Study of Data Updating for Forestry Land Resources on Single Map Based on Growth Model

doi:10.13466/j.cnki.lyzygl.2019.02.011

Abstract

Abstract:

Single map of forestry land resources is important for forest resources management.Application of stand growth model in updating stand factors of forest resources is meaningful in forest management practices.24 growth models were established based on data set of 4th forest resources survey in Kunming,which illustrate dynamic growth process of average stand height,DBH,number of trees per hectare,volume per hectare and mean increment of volume by model link.Fitting goodness of height growth is between 0.21 and 0.64.Fitting goodness of DBH growth is between 0.37 and 0.78.Fitting goodness of volume growth per hectare is between 0.44 and 0.92.There is a difference of 0.37 million cubic meters between predictions through stand growth model and observations.There is a difference of 2.91 million cubic meters between predictions through volume growth rate in Yunnan province and observations.24 models for different tree species could meet the requirement of data updating for single map of forestry land,which could provide support for policy making in forest management by mastering fluctuation of forest resources.

Key words: forestry land resources on single map, growth model, data updating, optimal algorithm, model linkage

CLC Number:

S757.2
S711

WU Heng, ZHU Liyan, LIU Zhijun, WU Xueqiong. Study of Data Updating for Forestry Land Resources on Single Map Based on Growth Model[J]. FOREST RESOURCES WANAGEMENT, 2019, (2): 73-79.

Figures/Tables 7

Tab.1

Statistics of samples for modeling

树种		样本量		林分因子(平均值±标准差)
树种		样本量		平均年龄 A/(a)		平均树高 H_D/(m)		平均胸径 D_g/(cm)		公顷株数 N/(株/hm²)		公顷断面积 G/(m²/hm²)		公顷蓄积 V/(m³/hm²)
华山松		19275		25±9.68		8.6±2.92		13.6±4.68		997±763.06		11.5314±6.33		54.2880±36.96
云南松		35821		25±7.45		7.7±2.35		11.7±3.41		1064±616.26		10.0465±4.48		41.2364±22.76
麻栎		244		24±11.60		7.7±2.31		10.5±3.90		1280±696.06		9.5700±4.66		38.7848±23.40
桤木		12788		22±7.93		11.5±2.84		16.7±4.77		580±405.51		10.6815±4.57		55.4405±30.39
银荆树		3737		15±5.64		8.8±2.29		12.4±3.61		869±566.13		8.8416±3.83		39.7316±21.16
赤桉		808		15±7.08		10.1±2.55		11.9±4.82		978±407.19		9.9267±5.35		49.8542±31.34
直杆桉		1814		10±4.03		10.3±2.68		10.3±3.58		2083±1106.20		14.2680±5.37		42.9819±23.59
蓝桉		5112		12±6.34		11.3±3.51		12.1±5.22		1419±1094.16		11.3683±5.49		62.3831±38.30
柳杉		85		15±6.32		8.3±3.12		11.0±4.40		1401±717.66		11.5562±5.90		53.6647±36.47
树种	样本量		林分因子(平均值±标准差)
树种	样本量		平均年龄 A/(a)		平均树高 H_D/(m)		平均胸径 D_g/(cm)		公顷株数 N/(株/hm²)		公顷断面积 G/(m²/hm²)		公顷蓄积 V/(m³/hm²)
冷杉	53		85±33.16		12.2±3.42		26.4±10.37		389±249.20		17.6247±9.80		125.7094±74.28
板栗	188		13±5.62		5.0±1.46		9.9±2.80		529±370.16		3.7708±2.35		9.7255±6.64
核桃	450		15±8.38		5.8±2.66		10.1±5.33		734±539.68		4.7863±3.44		16.2484±15.76
香椿	33		18±8.48		8.6±3.09		13.6±5.85		722±609.09		6.3136±3.02		25.1030±12.02
青冈栎	43		27±9.88		6.4±1.63		10.4±3.13		1287±645.97		9.6068±3.85		33.1465±16.61
油杉	2847		31±11.55		9.0±2.80		15.6±5.05		694±467.55		11.0017±5.35		50.7946±31.10
杉木	148		23±12.35		10.9±4.31		15.4±6.40		1084±920.90		15.0012±9.02		80.7264±57.83
栎类	19165		35±11.98		7.2±2.22		10.8±3.89		1317±843.01		9.9887±4.40		35.4861±20.47
柏类	3799		22±10.65		9.3±2.96		12.1±4.44		1162±876.88		10.7501±6.35		52.8504±39.76
桉类	6430		10±4.60		10.7±2.80		10.7±3.09		1588±856.57		12.4839±5.47		57.7253±31.19
樟类	140		9±5.24		5.2±1.89		10.0±2.98		1317±788.14		8.4986±3.57		22.0836±11.87
杨类	525		15±8.14		10.4±3.66		13.9±6.56		962±785.81		9.8473±5.30		52.9768±36.15
柳类	39		9±3.90		6.5±2.01		11.3±4.03		1038±662.08		7.9221±4.33		28.0487±20.23
硬阔	76		25±12.12		8.2±2.60		12.0±5.65		1135±698.13		9.4582±4.32		35.0684±20.51
软阔	407		16±9.21		6.3±1.97		9.1±4.38		1473±849.23		7.9546±4.51		27.7371±21.26

