Sampling Optimization of Stand Biomass Survey Based on Regional Characteristics Analysis

doi:10.13466/j.cnki.lyzygl.2023.02.008

Abstract

Abstract:

Forest biomass monitoring is the basic work to correctly understand and manage forest ecosystem.Analyzing existing data is an effective way to improve sampling efficiency.In this study,regional characteristics analysis tool was used with the data from the sixth to ninth continuous forest inventories of Sichuan Province in 2002,2007,2012 and 2017,respectively.Combined with the regional characteristics analysis,some samples from the selected samples by stratified sampling were selected to form annual sampling plots.Probability sampling estimation was applied for sampling efficiency analysis.The results show that the regional characteristics analysis can effectively reduce the variance within each stratification,which can be used as the prior information of spatial stratification sampling.With 95% reliability,the average accuracy of spatial stratified sampling of stand biomass estimation was 93.41%,which greatly reduced the workload of field survey,effectively improved sampling efficiency.

Key words: sampling technique, regional characteristics, spatial correlation, continuous forest inventories

CLC Number:

S718.556

WU Heng, LIU Lang, LU Chi, WANG Baoyun. Sampling Optimization of Stand Biomass Survey Based on Regional Characteristics Analysis[J]. FOREST RESOURCES WANAGEMENT, 2023, (2): 57-63.

Figures/Tables 6

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年份	正态分布检验			全局空间自相关性
年份	K-S检验	S-W检验	P值	Moran'sI指数	z-score值
2002	0.22	0.74	<0.01	0.31	85.92
2007	0.18	0.80	<0.01	0.30	83.25
2012	0.18	0.81	<0.01	0.28	78.77
2017	0.17	0.81	<0.01	0.20	79.70

样地分组	年度	聚类分布模式
		高值集聚			低值集聚			相异集聚			随机分布
		数量/ 个	均值/ (t/hm²)	变异系数	数量/ 个	均值/ (t/hm²)	变异系数	数量/ 个	均值/ (t/hm²)	变异系数	数量/ 个	均值/ (t/hm²)	变异系数
组1	2002	160	234.20	0.54	175	4.71	0.87	30	56.76	3.11	377	49.84	1.10
	2007	158	237.87	0.55	156	5.82	0.79	35	48.33	1.43	385	53.72	0.97
	2012	150	253.02	0.54	165	6.70	0.77	41	47.30	1.17	384	61.42	0.91
组2	2002	159	239.86	0.64	176	4.88	0.74	24	62.34	2.29	374	49.66	1.23
	2007	154	249.68	0.63	183	6.05	0.75	31	41.38	1.49	389	57.10	1.11
	2012	149	258.35	0.63	172	6.52	0.82	34	51.94	1.35	395	62.68	0.93
组3	2002	173	260.37	0.68	172	4.65	0.78	28	27.69	1.60	398	48.74	1.01
	2007	178	267.43	0.67	167	5.62	0.84	26	45.13	1.17	409	52.71	0.91
	2012	166	282.44	0.62	177	6.16	0.87	35	67.23	1.06	401	61.87	0.88
组4	2002	160	251.66	0.60	156	5.04	0.78	35	28.51	1.92	321	50.62	1.26
	2007	155	260.85	0.57	153	6.56	0.85	32	27.40	1.90	340	57.76	1.11
	2012	149	270.69	0.52	142	7.33	0.85	29	57.80	1.10	355	62.69	1.03
组5	2002	160	251.72	0.66	159	4.67	0.81	32	31.68	1.75	397	43.59	0.99
	2007	163	259.91	0.64	159	5.36	0.82	35	39.63	1.54	405	49.00	0.90
	2012	156	272.90	0.63	158	7.00	0.78	38	53.00	1.40	401	53.72	0.82

分层情况	年度	抽样分组
		组1		组2		组3		组4		组5
		分组样地	检尺样地	分组样地	检尺样地	分组样地	检尺样地	分组样地	检尺样地	分组样地	检尺样地
空间分层	2002	1.85	0.69	1.94	0.72	1.79	0.69	2.02	0.68	1.82	0.68
	2007	1.62	0.60	1.67	0.64	1.61	0.63	1.74	0.60	1.61	0.61
	2012	1.50	0.56	1.51	0.57	1.45	0.57	1.62	0.55	1.50	0.57
不分层	2002	2.73	0.99	2.76	1.01	2.76	1.05	2.73	0.97	2.75	1.03
	2007	2.73	0.99	2.76	1.02	2.76	1.07	2.73	0.97	2.75	1.06
	2012	2.73	0.99	2.76	1.02	2.76	1.07	2.73	0.96	2.75	1.04

分层情况	估计年度	抽样分组										森林资源连续清查
		组1		组2		组3		组4		组5		森林资源连续清查
		中值/亿t	精度/%	中值/亿t	精度/%	中值/亿t	精度/%	中值/亿t	精度/%	中值/亿t	精度	中值/亿t	精度/%
空间分层	2007	12.52	92.86	13.34	94.36	13.06	94.02	12.96	93.88	13.34	94.21	13.00	94.73
	2012	13.89	93.76	13.72	93.99	13.71	93.94	14.02	94.43	14.11	94.37	13.95	94.90
	2017	16.12	93.66	15.82	93.33	15.70	93.22	15.15	93.63	15.57	93.48	15.97	95.49
不分层	2007	13.19	94.91	13.27	94.94	12.91	94.77	12.79	94.66	12.98	94.38	13.00	94.73
	2012	13.87	94.93	13.98	95.01	14.25	95.03	13.50	94.97	14.13	94.93	13.95	94.90
	2017	15.41	94.76	16.05	94.84	16.40	94.93	16.06	95.16	16.15	94.99	15.97	95.49

年度	空间分层抽样分组
	组1		组2		组3		组4		组5
	R	RD/%	R	RD/%	R	RD/%	R	RD/%	R	RD/%
2007	0.97	4.30	0.95	-3.00	0.95	-0.50	0.96	0.30	0.95	-2.90
2012	0.97	0.50	0.97	1.90	0.97	2.00	0.97	-0.60	0.97	-1.30
2017	0.93	-1.00	0.94	1.10	0.94	2.00	0.97	-7.40	0.94	2.90