000057469	1.91556	0.92660	19.48760	0.04081	0.80135
13	其他软阔	0.000057469	1.91556	0.92660	16.96020	0.05504	0.93200

序号	树种组	模型评价指标						树高曲线参数
序号	树种组	R²	SEE	TRE	ASE	MPE	MPSE	c₀	c₁	c₂
1	落叶松	0.973	0.028	0.14	-0.17	3.59	21.23	30.764	0.02667	0.98574
2	油松	0.921	0.030	-0.23	0.20	1.82	22.32	32.335	0.01328	0.83823
3	侧柏	0.951	0.009	-0.34	-0.31	1.49	14.56	23.555	0.01334	0.64379
4	栎树	0.875	0.047	-0.80	0.03	1.95	20.14	12.270	0.06704	0.86862
5	桦木	0.953	0.029	-0.19	0.55	3.49	18.03	29.008	0.01844	0.76355
6	榆树	0.937	0.024	-0.34	-0.02	2.99	18.84	19.290	0.03232	0.79656
7	刺槐	0.955	0.032	-0.26	-0.17	2.01	16.51	274.420	0.00034	0.64875
8	杨树	0.962	0.105	-0.15	-0.28	1.41	13.95	23.572	0.05428	1.05775
9	山杨	0.979	0.025	-0.13	-0.28	3.15	15.89	19.637	0.05773	0.92914
10	柳树	0.878	0.072	-0.64	-0.14	4.87	19.13	13.943	0.15539	2.81866
11	经济树种	0.980	0.015	-0.29	-0.18	3.90	16.39	7.337	0.05790	0.46566
12	其他硬阔	0.910	0.037	-0.44	-0.08	2.11	19.18	14.053	0.04732	0.72648
13	其他软阔	0.922	0.027	-0.39	0.11	2.41	21.04	23.397	0.02037	0.77160

序号	树种组	相对误差RE/%		序号	树种组	相对误差RE/%
序号	树种组	老材积式	新材积式	序号	树种组	老材积式	新材积式
0	合计	-1.14	0.35	7	刺槐	3.54	0.26
1	落叶松	-19.74	-0.14	8	杨树	-10.78	0.15
2	油松	-9.42	0.24	9	山杨	7.47	0.13
3	侧柏	-36.59	0.34	10	柳树	22.85	0.64
4	栎树	-4.36	0.80	11	经济树种	47.76	0.29
5	桦木	-15.41	0.19	12	其他硬阔	23.14	0.44
6	榆树	21.97	0.34	13	其他软阔	13.37	0.39

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