Tab.1

Tab.2

Tab.3

Tab.4

Tab.5

Tab.6

Fig.1

References 11

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树种	模型	参数			拟合决定系数 R²	均方根误差 RMSE
树种	模型	a₁	a₂	a₃	拟合决定系数 R²	均方根误差 RMSE
华山松	(3)	15.1668	0.0281	0.7680	0.39		2.28
云南松	(3)	13.0147	0.0300	0.8095	0.27		2.01
麻栎	(3)	13.8793	0.0159	0.4869	0.38		1.83
桤木	(3)	14.9788	0.0558	0.6606	0.28		2.41
银荆树	(3)	10.4580	0.1235	0.7203	0.21		2.04
赤桉	(3)	20.2454	0.0197	0.4937	0.58		1.65
直杆桉	(3)	16.1709	0.0810	0.7090	0.46		1.97
蓝桉	(3)	22.7242	0.0232	0.4681	0.43		2.65
柳杉	(3)	41.4959	0.00631	0.6578	0.45		2.34
冷杉	(2)	16.2595	21.0431		0.23		3.03
板栗	(3)	9.5914	0.0263	0.5068	0.39		1.14
核桃	(3)	19.6944	0.0127	0.6854	0.46		1.95
香椿	(3)	14.1646	0.0570	0.9812	0.64		1.94
青冈栎	(3)	7.0517	0.1742	3.3704	0.30		1.39
油杉	(3)	18.5254	0.0125	0.6168	0.31		2.32
杉木	(3)	15.5477	0.0807	1.4011	0.53		2.97
栎类	(3)	19.2691	0.00464	0.5053	0.28		1.89
柏类	(3)	15.7162	0.0334	0.7062	0.51		2.07
桉类	(3)	19.0674	0.0246	0.3723	0.28		2.60
樟类	(3)	22.2570	0.00787	0.5197	0.53		1.30
杨类	(3)	19.1314	0.0377	0.6584	0.56		2.42
柳类	(3)	8.0315	0.4003	3.9954	0.54		1.40
硬阔	(3)	16.0533	0.00852	0.3915	0.25		2.28
软阔	(3)	13.2752	0.0141	0.4302	0.43		1.49

树种	模型	优化算法	参数					决定系数 R²	均方根误差 RMSE
树种	模型	优化算法	a₁	a₂	a₃	a₄	a₅	决定系数 R²	均方根误差 RMSE
华山松	(6)	LM	10.8455	0.6833	0.6178	0.00482	-0.1040	0.75	2.32
云南松	(6)	LM	7.8019	0.6224	0.6790	0.0112	-0.1027	0.68	1.91
麻栎	(5)	LM	4.0071	0.7257	12.0648	0.0745		0.58	2.52
桤木	(6)	DE	4.7759	0.7033	0.7066	0.0328	-0.0899	0.64	2.87
银荆树	(5)	SA	4.5477	0.6201	5.5581	0.1708		0.53	2.47
赤桉	(5)	SA	3.2852	0.9645	11.1999	0.0376		0.75	2.41
直杆桉	(6)	SA	4.6389	0.7019	0.5840	0.0244	-0.5045	0.73	1.86
蓝桉	(6)	SA	6.4954	0.7307	0.6709	0.0150	-0.2371	0.70	2.84
柳杉	(5)	SA	4.0571	0.7264	9.4807	0.2345		0.69	2.45
冷杉	(6)	DE	4.0749	0.9157	3.3569	0.0367	-0.1298	0.73	5.36
板栗	(5)	GA	4.4890	0.6394	4.6323	0.2066		0.37	2.21
核桃	(5)	SA	4.2648	0.8599	12.8270	0.1155		0.68	3.03
香椿	(5)	SA	8.7220	0.5893	13.9018	0.0545		0.77	2.74
青冈栎	(6)	DE	2.4397	0.9106	1.0877	0.0534	-0.6418	0.73	1.60
油杉	(6)	GA	5.4105	0.6570	1.1692	0.0381	-0.1649	0.61	3.14
杉木	(6)	SA	7.2310	0.5564	0.7295	0.0213	-0.2054	0.72	3.38
栎类	(5)	SA	3.9222	0.8444	19.9179	0.2011		0.64	2.32
柏类	(5)	DE	4.3003	0.7122	12.1546	0.1889		0.65	2.64
桉类	(6)	SA	7.3332	0.5246	0.4962	0.0182	-0.2289	0.57	2.03
樟类	(5)	GA	5.7169	0.4295	3.3318	0.0963		0.46	2.18
杨类	(5)	GA	2.7901	0.8940	9.2532	0.1254		0.69	3.63
柳类	(6)	SA	5.5724	0.5869	0.4133	0.0203	-0.8753	0.53	2.73
硬阔	(5)	LM	3.1190	0.9564	17.8099	0.3883		0.78	2.65
软阔	(5)	LM	2.9609	0.9373	8.8210	-0.1039		0.67	2.50

树种	模型	优化算法	参数					决定系数 R²	均方根误差 RMSE
树种	模型	优化算法	a₁	a₂	a₃	a₄	a₅	决定系数 R²	均方根误差 RMSE
华山松	(9)	DE	17.1334	1.2746	0.8622	0.0131	1.1318	0.84	14.83
云南松	(9)	DE	21.1359	1.0721	0.9506	0.0150	0.9120	0.75	11.28
麻栎	(9)	LM	7.0483	1.4570	0.8847	0.0209	1.1015	0.88	8.20
桤木	(9)	SA	8.4539	1.2093	0.7469	0.0508	1.3951	0.80	13.50
银荆树	(9)	GA	5.8544	1.2667	0.5253	0.0973	1.9580	0.90	6.85
赤桉	(8)	DE	5.5150	1.5964	11.0404	-0.8641		0.89	10.43
直杆桉	(9)	DE	5.2999	1.2063	1.0950	0.0771	0.5384	0.44	17.58
蓝桉	(10)	LM	15.4872	1.1068	2.7912	0.7369	0.9199	0.90	12.22
柳杉	(9)	LM	5.3468	1.6062	1.5521	0.0397	0.6727	0.87	13.25
冷杉	(9)	DE	15.6744	1.2854	1.2981	0.0246	1.0311	0.90	23.77
板栗	(10)	LM	15.2051	0.7666	2.8477	-0.0602	-14.3813	0.76	3.22
核桃	(8)	DE	1.7533	1.8237	10.7001	-0.6340		0.67	9.07
香椿	(9)	GA	4.5609	1.3186	1.3461	0.1539	1.0282	0.91	3.49
青冈栎	(9)	DE	1.8143	1.7940	2.8177	0.1294	1.0254	0.87	5.95
油杉	(9)	LM	7.8986	1.2715	1.0878	0.0445	1.0375	0.81	13.51
杉木	(9)	LM	24.6427	1.2005	0.7667	0.00764	1.3378	0.88	19.72
栎类	(9)	SA	11.2639	1.3207	0.8079	0.0152	1.2031	0.81	8.81
柏类	(9)	DE	13.1660	1.4416	0.7975	0.0105	1.1880	0.88	14.02
桉类	(9)	SA	20.6393	1.0217	0.5925	0.0178	1.4960	0.70	17.10
樟类	(10)	GA	12.9508	1.2688	3.6974	0.4638	1.0563	0.85	4.57
杨类	(9)	LM	9.6032	1.4134	1.0108	0.0274	0.9626	0.91	10.95
柳类	(9)	DE	10.6262	1.5481	0.8039	0.0203	1.1967	0.92	5.80
硬阔	(10)	SA	1.3499	1.9176	2.6431	0.6904	1.1924	0.85	7.99
软阔	(10)	GA	9.9638	1.6509	4.0322	1.6472	0.2881	0.91	6.37

年龄/a	树高/m	胸径/cm	断面积/ (m²/hm²)	株数/ (株/hm²)	单木蓄积量/ (m³/株)	蓄积量/ (m³/hm²)	蓄积平均生长量/ (m³/hm²)	蓄积生长率/%
10	6.1	8.2	9.25	1745	0.0181	31.6090	3.1609
20	9.6	12.7	13.03	1036	0.0563	58.3975	2.9199	4.99
30	12.0	16.0	15.90	787	0.1044	82.1362	2.7379	3.02
40	13.7	18.8	18.29	659	0.1568	103.3647	2.5841	2.10
50	14.9	21.1	20.38	583	0.2099	122.4301	2.4486	1.58

年龄/a	树高/m	胸径/cm	断面积/ (m²/hm²)	株数/ (株/hm²)	单木蓄积量/ (m³/株)	蓄积量/ (m³/hm²)	蓄积平均生长量/ (m³/hm²)	蓄积生长率/%
10	6.4	5.9	5.4936	2026	0.0072	14.6432	1.4643
20	9.0	12.7	6.5878	521	0.0486	25.3025	1.2651	10.67
30	10.9	16.4	7.2701	344	0.1007	34.6564	1.1552	6.24
40	12.5	18.6	7.7714	284	0.1517	43.1628	1.0791	4.37
50	13.8	20.1	8.1694	256	0.1990	51.0296	1.0206	3.34